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International Journal of Cloud Computing (ISSN 2326-7550) Vol. 2, No. 1, January-March 2014
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IJCC Editorial Board
Editors-in-Chief Hemant Jain, University of Wisconsin–Milwaukee, USA
Rong Chang, IBM T.J. Watson Research Center, USA
Associate Editor-in-Chief Bing Li, Wuhan University, China
Editorial Board Danilo Ardagna, Politecnico di Milano, Italy
Janaka Balasooriya, Arizona State University, USA
Roger Barga, Microsoft Research, USA
Viraj Bhat, Yahoo, USA
Rajdeep Bhowmik, Cisco Systems, Inc., USA
Jiannong Cao, Hong Kong Polytechnic University, Hong Kong
Buqing Cao, Hunan University of Science and Technology, China
Keke Chen, Wright State University, USA
Haopeng Chen, Shanghai Jiao Tong University, China
Malolan Chetlur, IBM India, India
Alfredo Cuzzocrea, ICAR-CNR & University of Calabria, Italy
Ernesto Damiani, University of Milan, Italy
De Palma, University Joseph Fourier, France
Claude Godart, Nancy University and INRIA, France
Nils Gruschka, University of Applied Sciences, Germany
Paul Hofmann, Saffron Technology, USA
Ching-Hsien Hsu, Chung Hua University, Taiwan
Patrick Hung, University of Ontario Institute of Technology, Canada
Hai Jin, HUST, China
Li Kuang, Central South University, China
Grace Lin, Institute for Information Industry, Taiwan
Xumin Liu, Rochester Institute of Technology, USA
Shiyong Lu, Wayne State University, USA
J.P. Martin-Flatin, EPFL, Switzerland
Vijay Naik, IBM T.J. Watson Research Center, USA
Surya Nepal, Commonwealth Scientific and Industrial Research Organisation, Australia
Norbert Ritter, University of Hamburg, Germany
Josef Schiefer, Vienna University of Technology, Austria
Jun Shen, University of Wollongong, Australia
Weidong Shi, University of Houston, USA
Liuba Shrira, Brandeis University, USA
Kwang Mong Sim, University of Kent, UK
Wei Tan, IBM T.J. Watson Research Center, USA
Tao Tao, IBM T. J. Watson Research Center, USA
Kunal Verma, Accenture Technology Labs, USA
Raymond Wong, University of New South Wales & NICTA, Australia
Qi Yu, Rochester Institute of Technology, USA
Jia Zhang, Carnegie Mellon University – Silicon Valley, USA
Gong Zhang, Oracle Corporation, USA
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Call for Articles
International Journal of Cloud Computing
Mission Cloud Computing has become the de facto computing paradigm for Internet-
scale service development, delivery, brokerage, and consumption in the era of
Services Computing, fueling innovative business transformation and connected
human society. 15 billion smart devices would be communicating dynamically
over inter-connected clouds by 2015 as integral components of various
industrial service ecosystems. The technical foundations of this trend include
Service-Oriented Architecture (SOA), business & IT process automation,
software-defined computing resources, elastic programming model &
framework, and big data management and analytics. In terms of the delivered
service capabilities, a cloud service could be, among other as-a-service types, an infrastructure service
(managing compute, storage, and network resources), a platform service (provisioning generic or
industry-specific programming API & runtime), a software application service (offering email-like
ready-to-use application capabilities), a business process service (providing a managed process for,
e.g., card payment), a mobile backend service (facilitating the integration between mobile apps and
backend cloud storage and capabilities) or an Internet-of-things service (connecting smart machines
with enablement capabilities for industrial clouds).
As the first Open Access research journal on Cloud Computing, the International Journal of Cloud
Computing (IJCC) aims to be a valuable resource providing leading technologies, development, ideas,
and trends to an international readership of researchers and engineers in the field of Cloud Computing.
Topics The International Journal of Cloud Computing (IJCC) covers state-of-the-art technologies and best
practices of Cloud Computing, as well as emerging standards and research topics which would define
the future of Cloud Computing. Topics of interest include, but are not limited to, the following:
- ROI Model for Infrastructure, Platform, Application, Business, Social, Mobile, and IoT Clouds
- Cloud Computing Architectures and Cloud Solution Design Patterns
- Self-service Cloud Portal, Business Dashboard, and Operations Management Dashboard
- Autonomic Process and Workflow Management in Clouds
- Cloud Service Registration, Composition, Federation, Bridging, and Bursting
- Cloud Orchestration, Scheduling, Autoprovisioning, and Autoscaling
- Cloud Enablement in Storage, Data, Messaging, Streaming, Search, Analytics, and Visualization
- Software-Defined Resource Virtualization, Composition, and Management for Cloud
- Security, Privacy, Compliance, SLA, and Risk Management for Public, Private, and Hybrid Clouds
- Cloud Quality Monitoring, Service Level Management, and Business Service Management
- Cloud Reliability, Availability, Serviceability, Performance, and Disaster Recovery Management
- Cloud Asset, Configuration, Software Patch, License, and Capacity Management
- Cloud DevOps, Image Lifecycle Management, and Migration
- Cloud Solution Benchmarking, Modeling, and Analytics
- High Performance Computing and Scientific Computing in Cloud
- Cloudlet, Cloud Edge Server, Cloud Gateway, and IoT Cloud Devices
- Cloud Programming Model, Paradigm, and Framework
- Cloud Metering, Rating, and Accounting
- Innovative Cloud Applications and Experiences
- Green Cloud Computing and Cloud Data Center Modularization
- Economic Model and Business Consulting for Cloud Computing
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International Journal of
Cloud Computing
January-March 2014, Vol. 2, No.1
Table of Contents
EDITOR-IN-CHIEF PREFACE iv Hemant Jain, University of Wisconsin–Milwaukee, USA
Rong Chang, IBM T.J. Watson Research Center, USA
RESEARCH ARTICLES 1 On The Financification Of Cloud Computing: An Agenda For Pricing And
Service Delivery Mechanism Design Research Robert J. Kauffman, Singapore Management University, Singapore
Dan Ma, Singapore Management University, Singapore
Richard Shang, Singapore Management University, Singapore
Jianhui Huang, The Conference Executive Board Asia Pte. Ltd., Singapore
Yinping Yang, Singapore Management University, Singapore
15 Impacts Of Multi-Class Oversubscription On Revenues And Performance In The
Cloud Rachel A. Householder, Bowling Green State University
Robert C. Green, Bowling Green State University
31 Optimization Of Operational Costs In Hybrid Cooling Data Centers
With Renewable Energy Shaoming Chen, Louisiana State University, US
Yue Hu, Louisiana State University, US
Lu Peng, Louisiana State University, US
45 A Broker Based Consumption Mechanism For Social Clouds Ioan Petri, Cardiff University, UK
Magdalena Punceva, University of Applied Sciences, Western Switzerland
Omer F. Rana, Cardiff University, UK
George Theodorakopoulos, Cardiff University, UK
Yacine Rezgui, Cardiff University, UK
58 Call for Papers: IEEE CLOUD/ICWS/SCC/MS/BigData/SERVICES 2014
Call for Articles: International Journal of Services Computing (IJSC)
Call for Articles: International Journal of Big Data (IJBD)
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Editor-in-Chief Preface:
Cloud Management and Assessment
Hemant Jain Rong Chang University of Wisconsin–Milwaukee, USA IBM T.J. Watson Research, USA
Welcome to the inaugural issue of International Journal of Cloud Computing (IJCC), the first open
access on-line journal on cloud computing. The increasing importance of cloud computing is
evidenced from the rapid adoption of this technology in businesses around the globe. The cloud
computing is redefining the business model of various industries from video rental (Netflix is enabled
by cloud) to small start-up companies (companies can be started with very little investment using
cloud infrastructure). The potential of cloud computing is even more promising. The cloud computing
combined with developments like internet of things can significantly change the life as we know today.
However, to deliver on these promises and to prevent clouding computing from becoming a passing
fad significant technical, economic, and business issues need to be addressed. IJCC is designed to be
an important platform for disseminating high quality research on above issues in a timely manner and
provide an ongoing platform for continuous discussion on research published in this journal. We aim
to publish high quality research that addresses important technical challenges, economics of sustaining
this environment, and business issues related to use of this technology including privacy and security
concerns, legal protection, etc. We seek to publish original research articles, expanded version of
papers presented at high quality conferences, key survey articles that summarizes the research done so
far and identify important research issues, and some visionary articles. We will make every effort to
publish articles in a timely manner.
This inaugural issue collects the extended version of five IEEE CLOUD 2013 articles in the general
area of managing Cloud computing environment.
The first article is titled “Cost-Driven Optimization of Cloud Resource Allocation for Elastic
Processes” by Schulte, Schuller, Hoenisch, Lampe, Steinmetz, and Dustdar. They present an approach
to address the cost-driven optimization of cloud-based computational resources, based on automatic
leasing and releasing of resource allocation for Elastic Processes. Empirical study and analysis are
presented.
The second article is titled “Recommending Optimal Cloud Configuration based on Benchmarking in
Black-Box Clouds” by Jung, Sharma, Mukherjee, Goetz, and Bourdaillet. They present a benchmark-
based modeling approach to recommend optimal cloud configuration for deploying user workloads,
based on various non-standardized configuration options offered by cloud providers. A search
algorithm is provided to generate a capability vector consisting of relative performance scores of
resource types. Experimental results are reported.
The third article is titled “Taming the Uncertainty of Public Clouds” by Schnjakin and Meinel. They
present a framework featuring improved availability, confidentiality and reliability of data stored in
the cloud. User data is encrypted together with the RAID technology to manage data distribution
across cloud storage providers. Experiments are conducted to evaluate the performance and cost
effectiveness of the presented approach.
The fourth article is titled “Cloud Standby System and Quality Model” by Lenk and Pallas. The
authors argue that contingency plans, replicate an IT infrastructure to the Cloud, are useful for disaster
preparedness. They propose a cloud standby approach together with a Markov-based model to analyze
and configure cloud standby systems.
The fifth article is titled “Efficient Private Cloud Operation using Proactive Management Service” by
Dong and Herbert. The authors present a distributed service architecture aiming to provide an
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automated, shared, and off-site operation management service for private clouds. A prototype system
and empirical study are presented.
We would like to thank the authors for their effort in delivering those five quality articles. We would
also like to thank the reviewers as well as the Program Committee of IEEE CLOUD 2013 for their
help with the review process. Finally, we are grateful for the effort Jia Zhang and Liang-Jie Zhang
made to this inaugural issue of International Journal of Cloud Computing (IJCC).
About the Editors-in-Chief
Dr. Hemant Jain is the Interim Director of Biomedical and Health Informatics
Research Institute, Roger L. Fitzsimonds Distinguished Scholar and Professor of
Information Technology Management at University of Wisconsin–Milwaukee. Dr.
Jain specializes in information system agility through web services, service oriented
architecture and component based development. His current interests include
development of systems to support real time enterprises which have situational
awareness, can quickly sense-and-respond to opportunities and threats, and can
track-and-trace important items. He is also working on issues related to providing quick access to
relevant knowledge for cancer treatment and to providing medical services through a virtual world.
Dr. Jain is an expert in architecture design, database management and data warehousing. He teaches
courses in database management, IT infrastructure design and management, and process management
using SAP. Dr. Jain was the Associate Editor-in-Chief of IEEE Transactions on Services Computing
and is Associate Editor of Journal of AIS, the flagship journal of the Association of Information
Systems.
Dr. Rong N. Chang is Manager & Research Staff Member at the IBM T.J. Watson
Research Center. He received his Ph.D. degree in computer science & engineering
from the University of Michigan at Ann Arbor in 1990 and his B.S. degree in
computer engineering with honors from the National Chiao Tung University in
Taiwan in 1982. Before joining IBM in 1993, he was with Bellcore researching on B-
ISDN realization. He is a holder of the ITIL Foundation Certificate in IT Services
Management. His accomplishments at IBM include the completion of a Micro MBA Program, one
IEEE Best Paper Award, and many IBM awards, including four corporate-level Outstanding
Technical Achievement Awards and six division-level accomplishments. He is an Associate Editor of
the IEEE Transactions on Services Computing and the International Journal of Services Computing.
He has chaired many conferences & workshops in cloud computing and Internet-enabled distributed
services and applications. He is an ACM Distinguished Member/Engineer, a Senior Member of IEEE,
and a member of Eta Kappa Nu and Tau Beta Pi honor societies.
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ON THE FINANCIFICATION OF CLOUD COMPUTING: AN AGENDA FOR PRICING AND
SERVICE DELIVERY MECHANISM DESIGN RESEARCH Robert J. Kauffman(a), Dan Ma(a), Richard Shang(b), Jianhui Huang(c) and Yinping Yang(a,d)
(a) School of Information Systems, Singapore Management University, Singapore(b) School of Business, Public Admin. and Info. Sciences, Long Island University Brooklyn, NY, USA
(c) The Conference Executive Board Asia Pte. Ltd., Singapore(d) Institute of High Performance Computing (IHPC), A*STAR, Singapore
Email: {rkauffman, madan}@smu.edu.sg, Di.Shang@liu.edu, jihuang@executiveboard.com, yangyp@ihpc.a-star.edu.sg
Abstract Pricing approaches to cloud computing services balance risks and interests between vendor and client, and optimize supply and consumption in terms of cost, uncertainty and economic efficiency. They also leverage the benefits of various services delivery mechanisms for reserved, on-demand, spot-price, and re-sold services in markets that have learned how to transact in full contracts and services instances. This is like a financial market: with services supply and demand, and opportunities to supply and purchase services with spot prices, or to sell or buy contracts for the delivery of future services. Our research suggests that the financification of the cloud computing services market represents a fundamental shift from the traditional model of software sales and large contracts outsourced to services vendors, to short-term contracts and computing capacity provision mechanism designs that are evolving similar to financial markets. We develop this perspective to explain the cloud vendor market, the provision of services, and the ways in which the financification of cloud computing will shape future offerings and the structure of the market. We see these changes in the market in the many ways that vendors offer cloud services of high value to organizations, while making more profitable business models possible.
Keywords: Cloud computing, economics, financification, intermediation, IT services, mechanism design, pricing, research directions, risk management, stakeholders.
___________________________________________________________________________________________________________________________
1. Introduction
Cloud computing is a means of providing commercial
information technology (IT) services to customers and
organizations. In traditional IT markets, CPUs, networks,
data storage and software applications are sold as products.
Customers own a perpetual license after a one-time payment,
but they have to pay for upgrades and other in-house IT
costs. In the past decade, many IT services vendors have
turned to cloud computing: they adjusted their services
provision and pricing schemes to permit “pay-as-you-go”
access so that customers are able to pay for usage or
subscribe to the computing resources they need.
The underlying technologies that empower cloud
computing services are not entirely new. They originated
from the idea of “computation … as a public utility” in the
1960s (Garfinkel 2011). Virtual private network (VPN)
services in the 1990s and grid computing in the early 2000s
were predecessors of today’s cloud computing services.
Amazon played a key role in the development of cloud
computing by providing cloud services to external
customers and launching Amazon Web Services (AWS) on
a utility computing basis in 2006. With the entry of many IT
giants such as Google, Microsoft, Oracle, and IBM, the
cloud services market has become prosperous but more
competitive.
Initially, there were three main types of cloud computing
services in the market: infrastructure-as-a-service (IaaS),
platform-as-a-service (PaaS), and software-as-a-service
(SaaS). As the cloud market matured, more categories
emerged, such as data storage-as-a-service (DSaaS),
hardware-as-a-service (HaaS), desktop-as-a-service (DaaS),
business process-as-a-service (BPaaS), data analytics-as-a-
Service (DAaaS) and others (Rimal et al. 2009).
Some industry reports have suggested the huge market
potential for cloud computing services. The New York-
based 451 Research (2013) forecasted that the cloud
computing market revenue would grow at a compound rate
of 36% to US$20 billion by the end of 2016. Gartner (2012)
reported that annual IT spending on cloud service brokerage
services would have reached US$100 billion by late 2014.
In addition, Transparency Market Research (2011) reported
that the cloud computing services market was valued at
US$79.6 billion in 2011, and would grow at a compound
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annual growth rate (CAGR) of 23.21% and hit US$148.9
billion in 2014, higher than Gartner’s estimate. It also
forecasted that the market value of services production
would reach US$205.4 in 2018, based on a slower CAGR of
8.39%.
There are conflicting viewpoints on costs, performance,
compliance and management, about whether cloud services
are a better alternative to in-house systems, however. Prior
research has pointed out that the monetary cost of running
scientific data-intensive applications using Amazon.com’s
Simple Storage Service (S3) is out of reach for some clients,
because the storage services – including availability,
durability, and access performance – can be expensive and
not altogether necessary (Palankar et al. 2008). This has
been especially true for small and medium enterprises
(SMEs). Deelman et al. (2008) presented other results
though: with storage cost reductions, using cloud services is
cheaper than in-house systems for data-intensive
applications.
Different concerns have been raised regarding the
pricing of cloud computing services (Yeo and Buyya 2007).
Should the services be simple or complex? The menu of
available services now has become quite complex. Instead
of only fixed-price, fixed-menu items, today vendors are
permitting their services to be offered in highly
customizable configurations for customers with different
profiles. The granularity of selectable service components
that are charged separately has become smaller too: from the
size of storage to the number of read/write operations.
Clients who prefer this may know how to configure the
services to achieve customization. But this also increases the
difficulty of cost estimation, because the prices will change
with the configurations. So clients who want simple services
offerings that come with simple fixed-prices may not want
this.
Furthermore, the fact that clients have to put all or part
of their data on the cloud has created concerns about the
control and security of sensitive data that reside there.
Clients will not be able to access their data if the cloud
computing services are down, and they have not backed
them up properly. Sometimes it may be impossible to back
up in a timely way: this is because of the size of the data and
the limitations on the network bandwidth.
Previous studies have recognized the complexity and
importance of appropriate pricing strategy (Demirkan et al.
2008, Durkee 2010, Marston et al. 2011). The changing
cloud services industry invites fuller investigation of pricing
strategies to identify factors that reflect vendor concerns
when they make pricing decisions and evaluate their
services. Some key considerations may be missing in
current industry practice.
This research aims to provide insights to help managers
understand the complex ecosystem of the cloud computing
services market. We also will make meaningful predictions
about future changes in vendor pricing and services
provision. We ask: (1) What factors are driving the
emergence of the new services practices? (2) What
characteristics of pricing and services provision are likely to
persist in the market? (3) And what are the potential
directions for future market services provision and pricing
mechanisms?
In this article, we argue that financification, which has
been discussed in the context of IT outsourcing by Bardhan
et al. (2010a), is also suitable for cloud computing services
and other new kinds of IT services. Financification refers to
the technology-enabled practices associated with financial
market operations, revenue yield management and financial
risk management. With respect to cloud computing services,
it is intended to mean that cloud services vendors and clients
will be increasingly subject to financial market-like
conditions. Services will have bid and ask prices, just like
financial securities assets and derivatives in the stock
market.
Similar to the risks that participants in a financial market
face (securities buyers and sellers, and financial
intermediaries), vendors, clients and cloud services
intermediaries also face multiple sources of risk related to
the provision, use, and management of IT services that are
similar to financial market-like operations. Cloud services
vendors, in particular, face uncertainty in services demand,
and their clients have to balance the benefits of cloud
computing services with the risks related to control and
information security, continuity of services, and integration
with other software applications. On the other hand, cloud
computing services are built on advanced IT, so many
heretofore manual processes can be automated. This makes
it possible for vendors and clients to rapidly adjust and
create value amid the emerging changes and risks. It also
opens up the possibility for cloud intermediaries to add
value in the market.
This article makes three contributions to research on
cloud computing services. (1) Based on financial market
concepts and theory, we provide an analysis of the cloud
computing services market and identify key elements that
are present that make it like a financial market. (2) We
assess the paths for the future development of the cloud
computing services market, based on its increasing
financification. (3) We also offer research directions that
will support the achievement of best practices and good
market design.
2. Financification and the Design ofMarket-Based Economic Exchangein IT Services
We next discuss characteristics of cloud services, the
functioning of the related services market, and the ways in
which it has become increasingly financified – in other
words, more and more like a real-world financial market.
2.1. Cloud Computing Services: Characteristics
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IT services facilitate the processing, manipulation and
managerial use of information with the help of computers,
networks, communication devices, and technologies that
interoperate all these devices and networks. Leavitt and
Whisler (1958) defined IT services in terms of the
technologies used for processing large amounts of
information, applying statistical and mathematical methods
to decision-making, and simulating mental processes with
computers. The definition highlights the role of IT, and IT
services by association, in enhancing the capability of
human beings to rapidly process large amounts of complex
data.
Cloud computing technology enables software
applications to be easily scaled up and down, and allow
companies to gain agility in prototyping, developing, testing,
and deploying new applications and services. They include
technologies that enable data and application portability.
They also comprise virtualization and parallelization
techniques that enable better computing power utilization
(Williams et al. 2012), web-scale resources and data
management technologies (Birman et al. 2009). Finally,
they cover large-scale event notification and multi-tenancy
technologies (Zhou et al. 2010), service-oriented
architecture (Bardhan et al. 2010a) and web services
(Newcomer and Lomow 2004). Cloud computing
technology has created new possibilities for IT services
provision and business operation in ways that are
dramatically different from what was available before.
Cloud computing services have some important
characteristics. First, they are similar to information goods:
the cost of providing an additional unit of services is
negligible. This is true for an additional hour of cloud
services or a new client. Such an approach compares
favorably to the large investment that is required in the
infrastructure to power the services (Varian 1995). Cloud
computing services are also similar to experience goods:
clients have imperfect information about services until they
have tried them out (Shapiro 1983). In addition, cloud
vendors sell services in units that are charged based on the
amount of time a client uses them, making the services
similar to perishable goods, whose value diminishes over
time and cannot be restored (Bardhan et al. 2010a). Finally,
unlike electrical and water utilities, cloud computing
services address multiple purposes. They take various forms,
including storage, computation, networking and applications,
and can be consumed separately and jointly.
2.2. A Profile of the Cloud Computing Market
The cloud computing services market is an ecosystem
that includes different stakeholders that play several
different kinds of roles, including component, service
provision, and infrastructure roles (Adomavicius et al. 2008).
The IT services industry supports the operation and
management of clusters of computers and servers with
specialized code that enables the efficient allocation of
computing resources. These key components make cloud
computing possible. An example of related stakeholders that
play the component provision role in cloud computing is
virtualization solution providers. They have the technical
and technology capabilities to help organizations build their
own data centers and private clouds. The players include
VMware, Citrix, Oracle, and Microsoft Virtual, and others.
A difference between cloud computing and traditional IT
services is that they are delivered via networks, including
the Internet, mobile networks, and private networks.
Network services providers operate local or wide area
networks, mobile networks, satellite networks, or other
types of networks through which clients can transfer data.
Other firms, such as telecom and broadband services
companies, satellite operators, and so on also play the role
of infrastructure providers.
A third role that we observe is application services
providers. Stakeholders in this role are services providers
that directly interact with cloud computing clients. They
mainly are software application providers that deploy
products on cloud platforms enabled by different
infrastructure technologies. Clients subscribe to applications
provided by these stakeholders, and they address clients’
business needs, computing requirements, in multiple
industry settings. The coverage spans online gaming to
scientific computing, and more.
When cloud services providers initially emerged, they
used delivery and pricing mechanisms in a pull-and-lock
mode. Clients locked the resources for however long a
period they needed once they successfully launched a
service instance in the cloud. Later, the services began to be
provided in a pull-and-lose mode. This enabled the vendor
to sell idle computing resources in a cheaper but more
flexible way. A downside was that clients might lose access
to computing resources even though they launched service
instances. The vendor could redirect the resources wherever
they provided a higher return.
On the pricing side, cloud computing services vendors
employ a number of different pricing mechanisms,
including usage-based, subscription-based, and a hybrid mix
of fixed-price and usage-based pricing. Even for a specific
type of pricing though, variations exist in the market. For
example, cloud services subscription plans can differ in the
length of the subscription period (a month, a quarter, or a
year), the limit on the number of user accounts that can
access the cloud within a subscription plan, and the number
of applications that can be hosted.
Prices have been decreasing over the last decade,
suggesting evidence for the growth and maturation of the
cloud computing services business. For example, Amazon
Web Services (AWS), the dominant vendor in the market,
has reduced the prices of its services offerings many times
(Lauchlan 2013). Amazon successfully promoted cloud
services and achieved a 30% market share by 2013, with
more than US$1 billion in revenues in a market with high
estimated annual growth (Nichols 2014). Another sign of
the industry becoming more competitive is the price war
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that arose between Amazon and Google, shortly after
Google introduced its own Compute Engine in March 2014
(Jackson 2014).
In this competitive context, clients face the challenge of
being aware of which types of tasks cloud computing
services suit the best. They might be unclear about the total
cost of cloud computing services adoption (Durkee 2010). It
also is not easy for clients to monitor their cloud services
usage, and know the total payments due to the uncertain
demand and complex pricing structures of cloud services
(Weinhardt et al. 2009). These things have slowed down the
adoption of cloud computing services (Perry 2010).
2.3. The Financification of the Cloud Market
Our observations on the cloud computing services
market have focused on new services delivery, pricing
mechanisms, and new stakeholders.
Industry surveys from the Cloud Information Forum
(2011, 2012) have suggested that the primary drivers of the
adoption of cloud services are flexibility, cost savings and
low adoption cost, while the major obstacles are concerns
about data security and privacy, reliability and contractual
liability. So cloud services providers need to meet their
customers’ needs and requirements with a more flexible and
collaborative approach. There is also a need to address the
vendor and client risks better, so it is possible to optimize
supply in the presence of shifting demand, and new pricing
and services delivery mechanisms.
They motivate us to explain how the financification of
the cloud services market is progressing in a fuller way. To
understand this perspective, consider some of the key
features of financial markets: (1) bid and ask prices for
securities; (2) spot, forward and futures prices; (3) liquidity
versus depth; and (4) hedging and risk management. Seeing
these in the cloud computing market will suggest a
progression toward financification.
