Exploring power-efficient provisioning for online virtual network requests

Gang Sun, Hongfang Yu, Vishal Anand, Dan Liao, Lemin Li School of Communication and Information Engineering

University of Electronic Science and Technology of China Chengdu, China

gangsun@uestc.edu.cn

Abstract— In the paradigm of cloud computing, multiple users can share the cloud infrastructure resources. The application or service requests from a user can be abstracted as a virtual network (VN) request and submitted to the cloud-based datacenters. How to map a virtual network onto the cloud infrastructure network is a challenging issue in cloud resource provisioning. Current researches about virtual network mapping problem mainly focus on resource efficiency, which can be classified into resource efficient VN mapping or cost efficient VN mapping. However, the amount of power or energy consumed by datacenters takes a large part of all of the power consumption. This may not only leads to a higher expenditure on the operation of datacenters but also contribute to carbon emissions and the greenhouse effect. In this work we study the problem of power efficient virtual network mapping and propose a framework and algorithm for solving this problem. The simulation results show that our approach performs well.

Keywords-power efficien; provisioning; virtual network request; embedding; datacenters

I. INTRODUCTION Cloud computing works as a new paradigm that enables

transparent resource sharing over multi state-of-the-art data centers for the on-demand provisioning of various application requests based on the model of pay-as-you-go. The application requests form users can be instantiated as virtual machines (VMs) which allow the isolation of applications from the underlying hardware and other VMs, and the customization of the platform to suit the needs of the end-user, and hosted on hundreds of thousands of interconnected servers in multi data centers. Until recently, to further mine the potential of cloud computing paradigm, cloud computing provides only pursue the high performance while deploying cloud data centers without considering power consumption. However, the amount of energy consumed by an average data center is equivalent to that of 25,000 households [1]. According to Amazon’s estimation on its data centers, expenditures on the cost and operation of the servers measured up to 53% of the total budget, while the expenditures on energy consumption account for to 42% of the total [2]. Moreover, higher power consumption leads to some other critical problems, such as reducing the lifetime if devices, wasting energy and emitting CO2 result in globe warming. Thus, it is necessary to pay attention to power efficient provisioning in cloud computing, while complying with service level agreements (SLAs).

In cloud computing, multi geographical separately servers or server clusters interconnected by a physical network (WDM network works as the best choice for the physical network because of its advantages of high speed, transparent transmission and abundant bandwidth resources [3]) constitute the cloud infrastructure (data centers). Therefore, the power consumed by information technology (IT) equipment of data centers mainly consists of two parts: communication power consumption (power consumed by networks) and processing power consumption (power consumed by servers). Generally, communication power account for about one third of the total IT power consumption, while the remaining two thirds are consumed by servers [4]. Other power consumption of datacenters contributed by cooling and power distribution systems beyond the scope of this study and does not considered in this work.

The issue of reducing the power consumption of datacenter is challenging and complex since an increasing number of applications such as web service, large-scale simulation, high-performance computing and virtual labs have been deployed in private or public clouds [5]. Green cloud computing aims at efficient utilization of cloud infrastructure and lowering power consumption [6], which is indispensable for paving the way for economic, environment-friendly and development-sustainable cloud computing. The customer’s service or application requests submitted to cloud-based datacenters can be abstracted as a virtual network (VN) request. In cloud computing paradigm, multiple VN requests may be mapped or embedded onto the same cloud infrastructure and share the underlying physical resources. For promoting the development of green cloud computing, the physical resources of cloud infrastructure must be managed in a power-efficient manner.

