A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual...

22
A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1 F. Antony Xavier Bronson, 2 S.P. Rajagopalan and 3 V. Sai Shanmuga Raja 1 Department of Computer Science and Engineering, Dr.M.G.R. Educational & Research Institute University, Chennai, Tamil Nadu, India. 2 Department of Computer Applications, Dr.M.G.R. Educational & Research Institute University, Chennai, Tamil Nadu, India. 3 Department of Computer Science and Engineering, Shanmuganathan Engineering College, Arasampatti, Pudukottai, Tamil Nadu, India. Abstract Cloud computing is an alluring emerging technology due to offering services based on the request by the user, by the process of virtualization. Since cloud computing platform offers services based on-demand it has been generally utilized as a part of the field of variousemerging IT infrastructure. In cloud platform every application runin individual virtual machine (VM) for execution of serviceswithin the host. Since cloud platform works on-demandservices it have to adapt to various application in single time consequently it is important to adopt an effective methodology for balancing memory usage in cloud network. For an effective use of accessible memory existing methodologies utilizes probability distribution technique for allocating resources in cloud platform yet still there exists an absence of use of accessible memory in cloud platform. For a dynamic memory allocation in VM in cloud platform an effective methodology is proposed in this paper. For memory allocation among virtual machine in cloud platform proposed approach utilizes Cloud Vertical Elasticity Manager (CVEM), memory reporter (MR), Memory Oversubscription Granter (MOG). In the proposed system MOG utilizes scheduler for dynamicallocation of memory within host. Further to balancing available memory within the host flexibility standard has been embraced in this paper for dynamic memory allocation within the available host in cloud platform. Key Words:Cloud platform, memory management, virtual machine (VM), elasticity rule, scheduler. International Journal of Pure and Applied Mathematics Volume 119 No. 15 2018, 1423-1444 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/ 1423

Transcript of A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual...

Page 1: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

A Dynamic Memory Allocation Strategy for Virtual

Machines in Cloud Platform 1F. Antony Xavier Bronson,

2S.P. Rajagopalan and

3V. Sai Shanmuga Raja

1Department of Computer Science and Engineering,

Dr.M.G.R. Educational & Research Institute University,

Chennai, Tamil Nadu, India. 2Department of Computer Applications,

Dr.M.G.R. Educational & Research Institute University,

Chennai, Tamil Nadu, India. 3Department of Computer Science and Engineering,

Shanmuganathan Engineering College,

Arasampatti, Pudukottai, Tamil Nadu, India.

Abstract

Cloud computing is an alluring emerging technology due to offering

services based on the request by the user, by the process of virtualization.

Since cloud computing platform offers services based on-demand it has

been generally utilized as a part of the field of variousemerging IT

infrastructure. In cloud platform every application runin individual virtual

machine (VM) for execution of serviceswithin the host. Since cloud platform

works on-demandservices it have to adapt to various application in single

time consequently it is important to adopt an effective methodology for

balancing memory usage in cloud network. For an effective use of

accessible memory existing methodologies utilizes probability distribution

technique for allocating resources in cloud platform yet still there exists an

absence of use of accessible memory in cloud platform. For a dynamic

memory allocation in VM in cloud platform an effective methodology is

proposed in this paper. For memory allocation among virtual machine in

cloud platform proposed approach utilizes Cloud Vertical Elasticity

Manager (CVEM), memory reporter (MR), Memory Oversubscription

Granter (MOG). In the proposed system MOG utilizes scheduler for

dynamicallocation of memory within host. Further to balancing available

memory within the host flexibility standard has been embraced in this

paper for dynamic memory allocation within the available host in cloud

platform.

Key Words:Cloud platform, memory management, virtual machine (VM),

elasticity rule, scheduler.

International Journal of Pure and Applied MathematicsVolume 119 No. 15 2018, 1423-1444ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

1423

Page 2: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

1. Introduction

Cloud computing is becoming most popular IT service transmission model in

recent and it’s still evolving. NIST defines five key qualities of Cloud

computing as; Resource on demand, Elasticity, Resource pooling, Broad

network access and Measured service [P. Mell, T. Grance, (2009)]. These

qualities have made the Cloud exceptionally prominent. Different other

computing technologies, developed throughout the years, do constitute the

Cloud computing. This incorporates Virtualization, Distributed Computing,

Utility computing, Service-Oriented Architecture, Networking, Storage and so

on [Baranwal, G., & Vidyarthi, D. P. (2015)]. The topic of resource

apportionment algorithm for Cloud Computing podium has been focus of recent

interest. In this context, the goal is to allocate a set of services understanding a

service as a distinguishable workload onto a set of physical machines, in order

to improve resource use, or to guarantee strong Quality of Service guarantees, or

to restrict the quantity of migrations utilized, among others. On the algorithmic

side, several solutions and methods have been proposed to compute such

designations: manybased on variations of standard Bin Packing calculations

(like First Fit or Best Fit), some are more engrossedon the dynamic aspect and

deliver online principles to compute new valid allocations. There is no clear

agreement in the community on which parts of the issue are the most important

(dynamicity, fault tolerance, multidimensional resources, additional user-

supplied constraints), neither on the formal algorithmic models to consider such

aspect into accounts [Beaumont, O., Eyraud-Dubois, L., & Lorenzo-del-Castillo,

J. A. (2016)].

In recent years, desktop virtualization through Desktop-as-a-Service (DaaS)

offerings has become an increasingly insensible decision for ventures due to: the

flawlessly acknowledged advantages of centralized desktop organization,

standard security configurations, information protection and simplifies

administration of dainty customers on ''virtual desktop clouds'' (VDCs)

[L.Yan(2011)]. Specifically, perceived client QoE in virtual desktops (VDs) is

sensitive to network degradation and can't endure bursty cross-activity designs

[M. Fiedler, T. Hossfeld, P. Tran-Gia(2010)] [P. Calyam, S. Rajagopalan, A.