We have already discussed bid and ask prices, and the
different ways in which cloud computing services prices can
be quoted. Note that there is currently no cloud services
market exchange that handles cloud computing futures
contracts, as are handled by financial market exchanges
when futures contracts for foreign exchange (FX),
derivatives or other financial instruments are involved.
There are some hints that options and forward contracts for
cloud computing may be coming though (Rogers and Cliff
2012b), with recent research on market mechanisms and
demand revelation in IT services (Rogers and Cliff 2010,
2012c; Wu et al. 2008). Related issues have been explored
before for options on IT resources (Clearwater and
Huberman 2005, Yeo and Buyya 2006) and grid computing
(Clearwater and Huberman 2005, Sandholm et al. 2006).
Some of these approaches have been conceptualized similar
to financial options and forward contracts on FX, for
example, which trade in the over-the-counter (OTC) market
between broker-dealers based on bilateral negotiation, but
not as standardized contracts in financial market exchanges.
Other related issues that have been studied include
financial risk management for IT services resources, and the
benefits associated with matching risks between the vendor
and client sides (Benaroch et al. 2010) include, for example,
the technology-enabled practices associated with resource
management, revenue yield management, and risk
management of IT services (Benaroch et al. 2010, Rogers
and Cliff 2010, Kauffman and Sougstad 2008a, 2008b). This
is like portfolio management, the focus on asset pricing, and
the emphasis on financial risk in investments and markets,
only for the IT services market (Bardhan et al. 2010a,
2010b).
In addition, we view the cloud computing services
market as an IT services ecosystem that consists of
interdependent stakeholders that have created the conditions
for the emergence of a near-financial market in this services
arena. There are cloud computing services vendors, cloud
technology services providers, services brokerages,
application service providers, and services clients. Services
vendors provide the major categories of cloud computing
services, including IaaS, PaaS, SaaS, and other “X-as-a-
service” offerings. Cloud technology services providers
further include vendors that offer the technologies that
enable cloud computing. They use virtualization
technologies, application parallelization, large-scale storage
solutions, monitoring and billing technologies, and other
capabilities. Vendors and clients exchange value to achieve
joint economic gain. In addition, market intermediaries and
brokers, similar to those in financial markets, facilitate the
search for and matching of clients with appropriate services
vendors, smooth transaction handling, and offer peripheral
services (Huang 2013, Huang et al. 2013b).
The financification of the cloud computing services
market supports effective practices to enhance market
performance and avoid market failure (Shang et al. 2012,
2013). The mobilization of cloud services resources and
their allocation to productive uses can be coordinated via
vendor pricing that permits clients to discover their
willingness-to-pay, much the same as what occurs in
financial markets and with revenue management (Kauffman
and Ma 2013). In addition, new instruments, similar to
financial instruments in financial markets, can be created to
support the transfer of cloud services resources from one
client to another, a broker to another broker, and so on. Spot
prices for services that change in the market based on
supply and demand, and longer-term lock-in of the services
provide this kind of flexibility (Huang 2013). This form of
economic exchange via trading will help to increase the
liquidity of cloud computing services, creating fuller
utilization and greater market-generated welfare (Huang et
al. 2013a, 2013b). This also provides more flexibility for
when, how, and how many resource units are consumed in
the market, and permits conversion of unutilized resources
into money.
We also are observing new forms of intermediation in
cloud services, similar to other e-commerce markets
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(Cartlidge and Clamp 2014, Rogers and Cliff 2012a). Cloud
services brokerages have emerged as digital intermediaries
in the services market, creating value for both cloud clients
and vendors (Gartner 2012). A recent industry article
reported on 35 cloud brokerage firms (Panettieri 2013).
They generate value for clients by supporting cloud services
intermediation, aggregation, and arbitrage. They also offer
value-added customization services, making cloud
computing frictionless and refined. They also facilitate its
integration with a firm’s internal software systems, reducing
the risk of mistaken adoption for clients, similar to what we
observed in the 2000s for electronic markets (Dai and
Kauffman 2004). Cloud computing services also can be
tweaked to meet clients’ needs and still be profitable for the
vendor.
3. State-of-the-Art in CloudComputing Pricing and ServicesProvision
To get a comprehensive view of the cloud services
market, we examined 19 cloud services vendors and 27
services offerings that they provide. They include four
major types of cloud computing services: IaaS, PaaS, SaaS,
and brokered cloud services. IaaS delivers computer
infrastructure based on virtualization technology. PaaS has
an additional layer on which clients can run applications
without knowing how the underlying infrastructure is
implemented. SaaS provides application services that
function as locally-installed software (Vaquero 2008).
Cloud services intermediaries, aggregators, and arbitrageurs
provide brokered cloud services in the market as well
(Gartner 2012). (See Appendix A.)
We reviewed the cloud computing services offerings and
collected pricing information from the major market players.
Our criteria for selecting a vendor were: (1) the vendor must
have made pricing information on all its services available
on its official web site; and (2) the vendor must have been
selected at least once for review in Gartner’s Magic
Quadrant Report (Leong and Chamberlin 2010, 2011;
Leong et al. 2012, 2013). The reports list cloud computing
services vendors that are leaders in the market, in terms of
revenue and market share. This is useful information. It
helps to ensure that we are sampling from an appropriate set
of cloud services vendors in the market, so the right kinds of
firms are represented, which makes our results more
meaningful.
We next offer our reading on the state-of-the-art in the
cloud computing services market, inclusive of current
services provision and pricing mechanisms, and their trends,
based on our observations and analysis.
3.1. Services Delivery and Pricing Mechanisms
Table 1 shows that most PaaS, SaaS, and cloud
brokerage services vendors offer reserved services delivery.
Cloud computing services with reserved resources are pre-
committed resources for clients by the vendor. Clients can
choose from several options for the length of the reservation
period predefined by the services vendor. For example,
Amazon EC2 offers clients with reserved compute instances
for a period of one year or three years. From the vendors’
point of view, reservations benefit them by reducing their
demand uncertainty. Any pre-paid reservation fees can
enhance a vendor’s cash flow, and generate lock-in with
clients.
Associated with the reserved services is reservation-
based pricing, which varies with the type of services offered.
Reservation-based pricing has been popular in the restaurant
and hotel industry; it typically results in increased vendor
revenue (Alexandrov and Lariviere 2008). In the case of
hotel reservations, rooms usually are scarce in popular
attraction areas, so travelers must be willing to pay for
reservations. The same rationale does not hold in cloud
services though. Computing capacity is expandable at a
relatively low cost. So clients have little incentive to reserve
services (Meinl et al. 2010). The situation will change when
the cloud services market becomes more competitive and
new demand emerges due to advances in related
technologies though. These will include sensor technologies
that enable wearable devices connected to cloud-based
health informatics services, and web-based data analytics
that help companies gain insight into their operations,
customers and the marketplace for their products.
Table 1. Services delivery and pricing mechanisms
RESERVAT
ION-BASED
PRICING
USAGE-BASED PRICING TECHNICAL
SUPPORT-
BASED
PRICING FIXED
PRICE
SPOT
PRICE
Reserved
services
delivery
Amazon,
Google,
Microsoft,
Rackspace,
GoGrid,
Joynet, HP
FlexiScale,
Amazon,
Google,
Microsoft,
Rackspace,
GoGrid,
Joynet, HP,
FlexiScale,
On-
demand
services
delivery
Amazon,
Google,
Microsoft,
Rackspace,
GoGrid,
Joynet, HP,
FlexiScale,
Spot-price
services
delivery
Amazon
Brokered
services
delivery
PiCloud,
CloudSigma,
ProfitBricks
On-demand services delivery, which provides reserved
resources based on pay-per-use, also has been widely
adopted in the cloud computing services market. On-
demand services delivery performs in an interesting way.
Once a client launches a job, the vendor will set aside
capacity for the job until the client terminates it. Clients are
charged based on the usage of services, as well as by the
amount of time that the services are used. Usage-based
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pricing is optimal for information goods that have negligible
marginal production costs (Maskin and Riley 1984), such as
movie CDs or software apps. Most IaaS vendors in our
survey employ usage-based pricing. The prevalence of
usage-based pricing among IaaS offerings is inconsistent
with the findings in Fishburn et al. (2000) and Sundararajan
(2004), though it matches the findings in an earlier study by
Maskin and Riley (1984). The key difference in these
studies is
whether the transaction costs associated with usage-based
pricing are negligible. IaaS vendors commonly implement
highly automated management systems, which generally
have low transaction costs. So it is reasonable for IaaS
vendors to adopt a pure usage-based pricing scheme.
Prior research suggests that fixed-fee pricing together
with usage-based pricing always outperforms pure usage-
based pricing (Sundararajan 2004). Such two-part tariff
pricing is never worse than any non-linear pricing strategy
(Masuda and Whang 2006, Png and Wang 2010). These
findings are consistent with pricing practices in the cloud
market also. Many PaaS and SaaS vendors have adopted the
two-part tariff pricing model, for example. Clients typically
pay a monthly subscription fee for pre-assigned usage
quotas, and pay an additional price if the usage exceeds
them.
The resource acquisition and allocation of spot-price
services delivery differ from those of reserved resources.
There is no commitment on the part of the vendor to
guarantee access at a given time, other than via the client’s
willingness to pay the spot-market price for services. The
acquisition of resources for spot-price services varies
according to a client’s bid price valuation and the changing
relationship between supply and demand. Clients submit
bids representing the maximum unit prices they are willing
to pay for a predefined type of spot-price service. As soon
as the service price in the spot market goes above the client's
bid price, the vendor will terminate its in-process services.
As a result, computing tasks running as spot-price services
may occasionally be interrupted due to price spikes in the
market. Clients will receive service allocations that are
affected by the interplay between supply and demand, and
bear risks of service termination that are not controllable by
themselves. On the other hand, spot-price services are
cheaper – most of the time, spot-price services represent less
than 25% of all tasks that use reserved resources. The cost
savings from using spot-price resources are attractive for
clients who need cloud services for compute-intensive but
time-insensitive tasks, such as scientific computing, web
crawling, and data analytics.
A variety of resource allocation approaches have
emerged recently, involving predictive analytics, machine
learning, and other models that support value-conscious use
of limited server resources (Das et al. 2011, Mazzucco and
Dumas 2011). Empowered by these resource management
techniques, brokered services delivery is able to provide less
costly and more reliable services to clients. For example,
PiCloud (now owned by DropBox), a computing services
broker that connects clients to Amazon’s cloud services,
emphasizes a positive customer experience. It delivers
results 33% faster and meanwhile saves clients 65% in total
costs compared to spot-price purchases, while still running
85% of jobs as Amazon spot-price services instances (Elliott
2012). Brokered services delivery can be provided in
various ways, such as management services provided as
subscription plans that support day-to-day management of
cloud computing services from various vendors, or value-
added services that are charged by usage. Clients usually do
not have full control of the resources. Instead, services
brokers make it transparent to clients how they acquire,
integrate and manage resources from different services
vendors. They also provide clients with interfaces to
configure and manage their usage.
Most major vendors apply technical support-related
pricing to different technical support plans with different
levels of expertise for client engagement. In general, SaaS
vendors provide greater flexibility in technical support
options, while IaaS and PaaS vendors offer more limited
options for their clients. This may be due to the relative
simplicity of IaaS and PaaS services. For example, IaaS
clients can terminate the services and shift to other vendors
anytime without incurring high costs. In contrast, SaaS
services typically contain functions that are provided only
by a particular vendor. More technical support from the
vendor is needed when problems occur, and a switch to
other SaaS vendors is generally difficult.
3.2. Services Delivery and Pricing Innovations
Amazon has been an innovator in cloud computing
services delivery and pricing mechanisms. It first introduced
its Elastic Compute Cloud (EC2) services in 2006, and used
an on-demand services delivery with a pay-per-use pricing
mechanism. Payments were based on actual usage, charged
by the hour. Since then, Amazon and its competitors
introduced a series pricing innovations in the market.
In 2009, Amazon announced two other new services
delivery mechanisms: EC2 reserved instances and EC2 spot
instances. With the reserved services delivery mechanism, a
client must pay a fixed fee up front to reserve services. The
client still needs to pay for actual usage, but the per-hour
rate will be lower than that in the on-demand pay-per-use
model that Amazon introduced in 2006. Spot-price services
delivery uses a different pricing model that is auction-based.
The major difference between spot-price services and the
other options that Amazon offered was that the spot-price
services were subject to interruption initiated by the vendor.
This pricing mechanism allowed Amazon to ration its idle
computer resources based on client willingness-to-pay.
In spite of its innovative services and pricing design,
Amazon has more or less locked itself into a specific billing
cycle: it always charges clients by the hour. Others are
pricing their services in a more innovative way. For
example, in 2011, CloudSigma, a Zurich-based IaaS vendor,
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announced a burst-pricing scheme that had a billing cycle as
short as five minutes. This is similar to the practices that
some telecom services firms used. They initially offered
monthly subscription plans only, and then started to offer
per-second billing. It is likely that the cost associated with
metering and billing in such a short interval is lower now. In
2012, PiCloud offered its clients even more value by
providing a usage-consolidation service. A client could use
1,000 compute instances, each active for one second only,
and then pay the price for using one compute instance for
1,000 seconds. This would have cost the client 1,000
instance hours via Amazon EC2. The emergence of
configurable cloud computing services offerings reflects
advances in managing virtualized computing resources.
Cloud vendors can now give more flexibility to their clients
than ever before.
4. Next-Generation Mechanisms
In the cloud computing services market today, we
observe the pull-and-lose mode of services delivery that
emerged after the pull-and-lock mode, which was the
default in the early days for cloud computing services.
This change in the services delivery mechanisms reflects
the needs of services vendors for more flexibility in selling
and re-allocating their resources, and their desire to
penetrate the IT services market. On one hand, a large
investment in infrastructure puts pressure on cloud services
vendors to recover their investment. On the other hand,
cloud computing services still are underused, despite their
capability to accommodate all kinds of client needs. We can
see this hold-up based on different concerns in adopting
cloud computing expressed by leaders in sectors that rely on
or heavily use IT services, such as financial services and
healthcare.
Cloud computing services are still in the process of
maturing and fast growth. New functionalities are being
added to existing services offerings, and totally new services
are being introduced. In the process, some clients will
naturally be resistant to trying out cloud computing services,
unless there are more options that mitigate both the
operational and financial risks for them. They also will need
more support to transition from legacy systems to the cloud,
a big challenge for many organizations.
We next will offer insights on new services provision
and the kinds of pricing mechanisms needed for the future
development of cloud computing services.
4.1. Quantity Discounted Pricing: Trade Cost Reduction with Demand Uncertainty
It is common in pricing strategy that a services vendor
uses quantity discounts to give buyers incentives to
purchase greater than the usual quantity. Research has
shown that second-degree price discrimination, especially
non-linear pricing strategies such as quantity discounts, is an
effective way for vendors to segment clients, gain market
power and obtain higher profit (Goldman et al. 1984,
Monahan 1984).
In the current cloud market, only storage service
vendors provide quantity discounts in the form of ladder-
shaped tariffs. They offer clients who use the services
bigger discounts on the unit prices. Other than that, quantity
discounts are rarely used in any other categories of cloud
services.
For example, for an Amazon EC2 on-demand standard
instance (small) running on Linux or Unix, the price is fixed
at $0.06 per instance-hour. There is no unit price difference
for a customer who runs 10 instance-hours versus one who
runs 10,000 instance-hours. For information goods, past
research indicates that usage-based pricing with a quantity
discount strategy is optimal when there are no transaction
costs (Maskin and Riley 1984). So it will be an option for
cloud vendors to use quantity discount pricing to incentivize
their clients to consume more services.
4.2. SLA-Based Services Delivery: Flexible Quality Guarantees, Costs and Compensation
Cloud services are experience goods: their tangible
features do not fully reveal their true value. Software
outsourcing contracts have a similar issue due to
information asymmetry (Dey et al. 2010). Enhancing the
completeness of the contract can potentially overcome this
problem, but at a high cost (Hart and Moore 1999). In the
practice of software outsourcing contracting, most vendors
specify the penalties applicable when delivery is delayed
(Whang 1992). Clients also have the right to terminate their
contracts, although this may be explicitly priced in a way
that the vendor can assure it will not be left with idle
capacity that it spent money to create (Benaroch et al. 2010).
In cloud computing, service level agreements (SLA)
serve as incomplete contracts between a client and a
services vendor, similar to other IT and grid computing
services (Li and Gilliam 2009, 2010; Li et al. 2010). Service
uptime guarantees are often stipulated in an SLA, like an
uptime guarantee of 99.9%, and terms specifying service
characteristics and penalties. In current practice, many IaaS
and PaaS vendors include both uptime guarantee and
penalty terms in their SLAs; few SaaS vendors do this
though.
All the vendors we reviewed, except for Salesforce,
provide uptime guarantees. And some IaaS vendors are
offering different uptime guarantees for different types of
services. For example, Amazon provides a 99.9% uptime
guarantee for S3, and a 99.95% uptime guarantee for EC2.
Rackspace provides a 99.9% uptime guarantee for storage
services and a 100% uptime guarantee for network
availability.
Most of the SLAs include uniform penalties that the
vendor must pay to all sorts of clients. Some issues are
ignored by this penalty design approach though. For
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example, client attitudes toward the risk of services
downtime differ across applications and periods. Mission-
critical enterprise applications typically carry a much higher
cost for services downtime than non-critical applications
(Hiles 2005). To meet the diverse expectations, the vendor
may wish to consider including customized penalty terms
that are expected to outperform uniform penalties. It may be
mutually beneficial to provide functions for negotiating
penalty terms to satisfy different types of clients. With the
technology affordance of SLA-oriented resource
management of cloud vendors, future services delivery will
differentiate among and satisfy service requests based on the
desired utility of users, balancing risk concerns and service
costs.
4.3. Cloud Computing Services Market Evolution: Toward Financification
Next, we discuss how the cloud computing services
market has been changing,
and how the financification of
the cloud market will reshape
and guide its future evolution.
When usage-based on-
demand services were first
introduced in the market, they
nicely addressed the early
adopters’ uncertainty about
services quality, and to what
extent users needed cloud
resources. With the pay-as-
you-go mechanism, users
were subject to potential risk
of the unavailability of cloud
resources when they needed
them, and the potential for
price increases in the future.
With the financification of the
cloud, we expect an options
and futures contract market
for cloud services to emerge so users of on-demand services
will be able to hedge their risks. In the context of IaaS,
Rogers and Cliffs (2012b) proposed a pricing method that
combines options contracts with on-demand purchasing.
They show that options contracts can
provide clients with flexibility and cost-savings, as well
give the vendor improved server utilization.
Later, with the improvement of service quality and
adoption of cloud, reservation-based services were
introduced to users who wanted to avoid the uncertainty of
availability and price fluctuation. They were subject to the
risk of being locked in and not being fully satisfied with the
services, and they also may have over-estimated their cloud
resources needs. It is conceivable that the financification of
the cloud will also address these additional problems.
Exchange-like markets for cloud services will likely emerge
so users of reserved services can resell unutilized resources.
With the huge investments in cloud computing capacity
that have been made since Salesforce.com emerged, many
vendors now face the spectre of unutilized capacity due to
shifting supply and demand. Spot-price services were
introduced as a way for vendors to monetize their unutilized
capacity. The services were subject to interruption risk
though. So today, the logical next stage of evolution that
will occur in an increasingly financified market is the
further development of cloud brokerage services, which will
provide leverage for more economical use of spot-price
services. An example of this is Amazon’s 2013 launch of its
EC2 Reserved Instance Marketplace, in which users can
resell their unutilized balance of reserve instances to other
clients.
Looking toward the future, we expect to see further
development and evolution of the cloud computing services
market, related to its technical aspects, and the mechanisms
that structure the offering, pricing, purchase, and delivery of
services. See Figure 1 for a summary.
Although the figure may be misinterpreted as
suggesting that the cloud computing services market is an
integrated market, it actually is quite fragmented. There are
variations in how services are provided and consumed, and
how vendors compute what their clients will pay. Thus,
there is potential for a more efficient services market that
subsidizes new clients who have uncertainty about adoption,
use and workload management, and have to deal with
contingent conditions in their day-to-day operations.
In addition, the current marketplace has many
constraints on what resources are available and how they
can be traded between clients. Take Amazon’s EC2
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Reserved Instance Marketplace as an example. Users can
resell their unutilized instances to other users. However, in
the current practice, the time available to consume a
reserved instance often is rounded to the nearest month, and
the marketplace will charge a service fee of 12% on the
price. These constraints mitigate the marketplace’s ability to
fluidly facilitate the trading of unutilized resources,
reducing their market liquidity. We believe that a future
financial market exchange-like marketplace will be needed
to optimize resource allocation and re-allocation to
effectively promote the adoption of cloud computing
services.
Finally, because IT services are subject to risk for
quality, cost, delivery, availability, it is likely that
insurance-related products will emerge for them in the
future (Accenture 2010, Cohen 2013). The establishment of
cloud services benchmarks (Yi et al. 2010) and the
maturation of actuarial analysis of cloud services risks will
support this future development, similar to what we have
seen with other IT services (Bardhan et al. 2010a, Gillam et
al. 2013, Kauffman and Sougstad 2008a). An example is
CloudInsure (www.cloudinsure. com), a Rye, New York-
based cloud computing insurance administrator that
specializes in IT services risk transference. Another is MSP
Alliance’s (www.mspalliance.com) managed services
insurance, which offers vendors indemnification against
liabilities from providing cloud services.
5. Research Directions for CloudComputing Mechanism Design
The range of issues that are related to the financification
of cloud computing deserve closer scrutiny. This can be
achieved by laying out a research agenda related to the
fundamental mechanism design issues for cloud computing
services. The issues identified are: the supply and demand
relationship and demand estimation; services offerings and
the structure of market prices; contracting, incentive-making,
and risk mitigation; third-party services and the value of
intermediated cloud services; and future innovations that
have the potential to reshape the entire market.
We begin with the first issue in this research direction on
the supply and demand of cloud computing services:
Research Direction 1 (Supply and Demand of
Cloud Computing Services in the Market).
Understanding the future functioning of the cloud
computing services market requires a basic knowledge
of how supply and demand interact with one another.
An important research direction to pursue involves
developing theoretical models and empirical studies
that will enable senior management to obtain more
knowledge of how supply and demand will play out in
the future, as the conditions, competition and
capabilities in the market change.
Since production and consumption of cloud computing
services are growing globally, it is important for researchers
to assist industry and government observers to establish
measurements and estimates of this area of services in the
economy. For example, Gartner’s estimates on cloud
services were recently expanded to US$180 billion by 2015
(Flood 2013). Seagate estimated that US$79 billion in cloud
computing hardware and equipment will ship by 2018. The
healthcare industry, for example, will use cloud computing
for 600 million images processed each year, and move from
only 15% today to 50% of diagnostic images stored in the
cloud by 2016 (Cox 2013).
These statistics are just the tip of a big iceberg though,
and other issues deserve attention (Woods 2014). Key
considerations are building models for national-level cloud
computing services growth – both in terms of what is
demanded and what is supplied. Some observers view rapid
growth of cloud computing as inevitable (Mason 2013,
Weinman 2009). Within specific industries, there exists the
issue of how different business processes and computing
workloads will be affected by cloud computing services
growth. How much money will be saved? Will downsizing
of organizations occur, shifting cloud computing demand?
And how long will it take to reap business value from cloud
computing, and what can be done to accelerate it?
Research Direction 2 (Services Offerings and the
Structure of Market Prices). There is a need to
pursue new bases for innovation related to cloud
computing services and market mechanism design,
and pricing and quality strategies. This will be a
fruitful research direction because it is necessary and
valuable to develop and test new business models,
pricing approaches, and mechanism design algorithms.
The demand for cloud computing services is driven by
the variety of the client needs and the quality of the services
that are offered, the price structures and price levels at
which they are offered, and the mechanism designs that
meter their delivery. This opens up a broad spectrum of
issues for research. For example, what future business
models are likely to be effective in supporting services that
will create higher demand? Will they be private-label
services with branded performance and unique qualities? Or
will they be more commoditized services whose provision is
driven by the cost leadership of large-volume, high service-
scope vendors? The financification of the cloud computing
services market is likely to be driven toward greater service
commoditization, thinner margins where the services are
provided without recognizable innovations that create value,
and increasing homogeneity in the functionality of the
services that are offered. There are opportunities to conduct
analytical and computational modeling research to assess
the relative performance of different kinds of mechanisms
under different assumptions about future growth and
demand. It will be especially useful to understand the extent
to which prices are dispersed or concentrated across
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different vendors who offer similar services.
Also, managers will find it beneficial to understand
more fully how to do effective statistical modeling of spot-
price cloud computing instances in public markets (Javadi et
al. 2011) and how spot prices change in these kinds of
market environments (Javadi et al. 2013). In future markets,
data analytics for different kinds of cloud computing
instances, and how demand and supply interact over time in
the presence of managerial choices on pricing for them, will
become strategic capabilities for cloud computing vendors
and their clients. Cloud computing services should be built
on vendor, client, and market informedness (Li et al. 2014),
so that it is possible to gauge prices under normal operating
conditions, as well as for peak loads in the market (Mattess
et al. 2010).
Research Direction 3 (Incentives, Contracting, and
Risk Mitigation). These issues motivate another
research direction that involves economic analysis of
incentives, assessment of information asymmetries for
SLA contracts, statistical analysis of the sources of
risk, and financial economic modeling of risk and
return.
Incentives, contracts and risk mitigation are different
facets of the same basic problem in cloud computing
services. Vendors need to design services and operate
mechanisms, supported by effective and balanced contracts,
so that it is possible for the client side to believe that there is
fair play with the sharing of the benefits and value arising
from services provision. For example, a vendor may grant
its client a real option involving the right but not the
obligation to switch from fixed-price contracted services to
spot-price instance purchases.
Benaroch et al. (2010, p. 319) argued: “When an IT
services vendor permits a client firm to exercise its
flexibility to switch sourcing modes, it essentially is offering
an opportunity to the client to achieve a marginal cost
advantage per IT service unit at varying demand levels.
There may be a loss of business for the vendor and a value
gain for the client. ... For the client, there will be the
irreversible switching costs of searching for a new vendor,
and whatever re-contracting costs arise in the process ...