In this work we study the power-efficient VN provisioning problem and propose a power-efficient provisioning scheme for VN requests which enables intelligent using the resources of the cloud infrastructure and results in reducing the total power consumption of datacenter. We consolidate VN nodes onto fewer servers and turn some unnecessary servers off, as well employing traffic grooming strategy to groom the traffic of WDM network into fewer wavelengths and shut down some unnecessary wavelength on fiber link for saving power consumption. This research is mainly to study the challenging issue of green cloud computing intelligent and power-efficient resource management, and propose efficient scheme and algorithm for VN provisioning to ensure that not only to

2012 IEEE 12th International Conference on Computer and Information Technology

978-0-7695-4858-6/12 $26.00 © 2012 IEEE

DOI 10.1109/CIT.2012.34

51

2012 IEEE 12th International Conference on Computer and Information Technology

978-0-7695-4858-6/12 $26.00 © 2012 IEEE

DOI 10.1109/CIT.2012.34

51

2012 IEEE 12th International Conference on Computer and Information Technology

978-0-7695-4858-6/12 $26.00 © 2012 IEEE

DOI 10.1109/CIT.2012.34

51

2012 IEEE 12th International Conference on Computer and Information Technology

978-0-7695-4858-6/12 $26.00 © 2012 IEEE

DOI 10.1109/CIT.2012.34

51

comply the service level agreements (SLAs), but also lower the power consumption.

II. POWER-EFFICIENT VIRTUAL NETWORK PROVISIONING

A. Cloud Infrastructure The servers or server clusters of cloud-based datacenters

spread across multiple geographical physical locations are interconnected by a mesh WDM network constitute the cloud infrastructure. Similar to our previous works [7-9], we model the cloud infrastructure as a weighted graph GS= (N, L, AN, AL), where N and L denote the set of sites and fiber links, respectively. Each site n, n N, composed by a server (server cluster1) and WDM node2, which with both server resources (such as CPU, memory and storage) and communication resources (such as wavelength and switches). An example of cloud infrastructure is shown in Figure 1 (a). The notation AN and AL denote the attributes of sites and fiber links, respectively. The typical attributes of sites include server resource capacity and communication resource capacity. The typical attribute of fiber link refers to the wavelength capacity.

Fig.1 Examples of cloud infrastructure and VN request

B. Virtual Network Request The task/application requests and communication demands

among these tasks for the purpose of data and information exchanging which submitted to a cloud-based datacenters can be abstracted as virtual network request. That is, each task or application request represented as a node, called virtual node; and each communication demand represented as an edge, called virtual edge. Similar to the substrate network, we model the virtual network request as an undirected weighted graph GV= (V, E, RV, RE), where V represents the set of virtual nodes and E denotes the set of virtual edges. Virtual nodes and edges are associated with constraints on resource requests, denoted by RV and RE, respectively. For each virtual node v, v V, we use req(v) to denote the amount of server resources requested from a specific substrate node. For each virtual edge e, e E, we employ b(e) to represent the bandwidth request. Figure 1(b) presents an example virtual network request, where the numbers in rectangles next to the virtual nodes represent the amount of server resources requested by the virtual nodes and the numbers next to the virtual edges represent the bandwidth requirement of the virtual edges.

1 In this paper we assume that each site includes only one server. 2 In the rest of this paper, we will abuse N to refer to the set of servers or the set of WDM nodes.

C. Power-efficient VN Provisioningt For provisioning a virtual network request, the components

of cloud infrastructure (physical servers and WDM network equipment) need to consume a certain amount of power. This power consumption can be divided into two parts: workload-dependent power and workload-independent power. In this work, we refer to the workload-independent power consumption as “idle power”. The idle-power consumed by cloud infrastructure can be reduced by a power-efficient VN provisioning scheme. Therefore, the basic idea of power efficient VN provisioning is that turning off the lightly loaded equipment of cloud infrastructure by consolidating VMs onto fewer physical servers and routing communication demands on fewer fiber links.