Selvadhurai, A. Venkataraman, A. Berryman, S. Mohan, R. Ramnath(2013)].

The majority of the prior research abstained from focusing on network

enhancement strategies for handling user QoEdeterioration since it is

challenging to control routing dynamics in the Internet. The research community

has been mounting asset allocation plans, for example, [L. Deboosere, B.

Vankeirsbilck, P. Simoens, F. De Turck, B. Dhoedt, P. Demeester(2012)][ K.

Beaty, A. Kochut, H. Shaikh(2009)] so as to increase the client experience of

VDCs based on thin client convention adaptations [Calyam, P., Rajagopalan, S.,

Seetharam, S., Selvadhurai, A., Salah, K., &Ramnath, R. (2014)].

In recent years, cloud computing has fascinated courtesy from industry,

government and hypothetical worlds. A cumulative quantity of applications

International Journal of Pure and Applied Mathematics Special Issue

1424

Page 3: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

makes broad use of cloud assets, as indicated by an on-demand, self-service and

pay-by-use plan business model, with the dynamic selection of cloud-based

administrations by numerous areas of modern society. One of the fundamental

reasons of this achievement is the probability of procuring virtual assets in a

dynamic and elastic way. Specifically, the developing virtualization

technologies permit different virtual machines (VMs) to run simultaneously on a

single physical host, called host machine (HM). Each VM, thus, has its

operating system, middleware and applications, by utilizing a segment of the

fundamental hardware assets capacity (CPU power, memory, store capacity and

network bandwidth) [Weiwei Lin, BaoyunPeng, Chen Liang, Bo Liu(2013)].

Cloud flexibility is the key component for implementing server consolidation

procedures. It allows for an on-demand relocation and dynamic reallocation of

VMs [M. Ficco, C. Esposito, Henry Chang, Kim-Kwang Raymond

Choo(2016)]. NIST describes flexibility as the capacity for clients to rapidly

purchase on-demand and consequently discharge as many resources as required,

preferably giving to the client the inclination that the cloud resource abilities are

boundless [L. Badger, R. Patt-Corner, J. Voas,]. Particular flexibility control

components are actualized to decide when and how to scale-up or scale-down

virtual resources, as per the provider’s own optimization destinations and

additionally with user-defined settings and prerequisites formalized inside the

context of a particular Service Level Agreement (SLA) between the cloud

provider and the customer. So as to do this, resource use data, such as CPU load,

free memory and network traffic volumes, for all the accessible HMs, must be

persistently collected and scrutinized on-line. Three unique methods are utilized

in the implementation of cloud flexibility solutions: replication, migration and

resizing [Gu. Galante, L.Carlos E. de Bona,(2012)], resulting in various

procedures and methodologies for handling resources allocation inside a cloud

infrastructure [Ficco, M., Esposito, C., Palmieri, F., & Castiglione, A. (2016)].

Flexibility [Waldspurger, C. A. (2002)], or the capacity to rapidly endowment

and discharge resources, is one of the necessary qualities of Cloud Computing.

Horizontal flexibility is generally utilized to endowmentadditional

computational nodesin-order to maintain the quality of service conveyed by an

architecturedeployed on a Cloud platform, extraordinarily after an expansion in

the number of users or workload. Horizontal flexibility has been

extensivelystudied previously, with services effectively accessible for public

Clouds, for example, Auto Scaling 1 for Amazon Web Services (AWS), and

Heat 2 for OpenStack.

Rather, vertical flexibility enables to increment and reduction in the amount of

assets allotted to a solitary Virtual Machine (VM). The increased backing to

method such as memory ballooning[Efficient Memory Management Techniques

In Cloud Computing.] and CPU hot plugging by prevalent hypervisors such as

KVM, Xen or VMware makes ready for vertical flexibility to be received by

Cloud platforms. However, prominent open source CMPs such as OpenNebula

and OpenStack don't currently provision vertical flexibility without stopping the

International Journal of Pure and Applied Mathematics Special Issue

1425

Page 4: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

VMs. As an illustration, the KVM hypervisor completely supports memory

ballooning in order to enthusiasticallymodify the dispensed memory to a given

VM with no downtime and the primary Operating Systems (OSs) provision this

feature. However, CMPs require to stop the VM so as to change its apportioned

memory.

Memory is a critical hardware resourcestalwartly associated to system

enactment. Consequently, cloud computing, which permits end-client to utilize

the memory existing in the cloud, should consider effective memory

administration arrangements to deliver good enactment to end-clients [Kundu,

A., Banerjee, C., Guha, S. K., Mitra, A., Chakraborty, S., Pal, C., & Roy, R.