Thus, switching between sourcing modes presents … a
trade-off …”
The research on IT services beyond cloud computing
services, especially for outsourcing and fixed services
contracting has explored a number of modeling perspectives
that are likely to be useful in the cloud computing context.
An example is the work of Alvarez and Stenbacka (2007).
They explored how to model IT sourcing and backsourcing
decision-making, so that it is possible to adjust the
contractual acquisition of services when services demand
falls in a flexible way, with a fair price charged by the
vendor. Techopitayakul and Johnson (2001) studied another
research context: application service provider (ASP)
operations. They modeled decision-making under
uncertainty for the value of the software that is used, the
number of users, and the overall usage level. They assessed
a vendor’s offering, including usage-based pricing versus a
flat subscription fee, back-sourcing to in-house computing,
and contract abbreviation. The research is especially
interesting for its inclusion of how learning effects from
service consumption for the vendor and client play into the
valuation of contract terms for IT services.
Research like this offers tremendous motivation to
researchers and managers to port some of these ideas from
statistical analysis, risk management, and financial
economics into cloud computing consulting and services
management practice (Bardhan et al. 2010). Due to the
information asymmetries that are present in cloud
computing, the vendor sees the market as a whole but the
client only knows its own demand (Stantchev and Tamm
2012). There are ample opportunities for process-perfecting
third-party information and data analytics providers to enter
the market, increase vendor and client informedness, and
improve their welfare (Knapper et al. 2011).
Research Direction 4 (Third-Party Services and the
Value of Intermediated Cloud Services). Digital
intermediation in the cloud computing services market
is a key target for mechanism design innovations. The
creation of new knowledge about the industrial
organization and optimization of IT services and cloud
computing intermediation will offer high scientific
payoffs and positive business returns on investment for
research that deals with the hard problems in this
context.
An intermediary’s position in a technology ecosystem
is determined by its viability and sustainability. The
intermediary will demonstrate viability when it creates
economic value for other participants in the ecosystem (e.g.,
buyers and suppliers in supply chain management, or clients
and vendors in cloud computing) in excess of the value
produced in its absence. This value difference must be
sufficient for the intermediary to earn a profit, so it will
maintain its incentive to participate and supply services
(Kauffman et al. 2010). The intermediary will demonstrate
sustainability when it is continuously able to create value
over time through the service transactions it supports to
generate profits that cannot be achieved without similar
market organization.
Beyond these basic observations though, how will we
know which intermediated solutions will work in the market,
and which will not? For example, will it be market structure,
competitive positioning, service pricing strategy, service
quality, or information security that will be the foremost
considerations? What kinds of models and business policies,
and what kinds of empirical evidence and business results
will make it clear what works and what does not? Cloud
computing technology platforms will do well when their
installed base of clients is high, the demand for their
services is relatively stable, and their growth trajectory
International Journal of Cloud Computing (ISSN 2326-7550) Vol. 2, No. 1, January-March 2014
11 http://www.hipore.com/ijcc/
looks positive for the future. But investments in cloud
services intermediation, similar to every other facet of
business in a modern economy, will be subject to the
vagaries of competition, vendor strategy errors, sufficient
compatibility, and mistaken services and mechanism
designs. As a result, undertaking research that provides a
deeper understanding of how cloud computing services
firms need to design and operate their businesses also has
the potential to produce useful new knowledge about their
market performance.
Research Direction 5 (Future Innovations in
Technologies, Services and Infrastructures).
Research that identifies the basis for future
innovations in the cloud computing services market
will be of high value, especially if it is possible to
explain and predict how and why, and under what
circumstances, the changes are likely to be observed.
Technology and technology-based services forecasting
are among the most difficult tasks that business and
technology analysts need to undertake in the current
business environment. One perspective on future
innovations and markets for cloud computing services is
that technological innovation will be supply-led, with the
innovations on the vendors’ side, with market demand being
transformed in the process (Adomavicius et al. 2011).
Another related perspective is that cloud computing services
innovation will be demand-led: the more the market
demonstrates its willingness-to-pay for new services, the
harder will vendors work to innovate and drive profit from
the new business. So an important research direction for
cloud services is to study, forecast and analyze how future
innovations will take place and what are their possible
adoption and diffusion paths.
6. ConclusionThis article offers useful contributions for research and
practice. On the research side, it shares a new perspective
for the organization of cloud computing services markets,
supply and demand for services, market mechanisms and
pricing approaches, contracts and incentives, and third-party
intermediation. The cloud computing services market
exhibits key features of financial markets, including: (1) bid
and ask prices for services; (2) spot, forward and futures
prices; (3) services liquidity and services depth; and (4)
opportunities to apply hedging and risk management. We
illustrated this with spot prices and dynamic prices, with
cloud computing insurance, with brokered cloud services,
and other compelling examples.
Our central goal in this article was to demonstrate a
practice-led set of scientific observations that can be
interpreted from the perspective of relevant theory from
financial economics – and its ties to related markets. We are
pleased to offer the financification of the cloud computing
services market contribution to managerial understanding of
a leading example of the dramatic changes made possible
due to a revolution in technology – computing in the cloud –
and the continuing evolution of the IT services practices that
have occurred around it. Through the lens of financification
that we have offered, managers and consultants who are
trying to understand current and future markets for cloud
computing will be empowered to make more confident
predictions and thoughtful explanations for what is to come.
A number of future challenges based on our perspective
are likely. How far will cloud computing services go in
terms of the extent of financification we will see?
Technological, economic, business, and competitive factors
are all likely to play a role in the future. We have not
answered all of the questions that an informed group of
researchers and practitioners are likely to ask. Nevertheless
we have offered a practice-led view of what is likely to
happen in a marketplace that is subject to the inexorable
forces that all financial markets have experienced – as we
have seen in other sectors with perishable services,
including the hospitality, air travel, temporary labor services,
and television and radio entertainment sectors.
7. AcknowledgmentsThe authors acknowledge Singapore’s Agency for
Science Technology and Research (A*STAR) for its
generous support of this research, as well as the following
individuals at the Institute for High Performance Computing
(IHPC) for their input: Terence Hung, Li Xiaorong, Henry
Palit, and Qin Zeng. We benefited from the comments of the
International Journal of Cloud Computing editors, Hemant
Jain of the University of Wisconsin, Milwaukee, and Rong
Chang, at IBM’s T.J. Watson Research Center, and an
anonymous member of the journal’s editorial board. Huang
Jianhui thanks the Ph.D. Program in the School of
Information Systems at Singapore Management University,
and Singapore’s Ministry of Education for doctoral
fellowship funding from 2009 to 2013. In addition, Yang
Yinping benefited from the funding provided by her
Independent Investigator research grant at A*STAR.
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Appendix We reviewed 19 cloud computing services vendors that
offer 27 types of services, including 15 IaaS, 6 PaaS, 7 SaaS,
and 3 cloud services brokerage services. (See Table A1.)
Table A1. Cloud services vendors selected for this study
TYPE NAME VENDOR URL
IaaS
Amazon EC2 On-Demand Instance Amazon goo.gl/fEzlD
Amazon EC2 Reserved Instance Amazon goo.gl/fEzlD Amazon EC2 Spot Instance Amazon goo.gl/fEzlD
Amazon S3 Amazon goo.gl/BcG1n
Infrastructure-as-a-Service Alatum goo.gl/w0B9d
Enterprise VM Hosting nGrid goo.gl/ihEuI
CloudSigma CloudSigma goo.gl/20mev
Cloud Servers GoGrid goo.gl/6Z4bO Joyent Cloud Joyent goo.gl/xkcwA
Rackspace Cloud Servers RackSpace goo.gl/cSZEA
FlexiScale public cloud FlexiScale goo.gl/I9rwE
IaaS ProfitBricks goo.gl/weH6L
Google Compute Engine Google goo.gl/RehH4 HP Cloud HP goo.gl/ZV3Fo
CloudLayer Computing SoftLayer goo.gl/8VKj3
PaaS
Google App Engine Google goo.gl/RLtG8
CloudFare CloudFare goo.gl/Jqt9Q
Force.com Salesforce goo.gl/Lo8jj
Microsoft Windows Azure Microsoft goo.gl/rDwP5 Microsoft SQL Azure Microsoft goo.gl/rDwP5
Amazon Beanstalk Amazon goo.gl/Tpu0E
SaaS Service Cloud Salesforce goo.gl/7sjJf
Sales Cloud Salesforce goo.gl/PkojZ
International Journal of Cloud Computing (ISSN 2326-7550) Vol. 2, No. 1, January-March 2014
14 http://www.hipore.com/ijcc/
Chatter Salesforce goo.gl/g7Lqq
Jigsaw Salesforce bit.ly/g6i6Um
Google App for Business Google goo.gl/kxkeZ NetSuite Financial Management NetSuite goo.gl/dtqTH
Office 365 Microsoft goo.gl/Au3tM
Cloud Brokerage
PiCloud Public Cloud PiCloud goo.gl/JGbKT
RightScale Cloud Comm. Edition RightScale goo.gl/PDDwl
Integration Cloud Boomi goo.gl/oO3lz
Note: All information was collected from the vendors’ official websites in October
2013, and updated as of October 2014. Vendors may have changed their website
structures and content related to their services pricing after we completed our update.
Authors
Robert J. Kauffman is Associate Dean
(Research), Deputy Director of the Living
Analytics Research Center, and Professor of IS
at Singapore Management University. His
graduate degrees are from Cornell and Carnegie
Mellon. He is an expert in technology and
strategy, financial IS, IT services, and the
economics of IT.
Dan Ma received her Ph.D. from the Simon
School of Business at the University of
Rochester. She is an Assistant Professor of IS
and Management at the School of Information
Systems, Singapore Management University.
Her expertise is in economics and IS, IT
services, cloud computing, and game theory.
Richard Di Shang is an Assistant Professor of
MIS at the School of Business, Public Admin.
and Info. Sciences, Long Island University
Brooklyn. He received his Ph.D. in Business
(IS) from City University of New York. He
previously was a Scientist at Singapore’s
Agency for Science, Technology and Research
(A*STAR). He applies experiments to test
insights from economics for IT services,
information goods, and e-marketplaces.
Yinping Yang is a Scientist and Capability
Group Manager at the Institute of High
Performance Computing (IHPC), A*STAR,
Singapore. She is affiliated with the School of
Information Systems of Singapore Management
University as an Adjunct Faculty. She received
her Ph.D. in IS from National University of
Singapore. Her research brings design science
and behavorial science to the study of electronic
negotiation systems, social networking sites and
IT services.
Jianhui Huang is a Senior Research Analyst at
the Corporate Executive Board Asia Pte. Ltd.
His Ph.D. degree is from the School of
Information Systems, Singapore Management
University. His research interests include the
economics of IT, business model in IT services,
the impact of cloud computing, and IT value co-
creation.
International Journal of Cloud Computing (ISSN 2326-7550) Vol. 2, No. 1, January-March 2014
15 http://www.hipore.com/ijcc/
IMPACTS OF MULTI-CLASS OVERSUBSCRIPTION ON REVENUES
AND PERFORMANCE IN THE CLOUD Rachel A. Householder and Robert C. Green
Bowling Green State University greenr@bgsu.edu
Abstract Rising trends in the number of customers turning to the cloud for their computing needs has made effective resource allocation imperative for cloud service providers. In order to maximize profits and reduce waste, providers have started to explore the role of oversubscribing cloud resources. However, the benefits of oversubscription in the cloud are not without inherent risks. This work attempts to unveil the different incentives, risks, and techniques behind oversubscription in a cloud infrastructure. The discrete event simulator CloudSim is used to compare the generated revenue and performance of oversubscribed and non-oversubscribed datacenters. The idea of multi-class service levels used in other overbooked industries is implemented in simulations modeling a priority class of VMs that pay a higher price for better performance. Three simulations are implemented. The first two compare the results of different VM allocation policies without VM migration. The third implements VM migration in an oversubscribed, power-aware datacenter. Results show that oversubscription using multi-class service levels has the potential to increase datacenter revenue, but the benefit comes with the risk of degraded QoS, especially for non-priority customers. Keywords: cloud computing; oversubscription; resource allocation; revenue; CloudSim
__________________________________________________________________________________________________________________
1. INTRODUCTION Utilizing cloud services to meet computing needs is a
concept that is rapidly gaining in popularity as ``in the
cloud'' has become a catchphrase in mainstream society.
According to NIST, ``Cloud computing is a model for
enabling convenient, on-demand access to a shared pool of
configurable computing resources (e.g. networks, servers,
storage applications, and services) that can be rapidly
provisioned and released with minimal management effort
or service provider interaction'' (I. S. Moreno & Xu, 2012).
The resources offered by cloud service providers (known
from here on out as CSPs) can be classified under one of
three service models: Software as a Service (SaaS), Platform
as a Service (PaaS), and Infrastructure as a Service (IaaS).
On the lowest level, IaaS provides access to resources
such as servers, storage, hardware, operating systems, and
networking. Unlike SaaS and PaaS, the customer has the
ability to configure these lower-level resources. IaaS has
become increasingly popular (Wo, Sun, Li, & Hu, 2012) as
it allows customers, especially companies and organizations,
to outsource their IT needs. These companies simply request
the computing resources they desire (Ghosh & Naik, 2012)
and CSPs provide those resources with a high level of
assurance of their reliability and availability. The
outsourcing of computing resources has several benefits for
customers. Services are offered on a pay-as-you-go basis,
allowing customers to pay only for the resources they use.
CSPs handle much of the IT infrastructure management
tasks that customers once had to support themselves.
Additionally, data and services in the cloud are widely
available through the Internet via a variety of devices.
With all of these benefits, the number of customers
looking to migrate to the cloud is on the rise and the ability
of CSPs to efficiently host as many clients as possible on a
fixed set of physical assets will be crucial to the future
success of their business (Williams et al., 2011). Cloud
services are supplied to clients through virtualization
creating the impression that each user has full access to a
seemingly unlimited supply of resources. In reality, a single
physical machine must divide its finite set of resources
amongst multiple virtual machines (VMs). Much research
has been dedicated to developing optimum resource
allocation strategies in a non-overbooked cloud. For
instance, Feng et al. (2012a) uses concepts of Queuing
Theory to maximize revenues and increase resource
utilization levels while adhering to Service Level
Agreement (SLA) constraints and He et al. (2012) employs
a multivariate probabilistic model to optimize resource
allocation. While these strategies have been shown to
improve utilization, a high percentage of resources still sit
idle at any given time (Toms & Tordsson, 2013). As a result,
oversubscription of cloud services has become an appealing
solution to further optimize cloud efficiency. Much work
has been done to investigate cloud oversubscription without
specifically measuring the impact on revenues, and little
work investigates the affects of priority classes on revenues
in an oversubscribed cloud. To address this need, this paper
contributes by using the simulation tool CloudSim to
investigate how adding a priority class of VMs to an
oversubscribed datacenter affects VM debt and QoS. The
rest of the paper is organized as follows: Section 2 provides
a brief literature review; Section 3 provides an overview of
the concept of oversubscription; Section 4 briefly discusses
the economics of cloud computing; Section 5 compares
doi: 10.29268/stcc.2014.2.1.2
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cloud computing to other industries that overbook resources;
Section 6 describes the motivations and tools used for the
simulations; Section 7 shows Experiment 1 and its results;
Section 8 discusses Experiment 2 and its results; Section 9
Compares Experiments 1 and 2; Section 10 overviews
Experiment 3 and its results Section 11 reviews some
limitations and future work; finally, Section 12 concludes.
2. LITERATURE REVIEWThis chapter reviews the major research literature in the
area of oversubscribed cloud computing as it pertains to this
study. For organizational purposes, the research in this area
has been generally split into nine different categories:
memory oversubscription, bandwidth oversubscription,
CPU oversubscription, energy efficiency, optimal resource
allocation, multi-class users, maximizing revenue in the
cloud, measuring performance impact, and safe overbooking.
2.1 MEMORY OVERSUBSCRIPTION
Of all the computing resources, the ability to effectively
oversubscribe memory provides a challenging problem for
researchers (Hines et al., 2011). In their overview of
oversubscription, Wang et al. (2012) show that both
quiescing and live migration can be used independently to
remediate overload in a memory oversubscribed cloud. As a
continuation of this work, the authors quiesce VMs with
lower work values first to allow VMs with higher work
values to perform normally and to reduce the number of
migrations needed. Williams et al. (2011) present
Overdriver in an attempt to reduce performance degradation
that comes with memory overload. They use a new method
called cooperative swap for transient overloads and VM
migration for sustained overloads. Hines et al. (2011)
present a framework called Ginkgo that can be used to
automate the redistribution of memory among VMs. Ginkgo
uses an application performance profile along with other
constraints, such as performance level thresholds established
by the SLA, as criteria for memory allocation decisions.
2.2 BANDWIDTH OVERSUBSCRIPTION Bandwidth is another resource that can be
oversubscribed. As the cloud gains in popularity, network
traffic continues to increase which can lead to bottlenecks
and latency (Breitgand & Epstein, 2012). Jain et al. (2012)
focus on remediating overload using VM migration in a tree
topology data center network. They concurrently consider
the load constraints of servers and the traffic capacity
constraints of the tree edges to develop algorithms that
relieve as many hot servers as the network will allow. They
do so by migrating a portion of their VMs to cold servers.
Guo et al. (2013) focus on optimizing the distribution of
network traffic and throughput using a load balancing
technique in a fat-tree data center network.
Breitgand and Epstein (2012) attempt to improve
bandwidth utilization by applying concepts of the Stochastic
Bin Packing problem (SBP). They suggest three algorithms
to allocate VMs belonging to a single SLA class so that the
probability of meeting bandwidth demands is at least the
minimum calculated value.
Wo et al. (2012) use a greedy-based VM placement
algorithm that is traffic-aware to improve the locality of
servers running the same application. They also introduce a
revenue model designed to determine the overbooking ratio
that will maximize profits by reducing the number of SLA
violations.
2.3 CPU OVERSUBSCRIPTION Zhang et al. (2012) consider a cloud that has
overcommitted its processor power. They introduce a VM
migration algorithm called Scattered that focuses on
pinpointing the best VMs for migration based on evaluation
of their workload degree of correlation. Using two
variations of migration, standard migration and VM swap,
Scattered is shown to limit the number of migrations
required to relieve overload and can tolerate larger
overcommit ratios.
2.4 ENERGY EFFICIENCY Moreno and Xu (2012) focus on the value of
overbooking for greener computing. Using a multi-layer
Neural Network, they attempt to predict resource usage
patterns by studying historical data and using the results to
develop optimal allocation algorithms. When overload does
occur a Largest VM First approach is used to select VMs for
migration. Moreno and Xu (2011) attempts to use cloud
oversubscription to promote energy efficiency in real-time
cloud datacenters. Their approach uses customer utilization
patterns to more safely overallocate resources and considers
SLA and energy consumption in the process.
2.5 OPTIMAL RESOURCE ALLOCATION Ghosh and Naik (2012) evaluate the history of CPU
usage of VMs to establish a one-sided tolerance limit that
represents a threshold of risk based on the probability of
overload and SLA violations. They propose that these
analytics can be applied to develop a smart risk-aware
resource allocation tool that can be used to place incoming
requests.
Tomas and Tordsson (2013) propose a new framework
for VM placement. An admission control module
determines if a new job request can be deployed.
Applications are monitored and profiled to help predict their
behavior and predominant type of resource usage (i.e. bursty
CPU or I/O). A smart overbooking scheduler then
determines the best location for the application to be
deployed.
Breitgand et al. (2012) approach optimizing resource
allocation by creating the extended SLA (eSLA) that
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provides a probability for successful VM launch in addition
to an availability probability. The eSLA allows for more
aggressive overcommit ratios without increasing the number
of eSLA violations.
2.6 MULTI-CLASS USERS Patouni et. al (2014) briefly discuss introducing a
fairness factor for allocating physical resources in the cloud
amongst multiple classes of users. However, their concept is
not fully developed and they do not consider
oversubscription.
2.7 MAXIMIZING REVENUE IN THE CLOUD Feng et al. (2012b) considers maximizing cloud
revenues by implementing dynamic resource allocation. The
allocation is based on SLA values and considers other
factors such as pricing mechanisms, arrival rates, service
rate, and available resources. However, it does not consider
oversubscription in their model nor does it consider multiple
class levels.
2.8 MEASURING PERFORMANCE IMPACT Hoeflin & Reeser (2012) attempts to quantify the impact
overbooking has on cloud performance. The relationship
between overbooking level and VM utilization is considered
as well monitoring parameters such as CPU utilization and
throughput. These are used to suggest reasonable levels of
oversubscription based on SLA constraints.
2.9 SAFE OVERBOOKING Luis (2014) explores safe overbooking methods that use
the concept of application brownout to help mitigate
performance degradation caused by overload. Tomas &
Tordsson (2014) attempts to implement a risk-aware
overbooking framework. Fuzzy logic is used to measure the
risk of overbooking decisions to help identify the
appropriate level of overbooking.
While all of these works consider topics related to
individual aspects of this research, none of them look at the
impact of overbooking on both performance and revenues in
a multi-class system. This is the primary contribution of this
work.
3. OVERVIEW OF OVERSUBSCRIPTION To oversubscribe a resource means to offer more of that
resource than there is actually capacity for under the
assumption most customers will not actually consume their
entire portion. The goal is to diminish the sum of unutilized
resources and thus increase profits. This section provides a
basic summary of oversubscription in other industries as
well as in the cloud. It also discusses the risks inherent to
employing oversubscription in a cloud computing system.
3.1 OVERSUBSCRIPTION IN OTHER INDUSTRIES
Oversubscribing resources is not a concept unique to
cloud computing. Hotels overbook rooms. When there are
less no-shows than expected, customers must downgrade
rooms or go to another hotel and are compensated for their
denial of services (Noone & Lee, 2011). The healthcare
industry overbooks doctors' time which can lead to
increased waiting time for customers and physician
overtime costs when all patients arrive for their
appointments (Zacharias & Pinedo, 2013). Airlines have
been known to overbook seats (Coughlan, 1999) and cargo
space (Singhaseni, Wu, & Ojiako, 2013). When more
customers show up than predicted, they are typically moved
to another flight which causes delays and other
inconveniences for the customer. To better accommodate
passengers when this occurs, airlines will sometimes form
alliances with their competitors to expand the number of
flights bumped customers can be moved to (Chen & Hao,
2013). The impact of this alliance can be taken into
consideration when developing overbooking policies.
Additionally, the class system can be taken into
consideration with first-class flights typically having lower
levels of overbooking than coach (Coughlan, 1999).
3.2 OVERSUBSCRIPTION IN CLOUD COMPUTING Like the lodging, healthcare, and airline industries, cloud
computing provides ample opportunity for oversubscription.
In recent years, companies have started to notice that they
are only utilizing a small portion of their available resources
(resources being memory, CPU, disk, and bandwidth
capacity). In fact, CSPs on average use only 53% of the
available memory, while CPU utilization is normally only at
40% in most datacenters (Toms & Tordsson, 2013).
Simulations done to study the CPU utilization patterns of
individual VMs have shown that 84% of VMs reach their
maximum utilization levels less than 20% of the time
(Ghosh & Naik, 2012). The underutilization of resources is
a major concern to most CSPs considering the amount of
resources required to run and maintain large datacenters.
Datacenters require a great deal of infrastructure that
consumes large amounts of power (Moreno & Xu, 2012).
Oversubscription helps to maximize resource utilization
which can in-turn help to reduce these costs and increase
profitability.
In the area of cloud computing, a cloud is said to be
oversubscribed when the sum of customers' requests for a
resource exceeds the available capacity. There can be
oversubscription on both the customer's end or the
provider's end (Williams et al., 2011). Oversubscription
stemming from the customer occurs when they do not
reserve enough computing power to meet their needs.
Oversubscription on the provider's end occurs when CSPs
book more requested capacity than they can actually support.
This type of oversubscription is more common than the
former as many customers tend to reserve more resources
than they need (Ghosh & Naik, 2012). Thus, this paper will
focus on overbooking by the CSP.
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Figure 1a shows an example of a non-oversubscribed
cloud using memory capacity as an example. Each column
represents a VM sitting on top of the physical machine (PM).
Here 37.5% of the available physical memory is being
utilized. Conversely, Figure 1b shows a corresponding
example for an oversubscribed PM. This model increases
resource utilization to 68.75%.
(a). Non oversubscribed PM
(b). Oversubscribed PM
Figure 1. VM Memory Allocation.
3.3 RISKS OF CLOUD OVERSUBSCRIPTION Though there are valid motivations to oversubscribe
computing resources, the strategy is not without inherent
risks. In order to make oversubscription possible, providers
must make several assumptions about their customers. They
assume that not all customers will use any or all of their
requested resources. They also assume that not all customers
will show up to use their resources at the same time. By
making these assumptions, CSPs flirt with the possibility of
running out of resources that customers have legitimately
paid for. This can have costly consequences for CSPs, one
of which is overload.
Overload occurs when the infrastructure is strained to
meet demands as requests for resources near or exceed the
physical capacity. This can severely degrade the
performance of the cloud and even lead to outages and
failures for some customers (Moreno & Xu, 2012; Williams
et al., 2011; Toms & Tordsson, 2013; Ghosh & Naik, 2012;
Baset et al., 2012; Zhang et al., 2012; Hines et al., 2011;
Jain et al., 2012; Wang, Hosn, & Tang, 2012; Breitgand &
Epstein, 2012; Wo et al., 2012; Guo et al., 2013; Breitgand
et al., 2012). CPU overload can result in an abundance of
processes waiting to run in a VMs CPU queue (Baset et al.,
2012). This can degrade application performance and reduce
the ability of cloud monitoring agents to supervise VMs.
Disk overload, which is not thoroughly discussed in this
paper, can have similar consequences in regards to
performance reduction. If memory becomes overloaded
(usually classified by multiple VMs swapping out to the
disk) it can be devastating because it has the potential to
inhibit any application progress (Williams et al., 2011).
Large overheads and thrashing can seriously impede the
performance of the system (Williams et al., 2011; Baset et
al., 2012). Finally, network overload can cause bottlenecks
that slow progress and can lead to a reduction in resource
utilization and oversubscription gains (Baset et al., 2012;
Jain et al., 2012; Breitgand & Epstein, 2012; Wo et al.,
2012; Guo et al., 2013).