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Fig.2 VN requests provisioned in power-unaware ((c)) and power-efficient ((d)) manner

Figure 2(c) shows a power-unaware embedding solution for two he VN request (shown in Figure 2 (b)), where the VN nodes a, b and c in the first VN request are hosted by physical server S1, S2 and S3, and the VN edges (a-b), (a-c) and (b-c) are mapped onto physical paths (A-B), (B-C) and (C-A); the VN nodes d, e and f in the second VN request are hosted on physical server S4, S5 and S6, and the VN edges (d-e), (e-f) and (f-d) are mapped onto physical paths (D-E), (E-F) and (F-D). The power-unaware approach shown in Figure 2(c) aims at balancing the workload in underlying infrastructure. As a result, the workloads have been evenly distributed in the underlying cloud infrastructure. Thus, as shown in Figure 2(c), the total idle-power consumption is contributed by 6 physical servers, 6 WDM nodes and 6 fiber links.

In power-efficient approach as shown in Figure 2(d), the VN nodes a and d are both hosted on physical server S1, VN nodes b and e are consolidated onto physical server S2, VN nodes e and f are both mapped onto physical server S3; VN edges (a-b) and (d-e) are packed in the same physical path (A-B), (b-c) and (e-f) are both mapped onto physical path (B-C), (c-a) and (f-d) are packed into the physical path (C-A). As shown in Figure 2(d), the objective of power-efficient is to reduce the total idle power consumption by turning off unnecessary equipment of cloud infrastructure such as physical servers S4, S5, S6, WDM

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nodes D, E, F and fiber links (D-E), (E-F), (F-D). As a result, the total idle-power consumption is only contributed by 3 physical servers, 3 WDM nodes and 3 fiber links, that is the power-efficient approach has a 50% gain from idle-power consumption.

However, in power-efficient provisioning for VN request, as many equipment as possible need to be turned off for higher power-efficiency, which may lead to a heavy workload in some specific physical equipment of cloud infrastructure, such as server S1, S2, S3, and fiber links (A-B), (B-C), (C-A) shown in Figure 2(d). This will result in the emergence of hot-spot in the cloud infrastructure, as well influence the acceptance ratio of VN requests. In order to avoid the bottlenecks of underlying infrastructure, in this work, we introduce a workload threshold at each physical component of cloud infrastructure to control its workload. In our approach, the distribution of workloads in cloud infrastructure can be easily balanced by adjusting the parameter threshold. Thus, the two contradictory targets: power-efficiency and workload balance will be well trade-off for enhancing the acceptance ratio of VN requests.

III. POWER-EFFICIENT VN PROVISIONING ALGORITHM

A. Objective of PEVNP We focus on designing a power-efficient VN provisioning

algorithm for the online problem, where virtual network requests arrive and depart over time. From the cloud infrastructure provider’s point of view, power-efficient online VN provisioning algorithm would minimize the consumed power, thus contribute to reduce the operation cost. We introduce the notation of POW(t) that corresponds to the power consumption for provisioning VN requests at time t. Thus, the objective of our algorithm for minimizing long-term power consumption can be formulated as following:

0 ( )limTt

T

POW tMinimizeT

�

��

�� �� �

(1)

Where the total power consumption POW(t) can be calculated as following:

( ) network serverPOW t P P� � � � � (2)

Where Pnetwork and Pserver denote the power consumed by WDM network and servers, respectively; and 1� � � , they can be used to balance the importance of Pnetwork and Pserver.

B. Virtual Node Ranking Mapping a VN request onto a shared infrastructure network

includes two key steps: VN node mapping and VN edge mapping. In VN node mapping process, the resource of servers which host VN nodes would be fragmented. For efficiently utilization of server resources and high acceptance ratio, we rank the VN nodes according to their resource requirement before implementing VN mapping in our algorithm. For a VN node v, v V, the resource requirement RES(v) defined as equation (3).

( )( ) ( ) ( )

e Adj vRES v req v b e

��� � (3)

Where req(v) denotes the amount of server resources required by VN node v; the notation b(e) denotes the amount

of bandwidth resources requested by VN edge e; and Adj(v) be used to represent set of adjacent edges of VN node v.