(2010)]. Effective Memory administration is one of the intriguing issues

nowadays in Cloud due to the expanding need of incorporated data handling and

exigency of optimized memory management algorithm. The drifting Application

Service Provider (ASP) and Database-as-a-Service (DaaS) models are in need of

memory management conventions to be incorporated in Cloud in-order to

organizeexpectancy and load balancing issues. On demandresource provision is

the key in enhancing the data effectiveness of the Cloud. There is large amount

of drain in the assets over the Cloud platform, if the assets are assigned and left

indolent. Constant checks and observing is important to get hold of the indolent

assets. The best example in this area is the Amazon's Elastic Compute Cloud

(EC2) which is one of the befitting case of the practical usage of the Cloud. EC2

Cloud just allocates the assets to the virtual or genuine entities on demand. The

Cloud computer architecture requests a clustered structure of the memory assets

as virtual entities. Gone are the days when memory administration was done

utilizing the static strategies. As Cloud environment is dynamic and

unpredictable, there is a strong need to ingrain the dynamic memory allocation

patterns in the Cloud based frameworks. One thing that must be ensured is the

avoidance of the "memory over commitments". This begin approach must be to

limit the memory allocation to the base level to avoid the drain of assets

[Efficient Memory Management Techniques In Cloud Computing]. A portion of

the current memory administration procedures are expressed in following

segments.

Conventional Management Policy

Conventional memory management approach replaces the chunk in shared

memory affording existing substitution strategies such as Random, FIFO and

LRU. In the traditional memory management approach, qualities of data from

various reminiscences are not considered [Son, D. O., Choi, H. J., Park, J. H., &

Kim, C. H. (2013)].

Static Partition Management Policy

Static partitionmanagement approach divides the collective memory inside

memory administrator into two sections: memory1 and memory2. Each line of

shared memory is separated statically by predefined allotment proportion. For

an example, if the associativity is 16 and the predefined allotment proportion is

International Journal of Pure and Applied Mathematics Special Issue

1426

Page 5: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

4:12, front four chunks in every cache line are assigned to memory1 while the

other twelve chunks in every cache line are apportioned to memory2. The logic

for static partition memory management strategy is exceptionally modest,

consequential in little hardware overhead [Son, D. O., Choi, H. J., Park, J. H., &

Kim, C. H. (2013)].

Dynamic Partition Management Policy

Dynamic partition management policy partition the shared memory inside

memory administrator into two sections: memory1 and memory2. Contrary to

the static partitionadministration strategy, each line of shared memory is isolated

progressively relying upon the quantity of misses for every memory. For

example, if the associativity is 16 and the proportion of number of misses in

memory1 and memory2 is 3:1, front twelve chunks in every cache line are

assigned to memory1 while the other four pieces are assigned to memory2. If

the proportion of number of misses in memory1 and memory2 changes to 1:1,

then front eight chunks are allocated to memory1 though the other eight pieces

are allotted to memory2 [Son, D. O., Choi, H. J., Park, J. H., & Kim, C. H.

(2013)].

In this paper, we concentrate on cloud computing podium that contribute

infrastructure as service. Clients submit demands for assets as virtual machines

(VMs). Every requestspecifies the expanse of resources it needs as far as

processor power, memory, storage space etc. We call these as request jobs. The

cloud service provider (CSP) first logjams these requests and subsequently

schedules them on physical machines called servers. Every server has a

constrained measure of resources of every kind. This restrains the number and

types of professions that can be scheduled on a server. The set of professions of

every type that can be scheduled simultaneously at a server is known as

configuration. The bowedcasing of the conceivable configurations at a server is

the limit boundary of the server. The aggregate capacity of the cloud is then the

Minkowshi total of the capacity regions of all the servers [Maguluri, S. T.,

Srikant, R., & Ying, L. (2014)]. The unique Contribution of paper is stated as

follows:

The main aim of this paper to provide an effective memory management scheme

for cloud platform. For effective memory management scheme in cloud

environment following steps are adopted in this research article.

i. To develop an appropriate cloud platform which contains virtualization

machine for allocating resources to environment.

ii. For memory managing in cloud network incorporating Cloud Vertical

Elasticity Manager (CVEM), memory reporter (MR) and Memory

Oversubscription Granter (MOG).

iii. Developing scheduler for dynamic memory allocation in the MOG

within the host in cloud network.

International Journal of Pure and Applied Mathematics Special Issue

1427

Page 6: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

iv. For load balancing in available memory within the host elasticity rule

has been adopted in this paper for dynamic memory allocation within the

available host in cloud platform.

The paper is organized as follows: Section I provides the overview of cloud

platform and necessary for memory management in cloud platform. Also in this

section provides detailed description of existing approaches used in clod

platform. Section II of this paper reviewed about the recent articles and

researches for effective memory allocation scheme in cloud. In section III

overview of the cloud management platform alone stated which is followed by

the Proposed architecture as section IV. Section V of the paper provides

mathematical model developed for dynamic memory management scheme

adopted through this research. Finally section VI provides the clear description

of the results obtained in through the incorporation of elasticity rule in cloud

environment.

2. Related Works

Moltó, Get,al [Moltó, G., Caballer, M., & de Alfonso, C. (2016)] describes a

system that coordinates with the CMP to give programmed vertical elasticity to

adapt the memory size of the VMs to their present memory utilization,

highlighting live relocation to prevent overload situations, without downtime for

the VMs. This empowers an improved VM-per-host consolidation proportion

while keeping up the Quality of Service for VMs, since their memory is

progressively expanded as essential. The flexibility of the encroachment is

surveyed by means of two contextual analyses based on OpenNebula featuring

(i) horizontal and vertical versatile virtual clusters on a production Grid

infrastructure and (ii) flexible multi-tenant VMs that run Docker ampoules

combined with live migration technique. The results demonstrate that memory

oversubscription can be coordinated on CMPs to convey automatic memory

administration without extremely affecting the performance of the VMs. This

results in a memory administration system for on-premises Clouds that

components live migration to securely empower transient oversubscription of

physical resources in a CMP.