If overload is not managed, CSPs further run the risk of
violating its Service Level Agreements (SLAs). SLAs
provide customers with a sense of security by providing a
level of assurance that their requested resources will be
available and operational when they need them (Ghosh &
Naik, 2012). Some SLAs are legally bonded, which means
that companies could be forced to provide compensations to
customers for SLA violations. Even one SLA violation can
be costly to CSPs and so developing an oversubscription
policy that considers SLA constraints is crucial for effective
implementation.
3.4 OVERLOAD PREVENTION AND MITIGATION In developing a model for oversubscription, CSPs must
take both proactive and reactive steps to reduce overload.
Studying client habits is one predictive measure taken to
determine how best to allocate resources thus optimizing
oversubscription while reducing performance degradation
from overload (Moreno & Xu, 2012; Williams et al., 2011;
Toms & Tordsson, 2013; Ghosh & Naik, 2012; Hines et al.,
2011; Wang et al., 2012; Wo et al., 2012; Breitgand et al.,
2012).
Even with proactive resource allocation models in place,
overload can still occur. When it does, strategies to
effectively detect and manage overload must be employed.
A basic description of some common overload remediation
techniques are discussed by Baset et al. (2012) and are as
follows:
Stealing is the act of taking resources from underloaded
VMs and giving them to overloaded VMs on the same
physical host. Memory ballooning is a common
example of this and it is often a capability installed in
hypervisors that use this as a first line of defense
against overload.
Quiescing is the act of shutting down VMs on an
overloaded PM so that the remaining VMs can function
at normal performance levels (Baset et al., 2012; Wang
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et al., 2012). The VMs that are shut down will resume
once overload subsides.
VM Migration is the act of moving a VM from one
physical machine to another (Moreno & Xu, 2012;
Williams et al., 2011; Baset et al., 2012; Zhang et al.,
2012; Jain et al., 2012; Wo et al., 2012). This is
typically done in systems that have VM storage located
on Storage Area Network (SAN) devices as opposed to
local memory. In the former situation, only a memory
footprint of a VM needs to be moved while in the latter,
VM migration to the disk can encumber the datacenter
network.
Streaming disks serves as a solution to reduce the
network costs of migration to the disk. Here, a portion
of the VMs disk is transferred to another PM. When the
transfer is completed, the migrated VM can access disk
space on both the original and new PM. Once the
network traffic is low, the two disks can be reunited.
Network memory can reduce load caused by swapping
on the PM disk by allowing the memory of another PM
to be used for swapping over the network.
4. THE ECONOMICS OF CLOUD
COMPUTING As discussed in Malkowski et al (2010), the profit
model of cloud computing can be described as a trade-off
between two components: the infrastructure cost model and
the provider revenue model. Revenue is calculated as
earnings minus penalties. Overall profit is realized by
calculating the revenue minus the infrastructure cost.
In terms of costs, cloud datacenters require a great deal
of infrastructure that consumes large amounts of power in
operating the hardware and maintaining the environmental
factors. Additionally, datacenters necessitate educated and
skilled employees to manage and maintain their systems.
Cloud revenue is a product of several factors, namely
the pricing model(s) implemented, the number of customers
served, and the number of SLA penalties incurred. Whereas
traditional software business models require a one-time
payment for unlimited use typically for one computer, cloud
pricing models are inspired by those of utility companies
and charge customers on a consumption basis. Cloud
providers can offer a variety of pricing tiers. For instance,
Amazon EC2 offers free, on-demand, reserved, and spot
instances. Within these tiers, variations in VM specifications
allow for further price customization and accommodate
customers with diverse needs such as varying memory,
bandwidth, and CPU requirements.
Some research has considered pricing models that allow
customers to pay for higher priority. In an experiment
performed in Macias et al. (2010), tasks are assigned either
a gold, silver, or bronze priority. The gold tasks have a price
50% higher than the bronze, and the silver tasks have a price
20% higher than the bronze tasks. They proved that
dynamic pricing always generates the highest revenue over
fixed pricing because it adapts to all possible scenarios.
Simulations discussed later in this paper show CSPs can use
fixed pricing to increase revenue, but too many high priority
customers can overwhelm those who have paid for regular
service.
Penalties in the form of SLA violations detract from the
revenue generated by the pricing model. SLA violations are
caused when utilization within the cloud is too high leading
to performance degradation. Some SLAs have a penalty
price that companies pay out of pocket when a violation
occurs. If this is the case, CSPs risk losses in both revenue
and customers.
Overall the total profits generated by the profit schema
of cost-revenues is driven by the number of customers being
served and the amount of resources they consume. If
customer utilization within the cloud is too low, then
infrastructure costs could outweigh revenue (Assuno et al.,
2009). On the other end of the spectrum, if the datacenter
becomes overwhelmed by the load of customers, SLA
penalties will limit profits. These factors must be considered
when searching for the most optimal solution for pricing in
the cloud. A solution is considered to be optimal if it
maximizes the revenue while not violating SLAs or
degrading service. The simulations in future sections begin
to take a look at one part of this equation, namely the
revenues generated from customer utilization of the service.
5. COMPARING CLOUD COMPUTING TO
OTHER INDUSTRIES There are noticeable similarities between overbooking
in other industries and the cloud. Overbooking too many
appointments in a doctor's office can lead to longer wait
times as the doctor tries to see all patients. This is analogous
to how overbooking a resource such as CPU can potentially
lead to longer wait times if overload occurs. Airlines often
have class systems that outline the level of services a
passenger is entitled to. First class is more expensive than
coach, but these passengers also receive a more luxurious
flight. Similarly, some clouds use levels of service to offer
customers flexibility in selecting a price for the computing
experience they need (Baset et al., 2012). In all industries,
waiting times or lack of service availability is a potential
consequence that can lead to customer dissatisfaction.
Researching the application of oversubscription in other
industries raises new questions that can be addressed. Some
of these questions are listed below:
How much should customers be compensated in the
event of an SLA violation to best maintain retention
rates?
Is one type of compensation (i.e. offering vouchers for
computing resources as opposed to monetary
compensation) better able to placate angry customers?
Similar to airline practices, could CSPs benefit from
alliances with competitors by assigning workloads to
them in times of overload? If so, what impact would
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that have on overbooking models when compared to
more traditional models?
Can having levels of service similar to airline classes
improve overbooking policies by implementing varying
degrees of overbooking to the different levels?
The answers to these questions may give CSPs an edge
in the highly competitive market that is outsourcing
computing power, yet there are many ways in which cloud-
based oversubscription differs from other industries. Other
industries tend to maximize oversubscription by optimizing
a single primary resource. CSPs can oversubscribe several
resources with hopes to optimize the use of existing
computing infrastructures. When these oversubscription
methods fail, the results may be felt by all subscribers of the
service. Outside of the cloud, the impact of oversubscription
is not always universal. For this reason, proper optimization
of resources within the cloud is extremely important as it
allows providers to offer competitive pricing and also
ensures customer satisfaction.
6. SIMULATING THE EFFECTS OF
OVERSUBSCRIPTIONMuch research on oversubscription focuses on
maximizing resource utilization without exploring the
economic effects in detail. Thus, the simulation tool
CloudSim is used to investigate the difference in revenues
and QoS between oversubscribed and non-oversubscribed
datacenters as a first step towards this goal. Inspired by the
multi-class service model of airlines, additional simulations
are employed to examine these metrics in a priority class
scenario. The simulations attempt to shed light on the
answers to three main research questions: 1) Does
oversubscribing the datacenter CPU have the potential to
increase revenue? 2) Does adding priority to the
oversubscription scheduling policy have the potential to
increase revenue? 3) How does giving priority to some
VMs in an oversubscribed datacenter affect the performance
and debt of both the priority and non-priority VMs?
6.1 CLOUDSIM CloudSim is a discrete event simulator built upon
GridSim that allows for the simulation of virtualized cloud
computing environments. The version of CloudSim used in
these experiments is 3.03.
Experiments 1 and 2 focus on utilizing different VM
scheduling policies within the CloudSim framework,
namely the TimeShared, TimeSharedOverSubscription, and
the TimeSharedOverSubscriptionPriority policies, the last
of which was developed for this research. Experiment 3
focuses on adjusting the power VM allocation policy so that
the TimeSharedOverSubscriptionPriority VM scheduling
policy can be implemented in a power-aware datacenter
with VM migration turned on. Experiment 3 utilizes the
power classes within CloudSim that are introduced in
(Beloglazov & Buyya, 2012).
6.2 CALCULATING DEBT In the simulations, the base price of a regular VM
instance is $.09 per hour. This is the same as the cost of a
standard small, pay-as-you-go VM instance on Windows
Azure.1 For priority VMs, the price is $0.15 per hour. The
price set for priority VMs is intended to allow comparison
of revenues in the priority and non-priority oversubscription
configurations. However, further research may explore the
optimum price for a priority instance given the factors that
affect revenues.
As is typical with most cloud IaaS providers, customers
are only charged for the time that their instances are running.
The simulations follow this precedent by only charging
VMs for the total actual CPU time they use. However, in the
simulations, CPU time is not rounded up to the next hour as
is the case with many IaaS providers.2 3
The calculations
used for computing the total datacenter debt are represented
by the formula below.
𝐷 = 𝑐𝑛 ∑𝑡𝑛
3600𝑣𝑛∈𝑉
+ 𝑐𝑝 ∑𝑡𝑝
3600𝑣𝑝∈𝑉
(1)
where:
𝐷 = Total Datacenter Debt 𝑉 = Set of all VMs in the Datacenter
𝑣𝑛 = Subset of VMs that are non-priority𝑣𝑝 = Subset of VMs that are priority
𝑐𝑛 = Hourly charge for a non-priority VM𝑐𝑝 = Hourly charge for a priority VM
𝑡𝑛 = Cumulative CPU time for a non-priority VM𝑡𝑝 = Cumulative CPU time for a priority VM
7. EXPERIMENT 1: SCALING INITIAL MIPSALLOCATION
The first Experiment was initially presented in
Householder et al. (2014). As an extension, this work
conducts the simulations using 500 hosts. In Experiment 1,
the initial mips allocation is used to scale back non-priority
VMs.
7.1 SIMULATION SETUP In Experiment 1, three main scenarios are simulated and
compared. In the first scenario, the workloads are executed
using a time-shared (TS) VM scheduling policy that does
not allow for oversubscription. All VMs are given 6,000
1 http://www.windowsazure.com/en-
us/pricing/details/virtual-machines/ 2http://www.rackspace.com/cloud/servers/pricing/
3 http://www.rackspace.com/cloud/servers/pricing/
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mips per physical element (PE) and are charged $0.09 per
hour. In the second scenario, the time-shared
oversubscription (TSO) VM scheduling policy is employed
in a non-priority setting. All VMs are allotted 6,000 mips
per PE and are charged at a rate of $0.09 per hour. In the
third scenario, the TSO VM scheduling policy is also
employed, but a percentage of the VMs are given priority
over the others so that their jobs run faster than they
typically would in a non-priority configuration.
Table 1. Cost and MIPS of Priority and Non-Priority VMs.
VM Instance Type Price Per Hour ($) MIPS
Non-Priority 0.09 5,000
Priority 0.15 6,000
In order to give a subset of the VMs priority, they are
allotted 6,000 mips per PE while the other non-priority VMs
are scaled back to 5,000 mips per PE. The priority VMs are
charged $0.15 per hour while the non-priority VMs are still
charged $0.09 per hour. The differences between priority
and non-priority VM instances can be seen in Table 1.
Each simulation sets up a single datacenter comprised of
500 physical hosts. Each host is given 10 GB/s of
bandwidth, 16 GB of RAM, and 1024 GB of storage. Each
host is also allocated 48,000 mips distributed across 8 cores,
allowing each core up to 6,000 mips. The hosts are
dynamically carved into VMs using
VMAllocationPolicySimple which assigns a VM to the host
with the fewest cores currently in use (Calheiros et al.,
2011). Each VM is given 100 MB/s of bandwidth, 2 GB of
RAM, and 10 GB of storage. Additionally, each VM is
allocated 2 cores. mips per core are allocated to each VM
based on the VM instance type as is noted in Table 1. The
configurations for the hosts and VMs can be seen in Table 2.
Table 2. Experiment 1 Specifications.
Resources Host VM
Bandwidth (MB/s) 10,240 100
RAM (GB) 16 2
Storage 1,024 10
MIPS 48,000
Cores (PEs) 8 2
Each scenario is tested using three different workload
files obtained from the Parallel Workloads Archive.4 These
files represent the logs of actual workloads ran on real
systems and, in this study, consist of the following three
workloads: Workload 1 (NASA-iPSC-1993-3.1-cln.swf.gz),
Workload 2 (OSC-Clust-2000-3.1-cln.swf.gz), and
Workload 3 (LLNL-Atlas-2006-2.1-cln.swf.gz).
The workloads are used to generate cloudlets which are
jobs assigned to the VMs (Calheiros et al., 2011). Within a
VM, the cloudlets are scheduled using the
CloudletSchedulerTimeShared scheduling policy that
4 http://www.cs.huji.ac.il/labs/parallel/workload/
implements a round robin algorithm. For each workload,
simulations are run for 5 different configurations. These
configurations include time-shared (TS), time-shared
oversubscription non-priority (TSO-NP), time-shared
oversubscription with 10% of the VMs given priority (TSO-
P 10%), time-shared oversubscription with 20% of the VMs
given priority (TSO-P 20%), and time-shared
oversubscription with 40% of the VMs given priority (TSO-
P 40%). For each of these configurations, simulations for
1000, 2000, 3000, 4000, and 5000 VMs are run.
7.2 EXPERIMENT 1 RESULTS Table 3 shows the total datacenter debt for the three
workloads in each of the five configurations for 1000, 2000,
3000, 4000, and 5000 VMs. When the datacenter has 1000
and 2000 VMs, it is not overloaded and so all VMs
successfully execute their jobs in all five configurations. As
a result, the TS and TSO-NP configurations produce the
same debt for 1000 and 2000 VMs since all VMs are
charged the same price and oversubscription has no effect.
Notice that 1000 VMs setting acquires the longest running
time and the most debt. When the workload is spread out
across 1000 VMs, only half of the potential resources in the
datacenter are used, resulting in slower execution times and
greater costs.
Table 3: Exp 1 Total Datacenter Debt
For 3,000-5,000 VMs, the datacenter debt in TSO-NP is
higher than in TS. This is because the datacenter is
overloaded. With each of 500 physical hosts having 48,000
mips, the datacenter can only allocate up to 24,000,000 mips.
In the TS scheduling policy, each of the VMs is given the
full 6,000 mips at all times for both cores. This means that
in a TS configuration, 2000 VMs need 6,000 mips per core
* 2 cores per VM * 2000 VMs = 24,000,000 mips. This is
the maximum physical capacity of the datacenter. Any
VMs over 2000 will fail in a TS scheduling policy. As a
result, 1000 VMs fail in the TS simulation for 3000 VMs,
2000 VMS fail in the TS simulation for 4000 VMs and 3000
VMs fail in the TS simulation for 5000 VMs. Conversely,
all VMs execute successfully in the TSO-NP configurations.
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Thus, the TSO-NP allows for more VM instances to be
created and generates more debt as a result. These results
show the limitations of a non-oversubscribed datacenter in
terms of potential revenue.
While the TSO-NP configuration generates more debt,
it comes with a price. Table 4 shows the cumulative
datacenter CPU times for all VMs. Since the datacenter is
not overloaded for 1000 and 2000 VMs, again there is no
difference in the time it takes for all VMs to execute their
jobs between the TS and TSO-NP configurations. However,
when the datacenter is overloaded, TSO-NP continues to
accept VMs which degrades the performance by causing all
jobs to run longer. The more VMs created in the datacenter,
the longer it takes for all VMs to execute successfully.
These results show that oversubscription comes with a cost
to the QoS and must be implemented smartly to prevent a
loss of customers and/or an increase in SLA violations, both
of which would offset the potential economic benefits of
oversubscription.
Table 4: Experiment 1 Total Datacenter Actual CPU Time
(Hrs)
When adding the opportunity for some VMs to pay a
higher price in order for their jobs to run faster, it creates
further opportunities for revenue generation. This is
indicated in Table 3 as TSO-P configurations generate more
total datacenter debt than the TSO-NP configurations.
Further, as the percentage of VMs that are given priority
increases, the datacenter debt also increases, even when the
datacenter is not overloaded at 1000 and 2000 VMs. This is
logical because some VMs are paying more for their
instances.
8. EXPERIMENT 2: A NEW SCALING
MECHANISM The second set of simulations (known from here on out
as Experiment 2) takes a different approach to scaling back
non-priority VM mips. Experiment 1 allots each VM 2 CPU
cores. Upon instantiation, priority VMs are allotted their full
6,000 mips per core and the non-priority VMs are scaled
back to 5,000 mips per core. This means that non-priority
VMs are scaled back regardless of if overload occurs or not.
When overload does occur despite the initial scale-backs,
the VmSchedulerTimeSharedOversubscription class in
CloudSim scales back and redistributes the available mips
for all VMs using the following formula:
𝑆 =𝑇𝐴
𝑇𝑅 (2)
where:
𝑆 = Scaling Factor
𝑇𝐴 = Total available mips on host 𝑇𝑅 = Total required mips by all VMs
As a result of these settings, each VM in Experiment 1
has the potential to be scaled back at some point from its
initial 6,000 mips request. Experiment 2 addresses this issue
by only scaling back VMs that are not priority when an
overload occurs.
8.1 SIMULATION SETUP Experiment 2 has the same physical capacity as
Experiment 1. In the first round of simulations, the
datacenter consists of 500 physical machines. Each PM is
allotted 10,240 MB/s in bandwidth, 20 GB RAM, 1024 GB
of storage, 48,000 mips, and 8 cores. Each VM is allotted
100 MB/s bandwidth, 2 GB of RAM, 10 GB of storage, and
2 cores. In Experiment 2, the mips are only redistributed
when overload occurs. Upon instantiation, the simulation
allots the full 6,000 mips to both priority and non-priority
VMs. When overload occurs, the newly created VM
scheduling policy for oversubscribing with priority takes
over to scale back only the non-priority VMs. This is done
using the following formula:
𝑆 =𝑇𝐴 − 𝑇𝑅𝑝
𝑇𝑅 − 𝑇𝑅𝑝
(3)
where:
𝑆 = Scaling Factor
𝑇𝐴 = Total available mips on host 𝑇𝑅 = Total required mips by all VMs
𝑇𝑅𝑝 = Total required mips by priority VMs
As in Experiment 1, the prices for the VMs are fixed
with non-priority instances costing $0.09 per hour and
priority instances costing $0.15 per hour. Also as in
Experiment 1, the simulations are run on the three
workloads from the Parallel Workloads Archive.
8.2 EXPERIMENT 2 RESULTS Table 5 shows the resulting VM debt for each of the
three workloads while Table 6 shows the resulting CPU
times. Similar to Experiment 1, the TS and TSO-NP
configurations for a given workload produce the same VM
debt and CPU times when the datacenter is not overloaded
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(1000 and 2000 VMs). However, unlike Experiment 1, the
CPU times do not change for a given workload in the
underloaded scenarios even as the number of priority VMs
is increased, showing that Experiment 2 does not scale non-
priority VMs unnecessarily. With the consistent CPU times
in underloaded scenarios, the VM debt results for 1000 and
2000 VMs indicate that adding more priority VMs has the
potential to increase VM debt without degrading the QoS.
For the overloaded scenarios of 3000, 4000, and 5000
VMs (with the exception of TSO-P 40% for 5000 VMs),
Experiment 2 results show that the VM debt increases as the
number of priority VMs increases. However, the overall
execution times also increase for the workload as the larger
number of priority VMs further scales back the mips
allocated to non-priority VMs. Thus, the benefits of
increased revenues overall come at the risk of increasingly
degraded QoS and costs for non-priority customers. This
decline in QoS for non-priority customers reaches an
extreme value in the TSO-P 40% simulations. Here, the
priority VMs utilize all of the physical resources which
means that non-priority VMs are scaled back to zero and
their jobs cannot be executed. Thus, Experiment 2 has
limitations on the number of priority VMs it can host.
Table 5: Exp. 2 Total Datacenter Debt
9. COMPARING EXPERIMENTS 1 AND 2Figure 2 shows a comparison of the results for
Experiment 1 and Experiment 2. While both experiments
were run on all three workloads, the overall trends remained
consistent and so Workload 3 is shown as a representation
of the results.
When the datacenter is not overloaded, as is the case for
1000, and 2000 VMs, both experiments are equivalent in the
TSO-NP simulations. Additionally, for the TSO-P
simulations, results for the priority VMs are also equivalent
when the datacenter is not overloaded. This is because they
are never scaled back at any time. However, in the same
TSO-P simulations, Experiment 1 shows degraded
performance leading to increased costs for the non-priority
VMs. These results are attributed to the fact that in
Experiment 1, the non-priority VMs are being scaled back
to 5,000 mips even when no overload has occurred. This
contrasts from Experiment 2 in which no VM is scaled back
unless there is overload. These results can be seen in Figures
2a-2d .
Table 6: Exp. 2 Total Datacenter Actual CPU Time (Hrs)
When the datacenter faces a potential overload scenario,
as is the case with 3000, 4000, and 5000 VMs, Experiment
2 is favorable for the priority VMs by providing lower costs
and faster execution times whereas Experiment 1 is
favorable for the non-priority VMs for the same reasons. In
Experiment 2, priority VMs are never scaled back. Instead,
when overload occurs, the mips remaining after the priority
VMs are allocated are scaled and redistributed to the non-
priority VMs. In Experiment 1, the non-priority VMs are
initially scaled back to 5,000 MIPs. However, if overload
occurs, the mips are scaled back and redistributed amongst
all VMs, even those with priority. Thus, Experiment 1
allows the effects of overload to be felt overall at an earlier
stage. While Experiment 2 does not allow for scaling of
priority VMs, this also presents a new limitation which can
be seen with TSO-P 40% configuration for 5000 VMs.
Without scaling priority VMs, Experiment 2 only allows for
a maximum of 2000 priority VMs to be hosted. Any VMs
beyond 2000 results in failure as not all VMs can be
instantiated. Notice for Experiment 2 that the TSO-P 40%
has a much lower debt and CPU time than the others. This is
because while all 2000 priority VMs are allowed to be
instantiated at their full capacity, it causes
the remaining 3000 non-priority VMs to be scaled back to
zero, and so they cannot execute their jobs. In order to
implement this scheduling policy, CSPs must be careful to
set boundaries to ensure that the number of priority VMs
does not exceed the physical capacity of the datacenter. So,
while Experiment 2 tends to better benefit priority VMs,
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(a) VM Debt 1,000 VMs.
(c) VM Debt 2,000 VMs.
(e) VM Debt 3,000 VMs.
(g) VM Debt 4,000 VMs.
(i) VM Debt 5,000 VM.s
(b) CPU Time 1,000 VMs.
(d) CPU Time 2,000 VMs.
(f) CPU Time 3,000 VMs.
(h) CPU Time 4,000 VMs.
(j) CPU Time 5,000 VMs.
Figure 2: Workload 3 Simulation Comparison
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Experiment 1 has the potential to host more VMs resulting
in higher potential for oversubscription.
Both Experiments 1 and 2 indicate that allowing some
VMs to pay more for priority allocation has the potential to
increase revenues. Unfortunately these benefits come at the
risk of SLA violations due to decreased performance.
Additionally, some of the increased VM debt generated
comes from charging non-priority VMs for extra CPU time
acquired due to their resources being scaled back. This
raises the question of fairness to non-priority VMs.
If implemented in a real datacenter, the higher costs and
extended execution times may lead to unhappy customers
that will seek other CSPs for further requests. In the next
experiment, some of the concerns regarding fairness to non-
priority VMs and SLA violations are addressed.
10. EXPERIMENT 3: OVERSUBSCRIPTION
WITH VM MIGRATION The third set of simulations (known from here as
Experiment 3) utilizes the VM scheduling policy
implemented in Experiment 2 in conjunction with VM
migration in a power-aware datacenter. With this
combination, non-priority VMs can be scaled back to allow
a PM to host more VMs. When a host becomes over-utilized
past a certain threshold value, VMs can be migrated from
the over-utilized host to an underutilized host. Additionally,
when all VMs from an under-utilized host can be migrated,
the migration is implemented and the newly empty under-
utilized host is turned off to conserve energy.
10.1 SIMULATION SETUP The migration simulations implement the power classes
that can be found in CloudSim 3.0 and were developed in
Beloglazov and Buyya (2012). In these simulations, there
are 250 hosts. Each host is allotted 48,000 mips and 8 cores
which means there are 6,000 mips/core. Hosts are given 18
GB of RAM, 1 Gbit/s of bandwidth, and 1000 GB of
storage. In the datacenter, half of the hosts are HP ProLiant
ML110 G4 servers and the other half are HP ProLiant ML
110 G5 servers. A power model is implemented that
classifies the power consumption for each server type at
different load levels. These consumption values for each
server are based on real data. The Hosts are carved into
homogeneous virtual machines. Each VM is given 2 PEs
and 6,000 mips/PE. Each VM is also allotted 1.7 GB of
RAM and 0.1 Gbit/s of bandwidth.
PlanetLab Workloads. For these simulations,
cloudlets are generated using the dynamic cloudlet
scheduling policy in CloudSim which creates a VM to host
each job in the workload. The workloads used for these
experiments are two PlanetLab workloads. The description
of the workloads can be seen in Table 7. Each workload
represents a workload trace for 1 day from a random server
in PlanetLab (Beloglazov & Buyya, 2012). Inside each
workload file, there are jobs that represent data from virtual
machines on that given server in the given day. These jobs
will become cloudlets and each cloudlet is hosted by a
single VM. Inside of each job file, there are 288 random
samples of values that indicate the CPU utilization
percentage for that VM in 5 minute intervals.