C. Differentiated Pricing Strategy for Routing In VN edge mapping process, a VN edge would be assigned

to a path on the infrastructure network. We assume that VN nodes u and v are hosted on server nodes s1 and s2, respectively. There are multiple paths between s1 and s2, we pick the path which with minimum power consumption as the corresponding physical path of VN edge (u, v). That is the principle of Min-Power Path First (MPPF). However, if we only adopting MPPF principle for VN edge assignment, VN edges will be mapped onto as fewer links of infrastructure as possible (it follows from Figure 2), then bottleneck/hotspot links appear. Since most VN requests are rejected due to bottleneck links of infrastructure [10], we consider load balancing of infrastructure links in our online VN mapping problem for improving the acceptance ratio and enhancing the revenue of InP.

We introduce a differentiated pricing strategy to set the link weight in routing in our approach for enabling the traffic is evenly distributed throughout the infrastructure network. The main idea of differentiated pricing strategy is that when the link resource utilization falls into a specific range, the weight (power consumption) of the link is normal; otherwise, the link weight will be enlarged. The differentiated pricing strategy will guide and implement the uniform mapping of VN edges over the infrastructure network. For link l, the differentiated pricing strategy used to set link weight ( )Weight l can be formulated as in equation (4).

, (0, ]( )

, (0, ]

lbasic

lbasic

P rWeight l

P r

�

� �

� ��

� �� � �

(4)

Where lbasicP denotes the power consumption of link l; (0,1]r� is

the resource utilization of link l; (0,1]� � is the bound of

resource utilization of link l; and 1� � is the penalty factor.

For a link l, the resource utilization r can be contributed by all of the corresponding paths of VN edges which traverse it. Thus, the resource utilization of link l can be calculated as in equation (5).

: ( )( )

( )e l Path e

b er

capa l��

� (5)

Where e E, denotes a VN edge; b(e) is the amount of resource requirement of VN edge e; Path(e) denotes the corresponding path of VN edge e, it is a set of links; and capa(l) represents the resource capacity of link l.

D. Power-efficient VN Provisioning Algorithm In the online VN mapping problem, we use notation Q to

denote the set of arriving VN requests. Our main interest is to find a power-efficient mapping solution for each VN request. For a VN request need to be provisioned, we first compute the resource requirement RES(v) of each VN node according to equation (3). Then sort the VN nodes in a descending order by RES(v). In the VN edge mapping process, we introduce differentiated pricing strategy to set the link weight according to equation (4) while routing a path on infrastructure network

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for a VN edge. We focus on finding a path with minimum weight (i.e. minimum power consumption) for each VN edge. All of the allocated resources including server resource and link resource will be release while a VN request expires. The pseudo code of PEVNP algorithm is shown in Figure 3.

PEVNP Algorithm 1: Initialization: initialize the infrastructure network and VN

request queue Q 2: if Q is not empty then 3: VNx� Q.pop 4: for each VN node of VNx do 5: Compute RES(v) according to equation (3) 6: end for 7: Sort the VN nodes of VNx according to RES(v)

Store the ordered VN nodes into set Nsort

8: for all v Nsort do 9: Mapping the VN node onto server node; 10: for each VN edge e which adjacent to VN node v do

Set the weights of links according to equation (4); Compute a path with minimum weight (i.e,

minimum power consumption) for VN edge e by using Dijkstra algorithm [11]

11: end for 12: end for 13: Updating the infrastructure resource record 14: end if 15: if there are expired VN requests then 16: release the resources allocated before 17: end if 18: if the termination condition is not satisfied then 19: go to step 220: end if 21: Terminate the procedure

Fig. 3 Pseudo code of PEVNP algorithm

IV. SIMULATIONS AND ANALYSIS

A. Simulation Environment In our simulation we employ CERNET (shown in Figure 4)

as the cloud infrastructure network. We assume that each fiber link consists of 8 wavelengths and each wavelength with a bandwidth capacity of 40G; and all node resource capacities are assumed to be 400 units.