Beltrán, M [Beltrán, M. (2016)], defines a new flexibility metric capable of

considering its four fundamental segments, scalability, exactness, time and cost,

autonomously of the service level (framework, stage or programming). Besides,

an exploration method to evaluate the behavior of service flexibility and a

benchmarking tool to systematize this investigation was presented. The principle

flexibility empowering agents of cloud administrations are distinguished and

investigated utilizing this metric, system and apparatus via real use cases on

private and public cloud, making intriguing determinations about this essential

execution part of cloud administrations [Beltrán, M. (2016)].

Naskos, A et, al [Naskos, A., Stachtiari, E., Gounaris, A., Katsaros, P.,

Tsoumakos, D., Konstantinou, I., &Sioutas, S. (2014)] describes to make the

International Journal of Pure and Applied Mathematics Special Issue

1428

Page 7: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

advancement of versatility arrangements more formalized and dependable. In

this work make two distinct commitments. In this methodology previously

proposed an extensible approach to enforce elasticity through the dynamic

instantiation and online quantitative verification of Markov Decision Processes

(MDP) utilizing probabilistic model checking. In second stage proposed solid

flexibility models and related versatility approaches. He evaluated the choice

approaches utilizing both real and syntactic datasets in cluster of NoSQL

databases. From analyses it is scrutinized that proposed approach enhances upon

the state of art in fundamentally expanding user defined utility values and

diminishing user defined threshold infringement. This work introduced a formal,

probabilistic model checkingbased methodology to deal with resizing an

application cluster of VMs so that flexibility choices are responsive to

quantitative examination. The proposed MDP flexibility models and related

flexibility approaches that depend on the dynamic synthetic datasets. The

authors additionally conducted experiments utilizing both genuine and synthetic

datasets and the authors exhibited results demonstrating that he can

fundamentally expand user defined utility values and decline the recurrence of

user defined threshold violations.

Barba-Jimenez et,al [Barba-Jimenez, C., Ramirez-Velarde, R., Tchernykh, A.,

Rodríguez-Dagnino, R., Nolazco-Flores, J., & Perez-Cazares, R. (2016)],

proposed an Increasing accessible and ubiquity of cloud storage as a Service

(STaaS) other options to customary on-line video excitement models, which

depend on costly Content Delivery Networks (CDNs) has been proposed in

[Barba-Jimenez, C., Ramirez-Velarde, R., Tchernykh, A., Rodríguez-Dagnino,

R., Nolazco-Flores, J., & Perez-Cazares, R. (2016)]. In this paper, the author

introduced a versatile diagnostic solution model to guarantee Quality of Service

(QoS) while providing Video-on-Demand (VoD) utilizing several third party

flexible cloud storage servive. In the first place, he separately gathered cloud

storage start-up deferrals and describe them to demonstrate that they are heavily

tailed. At that point, he accomplishes a meta-portrayal of these delays utilizing

Principal Component Analysis (PCA) to create a trademark cloud delay trace.

By utilizing different estimation strategies of the Hurst Parameter, he has

demonstrated this new trace (also heavy tailed) exhibits self-similarity, a

property not sufficiently considered in cloud storage situations. At last, the

author seek after stochastic demonstrating utilizing different overwhelming

tailed possibility, disseminations to infer expectation models and flexibility

parameters from the cloud VoD framework. The authors obtained a stochastic

self-comparative model and contrast it with trace based reenactment results by

testing different overwhelming tailed likelihood dispersions, meta-cloud

versatility qualities and Hurst parameters. Since the methodology improves

QoS, the author promises a specific video start-up delay for various arriving

customers. This is a robust obligation for a VoD service, because conventional

cloud approaches frequently concentrate on a best-effort paradigm optimizing

performance, expense, and data transfer capacity, among different parameters.

International Journal of Pure and Applied Mathematics Special Issue

1429

Page 8: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

Coutinhoet,al [Coutinho, R. D. C., Drummond, L. M., Frota, Y., & de Oliveira,

D. (2015)], built up a novel apparatus that can catch basic quality

measurements, such as Net Utility and Service Response Time, which can be

utilized to evaluate VDC platform status. This device permits CSPs, researchers

and teachers to plan and confirm different resource apportionment scheme

utilizing both prompt and emulation as a part of two modes: ''Run Simulation''

and ''Run Experiment'', respectively. The Run Simulation mode permits clients

to test and visualizeresource provisioning and situation patterns on animation

system. Run Experiment mode permits testing on a realsoftware defined

network tested utilizing rivaled virtual desktop application traffic to make a

sensible environment. Results from utilizing our tool exhibit that a substantial

increment is perceived userQoE can be accomplished by utilizing assortment of

the accompanying procedures incorporated in the gadget: (i) enhancing Net

Utility through a ''Cost-Aware Utility-Maximal Resource Allocation

Algorithm'', (ii) evaluating values for Service Response Time utilizing a ''Multi-

stage Queuing Model'', and (iii) fitting load balancing through software defined

networking adaptation in the VDC tested.