Table 7. Description of Workloads. Date Name Number of
VMs
Mean
Utilization
03/06/2011 20110306 898 16.83%
04/20/2011 20110420 1033 15.21%
Implementing VM Migrations. The method for
implementing VM migration is the same as in Beloglazov
and Buyya (2012) and can be seen in Algorithm 1. The VM
selection policy used to determine which VM should be
migrated from an overloaded host is the Minimum
Migration Time Policy. With this policy the migration time
is estimated using the amount of RAM utilized by the VM
divided by the free network bandwidth available for the
given host. Upon migration, there is an average performance
degradation that can be estimated as 10% of the CPU
utilization. In these simulations, a destination host for a VM
only receives 10% of the migrating VMs mips meaning that
any migration could potentially lead to an SLA violation.
Figure 3: Algorithm taken from Beloglazov and Buyya
(2012).
TSO VM Allocation Policy. A time-shared VM
allocation policy for oversubscribing with priority has been
created for these experiments. It is very similar to the power
static threshold policy used in (Beloglazov & Buyya, 2012).
The primary difference between the two is that upon initial
allocation of a VM to a host, the VM is allowed to be
created and assigned even if there are not enough available
mips on the host machine. As long as the host machine has
not reached its maximum capacity with priority VMs, this
allocation policy can assign a VM to an overloaded host and
then implement the time-shared oversubscription VM
scheduling policy for priority (introduced in Experiment 2)
to scale back the non-priority VMs. Like with the power
static threshold policy, there is a threshold for which a host
is considered overloaded. In these simulations, that
threshold is reached when 90% of the CPU is utilized.
Calculating SLA and Energy Metrics. Calculations
for the SLA violation metric as well as the energy metric are
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identical to those utilized in (Beloglazov & Buyya, 2012).
SLA violations are calculated using two metrics. The first is
the percentage of time that a host receives 100% CPU
utilization and the other is the overall performance
degradation that is caused by VM migrations. The product
of these two metrics is used to determine the SLA violation
metric.
As previously described, energy consumption is based
on a power model for two server types. These values are
based on real data for the server that identifies the power
consumption in watts for various loads.
Compensating Non-Priority VMs. The results of the
simulations in Experiments 1 and 2 show that the QoS for
non-priority VMs may become severely degraded due to
increased running times for their jobs. As a result of these
increased running times, the debt calculation in Equation 1
used in the first two experiments may be unfair to non-
priority customers as their longer running jobs lead to
increased VM debt. Thus, in Experiment 3, a compensation
factor is added to make the cost to non-priority customers
more fair.
In order to calculate the compensation factor for non-
priority VMs, the running times for each VM is first
estimated using Equation 4 where 𝑅 is the estimated run
time, 𝐿 is the cloudlet length, ��is the average mips, and 𝐶 is
the number of cores.
𝑅 =𝐿
��𝐶(4)
This estimation is calculated for each non-priority VM
and the results are summed together. Note that since a job in
a PlanetLab workload changes its CPU utilization every 5
minutes, the average mips being utilized for each job is used
to estimate the total requested mips for the VM.
The extra debt accumulated by non-priority VMs due to
degraded service is calculated by taking the difference in
actual and estimated run times and multiplying it by the
price for a non-priority VM using Equation 5 where 𝐷𝐸𝑥𝑡𝑟𝑎
is the extra debt and 𝑇 is the run time.
𝐷𝐸𝑥𝑡𝑟𝑎 = (𝑇𝐴𝑐𝑡𝑢𝑎𝑙 − 𝑇𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑) ∗ 0.09 (5)
Finally, the extra debt is subtracted from the original
debt value calculated to get the adjusted debt value using
Equation 6.
𝐷𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 = 𝐷𝐴𝑐𝑡𝑢𝑎𝑙 − 𝐷𝐸𝑥𝑡𝑟𝑎 (6)
10.2 EXPERIMENT 3 RESULTS Comparing VM Debt and CPU Time. Figures 4 and 5
compare the VM debt and CPU times for the cases when
VM migration is either off or on. When VM migration is on,
both the CPU time and the VM debt tend to be slightly
higher than when VM migration is off. This is likely
because there is some overhead incurred from the VM
migrations. Some minor exceptions to this can be seen.
(a): VM Debt
(b): CPU Time
Figure 4: VM Debt and CPU Time for Workload 20110306
without VM Migration.
(a): VM Debt
(b): CPU Time
Figure 5: VM Debt and CPU Time for Workload
20110306 with VM Migration.
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For instance, in Figure 4b the CPU time is slightly lower
when migration is on for 100% priority. Similarly, in Figure
4b the VM debt is slightly lower when VM migration is on
for 0% priority. Further examination of the logs indicate that
in the allotted simulation time given, one less cloudlet
finished its job in both of these scenarios, thus slightly
skewing these factors.
The results in Figures 4 and 5 show that for both
workloads, the overall CPU time remains fairly consistent
even as a higher percentage of priority VMs are allotted.
This indicates that combining migration with the VM
Scheduling policy that scales back non-priority mips could
potentially offset some of the performance degradation
accumulated from adding higher priority percentages.
However, it is unclear from these results whether or not the
priority VMs are receiving any improved performance.
While the CPU times remain fairly consistent as more
priority VMs are added, the VM debt tends to increase even
with the non-priority VMs being compensated for slower
execution times. This suggests there is potential for
increasing revenues using this model.
Non-Priority Compensation. Adding the
compensation factor proves to better balance out the costs
for non-priority VMs. Figure 6 shows the amounts that the
VM debt was adjusted for both migration on and off. Notice
that VMs tend to be compensated slightly more when VM
migration is on. This could be because the VMs are also
being compensated for the overhead incurred due to
migration also.
While this metric attempts to make the datacenter
configuration fairer for non-priority VMs, there is the
potential that non-priority VMs will still pay slightly more
for their units due to slower job execution times caused by
oversubscription. This is the result of using an average mips
value to estimate the run time of a given cloudlet. Future
work should make this estimation more robust so as to more
completely compensate non-priority VMs for the extra costs
resulting from degraded QoS.
Figure 6: VM Debt Adjustments.
Number of VM Migrations. The number of
migrations for the two workloads can be seen in Figure 7.
These results indicate that allowing a large ratio of priority
VMs tends to increase the overall number of VM migrations.
On a much larger scale, this could have dire implications if
the number of VM migrations causes enough performance
degradation to cause SLA violations. While the SLA
violation metric discussed in the next section is low overall,
it does tend to rise as the number of VM priority VMs
increases. Also tracked was the number of priority and non-
priority migrations. Tables 8 and 9 show these values for
each workload and each percentage.
Figure 7: Number of VM Migrations.
Table 8: Number of migrations by priority workload 20110306
Percent Priority Priority
Migrations
Non-Priority
Migrations
0% 0 944
10% 79 862
20% 203 738
30% 333 604
40% 427 510
50% 539 398
60% 619 324
70% 718 225
80% 864 79
90% 944 26
100% 1016 0
SLA. Figure 8 shows the SLA violation metric for both
of the workloads. In the given simulation limit, both
workloads start with slightly higher values as no VMs are
given priority. This value then drops as a small percentage
of priority VMs are allotted. Finally, as nearly all VMs in
the workload are priority, the SLA metric again is higher.
However, the overall values for this metric are considerably
low and so it suggests that the combination of scaling non-
priority VMs and VM migration could potentially be
implemented with low levels of SLA violations. It is worthy
to note that this metric is only captured when VM migration
is turned on. Future work should include a metric that can
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compare the SLA dynamic for migration on and migration
off scenarios.
Table 9: Number of migrations by priority workload 2011042.
Percent Priority Priority
Migrations
Non-Priority
Migrations
0% 0 991
10% 87 898
20% 215 770
30% 325 647
40% 450 522
50% 561 418
60% 670 312
70% 816 166
80% 919 74
90% 979 51
100% NA NA
Figure 8: SLA Violation Metric for Migration.
Energy Consumption. Results of this experiment in
terms of energy consumption are shown in Figure 9. Of note
is that when VM migration is used, energy consumption
generally tends to increase along with the percent of priority
VMs. This is sensible as more VMs running while using full
resources should consume a greater amount of energy.
Overall, the use of VM migration as coupled with
oversubscription techniques tends to lead to greater energy
savings.
11. LIMITATIONS AND FUTURE WORKThese simulations are a continuation towards
understanding how adding priority classes to an
oversubscribed datacenter can impact cloud profits and QoS.
However, there are limitations to the current work that need
to be addressed in order to strengthen its value as a well-
rounded economic study that can be applied to real
datacenters. These simulations only consider three
workloads for Experiments 1 and 2 and two workloads for
Experiment 3. Moreover, they are limited to the scope of
CPU oversubscription and on-demand VM instances. While
this work extends previous works by attempting to consider
infrastructure costs such as energy consumption and SLA
violations, more work can be done to further develop the
(a) Energy consumption using VM migration.
(b) Energy consumption without using VM migration.
Figure 9: Energy Consumption.
difference in these metrics in both migration on and
migration off scenarios and how these metrics could impact
overall profits.
Future work seeks to address these limitations by
applying the simulations to more workloads and widening
the scope to consider other pricing tiers (spot, reserved, etc)
as well as considering oversubscription of other resources
such as bandwidth and memory. Future work will also
broaden the scope to consider the impact that other aspects
of the profit schema, such as infrastructure costs and SLA
penalties, have on the overall profits. While this work
addresses some of these, their economic impact has yet to be
explored. Investigations on the impact of additional priority
classes as well as identifying and comparing fair
compensation algorithms for non-priority customers whose
jobs are slowed in lieu of preferred VMs will be conducted.
Finally, future work will explore optimal resource allocation
under SLA constraints for overbooked cloud computing
systems with multiple preference classes.
12. CONCLUSIONSResource allocation models in cloud computing
infrastructures tend to allow large fractions of resources to
sit idle at any given time. The ability to improve resource
utilization and decrease waste can significantly increase the
profits of a CSP. This paper discusses the application of
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oversubscription techniques to achieve this goal and
introduces overload as a major risk of oversubscription. The
effects of oversubscription on datacenter revenues and VM
running times are investigated using three experiments. The
first two experiments implement and compare two different
VM Scheduling Policies in a non-power aware datacenter
with no VM migration. The third experiment analyzed the
same factors in a power-aware datacenter with VM
migration turned on and off. Simulation results suggest that
oversubscription does have the ability to allow CSPs to host
more VMs and increase revenues. Adding the opportunity
for some VMs to pay more for a priority instance can
increase revenues further. However as the research suggests,
the results demonstrate that oversubscription in any capacity
comes with the risk of degraded QoS when the datacenter
becomes overloaded. In the priority scenarios, the
degradation in QoS is minimized for priority VMs but
comes at a cost to the debt and running times of the non-
priority instances. Thus, in order for oversubscription to
provide CSPs with economic benefits, it must be
implemented in a smart and balanced manner in order to
limit the degradation to QoS that can lead to a loss of
customers and SLA violations.
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AuthorsRachel Householder has a B.Sc. in
Integrated Mathematics and a M.Sc. in
Computer Science from Bowling Green
State University. She worked as a
Research Assistant for two years at the
Institutional Research Office and as a
high school mathematics teacher for three
years. Her research interests include cloud computing and
business intelligence.
Robert Green received his B.S. in
Computer Science & Applied
Mathematics from Geneva College in
2005, his M.S. from Bowling Green
State University in 2007, and his Ph.D.
from the University of Toledo in 2012.
He currently serves as an Assistant
Professor of Computer Science Professor at Bowling Green
State University. His research interests including Cloud
Computing, High Performance Computing, Population-
based Metaheuristics, Software Development (Mobile
Applications and Web Development), and the modeling,
evaluation, and analysis of complex systems.
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
31 http://www.hipore.com/ijcc/
OPTIMIZATION OF OPERATIONAL COSTS IN HYBRID COOLING
DATA CENTERS WITH RENEWABLE ENERGYShaoming Chen, Yue Hu, and Lu Peng
Division of Electrical & Computer Engineering Louisiana State University, LA, US {schen26, yhu14, lpeng}@lsu.edu
Abstract
The electricity cost of data centers dominated by server power and cooling power is growing rapidly. To tackle this problem, inlet air with moderate temperature and server consolidation are widely adopted. However, the benefit of these two methods is limited due to conventional air cooling systems ineffectiveness caused by re-circulation and low heat capacity. To address this problem, hybrid air and liquid cooling, as a practical and inexpensive approach, has been introduced. In this paper, we quantitatively analyze the impact of server consolidation and temperature of cooling water on the total electricity and server maintenance costs in hybrid cooling data centers. To minimize the total costs, we proposed to maintain sweet temperature and ASTT (available sleeping time threshold) by which a joint cost optimization can be satisfied. By using real world traces, the potential savings of sweet temperature and ASTT are estimated to be average 18% of the total cost while 99% requests are satisfied compared to a strategy which only reduces electricity cost. The co-optimization is extended to increase the benefit of the renewable energy and its profit grows as the more wind power is supplied Keywords: Data Center, hybrid cooling, cost optimization, renewable energy __________________________________________________________________________________________________________________
1. INTRODUCTION The total cost of ownership (TCO) in data centers
consists of onetime capital costs incurring only at the
beginning or upgrade stage of data centers and monthly
recurring operational costs including electricity cost,
maintenance cost and salaries (Barroso, L. A. and Hölzle, U.
2009). According to recent reports (Ahmad, Faraz and
Vijaykumar, T. N. 2010), the TCO is dominated by the
operational costs, among which salaries are largely not a
technical but an economic factor. Therefore, we focus on
optimization of electricity and maintenance costs in this
work.
The growth of the cost of electricity consisting of server
power and cooling power outpaces expectations. In 2011,
U.S. data centers spent about $7.4 billion in electric power
among which server power and cooling power contribute
significantly to the total (Pelley, S. et al. 2009). Several
studies try to throttle this increase, though few of them
consider the cost of server maintenance.
Prior works employ two methods to reduce energy cost:
increasing server consolidation and increasing inlet air
temperature. Server consolidation is a powerful tool which
has been widely adopted to gain high energy efficiency of
server, which results from keeping active servers in high
utilization by turning off overprovisioned servers (Qouneh,
A, et al. 2011). As an alternative approach to save server
power, Dynamic Voltage and Frequency Scaling (DVFS) is
also used (Elnozahy, M. et al, 2002). However, the benefit
of DVFS is shrinking because the leakage power is
increasing and the voltage of processors is getting very close
to its limit (Meisner, D. et al. 2009). In addition, DVFS only
affects CPU power which amounts to 30% of server power
(Pelley, S. et al. 2009). Server consolidation remains as an
effective and practical method to save server power.
To reduce cooling power, increasing inlet air temper-
ature is a common method since increasing inlet air
temperature by just one degree can reduce cooling energy
consumption by 2 to 5 percent (California Energy
Commission n.d.). However, the room of inlet air
temperature can be raised is very limited due to the
constraint of server temperature below the critical
temperature. To keep the constraint with a low cost, there
are several prior works advocating thermal-aware workloads
placement which distributes workloads according to the
thermal map of data centers (Moore, J. et al. 2005).
Unfortunately, these methods cannot maintain energy
efficiency of traditional air cooling by keeping high inlet air
temperature when data centers are in high utilization
(Qouneh, A, et al. 2011). Therefore, a novel cooling system
is demanded.
As a practical and inexpensive solution of liquid cooling
(Huang, W. et al. 2011), a hybrid cooling system which
combines air and liquid cooling has been proposed and
deployed in data centers such as Aquasar, the first hot water
cooled supercomputer prototype (Zimmermann, Severin, et
doi: 10.29268/stcc.2014.2.1.4
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
32 http://www.hipore.com/ijcc/
al July 2012). The hybrid cooling system uses water to cool
down high power density components such as processors
and memory which dominate total heat dissipated in servers,
while other auxiliary components show low power density
are still cooled down by air cooling. In this way, hybrid
cooling can remove a mass of heat from datacenter with less
power than conventional air cooling.
In addition to the electricity cost coming from servers
and cooling systems, hardware maintenance cost is also
considerable. According to a typical new multi-megawatt
datacenter in the United States, the cost of server repair and
maintenance is approximately 50% of the costs of server
power and cooling power (Barroso, L. A. and Hölzle, U.
2009). Based on the empirical data of a HPC datacenter,
disks are the most frequently replaced components.
Resulting from server consolidation, frequent turning off
servers or transition between active state and sleeping state
incurs the cost of disk maintenance due to the limited start-
stop cycles for disks (Chen, Y. et al. 2005). Additionally,
higher inlet water temperature increases the cost of CPU and
memory maintenance, since every 10°C increase over 21°C
decreases the lifetime reliability of electronics by 50%
(Patterson, M. K. 2008). Therefore, rather than restricting
chip temperature below a certain threshold, we can balance
the saving of the electricity costs and the increase of the
costs of hardware maintenance through manipulating inlet
water temperature and smoothing the variation of the
number of active servers.
On the other hand, as three years electricity bills of
modern data centers grow over the server equipment cost
(Brill, K. 2007), the sustainability of data centers is
becoming one of top concerns of their owners. Driven by
soaring conventional energy price and the global warming,
the owners swift their power sources to renewable energy
such as wind, solar, and tidal power. We focus on
integrating wind power into our proposed optimization of
electricity and server maintenance costs since wind energy
is cheaper and widely used to power large-scale facilities
(Patel, M. 1999).
The contributions of our work are shown in the
following.
• We set up analytical models for server power, cooling
power and hardware maintenance model in hybrid cooling
data center for the quantitative evaluation. To our best
knowledge, we first build a comprehensive framework
which covers the evaluation of these costs. This framework
provides foundations to optimize the total cost in hybrid
cooling data centers.
• We propose a tradeoff between electricity cost and
maintenance cost. In this work, we show that the typical
optimizations (high inlet water temperature and aggressive
server consolidation) which reduce only electricity cost
could hurt the maintenance costs.
• To minimize the electricity cost and the maintenance
cost, we develop a joint optimization scheme based on
dynamic optimal water inlet temperature and server
consolidation. Our simulation results show that the method
can gain considerable savings of these costs.
• We extend our cost optimization to exploit the benefit
of the wind power. It increases the cost saving of the wind
power and this benefit grows as the more wind power is
supplied based our experiment.
The rest of our paper is organized as follows: we
describe the structure of hybrid cooling in section 3. In
section 4, we build models related to electricity cost and the
cost of server maintenance. We propose cost optimization
methods in section 5. In section 6, we setup a datacenter
model with server performance model and response analysis.
In section 7, we analyze the result of these two methods and
show their potential savings. Finally, we conclude the paper
in section 8.
2. RELATED WORKPrior works of the cost optimization of data centers fall
into two categories: the optimization of electricity cost
(Raghavendra, R. et al.2008) and the optimization of
hardware maintenance cost. To decrease energy consumption
of datacenters, many studies were addressed from server
level (Meisner, D. et al. 2009 ,and Meisner, D. and Wenisch,
T. F. 2012) , rack level (Ranganathan, P. et al. 2006) and
data center (Chen, Y. et al. 2005, Fan, X, et al, 2007 , Lin,
M. et al. April 2011, and Srikantaiah, S. et al. 2008). These
focused on increasing server energy efficiency and reducing
server idle power. On the other hand, Moore et al. (2005)
introduced thermal-aware workloads placement to reduce
cooling power in traditional air cooling data centers. On the
contrary, other researchers employed advanced
infrastructures of cooling systems to solve energy
inefficiency of traditional air cooling (Barroso, L. A. and
Hölzle, U. 2009. Hwang, D. C. et al 2011, and Rubenstein,
B. A. et al 2010). However, all these works just aimed at the
reduction of either cooling power or server power.
To capture an abroad scope of energy savings, several
architects proposed approaches (Ahmad, Faraz and
Vijaykumar, T. N. 2010, Huang, W. et al. 2011, Pelley, S. et
al. 2009, Qouneh, A, et al. 2011) for optimization of cooling
power and server power. For an example, Pelley et al. (2009)
set up a comprehensive framework of total data center power
in data centers to optimize server power and cooling power.
Ahma, F. et al. (2010) proposed a joint optimization of
server power and cooling power with guaranteeing response
time. However, all of these works did not consider the
increment of the costs of hardware maintenance.
On the other hand, several papers discussed the issue
related to hardware maintenance in data centers (Li, S. et al.
2011, Schroeder, B. and Gibson, G. A. 2007, and Srinivasan,
J. et al. 2005). Schroeder et al. (2007) analyzed disk
replacement rate based on the empirical data, which inspired
researchers to study the reliability of hardware in servers.
Unlike the studies focusing on the optimization of
electricity cost or hardware maintenance in data centers, our
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
33 http://www.hipore.com/ijcc/
approach covered them both. Additionally, though Y. Chen
et al. (2005) minimized the cost of energy and disk
maintenance by combing DVFS and server consolidation,
the author did not discuss cooling cost and maintenance cost
of other components such as processors and memory in
servers.
3. HYBRID COOLING The structure of hybrid cooling in modern data centers is
shown in Figure 1. The closed liquid loop between the
chiller and racks is designed to remove heat dissipation from
the racks. The cool water absorbs heat dissipation from the
racks and returns back to the chiller with heat. In the closed
liquid loop of a rack, the water cooled in the intermediate
Heat Exchanger (HTX) is pumped into servers. In a server,
the water flowing through a liquid cooled plate takes away
power dissipated by processors and memory. Other auxiliary
components such as disks, power supply, and chipsets on
motherboard are still cooled by the air condition as
traditional data centers since these components dissipate less
power and, more importantly, exhibit lower power density
compared with processors and DRAMs.
4. COST MODELS To optimize the electricity cost and the hardware
maintenance cost, we setup the cost models which
quantitatively estimate the impact of server consolidation
and inlet water temperature on the costs when hybrid cooling
is used.
4.1 ELECTRICITY COSTS. The power of a typical data center includes server power,
cooling power and power distribution loss. For power
distribution loss, PDU and UPS draw 10% of load power
(Srinivasan, J. et al. 2005). In the following context, the
models related to server power and cooling power is
addressed.
F
or
serv
er power, Pservers consists of the sum of all active server
power and the sum of sleeping server power. The power for
all servers is written as:
𝑃𝑠𝑒𝑟𝑣𝑒𝑟𝑠 = ∑ 𝑃𝑆𝑒𝑟𝑣𝑒𝑟𝑠(𝑖) + ∑ 𝑃𝑠𝑙𝑒𝑒𝑝
𝑁𝐼𝑆
𝑗=1
(𝑗)
𝑁𝐴𝑆
𝑖=1
(2)
Here, NAS and NIS denotes the number of active servers
and sleeping servers consuming 6 Watts per server (Ahmad,
Faraz and Vijaykumar, T. N. 2010). For an active server, the
total power consists of the power of processors, the power of
memory and the power of other components. The equation is
listed as follows:
𝑃𝑆𝑒𝑟𝑣𝑒𝑟 = ∑ 𝑃𝑃𝑟𝑜𝑐𝑒𝑠𝑠𝑜𝑟
𝑁𝑆
𝑖=1
(𝑖) + ∑ 𝑃𝑀𝑒𝑚𝑜𝑟𝑦(𝑗) +
𝑁𝑀
𝑗 =1
𝑃𝑂𝑡ℎ𝑒𝑟 (3)
where NS and NM are denoted as the number of sockets
and the number of DIMMs in a server. To simplify the
equation, we assume that all servers in data centers have the
same number of sockets and the number of DIMMs.
For the power model of components in a server
(PProcessor, PMemory and POther), we adopt the linear power
model, which is shown as follows:
𝑃 = (𝑃𝑇𝐷𝑃 − 𝑃𝑖𝑑𝑙𝑒) ∗ 𝑈 + 𝑃𝑖𝑑𝑙𝑒 (4)
where PTDP and Pidle indicate the maximum power and idle
power of components while U denotes server utilization.
The configuration of power model in a server is shown in
Table 1. For processors, its idle power amounts to 10% of
the TDP (Chang, J. et al. 2010), while 4 HDD hard disks are
assumed to be installed in the server to fit memory intensive
applications. The specification is derived from a typical
server (Chang, J. et al. 2010).
According to the hybrid cooling structure, the cooling
power can be divided into two parts: the liquid power and air
cooling power:
𝑃𝑐𝑜𝑜𝑙𝑖𝑛𝑔 = 𝑃𝑙𝑖𝑞𝑢𝑖𝑑_𝑐𝑜𝑜𝑙𝑖𝑛𝑔 + 𝑃𝑎𝑖𝑟_𝑐𝑜𝑜𝑙𝑖𝑛𝑔 (5)
To estimate cooling power, E = Q/COP is employed
where E denotes the energy to remove the heat dissipation
Q from data centers and COP (Coefficient of Performance) is
defined as a metric to evaluate the efficiency of cooling
system (Moore, J. et al. 2005). According to prior studies
(Ahmad, Faraz and Vijaykumar, T. N. 2010),
COPair (coefficient of performance) can be derived in the
following equation: COPair = (0.0068 × T^2 + 0.0008 ×T + 0.458) where T is the inlet air temperature.
𝑃𝑡𝑜𝑡𝑎𝑙 = 𝑃𝑠𝑒𝑟𝑣𝑒𝑟𝑠 + 𝑃𝑐𝑜𝑜𝑙𝑖𝑛𝑔
+ 𝑃𝑝𝑜𝑤𝑒𝑟 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑙𝑜𝑠𝑠 (1)
Chiller
HTX
HTX:Intermediate Heat Exchanger
Rack
Hot Water
Cool Water
HTX
Hot Water
Cool Water
Rack
Server
Server
Air
conditi
on
Hot Water
Cool Air
Cool Air Processor
s and
DRAMs
Other
Electronic
Compone
nts
Cool Water
Liquid
Cooled PlateHot Air
Hot WaterServer
pump
HTX
Rack
pump
Cool Water
Figure 1: The Structure of Hybrid Cooling
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
34 http://www.hipore.com/ijcc/
The power of liquid cooling consists of the power of
chiller and the pump power (Hwang, D. C. et al 2011). The
chiller efficiency for a typical chilled water system is also
written as: COPliquid = E/Q (Beitelmal, M. H. and Patel, C.
D. 2006). COPcooled is written in terms of inlet water
temperature: COPliquid = T ∗ 0.18 − 0.4836 based on the
specification of water-cooled screw compressor chiller
(Catalog of Water-Cooled Screw Compressor Chillers. n.d.).