The virtual networks (VN requests) are generated randomly, such that the number of VN nodes is equal to a given number N and the average probability of connectivity of any VN node pair is about 0.3. In our simulation, the generated VN requests are with 4 VN nodes. All of these generated VN requests arrive as a Poisson process. The amount of node resource required by each VN node is uniformly distributed between 10 and 30; the link resource requirement of each VN edge is uniformly distributed between 10 and 20. The resource allocated to a VN requests will be released while this VN is expired.

We have implemented the algorithms compared in our simulations by using Microsoft Visual Studio 2005 and C++ programming language. All algorithms are run on a computer with 2G memory and 2.66GHz CPU.

Fig.4 Infrastructure network topology used in our simulation

B. Algorithms Compared Since most researches on VN embedding problem mainly

focus on resource efficiency, the algorithms proposed in these works are not comparable with our approach. The authors in [12] have researched the optimal VN mapping problem. For comparison purposes, we extend the algorithms proposed in [12] by modifying its outputs and apply it into the online mapping problem researched in this work. The algorithms compared in our simulations have been summarized in Table 1.

TABLE I. ALGORITHMS COMPARED

Notations Algorithm Description

PEVNPApproach proposed in this work for power efficient virtual network provisioning which consider the efficient power consumption of all the infrastructure components.

PUVNP Modified power unaware VN provisioning approach proposed in [12].

C. Simulation Results In Figure 5, we compare the average power consumption of

our approach and the approach proposed in [12]. It shows that our proposal, PEVNP, significantly outperforms on saving power consumption. We can see that our approach PEVNP can achieve gains about 15% compared with PUVNP. This is due to that we consider the power consumption while implementing the VN node and edge assignments, it contributes to reduce the power consumption for supporting the VN running. Moreover, introducing the load balancing (the parameter � used to control the load balance shown in equation (4)) while routing the paths for VN edges will lead to higher power consumption. This is because for achieving load balancing, some VN edges must be assigned onto the paths with more hops and then contributes to higher power consumption. Figure 6 shows the real-time power consumptions of different approaches. We can see that it is fluctuating over time within a specific range, since the VN requests are arriving and expiring over time.

Figure 7 illustrates the blocking ratio (i.e., reject rate) of VN requests during our simulation. It shows that our approach performs well on the performance of blocking ratio, i.e., the blocking ratio of our proposed approach is very close to that of the resource efficient strategy proposed in [12]. This is due to

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the benefits of introducing VN node ranking and traffic grooming into our approach while implementing mapping of VN nodes and edges. The figure also presents that relaxing the load balancing parameter � contributes to reduce the blocking ratio. Since more link resource allowed to be used to provision the VN requests.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

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Fig.5 Average Power Consumption

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100008.0k

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0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000.00

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Number of Arrived VN Requests Fig.7 Blocking Ratio Comparison

V. CONCLUSION In this work, we are the first to research the problem of

power-efficient provisioning for virtual network requests in cloud-based datacenters. We have developed an architectural framework and principle for this problem, in which we pack the VN edges and VN nodes into as fewer links and servers as possible for reduce the idle power consumption which leads to a lower total power consumption. However, blind pursuit of reducing power consumption must lead to hot spot in cloud infrastructure, as well result in high block ratio of virtual network requests. Thus, our framework makes a trade-off between power consumption and VN block ratio. We propose an efficient framework and algorithm to solve this problem, for lowering the total power consumption without violating the service level agreements (SLAs) while implementing the VN provisioning. The simulation results show that our approach with good performance on power efficiency and blocking ratio.

ACKNOWLEDGMENT We would like to thank the anonymous reviewers and editors for their invaluable works. This research is partially supported by Natural Science Foundation of China grant (No.60872032, 60972030 and 61001084), the National Grand Fundamental Research 973 Program of China (No.2007CB307104), and the Fundamental Research Funds for the Central Universities (ZYGX2010J002, ZYGX2010J009).

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