Galanteet, al [Galante, G., & de Bona, L. C. E. (2012)], proposed a methodology

named GraspCC-fed to deliver the optimum (or close optimum) estimation of

the amount of virtual machines to allocate for every work process. GraspCC-fed

broadens a formerly proposed heuristic based on GRASP for executing

standalone applications to consider research work processes executed in both

single-supplier and combined clouds. For the experiments, GraspCC-fed was

coupled to an adjusted form of SciCumulus work process engine for federated

clouds. Along these lines, he trust that GraspCC-fed can be an imperative choice

augmenttool for user and it can help deciding an optimumconfiguration for the

virtual cluster for parallel cloud-based investigative work processes.

3. Cloud Management Platform

Topology of the cloud computing focus under consideration is shown in figure

1. These procedures turn off a server pool to spare owner if its servers are not

currently serving any occupation.

At the point when two VMs (VM1 andVM2) have been conveyed by a CMP

(cloud Management platform) on the same physical host (A). Contingent upon

the scheduling configuration of the CMP this circumstance can be exceptionally

common. For example, OpenNebula can be constituted to utilize a packing

scheduler.

International Journal of Pure and Applied Mathematics Special Issue

1430

Page 9: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

Figure 1: Topology of Cloud Center

The VMs have a tendency to be apportioned to the same physical machine if

there is sufficient memory accessible. In KVM, a deployed VM has both a

memory size and a most extreme memory size trait. A VM can't develop beyond

the maximum memory size, which compares to the memory initially dispensed

when the VM was created. However, its memory estimate (the memory

currently apportioned to the VM) can go from the minimum amount of memory

to augment the OS, regularly in the request of 200–300 MB for a Linux VM

[Beaumont, O., Eyraud-Dubois, L., & Lorenzo-del-Castillo, J. A. (2016)], to its

greatest memory size.

Proposed Architecture

The main aim of this exploration is to enhance concert of the cloud computing

platform by improving memory assignment in VM. For memory augmentation

in VM in this research consolidated cloud vertical elasticity manager (CVEM),

memory reporter (MR) and Memory Oversubscription Granter (MOG) in cloud

platform. The principle issue in cloud platform is expressed in beneath. Figure 2

shows the architecture of the proposed system. Initially, KVM [Baset, S. A.,

Wang, L., & Tang, C. (2012)], a popular open source hypervisor that completely

underpins memory expanding.

Figure 2: Cloud Management Platform

International Journal of Pure and Applied Mathematics Special Issue

1431

Page 10: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

Initially, KVM [Baset, S. A., Wang, L., & Tang, C. (2012)], a popular open

source hypervisor that completely underpins memory expanding. Second,

OpenNebula [Moreno-Vozmediano, R., Montero, R. S., &Llorente, I. M.

(2012)], an open source Cloud Management Platform that deals with the life

cycle of VMs on a physical infrastructure. As indicated by [Moreno-

Vozmediano, R., Montero, R. S., &Llorente, I. M. (2012)], there are diverse

mechanisms to wander the issues that emerge with oversubscription: (i) stealing,

which permits a hypervisor to steal (really acquire) resources from underloaded

VMs running on the same physical host; (ii) quiescing VMs, so that a VM is

shut down and relocated offline to an underloaded physical machine; (iii) live

migration, to hot move VMs from an overload physical machine to an under

loaded one; (iv) streaming records, to exchange the minimum portion of a VM's

local disk to consent the VM to be started on another physical machine, and (v)

network memory, to utilize memory of another machine as a swap space over

the network. In this paper we focus both on memory expanding and live

migrationperformances together with its reconciliation in a CMP. We depend on

these procedures since they are completely sustained on most hypervisors and

by the fundamental OSs (including Linux and Windows). Subsequently, this

empowers to generate a framework that can be effectively coordinated in current

on-premises Cloud deployments to consistently influence these procedures.

Therefore, one or more VMs (in this case, just VM3) must be migrated to

another physical host to maintain the nature of service over the infrastructure

accomplished by the CMP. In our case, this includes live migration, rendering to

a explicit arrangement, so that no downtime is presented for the migrated VM.

The design of CloudVAMP comprises of three segments:

Cloud Vertical Elasticity Manager (CVEM). A specialist that

investigations the amount of memory actually required by the VMs and

progressively upgrades the memory apportioned to each of them, as

indicated by an arrangement of customisable tenets. It is a specialist that

inquiries the observingsystem of the CMP and has entry to the

hypervisors (e.g. ssh access to the physical hubs of the on premises

Cloud). It can choose to live migrate VMs in-order to re-establish the

level of provision under memory overload circumstances.

Memory Reporter (MR). An operator that running in the VMs and

reports to a monitoring system the free, utilized memory and use of the

swap space, by the applications in the VM. This data must be accessible

for CVEM, so it ought to be coordinated inside the CMP's observing

system (as it has been presently executed) or by depending on an

outsider checking framework (e.g. Ganglia).

Memory Oversubscription Granter (MOG). A system that advises the

CMP about the amount of memory that can be oversubscribed on the

hosts, to be considered by the scheduler of the CMP.