The water pump power is calculated by this equation
(Hwang, D. C. et al 2011):
𝑃𝑝𝑢𝑚𝑝 = 𝑁 ×𝑉𝑤 × 𝛥𝑃𝑤
𝜂𝑝𝑢𝑚𝑝
(6)
where N is the number of servers and Vw is the water volume
flow rate. ΔPw denotes the water side pressure drop based on
the flow resistance. Finally, ηpump indicates the pump
efficiency.
Overall, the cooling power of the data center is
calculated as follows:
𝑃𝑐𝑜𝑜𝑙𝑖𝑛𝑔 = 𝑄𝑙𝑖𝑞𝑢𝑖𝑑 𝑐𝑜𝑜𝑙𝑒𝑑
𝐶𝑂𝑃𝑙𝑖𝑞𝑢𝑖𝑑(𝑇𝑖𝑛𝑙𝑒𝑡_𝑤𝑎𝑡𝑒𝑟) ∗ 𝑡
+𝑄𝑎𝑖𝑟 𝑐𝑜𝑜𝑙𝑒𝑑
𝐶𝑂𝑃𝑎𝑖𝑟(𝑇𝑖𝑛𝑙𝑒𝑡_𝑎𝑖𝑟) ∗ 𝑡+ 𝑃𝑝𝑢𝑚𝑝
(7)
where t is a time interval during which server components
dissipate the heat Qliquid cooled and Qair cooled . The heat
Qliquid cooled removed by liquid cooling, while the heat
Qair cooled consisting of the heat dissipated other components
in active servers and inactive servers. Shown in the Table 1
is the configuration of hybrid cooling derived from (Hwang,
D. C. et al 2011). the pump power of a server is 0.6 watt and
is negligible compared to the chilling power.
Overall, the electricity cost of the data center is written as:
𝐸𝐶 = 𝐾$ 𝑃𝑡𝑜𝑡𝑎𝑙 (8)
Here, Ptotal and K$ respectively denote the power consumed
the data center and commercial KWH Billing Rate which
comes to 9 cents/KWH.
4.2 THE COSTS OF HARDWARE MAINTENANCE. As we have addressed in the introduction, arising
temperature and frequent consolidation could accelerate
server aging processes. Due to high power density of DRAM
and CPU, we focus on their maintenance cost. In addition,
even though hard disks have a low power density, their
limited number of lifetime start-stop cycles is heavily
impacted by frequent server consolidations. Therefore, we
also take the cost of disks maintenance into account.
Thermal model. To investigate the costs of processor
and memory maintenance, we have setup up thermal models.
The CPU temperature TC is calculated as follows from
(Intel® Core™2 Duo Processor E8000¹ and E7000¹ Series
and Intel® Pentium® Processor E5000¹ Series Thermal and
Mechanical Design Guidelines n.d.):
𝑇𝐶 = 𝑇𝑖𝑛𝑙𝑒𝑡 + (𝜃𝐶𝑃 + 𝜃𝑝) ∗ 𝑄𝐶 (9)
Here, Tinlet is the inlet water temperature and QC is the
power dissipated by the CPU. Thermal resistance of the
processor package and TIM (Thermal Interface Material)
layer is denoted by θCP . The value of θCP is derived from
(Hwang, D. C. et al 2011). The thermal resistance of cold
plate which varies with water flow is denoted by θp ,
according to the specification of Lytron CP20 cold plates
(Hwang, D. C. et al. 2011). For the reliability issue of CPU,
there is a threshold temperature for processor chips as 90°C
(Hwang, D. C. et al 2011).
For DRAM, the temperature TM is given as follows:
𝑇𝑀 = 𝑇𝑖𝑛𝑙𝑒𝑡 + (𝜃𝑀𝑃 + 𝜃𝑃) ∗ 𝑄𝑀𝑃 (10)
where QMP is the power dissipated by memory. Thermal
resistance of chip package of DRAM is denoted by θMP
derived from 错误!未找到引用源。 There is a threshold
temperature for DRAM as 85°C (Lin, J. et al 2007). The
characteristics of thermal package of DRAM, CPU and cold
plates are listed in the Table 1.
Thermal Reliability model of electronic devices. After
we have obtained the chip temperature of electronic devices,
we can predict the lifetime of electronic devices based on the
thermal reliability model of electronic devices. The main
factors to determine the lifetime of electronic devices are
power and chip temperature (EPSMA 2005). For memory,
the lifetime prediction model (Li, S. et al. 2011) is adopted.
MTTF (mean time to failure) is widely used to represents the
predicted lifetime of electronic components for
processors: MTTF = 1 λ⁄ . For the prediction of the lifetime
of processor and memory, λ is the number of failures per
million hours and calculated according to Military Handbook
MIL-HDBK-217F (Reliability Prediction of Electronic
Equipment. Military Handbook n.d. ).
𝜆 = (𝐶1𝜋𝑇 + 𝐶2𝜋𝐸)𝜋𝑄𝜋𝐿
(11)
Server Configurations
Part # TDP(w) Idle power(w)
Processor 2 150W 15W
Memory 8 10W 5W
Others - 124W 73.6W
Hybrid Cooling Configurations
Parameter Value
Tinlet_water (°C) 25
Tinlet_air (°C) 25
Vw (GPM) 1
ηpump 70%
ΔPw(psi) 4.2
Thermal Reliability Configurations
𝜃𝐶𝑃 (ºC/W) 0.3
𝜃𝑀𝑃 (ºC/W) 4.75
𝜃𝑝 (ºC/W) 0.03
Maintenance Cost Configurations
Start-stop cycles for disks 40000
CPU maintenance price ($) 300
Disk maintenance price ($) 200
Memory maintenance price ($) 150
Table 1 Configurations of simulated server
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
35 http://www.hipore.com/ijcc/
𝜋𝑇 = 0.1exp (−𝐸𝑎
8.617 × 10−5(
1
𝑇𝑝 + 273−
1
298))
(12)
Here, Ea is the effective activation energy (Ev) and TP is the
temperature of electronic devices. The parameters
(C1,C2,πE,πL πQ) are derived from (Reliability Prediction of
Electronic Equipment. Military Handbook n.d. ). We have
scaled the lifetime of CPU and memory according to recent
studies (Li, S. et al. 2011). The lifetime of CPU is expected
to be 7 years when chip temperature is 70 ºC (Srinivasan, J.
et al. 2005), while the expected lifetime of 2GB DRAM is 5
years when its temperature is 65 ºC (Li, S. et al. 2011).
4.3 THE COSTS OF HARDWARE MAINTENANCE. After the thermal reliability of electronic devices has
been introduced, we evaluate the costs of processors and
DRAM maintenance based on their thermal reliability is
given as follows:
RC = the cost of hardware maintenances /MTTF . For a time interval, MTTF is calculated based on their
thermal reliability model and current chip temperature. The
cost of a CPU, a disk and a memory maintenance are $300,
$200 and $150 respectively as shown in Table 1, according
to the maintenance ranging from $300 to $150(Barroso, L. A.
and Hölzle, U. 2009). Based on the thermal reliability model,
the cost of CPU and memory maintenance in an active server
is specified as follows:
𝑅𝐶𝑆𝑒𝑟𝑣𝑒𝑟 = ∑ 𝑅𝐶𝑃𝑟𝑜𝑐𝑒𝑠𝑠𝑜𝑟(𝑖)
𝑁𝑆
𝑖=1
+ ∑ 𝑅𝐶𝑀𝑒𝑚𝑜𝑟𝑦
𝐷𝑀
𝑗=1
(𝑗) (13)
Here, the costs of DRAM and CPU maintenance are
increased by higher inlet water temperature, though the
auxiliary components are still cooled down by air cooling.
Their little heat dissipation, much lower power density and
fixed inlet air temperature result in their little cooling power
and their stable maintenance cost. Additionally, the lifetime
of hard disks is heavily impacted by server consolidations
due to hard disk limited number of lifetime start-stop cycles
(Elerath , J. G. 2000), while the impact of utilization and
temperature is still unclear (Pinheiro, Eduardo, et al. 2007).
On the other hand, switching on/off servers incurs relatively
little maintenance cost of other components such as
processors and memory compared with that of hard disks.
The cost of disk maintenance is computed by the following
equation:
𝑅𝐶𝐷𝑖𝑠𝑘 = 𝑃𝑟𝑖𝑐𝑒
𝑠𝑡𝑎𝑟𝑡 − 𝑠𝑡𝑜𝑝 𝑐𝑦𝑐𝑙𝑒𝑠(14)
As we know, the number of lifetime start-stop cycles for
hard disks is 40000 (Chen, Y. et al. 2005).
Overall, the cost of hardware maintenance of data center
is listed as follows:
𝑅𝐶 = ∑ 𝑅𝐶𝐷𝑖𝑠𝑘
𝑁𝐷
𝑛=1
[𝑁𝐴𝑆(𝑡 − 1) − 𝑁𝐴𝑆(𝑡)]+
+ ∑ 𝑅𝑆𝑒𝑟𝑣𝑒𝑟(𝑘)
𝑁𝐴𝑆
𝑘=1
(15)
[𝐴]+ = 𝐴 𝑖𝑓 𝐴 > 0 𝑜𝑟 [𝐴]+ = 0 𝑖𝑓 𝐴 ≤ 0
where ND and NAS(t) respectively denotes the number of
disks in a server and the number of active servers in the data
center at the time t. [NAS(t) − NAS(t − 1)]+ represents the
number of servers which have been turned off.
Consequently, we have set up models for electricity cost
and the cost of hardware maintenance to evaluate our
approach which optimizes the total cost. The models have
been validated with the costs of our campus datacenters.
5. WIND POWER
Figure 2: The relationship between wind speed and
power
0
1
2
3
4
5
6
0 5 10 15 20 25
Win
d P
ow
er
(KW
)
Wind Speed (m/s)
Cut-in wind Speed
Rated Wind Speed
Cut-off Wind Speed
Workload
Prediction
Request
History
Server Monitor
Server Temperature
& Server Utilization
Server Manager
Future minimal
required number of
active servers
Estimated the Cost of
Hardware Maintenance
Thermal Manager
The Cost of
cooling power
Inlet water
temperatureTurn off or on servers
Figure 4: The overview of costs optimization system
Figure 3: The mismatch between wind power and power
consumption of data center
0
0.2
0.4
0.6
0.8
1
1.2
0 100 200 300
No
rmal
ize
d P
ow
er
Hours
Demanded Power Wind Power
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
36 http://www.hipore.com/ijcc/
Wind power is captured by wind turbines which converts
kinetic energy into mechanical energy used to produce
electricity. The output power of a typical wind turbine with
respect to the wind speed is shown in Figure 2 (Patel, M.
1999). The power is determined by three important wind
speeds: cut-in wind speed, rated wind speed, and cut-off
speed, and these speeds are specific to a wind turbine. When
the wind speed exceeds cut-in wind speed, the wind turbine
starts to generate electricity. Its power grows as the wind
speed increases, until it reaches the rated wind speed. The
relation between the power and the wind speed could be
shown in the equation: P = 0.5Cp ∅Av3 , whereCp denotes
the power efficiency, ∅ is the air density, A is the rotor
swept area , and v is the wind speed. When the wind speed
is between the cut-off wind speed and rated wind speed, the
output power meets its maximum capacity. The power
sharply drops to zero for protecting its blade assembly when
the wind power exceeds the cut-off wind speed.
For most wind farm sites, the wind speed at most time is
observed between the cut-in wind speed and the rated wind
speed (Patel, M. 1999). As a result, the output power is
greatly sensitive to the wind speed due to their cubic relation.
The resulted fluctuation of the power is shown in Figure 3 of
the wind power trace used in our experiment. Although the
average power demand derived from Saskatchewan-HTTP
trace is approximate to the total wind power in the example,
a considerable mismatch is expected due to their unrelated
factors for their fluctuation: diary human activities and local
weather condition. This mismatch leads to low wind power
usage or requires a huge capacity of energy storage to
reshape the wind power. However, the energy storage incurs
additional capital costs and wastes wind energy, since
required batteries are considerably expensive and their
round-trip energy efficiency ranges from 5% to 25%.
6. COST OPTIMIZATION IN DATA
CENTERS We formulate the total cost in equation (16) based on the
equations (8) (15) with constraints. Since we only focus on
the operational cost of data centers, we pick up a typical
specification for our heuristic data center shown in Table
1.There are two important decision variables Tinlet_waterand
NAS, while other variables are determined by available
servers, server performance and characteristics of traces,
which are also treated as parameters. For example, NS
denotes the total number of servers, while MINS denotes the
minimal required number of active servers which is
determined by traces. Our objective is to minimize the total
cost with the constraints:
min {TC = ∑ 𝑅𝐶𝐷𝑖𝑠𝑘
𝑁𝐷
𝑛=1
∗ [NAS(t − 1) − NAS(t)]+
+ ∑ RCServer(i
NAS
i=1
) + K$ ∗ (PIT
+ Pcooling)}
(16)
Subject to
TC ≤ 90 °C and TM ≤ 85 °C MINS ≤ NAS ≤ NS
The space of feasible solutions of this discrete
optimization is too large, resulting in that exhaustively
searching the global optimal solution is impossible. To
optimize the total cost of electricity and hardware
maintenance, we proposed to trace local optimal solution by
dynamically manipulating Tinlet_water and NAS
corresponding to the fluctuation of workloads.
6.1 THE OVERVIEW OF COST OPTIMIZATION SYSTEM. For the manipulation of Tinletand NAS, we proposed a
structure shown in Figure 4. In this structure, there are four
modules, Workload Prediction, Server Monitor, Server
Manager and Temperature Manger, working together to
reduce the total cost. The workload prediction collects
request history and predicts future request trend based the
history. The module also can predict the future minimal
required number of active servers. The server monitor
collects the temperature and utilization information of
servers and estimates the cost of hardware maintenance.
Acquiring the average server utilization from the server
monitor, the temperature manager adjusts inlet water
temperature. The Server manager dynamic allocates servers
according to the predicted future minimal required number
of active servers.
6.2 THE IMPACT OF INLET WATER TEMPERATURE To investigate the impact of the inlet water temperature
on the total cost, we divide the total cost into two parts: the
cost of cooling power and CPU and memory maintenance
which are affected by the inlet water temperature, and the
other costs which are unaffected denoted by C.
𝑇𝐶 = 𝐾$ ∗ 𝑃𝑐𝑜𝑜𝑙𝑖𝑛𝑔 + ∑ 𝑅𝐶𝑆𝑒𝑟𝑣𝑒𝑟(𝑖)
𝑁𝐴𝑆
𝑖=1
+ 𝐶 (17)
As the inlet water temperature increases, Pcooling
decreases based on the function of COP, while RCServer
increases at the same time according to equations (9)-(13).
There should be an optimal temperature to balance the cost
of cooling power and the costs of CPU and memory
maintenance. The optimal temperature (or sweet temperature)
is adjusted according to workloads since the two costs also
vary with the change of workloads.
6.3 THE IMPACT OF SERVER CONSOLIDATION. The other substantial variable NAS is facilitated by
server consolidation which lively migrate jobs cross servers,
with the upper bound of available servers and the low bound
of service level agreement. Under these constraints, its cost
and benefit are investigated in the following.
The cost of Server Consolidation. It is well known that
server consolidation could save the electricity cost.
Unfortunately, it increases the cost of disk maintenance,
according to equation (15). Furthermore, the transition
between the active state and the sleeping state, servers wastes
energy. We formulate the cost for server consolidation
denoted by Ccs . The cost Ccs per a server is calculated as
follows:
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
37 http://www.hipore.com/ijcc/
𝐶𝑐𝑠 = ∑ 𝑅𝐶𝐷𝑖𝑠𝑘
𝑁𝐷
𝑗=1
+ 𝑃𝑚𝑎𝑥 ∗ 𝑇𝑇 ∗ 𝐾$ (18)
where TT is the time of the two transitions including two
job migrations (20 seconds for one (Chen, Y. et al. 2005))
and two transitions between the active state and sleeping
state (5 seconds for ACPI S3 state (Linux Documentation
n.d.)). Therefore, TT is estimated to be 50 seconds. Pmax and
K$ respectively represent the maximum power for a server
and denotes commercial KWH Billing Rate.
The benefit of Server Consolidation. The reward of
server consolidation depends on the length of server sleeping
time for once turning off. In other word, the benefit is
determined by the length of the period of turning off servers
without violation of user level agreement. The length of this
period is referred as available sleeping time (AST) which
indicates the maximal server sleeping time. Thus, the benefit
of turning off N servers is denoted by Bsleeping × AST × N.
Here, Bsleeping denotes the benefit of turning off a server for
a minute.
To optimize server consolidation, we define available
sleeping time threshold (ASTT) as follow:
𝐶𝑐𝑠 = ∑ 𝑅𝐶𝐷𝑖𝑠𝑘
𝑁𝐷
𝑗=1
+ 𝑃𝑚𝑎𝑥 ∗ 𝑇𝑇 ∗ 𝐾$ (19)
When the available sleep time of servers is longer than
ASTT, the servers should be turned off. Otherwise, the server
should keep running. We design an algorithm shown in
Figure 5 based on the concept. Generally, the algorithm conservatively turns off servers to mitigate the cost of server
consolidation.
In this algorithm, the decision of turning off servers
requires the knowledge of Future Minimal Required Number
of Active Servers (FMRNAS) which is bound by the
constraint of service level agreement (SLA).The
performance of this algorithm depends on how accurately
FMRNAS is predicted. Therefore, we will introduce two
different predictions combined with the algorithm in the
following sections.
ASTT-P Available sleeping time threshold based on a
perfect prediction. Firstly, we assume that we have a
perfect predictor which indicates FMRNAS accurately.
Given this knowledge, ASTT-P is designed to minimize the
total cost by selecting an available sleeping threshold without
the impact of inaccurate predictions. The exact value of
optimal available sleeping threshold is impossibly obtained
by solving equation (18) since Bsleeping is slightly affected
by other factors such as inlet water temperature.
ASTT-AR: Available Sleeping time threshold based
on the autoregressive model (AR model). The adopted prediction based on the autoregressive model (Stoffer, D. S. and. Shumway, R. H 2010) which is widely used for
pattern prediction is listed in the following equation to
estimate FMRNAS:
𝑆��(𝑇) = (𝐾 + 1)(𝐶 + ∑ 𝐴𝑖 ∗ 𝑆𝑁(𝑇 − 𝑖))
𝑎
𝑖=1
𝑖 = 1 ⋯ 𝑎 (20)
where SN(T) denotes predicted FMRNAS at time T while
SN(T − i) denotes PMRNAS at time (T − i). C and Ai are
tuned to reduce overprovision servers and guarantee the
response time in offline. K is updated according to the
percentage of requests whose response time is satisfied.
When the percentage is below the requirement, K increases
to reserve more servers to handle spike requests. Otherwise,
K is decreased. The goal in this paper is to satisfy more than
99% requests. In our paper, we focus on the benefit of ASTT
by utilizing the mature pattern prediction, though it might be
replaced by advanced tools.
In the following section, the model of a datacenter is built
up to quantitatively evaluate the benefit of sweet temperature
and ASTT.
6.4 CO-OPTIMIZATION WITH WIND POWER The proposed optimization for the wind power is
designed to increase its benefit. Rather than merely targeting
at electricity costs, the optimization reduces the server
maintenance costs at the expense of increased power
consumption. The cost of such overhead could be avoided
when the wind power is larger than the electrical demand of
data centers. It could be explained by the modified objective:
min {TC = ∑ 𝑅𝐶𝐷𝑖𝑠𝑘
𝑁𝐷
𝑛=1
∗ [NAS(t − 1) − NAS(t)]+
+ ∑ RCServer(i
NAS
i=1
) + K$ ∗ (PIT
+ Pcooling − Pwind)}
(21)
Where Pwind denote the wind power at time t. There are
two scenarios regarding to the comparison between the wind
power and the power demand of data centers:
Pwind ≥ (PIT + Pcooling) : Power Over Sufficient
Period(POS period). With over sufficient wind power, the
only concern of this optimization is to reduce the cost of
server maintenance costs by lowering the inlet water
temperature and stopping turning off active servers. The
power consumption of data centers could be increased as
long as it is less than the wind power.
//NAS : the Current Number of Active Servers if NAS < FMRNAS [T]
NAS = FMRNAS [T] Else // Turn off servers If NAS > Max(FMRNAS [T,T+ ASTT]) // Turn off(NAS - Max(FMRNAS [T,T+ ASTT])) servers NAS = Max(FMRNAS [T,T+ ASTT]) Else // Do nothing pass
Figure 5: The algorithm based on ASTT
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
38 http://www.hipore.com/ijcc/
Pwind < (PIT + Pcooling): Power Insufficient Period (PI
period). When the wind power partially compensates the
power consumption of data centers, ASST-AR can reduce
the electricity costs and server maintenance costs together by
adjusting the inlet water temperature and the number of
active servers. Since the derivative of the total cost in the
factor of them is not affected by the wind power, our method
still reach the optimal point to minimize the total costs at
each interval.
Disk replacement cost. Predicting the comparison
between the wind power and the power demand in the
following intervals is substantial to reduce disk replacement
costs by exploiting the benefit of the wind power. The disk
replacement cost is amortized over the saving of the
electricity costs in the server sleeping time. The saving could
be reduced if the sleeping time includes some POS periods.
Consequently, the longer available sleeping time is
demanded to compensate the disk replacement cost, since
electricity saving can only be gained in the PI periods. The
portion of POS periods in the following time become key to
reduce disk replacement cost with the wind power. To
further reduce disk replacement cost, we design a POS
predictor which is similar to the classical CPU branch
predictor.
Wind Power ASST-AR. ASST-AR as well as sweet
temperature is extended to fully exploit the benefit of the
wind power based on the above discussion. The optimization
of sweet temperature is intuitive; the inlet water temperature
tracks the optimal value to balance the CPU and memory
replacement costs in PI periods, otherwise, it is fixed at the
lowest temperature to minimize the server maintenance cost.
The modified ASST-AR also shows distinct policies in
different periods to minimize the electricity cost and the
replacement costs of disks shown in Figure 6. During POS
periods, turning off active servers is prohibited to avoid
incurred replacement cost; otherwise, the original ASST-AR
still works. For capturing the immediately following POS
period, we design a predictor based on the recent history,
which is widely used in CPU branch prediction in Figure
6.The M is chosen to be 8, since we discovered that it is the
optimal value for our five traces. This modified co-
optimization is referred as Wind Power ASST-AR (WP-
ASST-AR) which reduces electricity and server maintenance
costs by utilizing the wind power.
7. EXPERIMENT SETUP
7.1 DATACENTER. Recalling the models related to the costs of electricity
and hardware maintenance, we combined them with server
performance model and real traces to simulate our prototype
data center which consists of 1024 servers cooled by hybrid
cooling.
Server performance model & response time analysis.
We assume a server in our datacenter provides 2.6
Gbytes/sec service rate and the mean of response time should
be bound by 6 ms for SLA (Chen, Y. et al. 2005). To
calculate the FMRNAS at a time interval, we use GI/G/m
model (Bolch, G. et al. 1998) to determine how many servers
can satisfy a demand based on the following equation:
�� =1
𝜇 +
𝑃𝑚
𝜇(1−𝜌)∗ (
𝐶𝐴2+𝐶𝐵
2
2𝑚) (22)
Pm = ρ
m+12
if ρ ≤ 0.7
Pm = ρm + ρ
2 if ρ > 0.7
where W is the mean response time. 1 μ⁄ is the mean service
time of a server. ρ =λφ
mf is the average utilization of servers.
λ, φ, . CA and CB are derived from trace characteristics
(Ahmad, Faraz and Vijaykumar, T. N. 2010). We use this
performance server and response time model to acquire the
minimal required number of active servers at every time slot.
For a time interval, we choose 5 minutes as the minus unit
(Ahmad, Faraz and Vijaykumar, T. N. 2010).
7.2 TRACES. We use five traces downloaded from the Internet traffic
Archive (Traces in the Internet Traffic Archive n.d.):
Clarknet-HTTP, NASA-HTTP, Saskatchewan-HTTP, UC
Berkeley IP and WorldCup. The lengths of them range from
14 days to 30 days and all of trace files cover several peak
requests. We have scaled the traces to meet our datacenter
performance.
7.3 WIND POWER TRACE. We calculated the wind power based on the relation
between the wind speed and the output power of wind
turbines (Patel, M. 1999), with the specific parameters such
as power efficiency from (错误 !未找到引用源。 The
fourteen day wind speed trace is derived from (Center for
Operational Oceanographic Products and Services n.d.).
Since the average power consumption cross web traces are
//Predictor If Wind Power > power Consumption & Predictor <M Predictor = Predictor + 1 If Wind Power < power Consumption & Predictor >0 Predictor = Predictor – 1 //NAS : the Current Number of Active Servers if NAS < FMRNAS [T] NAS = FMRNAS [T] Else // Turn off servers If NAS > Max(FMRNAS [T,T+ ASTT])&& (Predictor<M/2) // Turn off(NAS - Max(FMRNAS [T,T+ ASTT])) servers NAS = Max(FMRNAS [T,T+ ASTT]) Else // Do nothing pass
Figure 6: The algorithm of Wind power ASST-AR
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
39 http://www.hipore.com/ijcc/
different due to their distinct patterns, we scale the average
wind power to match the average power consumption for
data centers for each trace. To scrutinize the benefit of our
optimization, the average wind power is scaled to 50%, 75%,
100%, 125%, and 150% of the average power demand in
each trace, which are referred as 50%, 75%, 100%, 125%,
150% Wind Power(WP). The extra power for data centers
comes from the conventional power grid when the wind
power is less than the power demand.
8. RESULTS
8.1 THE IMPACT OF THE OPTIMIZATION BASED ON
SWEET TEMPERATURE As illustrated in equation (17), when the server power is
fixed, the total cost is only related to cooling and hardware
maintenance. Figure 7 illustrates the impact of the inlet
water temperature changing from 15°C to 35°C on the
cooling cost and the cost of hardware maintenance of our
datacenter with 30% utilization. These costs are normalized
against the total costs when inlet water temperature is 15°C.