International Journal of Pure and Applied Mathematics Special Issue

1432

Page 11: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

Figure 3a, 3b and 3c

The evidence of-idea usage depends on OpenNebula (ONE) and it is flawlessly

coordinated utilizing the parts that it offers. OpenNebula requires a cluster based

establishment in which the main services are introduced in the front-end node

(ONE Front-end in Fig. 3a) while the VMs are sent on the inside working hubs

(ONE Host in Fig. 3b), where the KVM hypervisor (different hypervisors are

supported as well) must be introduced. The design of CloudVAMP has been

executed by means of lightweight Python-based operators. For instance, CVEM

keeps runs beside ONE to acquire the observing data with respect to the real

memory utilization of all the VMs in the infrastructure. For that, we depend on

the MR, which runs in the VM. TheMRagent occasionally (as a matter of course

like clockwork in spite of the fact that it can be arranged on a for each VM

premise) reports the memory utilization to OneGate by legitimately

questioning/proc/mem info to get both the aggregate and free memory in the

VM as well as the use of the swap space. We depend on the contextualisation

components gave by OpenNebula to progressively organize in the running VM

the agent that occasionally screens the memory utilization and the memory

accessible reporting back to OneGate. This empowers CVEM to get to

incorporated observing data about the memory utilization of all the VMs

conveyed in the on-premises Cloud (as a matter of course additionally at regular

intervals). Notice that MR and CVEM are decoupled system which can work at

various frequencies. Also, the MR just reports significative memory changes so

it can run as often as possible.

Mathematical Design

For viable memory allocation scheduler about the measure of memory from the

hosts that can be over subscripted, we have generated an altered form of the

KVM Virtual Machine Manager (VMM) This rendition ascertains the amount of

stolen memory from every host and instructs theONE scheduler to utilize a

portion of it to allotted extra VMs in that host. We define the stolen memory for

a given physical host as the aggregate amount of memory that CVEM has

possessed the capacity to recover from the different VMs running on that

physical host, in our situation, using memory expanding by means of KVM.

CVEM resolves to extend or shrivel the VM's assigned memory relying upon

the real memory utilization reported by the MR to OneGate. Notice that the

current Allocated Memory (AM) to a VM is separated between the current Used

Memory (UM) by the applications running inside and the Free Memory (FM)

International Journal of Pure and Applied Mathematics Special Issue

1433

Page 12: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

and, in this manner, AM = UM +FM. The vertical flexibility rules actualized in

CloudVAMP expand on our past work [Moltó, G., Caballer, M., Romero, E., &

de Alfonso, C. (2013)] to maintain Memory Oversubscription Percentage

(MOP) of an extra 20% of the current UM. The objective is to keep that

additional measure of free memory on the off chance that the application

running in the VM begins asking for more memory. In previous work we

surveyed the conduct of various estimations of MOP, specifically 10% and 30%

to comprehend the tradeoff between reducing the free memory in a VM to the

detriment of increasing the odds of an application to start thrashing because of

absence of free memory in the event that the application requires a memory

increment [Moltó, G., Caballer, M., Romero, E., & de Alfonso, C. (2013)].

However, the vertical flexibility guidelines are just activated if the rate of free

memory of theVMis lesser than 80% or more prominent than 120% of the MOP.

This empowers the system to just respond when generous changes in the utilized

memory of the VM happen, thus removing unnecessary oscillatory memory

changes. In these circumstances, CloudVAMP progressively adjusts the VM

memory size utilizing (1),

* 1AM UM MOP (1)

Where AM is the recently assigned memory to the VM by the hypervisor and

UM is the current utilized memory by the applications in the VM. As an

example, a MOP of 20% implies that the flexibility guideline will only be

activated when the free memory of the VM is lower than 16% (80% of 20%) or

superior than 24% (120% of 20%) of the utilized memory of the VM. As an

illustration, if a VM has 1000 MB of AM and the application begins utilizing

900 MB, then the new AM will be 1080 MB (900 × 1.2).

The memory thresholds that trigger the vertical flexibility rules in a case VM

(left part of the figure) that was initially conveyed with 2500 MB and was

consequentlyreduced to 1200 MB (AM), of which 1000 MB are being utilized

by the application (UM) and 200 MB are the free memory (FM) gave by the

MOP (20% of the UM). Whenever the UM surpasses the Increase Memory

Threshold (IMT) or the Decrease Memory Threshold (DMT) the vertical

flexibility guideline (1) is pragmaticin order to maintain additional 20% free

memory. Any memory utilization changes between those thresholds won't

trigger the flexibility rules to avoid unnecessary oscillations. The elasticity

principle has been supplemented with a safeguard system when thrashing has

already happened inside a VM. In that case, the memory size increment should

be much bigger to promptly counteract the staggering impacts that thrashing has

in application performance [Denning, P. J. (1968)]. For that we utilize an

instrument that extraordinarily motivates in exponential backoff [Kwak, B. J.,

Song, N. O., & Miller, L. E. (2005)]. In the event that there is no accessible free

memory in the VM, an additional 50% of the contrast between the maximum

memory and the current dispensed memory is assigned. This empowers to

rapidly increase the assigned memory to the VM, endeavoring to abstain from

thrashing as quick as possible. If there is still lack of memory, the same

International Journal of Pure and Applied Mathematics Special Issue

1434

Page 13: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

additional allocation of memory is performed. Finally, if the third checking

interval still reports a shortage of memory in the VM (most likely because the

application running in theVMis inquiring for memory speedier than the rate at

which CloudVAMP is increasing the allotted memory to the VM), the VM is

assigned its greatest memory size. Notice that any overabundance of allotted

memory will be remedied in succeedingsteps by CloudVAMP by diminishing

the dispensed memory as indicated by the standard in (1), prompting a self-

administrative system.