Increasing inlet water temperature reduces cooling power
especially when the temperature is below 25°C. However,
high inlet water temperature increases the cost of hardware
maintenance of CPU and memory. Observed from Figure 7,
we can find an optimal inlet water temperature (25 °C in this
case) which minimizes the total cost when utilization is fixed
at 30%. In the following context, we will refer the sweet
temperature to the optimal inlet water temperature. This
observation justifies that high inlet water temperature is
reasonable in datacenters when the current average server
utilization is low (below 30%). Otherwise, high inlet water
temperature could hurt the cost of hardware maintenance
during the high utilization.
Figure 8 shows the cooling and hardware maintenance
costs of our datacenter when its utilization varies from 0% to
100%. The right vertical axis of the figure illustrates sweet
temperatures for different utilizations. In the figure, the total
costs for all utilizations are the lowest for the datacenter
cooled by water at corresponding sweet temperatures. When
the utilization of the datacenter is low, warm inlet water
temperature offers more benefit since the cost of cooling
power is larger than the cost of hardware maintenance (e.g.
in our simulation result, the cost of cooling power is 1.65
times of the cost of hardware maintenance when the
utilization is 10%). On the other hand, as the datacenter
utilization increases, we must keep a cold chilling water to
cool down the heating hardware and slow the growth of
hardware maintenance especially when their temperatures
are close to the critical temperatures. Consequently, to
minimize the total costs, inlet water temperature should be
dynamically adjusted according to the data center utilization.
8.2 THE IMPACT OF THE OPTIMIZATION BASED ON
ASTT. The total cost by employing ASTT-P with different
ASTT (ASTT from 5 to 80 minutes) is shown in Figure 9.
The total costs of five traces with different ASTT are
normalized against the total cost of five traces when ASTT is
5 minutes. Observed from this figure, the total cost of five
traces can be reduced considerably when we select an
optimal ASTT for them, though the best ASTT for five
Figure 7: The impact of inlet water temperature on the costs
of cooling power and hardware maintenance
0
0.2
0.4
0.6
0.8
1
1.2
15 20 25 30 35
No
rmal
ize
d C
ost
Inlet Water Temperature (°C)
Cooling costs Replacement Costs
Figure 8: The variation of Sweet Temperature and these
costs corresponding to the utilization of the data center
15
20
25
30
0
0.5
1
1.5
0 10 20 30 40 50 60 70 80 90 100
Tem
pe
ratu
re (°
C)
No
rmal
ize
d C
ost
Utilization (%)
The cost of cooling power
The cost of hardware maintenance
Figure 9: The Normalized total cost reduced by ASTT-P
when ASTT from 5 to 80 minutes in five traces
0.7
0.75
0.8
0.85
0.9
0.95
1
1.05
5 10 20 30 40 50 60 70 80
No
rmal
ize
d T
ota
l Co
st
ASTT (Minutes)
WorldCup UC Berkeley IP
Clarknet-HTTP NASA-HTTP
Saskatchewan-HTTP
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
40 http://www.hipore.com/ijcc/
traces are not the same (around 30 minutes to 50 minutes)
due to the small variation of the benefit of server
consolidation( Bsleeping ). In the following, we select 40
minutes ASTT as the optimal ASTT for ASTT-P in the five
traces. For ASTT-AR, we also obtained similar curves for
five traces, though the optimal ASTT (around 60 minutes) of
ASTT-AR for five trace is longer than that of ASTT-P due to
the inaccurate prediction and the relatively slow growth of
total cost. 60 minutes ASTT is selected as the optimal ASTT
for ASTT-AR in the five traces for the following analysis.
Figure 10 shows the comparison of the benefits of
ASTT-P (ASTT = 40 minutes) and ASTT-AR (ASTT = 60
minutes) for five traces. All the total costs are normalized
against the total cost of ASTT-P (ASTT = 5 minutes) in five
traces respectively. ASTT-P offers the most benefit
compared with ASTT-AR but requires an unreachable
perfect prediction. As a practical algorithm, ASTT-AR still
saves considerable cost while it guarantees the response time
of 99% requests in the datacenter.
8.3 JOINT OPTIMIZATION BASED ON SWEET
TEMPERATURE AND ASTT-AR
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
41 http://www.hipore.com/ijcc/
Figure 11 shows the benefit when we combine dynamic
optimal inlet water temperature (i.e. sweet temperature) and
ASTT-AR for the five traces. The total costs of five traces
are normalized against the total costs in five traces with
ASTT-P (ASTT = 5 minutes and inlet water temperature
fixed at 25 °C) as the baseline which represents a typical
scheme. Overall, the total costs of sweet temperature and
ASTT-AR offers 18% savings of total cost of five traces
compared with the baseline in arithmetic mean based on our
simulation results.
8.4 WP-ASST-AR
Figure 10: The total cost of ASTT-P with ASST (60 minutes) & fixed inlet water temperature (25 °C), ASTT-AR with
ASST (60 minutes) & fixed inlet water temperature (25 °C), and ASTT-P with ASST (60 minutes) & sweet temperature
in five traces.
0.6
0.7
0.8
0.9
1
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ASTT-P ( T = 25 °C ) ASTT-AR (T = 25 °C) ASTT-AR with sweet temperature
Figure 11: The total cost of ASTT-P with ASST (5 minutes), ASTT-AR with ASST (60 minutes), and ASTT-P with ASST-
P with ASST (40 minutes) in five traces
0.7
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ASTT-P (ASST = 5 minutes) ASTT-AR (ASST = 60 minutes) ASTT-P (ASST = 40 minutes)
Figure 12: The normalized costs in five traces of the simulated data center powered by 50%WP, 75% WP, 100% WP,
125% WP, 150% WP
0.3
0.5
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Baseline 50 % WP 75 % WP 100% WP 125% WP 150% WP
Figure 13: The normalized costs in five traces of the simulated data center powered by 50%WP, 75% WP, 100% WP, 125%
WP, 150% WP, and optimized by WP-ASST-AR
0.3
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42 http://www.hipore.com/ijcc/
The benefit of WP-ASST-AR is revealed by the
comparison between Figure 12 and Figure 13. Figure 12
shows the normalized costs in five traces of the simulated
data center powered by 50%WP, 75%WP, and 100%WP,
and 125%WP, and 150%WP with the baseline which merely
targets electricity costs. The total costs are normalized
against those of the baseline without the wind power. The
shrinking marginal profit of increasing the wind power could
be observed from that the total costs of 50%WP, 75%WP,
100%WP, 125%WP, and 150%WP are 0.77, 0.7, 0.66, 0.63,
and 0.61 in geometric mean respectively. This trend is
confirmed by the results of five traces. Figure 13 also shows
this normalized costs but with WP-ASST-AR. The similar
decrease of the marginal profit could be observed from that
the total costs of 50%WP, 75%WP, 100%WP, 125%WP,
and 150%WP are 0.67, 0.57, 0.51, 0.47, and 0.44 in
geometric mean respectively. However, the benefit of WP-
ASST-AR grows as the wind power increases based on the
facts that with 50%WP, 75%WP, 100%WP, 125%WP, and
150%WP are 0.1, 0.13, 0.15, 0.16, and 0.17 compared with
Figure 12.
Contributing the benefit of WP-ASST-AR, its higher cost
savings of the wind power could be discovered by the
comparison between Figure 14 and Figure 15. Figure 14
shows the total cost savings of the baseline yielded by
50%WP, 75%WP, and 100%WP, and 125%WP, and
150%WP. The increase of the savings shrinks as the wind
power grows, and this trend is also perceived in Figure 15
showing the cost savings with WP-ASST-AR. More
importantly, this cost saving is increased by WP-ASST-AR,
which is consistent to the results of the total costs. The cost
saving of the wind power increases from 22%, 29%, 31%,
35%, and 37% to 27%, 37%, 45%, 50% and 54% in
geometric mean respectively. It implies that the more wind
power are supplied, the more its cost saving could be
obtained by WP-ASST-AR.
9. CONCLUSIONSThe quick growth of electricity bill drives owners of data
centers to employ server consolidation and the high
temperature of data center However, the traditional air
cooling system offers limited benefit of these two approaches
due to its low energy efficiency of cooling power especially.
We build a comprehensive framework which covers the
costs of server power, cooling power, and hardware
maintenance. Based on the models, we introduce a joint
optimization of the costs of electricity and server
maintenance. The approach gains 18% savings of the total
cost and guarantees the response time of more than 99%
requests. In the future, our framework will incorporate
elaborated reliability models for state of the art servers and
power managements which are also important for
minimizing costs of data center owners.
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Clarknet-HTTP NASA-HTTP Saskatchewan-HTTP UC Berkeley WorldCup GM
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Authors
Shaoming Chen received the bachelor’s and
master’s degrees in Electronic and
Information Engineering from Huazhong
University of Science and Technology,
China. He is currently a PhD student in
Electrical and Computer Engineering, Louisiana State
University, majoring in Computer architecture. His research
interests cover the cost optimization and Green Energy in
data centers.
Yue Hu received his Bachelor’s degree in
Electronic Information Science and
Technology from Central South University,
China, in June 2009. He is currently a PhD
student in Electrical and Computer Engineering, Louisiana
State University. His research interests include branch
predictors, cooling techniques for microprocessors and data
centers, and energy-efficient microprocessor design
Lu Peng received the bachelor’s and
master’s degrees in computer science and
engineering from Shanghai Jiao Tong
University, China, and the PhD degree in
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
44 http://www.hipore.com/ijcc/
computer engineering from the University of Florida,
Gainesville, in April 2005. He is currently an associate
professor with the Division of Electrical and Computer
Engineering at Louisiana State University. His research
focuses on memory hierarchy systems, reliability, power
efficiency, and other issues in processor design. He also has
interest in network processors. He received an ORAU Ralph
E. Powe Junior Faculty Enhancement Award in 2007 and a
Best Paper Award from the IEEE International Conference
on Computer Design in 2001.
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
45 http://www.hipore.com/ijcc/
A BROKER BASED CONSUMPTION MECHANISM FOR
SOCIAL CLOUDS Ioan Petri1, Magdalena Punceva2, Omer F. Rana1, George Theodorakopoulos1 and Yacine Rezgui3
1 School of Computer Science & Informatics, Cardiff University, UK 2 Institute for Computer and Communication Systems, University of Applied Sciences, Western Switzerland
3 School of Engineering, Cardiff University, UK contact author: petrii@cardiff.ac.uk
Abstract The new consumption without ownership paradigm is leading towards a “rental economy” where people can now rent and use various services from third-parties within a market of “shared” resources. The elimination of ownership has increased the marginal utility of consumption and reduced the risks associated with permanent ownership. In the absence of ownership the consumption in the global marketplace has become more dynamic and has positively impacted various economic and social sectors. The concept of “consumption without ownership” can also be used in the area of cloud computing where the interaction between clients and providers generally involves the use of data storage and computational resources. Although a number of commercial providers are currently on the market, it is often beneficial for a user to consider capability from a number of different ones. This would prevent vendor lock-in and more economic choice for a user. Based on this observation, work on “Social Clouds” has involved using social relationships formed between individuals and institutions to establish Peer-2-Peer resource sharing networks, enabling market forces to determine how demand for resources can be met by a number of different (often individually owned) providers. In this paper we identify how trading and consumption within such a network could be enhanced by the dynamic emergence (or identification) of brokers – based on their social position in the network (based on connectivity metrics within a social network). We investigate how offering financial incentives to such brokers, once discovered, could help improve the number of trades that could take place with a network, thereby increasing consumption. A social score algorithm is described and simulated with PeerSim to validate our approach. We also compare the approach to a distributed dominating set algorithm – the closest approximation to our approach.
Keywords: Cloud computing; Social Networks; Dominating Set; Economic Models; Consumption; Ownership.
__________________________________________________________________________________________________________________
1. INTRODUCTIONIn recent years the mode of acquisition and use of resources
has changed significantly, with consumers expecting to use
a product from one vendor for a short amount of time, and
renting rather than owning the product. Resources/products
which fall within this remit have ranged from cars to movies,
games and music recordings. Such a change in emphasis has
been influenced by variability in markets affected by aspects
such as seasonality and the temporary nature of exchange.
Consumers are therefore motivated to participate in a
leasing economy where products are used for a shorter
period significantly preferring to rent than to purchase
(Bendell, 2007), (Levenson, 2007). The ability to participate
in such a sharing economy also provides greater choice for
both the consumer and the provider, enabling a much
greater flexibility in being able to switch between multiple
market offerings, thereby also likely to increase
consumption from consumers by not being restricted to
products or price constraints from a single vendor. The
absence of ownership also enables access to some existing
services that may be inaccessible previously due to high cost
of ownership (Living Planet, 2012). By engaging in such a
non-ownership market, consumers can have access to
greater and increased social status with less cost (Moore,
2008), (Russell, 2007).
Consumption without the cost of ownership has been
identified by economists (Winsper, 2007), (Zukin, 2008) as
a new paradigm in the emerging “sharing economy”.
Increasing the use of social networks, data
mashing/aggregation, availability of software platforms that
facilitate such service/data aggregation and the availability
of handheld devices providing easy access to such platforms
enable users and providers of resources/services to discover
each other and utilize trust relationships developed over
time. Such trust relationships are often encoded in
interaction patterns and behaviours that can be derived from
(on-line) social networks. These relationships therefore
provide the basis for evaluating people that one can trade
with. Increasingly, there is also reluctance in making large
capital purchases of equipment and hardware, making it
more lucrative for users to monetize their time and assets.
Trust and reputation play a central role in an economy based
on the “consumption without ownership” model, therefore
these pre-established relationships would be essential to
encourage greater transactions between participants.
doi: 10.29268/stcc.2014.2.1.3
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In a sharing market being proposed in this paradigm, market
actors (users requesting services and providers offering
them) can also act as micro-entrepreneurs or brokers.
Enabling discovery of suitable providers able to meet
particular, often specific and individual demand from
consumers, becomes an essential tenet in such systems,
especially with the ability to also associate some degree of
confidence in the likelihood of the provider being able to
meet their advertised capability. Consumers become
independent contractors, working for themselves with
control over their working time and working conditions. An
example is the case of ride-sharing companies such as Lyft,
Sidecar or UberX which own no cars themselves but they
sign up instead ordinary car owners: when people need a
ride, they can use mobile apps to find a driver nearby and
ask to be picked up. Airbnb represents another example of
over 300,000 listings from people making their apartments
and homes available for short-term rent, similarly
SnapGoods makes it possible for people to borrow
consumer goods from other people in their neighbourhood
or social network. A variety of examples exist today in other
sectors (Benny, 1973), (Surowieski, 2013).
As brokers play a key role in such a “consumption without
ownership” paradigm, identifying where such brokers
should be situated and how many are needed become
important challenges. Such brokers should also enable trust
relationships to be established between consumers and
providers to allow concerns about liability and competence
to be addressed. Ride sharing companies such as Sidecar,
Lyft and Uber often need to also implement and conform to
certain safety and driver regulations. We believe an
equivalent capability is needed for other domains.
As the demand for data and computational services
increases, the benefits of Cloud computing become
substantial. However, Cloud computing capabilities (as
currently provisioned) can prove to be limited when
accessed through a single provider. Due to vendor lock-in
and specialist data models required from a single vendor, it
is in a user’s interest to explore and interact with multiple
possible Cloud providers. Extending capabilities of Clouds
by using user owned and provisioned devices can address a
number of challenges arising in the context of current Cloud
deployments – such as data centre power efficiency,
availability and outage management. We have investigated
such “Social Clouds” in a number of contexts previously
(Chard et al., 2011), (Petri et al., 2012). Social Clouds are
developed using the observation that like any community,
individual users of a social network are bound by finite
capacity and limited capabilities. In many cases however,
other members (friends) may have surplus capacity or
capabilities that, if shared, could be used to meet the
fluctuating demand. A social cloud makes use of trust
relationships between users to enable mutually beneficial
sharing. Social Clouds are defined in (Chard et al., 2011) as
“a resource and service sharing framework utilizing
relationships established between members of a social
network.” The availability of storage resources and access
latency are also significantly improved – as storage
resources may be found in closer proximity to a user. The
establishment of such Peer-to-Peer (P2P) community
Clouds requires a robust mechanism for controlling
interactions between end-users and their access to services.
For instance, in the context of such a Cloud model, end-
users can contribute with their own resources in addition to
making use of resources provided by others (at different
times and for access to differing services) (Grivas et al.,
2010). There is also increasing interest in developing
“distributed Cloud” platforms, which are able to orchestrate
capability across multiple federated Cloud systems, see for
instance work on such a Cloud orchestration system from
Ericsson in the European UNIFY project (UNIFY, 2013).
In previous work, we have also investigated incentive
models for users to provide services to others (Chard et al.,
2011), (Punceva et al., 2012) – which can range from
bartering of resources, improving the social standing of a
participant within a community or obtaining a financial
reward. We focus on the last of these incentives in this work.
Often in such markets it is necessary for a client to discover
suitable providers of interest. This is generally undertaken
through the use of either a registry service (centralized) to
the use of a discovery request being propagated across the
network (a variety of approaches have been considered,
ranging from flooding, controlled “gossiping” to multiple
federated registries). We propose a decentralized approach
whereby some sellers or buyers may become brokers (or
“traders”) in order to improve their own revenue within a
market place, based on their social connectivity within a
network. We consider a number of graph theoretic measures
(such as connectivity degree, centrality, etc) to identify how
nodes within a social Cloud which were initially buyers or
sellers could turn into brokers – to improve their own
revenue and satisfy service requests within the market. We
map our problem into a dominating set problem in graph
theory and show how our results compare with a distributed
implementation of this algorithm.
In section II we identify the role of brokers within a P2P
market – and how the number of brokers influences the
interaction dynamics within the network. The main concepts
of our approach are outlined in section III. In section IV we
outline our overall methodology, with a description of the
social score algorithm and the metrics (degree & centrality)
used within the algorithm to identify nodes that could be
potential brokers. A description is also provided of the
PeerSim simulator we used to evaluate various scenarios.
Results are presented in section V, with Conclusions and
future work in section VI.
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47 http://www.hipore.com/ijcc/
2. RELATED WORKIntermediaries or brokers bring together participants (users
and providers) who have not directly interacted with each
other. Brokers have been used extensively in service-based
systems, primarily as an alternative to service “exchanges”
and registry services. Such brokers may be managed by
external third parties, who have an economic incentive to
provide accurate matchmaking support (utilizing both the
functional capability provided/required including other non-
functional attributes which have been acquired by a broker
over time, such as failure rate, performance, cost, etc)
between the capabilities of a provider and the demands of a
user. Brokering solutions are now beginning to emerge in
Cloud systems also – especially with the emergence of Web
sites such as CloudHarmony (which can support
performance benchmarking across over 100 different Cloud
providers). With Social Clouds (as identified in section I),
broker-based interaction becomes even more important, as
providers can exist over shorter time frames and offer
specialist capability (Sundareswaran, 2012), (Nair et al.,
2010).
Sotiriadis et al. (Sotiriadis et al., 2013) propose a meta-
brokering decentralized approach to manage interactions
between interconnected Clouds. The objective is to support
Cloud interoperability and resource sharing. In this
framework a broker acts on behalf of the user and generates
requests for resources from the Cloud system, based on the
contacted SLAs. The authors demonstrate that the meta-
broker model outperforms a standard broker when the
system contains a high number of concurrent users and
cloudlets submissions.
Sundareswaran et al. (Sundareswaran et al., 2012) propose a
broker-based architecture where brokers help end users
select and rank Cloud service providers based on prior
service requests, enabling users to negotiate SLA terms with
providers. An efficient indexing structure called the CSP
(Cloud Service Provider) index is used to manage the
potentially large number of service providers, utilizing
similarity between various properties of service providers.
The CSP-index can subsequently be used for
service/provider selection and service aggregation.
STRATOS (Pawluk et al., 2012) is a broker service to
facilitate multi-cloud, application topology platform
construction and runtime modification in accordance with a
deployer’s objectives. STRATOS allows an application
deployer to specify what is important in terms of Key
Performance Indicators, so that Cloud system offerings can
be compared and ranked based on these indicators. The
authors demonstrate how an application distributed across
multiple Clouds can decrease the cost of deployment. Duy
et al. (Duy et al., 2012) propose a benchmark-based
approach to evaluate and compare cloud brokers. A
benchmark called Cloud Broker Challenge (CBC) is
employed to describe the cloud providers, cloud consumers,
across 5 difficulty levels – inspired by the successes and
impact of Semantic Web Service Challenge (a set of
benchmark problems in mediating, discovering, and
composing web services) . By introducing difficulty levels
for Cloud brokering and associated scenarios, the authors try
to abstract the fundamental properties of various Cloud
providers to better understand how broker-based solutions
could be applied across multiple providers simultaneously
(Leskovec, 2010).
Our approach complements these situations, in that we
already assume that brokers play an important role within a
Cloud-based resource sharing environment. Our key
objective, instead, is to understand how many brokers
should co-exist within a system to enable better interaction
between users and providers, whilst at the same time
ensuring that the number of brokers is limited.
3. APPROACHA resource trading network has a particular relevance in
Social Clouds – as some resource users & providers may
have a more dominant position in the system, with greater
access to social opportunities for intermediation. The
question of where brokers should be placed within such a
social network becomes significant – primarily to: (i)
increase the flow of ‘goods’ (i.e. facilitate resource
exchange); (ii) increase social welfare within the community.
Social welfare, in this case, measures the number of
potential resource users who are able to find providers that
match their requirements, within their budgets. We consider
a marketplace where buyers and sellers can interact through
an intermediate broker T. The broker receives commission
for each transaction that it facilitates – the broker’s
objectiveis therefore to increase the number of transactions
they participate in and the commission per transaction that
they receive.
Our approach focuses on not having a pre-defined list of
brokers – but understanding how the strategic position of a
node within a network can lead it to be become a broker –
which we refer to as “broker emergence”. Our approach
makes use of two stages to achieve this:
1) Node selection – Select nodes with the highest social
score (as described in section IV-A).
2) Risk assessment – Evaluating the broker’s capacity
of making profit and the associated (financial) risk to
lose the investment.
Broker emergence may be formulated as a dominating set
problem. A dominating set for a graph G = (V, E) is a subset
D of V such that every vertex not in D is joined to at least
one member of D by some edge. The problem minimum
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
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Dominating Set (MDS) requires finding a dominating set of
minimum size. Our goal is to find a set of nodes that will act
as brokers among a network of socially connected nodes
here every node is either a buyer or a seller. The selected set
of brokers should satisfy the following condition:
Market Accessibility: Every non-broker node should be
connected to at least one broker.
Our Market Accessibility condition ensures that every non-
broker node can participate in a transaction either as a buyer
or as a seller. Therefore our problem can be formulated as
finding a Minimum Dominating Set (MDS): given a graph
G(V, E) that represents a social network where vertices
represent users and edges represent friendship links, finding
the set of broker nodes corresponds to finding the minimum
dominating set. The dominating set problem is a classical
NP-complete problem and several approximation algorithms
exist for finding MDS. Kuhn et al. (Kuhn, 2005) propose a
distributed approximation algorithm for finding a
dominating set of minimum size which means every node
uses only local information when executing the algorithm.
This algorithm is particularly suitable for large-scale
decentralised networks and we use it here.
As nodes can change their roles of buyers/sellers, a broker
may be connected to buyers only or sellers only. We
consider two alternatives to overcome this: (i) brokers will
attempt to connect to other brokers (perturbing the original
social structure); (ii) apply the (approximation) algorithm
for finding a connected minimum dominating set instead of
a minimum dominating set (which may not be connected).
Such algorithm although not distributed is presented in
Guha et al (Guha, 1998). It ensures that every broker node is
connected to at least one other broker node.
4. METHODOLOGY We consider a network with an associated set of peer-nodes
P={p1, p2, p3,…,pn}, and a sub-set
S={p1, p2, p3, ..., pm}, m < n, S ⊂ P , where S represents
the set of non-leaf nodes from P . We use two algorithms
for selecting broker nodes: social score algorithm and
dominating set algorithm.
A. Social Score Algorithm
We apply the social score selection algorithm over the set
of non-leaf nodes S. The selection process can be modeled
as a function f (x) : S → T , where the result is a sub-
set of peer-nodes T with the highest social score which we
consider as brokers. The selection protocol for brokers is
built around the notion of social score.
Figure 1. The selection
We use social score as a metric to evaluate nodes and select
brokers. Social score is calculated as an average of three
metrics used to assess the connectivity of a node within a
graph – a node that has greater potential to link other nodes
with each other has a higher social score. The metrics we
use are a node’s: (1) Degree Centrality, (2) Betweenness
Centrality and (3) Eigenvector centrality. Within a graph
G(V, E) where V is the set containing the number of
vertices and E is the set containing the number of edges, we
define the following metrics:
Node’s degree centrality – is simply the number of links
incident to the node:
deg(v) = DC(v)
Node’s betweenness centrality – Betweenness centrality
quantifies the number of times a node acts as a bridge along
the shortest path between two other nodes and is calculated
as the fraction of shortest paths between node pairs that pass
through the node of interest:
Vtstsp
vtspvBC ,)
),(
)/,(()(
where p(s, t/v) is the number of shortest paths between users
s and t that pass through node/user v, and p(s, t) is the
number of all the shortest paths between the two users s and
t.
Node’s Eigenvector centrality – defines the influence of the
node within a network – i.e. it measures how closely a node
is connected to other well connected nodes. It assigns
relative scores to all nodes in the network based on the
concept that connections to high-scoring nodes contribute
more to the score of the node in question than equal
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connections to low-scoring nodes. Hence the objective is to
make xi proportional to the centralities of its n neighbors,
i.e.:
n
j
jjii xAxEC1
,
1)(
λ is a constant. In vector notation this can be written as X =
λ Ax, where λ is an eigenvalue of matrix A if there is a non-
zero vector x, such that Ax = λx. Thus, we classify nodes
from the perspective of their social score which is calculated
as:
3
ECDCBCSS
Figure 1 illustrates the set of non-leaf nodes for which we
calculate the social scores as a basis to support broker
selection. Each selected node is given a certain amount of
capital which can either be the same for all nodes, or in
proportion to their social score. Algorithm 1 explains how
brokers are selected based on the social score associated
with nodes. The variable degree represents the number of
current connections whereas capacity represents the
maximum number of connections a node can support. This
variable can be either specified in a configuration file or set
as the maximum degree of the social graph: capacity =
max(degree).
Algorithm 1: Brokers Selection
1: len:=node.capacity; 2: pos:=0; 3: set:=null; 4: for i := 0 to networkSize by 1 do 5: len := pos; max := maxRounds; found := false; 6: while (!found) and (len>0) and (max>0) do 7: max−− ; r[i] := selectNewNode(); rpeer := null; 8: size:=getNodeDegree(r[i]); 9: brokerObserver.calculateScore(r[i].getIndex());
10: if (r[i].isActive()) and (capacity < r[i].capacity) then 11: rpeer := getNodeId(r[i]); 12: markNode(rpeer,r[i]); 13: addToSet(rpeer,r[i],set); 14: found:=true; 15: else 16: if (degree ≤ r[i].degree) and (r[i].degree <
rpeer.getTarget()) then 17: markNode(rpeer,r[i]); 18: addToSet(rpeer,r[i],set); 19: found := true; 20: end if 21: end if 22: end while 23: if (rpeer := null) or (r[i].IsBroker()) or (r[i].Size ≥
rpeer.getTarget()) or (r[i].isActive()) then 24: removeNode(rpeer); 25: end if 26: end for
We use a set variable for storing nodes and a marking
mechanism markNode for identifying all those brokers over
a set of simulation rounds maxRounds. The algorithm starts
by excluding the leaf nodes and calculating the social score
for each the non-leaf nodes. The brokers are then selected as
the nodes with the highest social score out of all non-leaf
nodes.
Algorithm 1 attempts to identify a minimum number of
brokers within the network. The algorithm uses a
classification criteria based on the social score measure
introduced above: nodes with higher social score are
considered better candidates as brokers. The target set of
brokers is composed by the minimum set of nodes with
highest social score whose total capacity is sufficient to
cover all the remaining nodes (sellers and buyers).
B. Dominating Set Approximation: Distributed
Algorithm
The dominating set distributed algorithm is an
approximation algorithm for solving the dominating set
problem from (Kuhn, 2005). The algorithm relies on a
linear programming (LP) formulation of the problem and
consists of two parts/algorithms: first algorithm calculates
the fractional solution to the LP problem and the second
algorithm does the rounding part. The algorithm runs in
constant time and has a provable approximation ratio.
Algorithm 2: LPMDS Approximation 1: x i := 0;
2: calculateδ(i) ;
3: γ(2 )(vi) := δi + 1; δ(vi) := δi + 1;
4: for l:=k-11 to 0 by -1 do 5: (∗ δ(vi) ≤ (∆ + 1)
(l+1)/k, zi := 0∗ ;
6: for m:=k-1 to 0 by -1 do 7: if (δ(vi) ≥ γ2
(vi)l/l+1
) then 8: send ’active node’ to all neighbors; 9: end if
10: a(vi) := |j ∈ Ni|vj is activenode | ; 11: if colori = gray then then 12: a(vi) = 0; 13: end if 14: a(vi) to all neighbors; 15: a(1 )(vi) := maxj∈Nia(vi) ; 16: ∗ a(vi) , a(1 )
(vi) ≤ (∆ + 1)m+1/k∗
17: if δ(vi) ≥ γ(2 )(vi)
l/l+1 then 18: x i := maxxi, a(1 )
(vi)(−
m+1
);
19: end if 20: send x i to all neighbors; 21: if 22: colori := gray ; 23: send colori to all neighbors; 24: δ(vi) := |j ∈ Ni|colorj = white |; 25: end if 26: ∗ zi ≤ (1 + (∆ + 1)
1/k)/γ
(1 )(vi)
(l/l+1)∗ 27: send δ(vi) to all neighbors; 28: γ(1 )(vi) := maxj∈Niδ(vj) ; 29: send γ(1 )
(vi) to all neighbors; 30: γ(2 )(vi) := maxj∈Niγ
(1 )(vj) 31: end for 32: end for
For an arbitrary possibly constant parameter k and
maximum node degree ∆, the algorithm computes the
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50 http://www.hipore.com/ijcc/
dominating set of expected size O (k∆2k log(∆)|DSOPT |) in
O(k2) rounds. Where |DSOPT | represents the size of the
optimal dominating set.The output of the algorithm is the
vector x defined for all vi∈V which has values 0 or 1 and
indicates whether a node is in the dominating set or not: if xi
= 1 then node vi is in the dominating set and if xi = 0 the
node is not in the dominating set. Initially each node
independently runs the first part of the algorithm (Algorithm
3 from Kuhn et al. (Kuhn, 2005)) and as a result returns a
fractional value between 0 and 1 for xi variables. In
accordance with this approach we use the following
notations. Initially all nodes are colored white. A node is
colored gray if the sum of the weights of xj for vj ∈ Ni
exceeds 1, i.e., as soon as node is covered. The degree of a
node vi is denoted δi. The largest degree in the network
graph is denoted ∆. The notation jNj i max)1(
is
the maximum degree of all nodes in the closed
neighborhood Ni of vi. Similarly )1()2( max jNj i
is
maximum degree of all nodes at distance at most two from
vi. Note that it is assumed each node knows its 2-hop
neighbors and these values therefore can be computed in at
most two communication rounds. A dynamic degree of a
node vi is denoted by and represents the number of white
odes in Ni, the neighborhood of vi. The output of the first
part Algorithm 3 in (Kuhn, 2005)) are fractional values for
xi and the second part (Algorithm 1 in (Kuhn, 2005)) does
the rounding: it takes fractional xi values as input and
rounds them to 0 or 1.
C. Modeling trading process
In our framework each trade is defined as a function f(t) : S
D where S represents a domain containing originating
nodes and D the domain containing destination nodes. Each
transaction f(t) brings an associated revenue for brokers and
is scheduled to happen at a specific simulation cycle. Within
the protocol we enable peer-nodes to change roles over time
such that buyers and sellers can become brokers or brokers
can become buyers or sellers. In order to validate our
hypotheses, PeerSim (Jelasity et al., 2010) was chosen as a
framework for simulating a number of different scenarios.
The PeerSim simulator uses separate source files for
programming different needed controllers of the simulation
process. We therefore employ a number of different
parameters and controllers for simulating the scenarios
reported in section V. We use an initialization controller
defining the various types of events that can happen during
the simulation and which need to be scheduled during the
simulation. Another controller is used for defining the
network variation at each simulation cycle (e.g. how the
network changes when adding new nodes to the network)
for each round of trading.
An additional controller is allocated as an observer that
collects the results for each experiment. The configuration
file also contains a number of simulation parameters:
•cycles: defines the maximum number of simulation
cycles for each experiment.
•maxCapacity defines the maximum number of
connections allowed for any given node.
•minCpacity defines the minimum number of
connections allowed for any given node.
•minTrades defines the minimum number of trades
scheduled to be run within the system as a whole.
•maxTrade defines the maximum number of trades
scheduled to be run within the system as a whole.
To support a dynamic network formulation – whereby nodes
may be added or removed from the network, we used an
additional network dynamics module.
• control.c1 peersim.dynamics.DynamicNetwork
• control.c1.type vtype
• control.c1.maxsize vmax
• control.c1.add vadd
• control.c1.step vstep
• control.c1.from vfrom
• control.c1.until vuntil
The DynamicNetwork is a module provided within PeerSim
which enables us to define a simulation with a differing
number of nodes at each simulation cycle. It includes
various Java packages initializing a network or modifying it
during simulation. The type parameter represents the type of
the node to be added, the maxsize parameter represents the
maximum number of nodes that one simulation process can
use; the add parameter defines the number of nodes injected
at each step; the step parameter defines the frequency in
cycles for each injected node. The parameter from specifies
the starting number of nodes to simulate while the until
parameter defines the maximum limit on the number of
nodes that the simulation can use.
Table 1. Simulation data set from epinions.com
Nodes 75879
Edges 508837
Nodes in largest WCC 75877 (1.000)
Edges in largest WCC 508836 (1.000)
Nodes in largest SCC 32223 (0.425)
Edges in largest SCC 443506 (0.872)
Average clustering
coefficient
0.2283
Number of triangles 1624481
Fraction of closed triangles 0.06568
Diameter (longest shortest
path)
13
90-percentile effective
diameter
5
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We use two different metrics to measure the status of the
system:
(i) Volume of trades: defining the total number of
trades f(t) taking place within the system;
(ii) Average revenue: measures the average revenue
per broker. In equation 5 n defines the number
of trades within the system, m defines the
number of brokers and val(f(ti)) represents the
associated revenue of each trade.
n
i
itfvalm
AR1
)))(((1
5. SIMULATION AND RESULTS Our simulation makes use of social network data about
epinions.com obtained from the Stanford Network Analysis
Platform (SNAP) project (SNAP, 2012).
We use this particular data set as it exposes trust
relationships that are formed within a social network for
product recommendation, exposing the Web of Trust
between individuals. We also felt that this data set is
representative of the types of buyer-seller-broker
relationship that we could foresee within a Social Cloud –
based on the referrals or recommendations made between
people. Table I provides a description of the Epinions data
set.
Experiment 1: This experiment measures how the number of
brokers evolves during the simulation in relation to the
initial network configuration – containing only buyers and
sellers.
Figure 5a. Brokers emergence
The number of nodes, number of edges, average network
degree provided in table I are used to initially start the
network in bi-partite (buyer, seller only) mode.
Brokers are gradually selected based on the algorithm 1
presented in section IV-A. From figure 5a it can be observed
that the simulator needs around 6 simulation cycles to select
brokers. During simulation, the process of broker selection
works in parallel to the actual trading (i.e. trading starts
when the first broker has been identified and continues
during simulation). After 6 simulation cycles the number of
brokers within the system becomes stable – although trading
within the network still continues to take place.
Figure 5b. Degree of brokers
Experiment 2 – This experiment presents how the average
number of nodes connected to brokers evolves during the
simulation – with the initial setup provided in table I.
The average numbers of nodes connected to brokers identify
sellers and buyers within the network.
From figure 5b we observe that the number of nodes
connected to brokers gradually increases within the first 6
cycles of the simulation. Hence, as the number of brokers
increases, the number of nodes (buyers/sellers) associated
with a broker changes. This process of a change in node
interactions (buyer/broker, seller/broker) is strongly related
to the process of broker emergence.
Experiment 3 – Volume of trades when increasing the
number of brokers. In this experiment we measure the
volume of trades when increasing the number brokers
within the network but keeping a fixed number of buyers
and sellers. For running this experiment we extended the
capacity of the network by adding new brokers to the
simulation process. This is ensured by the dynamics
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controller presented in section IV-C with the specific
parameter type specifying broker as the type of node to be
added.
Figure 5c. Volume of trades with brokers
From 5c it can be observed that an increase in the number of
brokers by 25% has a direct impact on the volume of trades.
When adding more brokers to the network, the routes
between sellers and buyers increases significantly, thus an
additional volume of trades is identified. In this experiment
the initial setup and the associated number of brokers are
specified by the social score calculated for each node.
Figure 5d. Volume of traders with buyers and sellers
Experiment 4 – Volume of trades when increasing the
number of buyers and sellers. In this experiment we
measure the volume of trades when increasing the number
of sellers and buyers within the network but keeping a fixed
number of brokers.
Experiments 3 and 4 are the only two simulations where we
change the structure of the network during the simulation to
better understand the impact of: (i) varying number of
intermediaries (Exp. 3); (ii) a change in demand/ supply
over time (Exp. 4). Whereas experiment 3 investigates how
an increase in the number of brokers impacts the volume of
trades, in this experiment we evaluate how the volume of
trades change when expanding the number of buyers and
sellers. As in the previous experiment, the increase of nodes
is handled by employing the dynamics controller with the
specific parameter type set to node. As illustrated in figure
5d an increase of 25% in the number of buyers and sellers
causes an increase in volume of trades. When more buyers
and sellers are added, the number of possible trade options
increases.
However, even if the increase of buyers or sellers causes an
increase in volume of trades, as the number of brokers and
the associated capital are limited the impact on volume of
trades is less significant than the increase in brokers
presented in previous experiment.
Figure 5e. Volumes of trades at broker degrees
Experiment 5 – Volume of trades with regard to broker
degrees. In this experiment we investigate how the volume
of trades evolves with reference to a (broker) node degree.
Brokers are selected according to their social score.
However, each broker has an associated degree parameter
specifying the number of current connections.
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Figure 5f. Average revenue per broker
In this experiment we analyze the relationship between the
average broker degree and the volume of trades. Figure 5e
presents various levels of trades with reference to the degree
of a broker. It can be observed that the volume of trades
increases for brokers with higher degrees such as 50 or 75
connections. The impact on volumes of trades for those
brokers with lower degrees is reduced as identified for
degrees of 15 and 25.
Experiment 6 – This is used to measure the average broker
revenue with regard to broker degrees. In addition to
measuring the volumes of trades we also try to quantify the
revenue for each broker. As it can be observed from figure
5f, the average revenue for brokers is strongly related to
their network degree. Whereas for brokers with degree of 15
respectively 25 connections, the impact is low, for brokers
with higher connectivity average revenue is significantly
increased. A higher degree for a broker gives an increased
number of options for performing trades thus leading to an
increased revenue.
Experiment 7 – The number of brokers compared to the
number of nodes in the dominating set when comparing the
Social Score Algorithm with The Dominating Set algorithm.
In this experiment we compare the performance of the
Social Score Algorithm with Dominating Set Algorithm
from the perspective of number of brokers respectively
number of nodes in dominating set. As presented in figure
5g, the dominating set has better performances than the
social score algorithm. It can be observed that at cycle 5 the
number of nodes in dominating set is with 11% lower than
the number of brokers whereas at cycle 30 the difference is
around 12.5%. The performance differences are determined
by two important particularities: (i) graph properties and (ii)
evaluation metrics.
Figure 5g. Number of brokers: Social score vs. Dominating
set problem
Experiment 8 – Volume of trades when comparing the
Social Score Algorithm with The Dominating Set Algorithm.
In this experiment we evaluate the social score algorithm
and the dominating set algorithm from the perspective of the
volume of trades they generate. Figure 5h shows that the
social score algorithm generates a higher volume of trades
than the dominating set algorithm.
Figure 5h. Volume of trades: Social score vs. Dominating
set algorithm
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This happens because the social score algorithm considers a
number of different network metrics for selecting the
brokers applied on a predefined social graph whereas the
dominating set algorithm seeks to optimize the dominating
set for an ad-hoc network.
Experiment 9 – Volume of trades when comparing the
Social Score Algorithm with the approach when we
incentivize peer-nodes to become brokers. For running this
experiment we assign an incentive to each peer-node in
order to become a broker. Hence, a peer-node i can decide
to become a broker because according to its subjective
decision function f(x), the broker role enables it to
maximize its revenue. Figure 5i shows that the social score
algorithm generates a higher volume of trades than the
incentivising approach.
Figure 5i. Volume of trades: Social Score vs. Incentivising
peers approach
This happens because the social score algorithm considers a
number of different network metrics for selecting the
brokers applied on a predefined social graph whereas in the
incentive approach some of the brokers can derive from
peer-nodes with poor network attributes such as low
connectivity, low centrality degree, etc. It can be also
observed that in the incentive approach the volume of trades
starts to decay after a certain simulation cycle.
This happens because the brokers derived from peers with
low connectivity are unable to generate a constant volume
of trades within the system.
Experiment 10 – Volume of trades when increasing the
demand. In this experiment we measure the volume of
trades when increasing demand within the system but
keeping a fixed number of buyers and sellers.
Figure 5k. Average revenue per broker when increasing
demand
In previous experiments we used a fixed demand identifying
a process where one broker can intermediate a single trade
between a buyer and a seller. Here, we consider that
between each buyer and each seller more than one broker-
intermediated trade can take place. Figure 5j illustrates a
comparison between the base case where there is a regular
demand and the cases where we increase the demand by
25% and 50%, respectively. We observe that the highest
differential increase in volume of trades is identified when
increasing demand by 25%.
This differential increase is determined by the capacity
parameter associated with every broker. In this experiment,
we assume that one broker has a configured capacity of
trades that can be intermediated. When increasing the
demand by 25% there is still enough capacity for brokers to
intermediate trades whereas when increasing the demand by
50% the brokers, due to limited capacity, cannot
intermediate all the trades. The request for resources
increases when the demand is increased, hence brokers will
intermediate more trades generating an increase in volume
of trades.
Experiment 11 – Average revenue per broker with an
increase in demand. In this experiment we investigate how
demand impacts the average revenue per broker. As outlined
in experiment 10, an increase in demand leads to an increase
in trade volume, and is affected by the degree of the broker
(Figure 5k). When a broker has a degree of 25, it can be
observed that the impact of demand on average revenue is
limited. When using a broker degree of 50 it can be
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observed that the demand correspondingly impacts average
revenue.
Figure 5j. Volume of trades when increasing demand
This difference between various levels of demand in terms
of average revenue is determined by the broker degree.
Although the demand is increased, in some cases a broker
can support only a specific number of trades in relation to
the configured capacity, thus the impact is often limited in
practice.
6. CONCLUSION Consumption without ownership represents an emerging
economical approach with applicability in many contexts.
Enhancing consumption with a broker based market
intermediation is a process commonly used in various
market scenarios to enable better interaction between buyers
and sellers. This concept has found applicability in P2P
markets with extension to Social Clouds where a number of
sellers and buyers are able to use and provide resources –
driven primarily by economic incentives and their reputation
in the market. We investigate a specific mechanism of
broker emergence – whereby nodes in a Social Cloud can
change role from buyers or sellers to brokers – in order to
improve their revenue. We identify the associated benefits
for supporting such broker emergence within a P2P
environment. We also describe how the identification of
such brokers can lead to an improved social welfare within a
community.
A number of scenarios are simulated in PeerSim, by
employing a heuristic social score algorithm for determining
the number of brokers within the network and the associated
generated volume of trades. We investigate how the
algorithm performs when adding more brokers respectively
buyers/sellers by measuring the volume of trades and the
average revenue. In addition we compare the social score
algorithm with a distributed dominating set algorithm.
Broker emergence provides a useful alternative to the pre-
identification of “brokers” within a Cloud system – and
could lead to a dynamic environment which adapts the
number and types of brokers available over time (as the
system connectivity and trade volumes (based on
supply/demand) change.
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Authors
Ioan Petri is a computer scientist
in School of Computer Science
& Informatics at Cardiff
University. He holds a PhD in
''Cybernetics and Statistics'' from
Babes-Bolyai University of Cluj-
Napoca, Romania. He has
worked in industry, as a software
developer at Cybercom Plenware
and then as a research assistant on
several projects funded by the Romanian Authority of
Research. Starting with 2009, he collaborated with the
School of Computer Science & Informatics, Cardiff
University, as an internship researcher in Distributed and
Parallel Computing. Between 2011 and 2014 he has worked
as a research associate in School of Engineering of Cardiff
University. Currently he is a member of BRE Institute of
Sustainable Engineering and a research associate in School
of Computer Science & Informatics, Cardiff University. His
research interests are cloud computing, peer-to-peer
economics and distributed systems.
Omer F. Rana is a
Professor of Performance
Engineering in School of
Computer Science &
Informatics at Cardiff
University and Deputy
Director of the Welsh e-
Science Centre. He holds a
Ph.D. in ‘‘Neural
Computing and Parallel
Architectures’’ from
Imperial College (University of London). He has worked in
industry, as a software developer at Marshall
BioTechnology Limited and then as an advisor to Grid
Technology Partners. His research interests extend to three
main areas within computer science: problem solving
environments, high performance agent systems and novel
algorithms for data analysis and management.
Magdalena Punceva is a Senior
Scientists at the Institute for
Computer and Communication
Systems (ISIC), HE-Arc, HES-
SO, Switzerland. She holds a
PhD in peer-to-peer networks
and distributed information
systems from the Swiss Federal
Institute of Technology in
Lausanne (EPFL). She has
worked as a Postdoctoral
Researcher at CERN and spent a
year as a Fulbright Visiting Reaserch Scholar at Rutgers
University, New Jersey, US. Her research interests are in the
area of large-scale networks and algorithms including social
networks, cloud computing and distributed systems.
George Theodorakopoulos is a
Lecturer at the School of
Computer Science & Informatics,
Cardiff University, since 2012.
From 2007 to 2011, he was a
Senior Researcher at the Ecole
Polytechnique Federale de
Lausanne (EPFL), Switzerland.
He is a coauthor (with John Baras)
of the book Path Problems in
Networks (Morgan & Claypool, 2010). He received his
Ph.D. (2007) in electrical and computer engineering from
the University of Maryland, College Park, MD, USA. His
research interests are in privacy, security and trust in
networks.
International Journal of Cloud Computing (ISSN 2326-7542) Vol. 2, No. 1, January-March 2014
57 http://www.hipore.com/ijcc/
Professor Yacine Rezgui is a
Professor in School of
Engineering at Cardiff
University and a BRE (Building
Research Establishment) Chair
in 'Building Systems and
Informatics'. He is a qualified
architect with an MSc (Diplôme
d’Etudes Approfondies) in
“Building Sciences” (obtained
from Université Jussieu - Paris
6) and a PhD in Computer Science applied to the
construction industry, obtained from ENPC (Ecole
Nationale des Ponts et Chaussées). He has then worked as a
researcher for CSTB (Centre Scientifique et Technique du
Bâtiment) and was involved in a number of national and EU
research projects in the field of document engineering
(DOCCIME), product modelling and Computer Integrated
Construction (ATLAS).
Call for Articles International Journal of Services Computing
Mission The International Journal of Services Computing (IJSC) aims to be a reputable resource providing leading technologies, development, ideas, and trends to an international readership of researchers and engineers in the field of Services Computing. To ensure quality, IJSC only considers extended versions of papers published at reputable international conferences such as IEEE ICWS.
From the technology foundation perspective, Services Computing covers the science and technology needed for bridging the gap between Business Services and IT Services, theory and development and deployment. All topics regarding Web-based services lifecycle study and management align with the theme of IJSC. Specially, we focus on: 1) Web-based services, featuring Web services modeling, development, publishing, discovery,composition, testing, adaptation, and delivery, and Web services technologies as well as standards; 2)services innovation lifecycle that includes enterprise modeling, business consulting, solution creation,services orchestration, services optimization, services management, services marketing, businessprocess integration and management; 3) cloud services featuring modeling, developing, publishing,monitoring, managing, delivering XaaS (everything as a service) in the context of various types ofcloud environments; and 4) mobile services featuring development, publication, discovery,orchestration, invocation, testing, delivery, and certification of mobile applications and services.
Topics The International Journal of Services Computing (IJSC) covers state-of-the-art technologies and best practices of Services Computing, as well as emerging standards and research topics which would define the future of Services Computing. Topics of interest include, but are not limited to, the following:
-Services Engineering-XaaS (everything as a service)-Cloud Computing for Internet-based services-Big Data services-Internet of Things (IoT) services-Pervasive and Mobile services-Social Networks and Services-Wearable services-Web 2.0 and Web X.0 in Web services-Service-Oriented Architecture (SOA)-RESTful Web Services-Service modeling and publishing-Service discovery, composition, and recommendation-Service operations, management, and governance-Services validation and testing-Service privacy, security, and trust-Service deployment and evolution-Semantic Web services-Scientific workflows-Business Process Integration and management-Service applications and implementations-Business intelligence, analytics and economics for Services
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Call for Articles International Journal of Big Data
Mission Big Data has become a valuable resource and mechanism for the practitioners and researchers to explore the value of data sets in all kinds of business scenarios and scentific investigations. New computing platforms such as cloud computing, mobile Internet, social network are driving the innovations of big data. From government initiative perspective, Obama Administration in United States launched "Big Data" initiative that announces $200 Million in new R&D investments on March 29, 2012. European Union also announced "Big Data at your service" on July 25, 2012. From industry perspective, IBM, SAP, Oracle, Google, Microsoft, Yahoo, and other leading software and internet service companies have also launched their own innovation initiatives around big data.
The International Journal of Big Data (IJBD) aims to provide the first Open Access publication channel for all authors working in the field of all aspects of Big Data. Big Data is a dynamic discipline. One of the objectives of IJBD is to promote research accomplishments and new directions. Therefore, IJBD welcomes special issues in any emerging areas of big data.
Topics IJBD includes topics related to the advancements in the state of the art standards and practices of Big Data, as well as emerging research topics which are going to define the future of Big Data. Topics of interest to include, but are not limited to, the following:
Big Data Models and Algorithms (Foundational Models for Big Data, Algorithms and Programming Techniques for Big Data Processing, Big Data Analytics and Metrics, Representation Formats for Multimedia Big Data)
Big Data Architectures (Cloud Computing Techniques for Big Data, Big Data as a Service, Big Data Open Platforms, Big Data in Mobile and Pervasive Computing)
Big Data Management (Big Data Persistence and Preservation, Big Data Quality and Provenance Control, Management Issues of Social Network enabled Big Data)
Big Data Protection, Integrity and Privacy (Models and Languages for Big Data Protection, Privacy Preserving Big Data Analytics Big Data Encryption)
Security Applications of Big Data (Anomaly Detection in Very Large Scale Systems, Collaborative Threat Detection using Big Data Analytics)
Big Data Search and Mining (Algorithms and Systems for Big Data Search, Distributed, and Peer-to-peer Search, Machine learning based on Big Data, Visualization Analytics for Big Data)
Big Data for Enterprise, Government and Society (Big Data Economics, Real-life Case Studies, Big Data for Business Model Innovation, Big Data Toolkits, Big Data in Business Performance Management, SME-centric Big Data Analytics, Big Data for Vertical Industries (including Government, Healthcare, and Environment), Scientific Applications of Big Data, Large-scale Social Media and Recommendation Systems, Experiences with Big Data Project Deployments, Big Data in Enterprise Management Models and Practices, Big Data in Government Management Models and Practices, Big Data in Smart Planet Solutions, Big Data for Enterprise Transformation)
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ISSN:2326-7542(Print) ISSN:2326-7550(Online)A Technical Publication of the Services Society
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