Scheduler Design

The KVMVMM monitor sent with scheduler has been amended in order to

inculcate the scheduler to oversubscribe the memory of the physical hosts. The

authenticquantity of memory accessible in the host that is conveyed to the

monitoring framework is the portion of physical memory attained by the

authentic observing system in addition to a rate O from the measure of memory

that could be stolen from the free memory accessible in the VMs. The scheduler

transported in ONE is uninformed of the memory reduction of the VMs and

determines the measure of memory accessible for virtual machines in one host

as the memory accessible in the host short the memory demanded by the VMs

when they were conveyed, as appeared in (2).

host

N N N

VM

HostVM Host VM (2)

Utilizing this approach, the ONE scheduler will deed if the hosts had more

memory accessible for the VMs and will attempt to convey new VMs in the

physical host even if the aggregate sum of memory asked for by the VMs is

more prominent than the physical memory accessible at the destination host.

The estimation of O can be arranged for the on-premises Cloud so as to increase

the degree of memory oversubscription. It is a rate so an estimation of 0%

implies that no memory oversubscription will be presented by CloudVAMP.

This implies the total of dispensed memory of all the VMs of a host in the on-

premises Cloud will never surpass the accessible memory of that host. An

estimation of 100% for O implies that CloudVAMP will attempt as much

oversubscription as probable. This mean to recover all the free memory from the

VMs to empower maximum oversubscription, since the CMP scheduler will

dispense extra VMs to the fundamental hosts. Notice this may require migrating

VMs more recurrently applications begin requesting extra memory. Likewise,

by no means Cloud VAMP will recover memory being utilized from the VMs

since that would dramatically affect the concert of application. At the end, this

parameter should be legitimately adjusted relying upon the requirements of the

on-premises Cloud.

As a final comment, notice that specific sort of application require low

dormancy reactions may incline toward not to be live migrated to different

hosts, which may have an impact (although moderatelysmall, as shown on this

issue study) on its enactment and the level of provision expected by the

International Journal of Pure and Applied Mathematics Special Issue

1435

Page 14: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

customer. If a Cloud benefactor needs to run applications that is exceptionally

profound to performance, this can be upheld in our framework by assigning the

VMs that run those applications to a subset of hosts that won't be monitored by

CloudVAMP. Thus, the apportioned memory to those VMs won't be condensed

and applications will keep running on the request for resources without being

relocated to different hosts. Additionally, notice that the objective of

CloudVAMP is not to apportion more resources to expand the performance of

an application however to recover the unused assets (specifically we concentrate

on the memory because hypervisors support their dynamic management)

without influencing the performance of the application. It is conceivable to

lessen the dispensed memory of a VM that is currently not being utilized by an

application for different VMs to utilize it. Obviously, contingent upon the

memory utilization designs, the application may require the additional memory

back and this may present an performance penalty. At last, these strategies can

be further customized for a particular on-premises Cloud contingent upon the

workload and application qualities.

max min *

100

i

i

P P UPE LinearPower

Where Pmax&Pmin = maximum and minimum power consumed by PDi

respectively. Utilization of Data Centre can be calculated by

_ _

_i

Total VM Allocated VMU

Total VM

4. Results and Discussion

For simulation we CloudSim 3.0 power module is utilized. CloudSim 3.0 gives

cloud simulation and predefined power model simulation. We have reenacted

proposed power and fault aware VM designation algorithm in Cloudsim power

cluster. Proposed algorithm is being tested over different test case with 3 servers

S1-S5 and linear power model. Power model specifically rely on upon use of

servers. Testing of proposed algorithm is finished with essential DVFS

(Dynamic voltage and frequency scaling) based programing which is a power

administration in servers. Testing is accomplished for 600, 800, 1200, 1400

solicitations. Server setup is as follows.

This contextual analysis highlights the deployment of a VM with Docker that

supports the disposition of compartments to have distinctive applications inside

the same VM. This methodology isolates infrastructure provision (from the

Cloud) and application deployment (utilizing Docker holders) which acquaints

critical advantages to deploy applications on numerous back-finishes. In multi-

tenant situations, where a solitary VM can be utilized to convey numerous

holders from various clients, it is expected a bigger variety in the memory

utilization designs, when contrasted with a solitary application running on a

solitary VM. This is the reason we trust that CloudVAMP can be helpful via

International Journal of Pure and Applied Mathematics Special Issue

1436

Page 15: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

automatic managing the allocated memory to the VM (or an arrangement of

VMs) as per the memory utilized by its dynamic holders. Fig. 4 portrays the

situation utilized, alongside the accompanying occasions:

A VM (VM1) is provisioned with a specific measure of RAM on a given

physical host of the on-premises Cloud.

CloudVAMP decreases the allotted RAM of VM1 since the memory

utilization of the VM after its boot is low (no application is being run

yet).

Docker is introduced and the first container is deployedbased on the

image with the Apache Tomcat application server. This will bring about

an increase of the allocation of memory to VM1, as requested by

CloudVAMP.

A second container is conveyed based on the same image. We expect a

memory increment, although somewhat bring down because of the

sharing of a few pages between the two container.

Since VM1 memory was diminished, there is sufficient free accessible

memory in the physical machine to have another VM (VM2), as decided

by the OpenNebula scheduler, which will be running in the same

physical host.

A memory-serious application is executed on a third container which

introduces memory pressure for VM1. We will utilize a manufactured

benchmark application that empowers us to control the memory

allotment design and to get the execution of the application (in

MFLOPS) as depicted.

CloudVAMP will attempt to increase the memory of VM1 however

since this would bring about memory overload, it will utilize a live

relocation methodology as a contingency plan. This includes migrating a

VM from the physical host to reestablish the capacity of VM memory

without overload.

When enough memory has been liberated from the physical host, VM1

can be allocated more memory. Remember that the VM memory size

won't have the capacity to develop past the amount of memory

determined when initially created the VM.

Figure 4: Memory Consumption

The previously stated sequence of occasions has been done in the same on-

premises Cloud. Fig. 4 demonstrates the real memory allocation of the VM that

International Journal of Pure and Applied Mathematics Special Issue

1437

Page 16: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

facilitated the diverse Docker containers. Above all else, the VM is deployed

with 4 GB of RAM at time prompt 1:00:00 (which relates to occasion An in Fig.

6). After a certain time, CloudVAMP distinguishes that the VM has enough free

memory and decides to reduce its allocated memory to slightly over than 500

MB (occasion B). At 1:01:57 we perform the establishment of required

packages to utilize Docker, which requests extra memory and results in an

occasional increment in the allotted memory to the VM. At 1:04:44, the

principal Docker holder is conveyed (C) which increases the memory necessities

for the VM, thus resulting in the increase of the allocated memory. The level of

allocated memory to the VM that can be seen from 1:06 until 1:07 is because of

the unfaltering condition of the VM, since once the Docker container are begun,

no movement is really performed with those containers, for clarity for this

situation study. At 1:07:36, the second holder is sent (D) which presents

memory pressure in the VM resultingin an occasional increment in the

designated memory, as per the increasing memory necessities for the containers,

which host Tomcat, a memory-concentrated Java application server. Within this

period we have deliberately sent different VMs inside the same physical host to

introduce memory pressure in the host when the analyzed VM starts requesting

more memory. At moment 1:09:01 the memory-concentrated application is

conveyed in the VM to drive a relentless memory utilization from 0 to 1000 MB

in two minutes and keep up that memory utilization for other 60 s (F). This

results in CloudVAMP to periodically build the allocated memory of the VM at

moderately similar memory chunks as per the occasional memory utilization

increment of the VM. At moment 1:10:36, there is so much memory pressure in

the physical host that no extra memory can be dispensed to the VM.

Despite the fact that the application is continually requesting more memory,

CloudVAMP can't apportion extra memory to the VM because the host is

beginning to become overloaded, as far as memory. In this circumstance, the

application may bring about in thrashing since it needs to depend on swap

memory. Since this circumstance would influence the performance of use, it is

vital to keep the memory over-load in the physical host. This requires live

migration strategies to move a VM far from the physical host so that the

accessible free memory can later be assigned to extra VMs running on the

physical host. In this situation CloudVAMP was designed to live relocate the

VM with minimal amount of allocated memory to prevent as fast as possible the

memory over-load circumstance. Remember that the time involved into live

migration is ordinarily related with the memory size, however it is much

dependent on the applications running inside, specifically the rate at which

grimy pages are made. Thusly, at around instant 1:10:36 (G) a VM other than

the one considered for this situation study is migrated away from the physical

host in a procedure that lasted a minute. Thusly, at instant 1:11:09 (H) the VM

can now be allocated more memory to agree to the increasing memory

necessities of the application. Not long after, the application is halted and the

contextual analysis is done. It is imperative to call attention to that the use of

CloudVAMP in an on-premises Cloud has empowered to progressively deal

International Journal of Pure and Applied Mathematics Special Issue

1438

Page 17: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

with the memory apportioned to the VMs and to mitigate the memory pressure

that emerges because of the oversubscription by means of live relocation

methods with no VM downtime.

1 :00 :0

0

1 :00 :0

5

1 :00 :1

9

1 :00 :3

2

1 :00 :4

8

1 :00 :5

4

1 :00 :5

8

1 :01 :0

4

1 :01 :1

7

1 :01 :2

2

1 :01 :3

6

1 :01 :4

90

0 .5

1

1 .5

2

2 .5

3

3 .5

4

4 .5

5

Tim e

Me

mo

ry (

GB

)

Figure 5: Memory Consumption Over Time

Notice that Fig. 5 likewise demonstrates the mflops that conveys the application,

to assess the effect of the memory oversubscription situation and the live

relocation of the VMon the performance of the application being executed. You

can see a diminishment of up to 15% in the MFLOPS conveyed by the

application which can be attributedmainly to inevitable thrashing and optionally

to live migration. In any case, this decrease is exceptionally transient and for

long running applications, may be insignificant. Furthermore, CloudVAMP can

be tweaked with a specific end goal to attempt to keep the applications from

thrashingat the expense of squandering extra memory by expanding, for

example, the estimation of MOP or reducing the estimation of O at the

infrastructure level. As a last remark, notice that specific kind of utilizations that

require low latency response may lean toward not to be live relocated to

different hosts, which may have an effect (althoughmoderately little, as

appeared working on this issue study) on its performance and the level of

service expected by the customer. In the event that a Cloud provider needs to

run applications that are exceptionally sensitive to performance, this can be

upheld in our system by designating the VMs that run those applications to a

subset of hosts that won't be checked by CloudVAMP. Along these lines, the

allocated memory to those VMs won't be diminished and applications will run

on the requested for resources without being migrated to different hosts.

Additionally, see that the objective of CloudVAMP is not to allocate more

resources to expand the performance of an application yet to recover the unused

resources (specifically we concentrate on the memory in light of the fact that

hypervisors support their dynamic administration) without influencing the

performance of the application.

It is conceivable to reduce the allotted memory of a VM that is at currently not

being utilized by an application for different VMs to utilize it. Obviously,

contingent upon the memory utilization designs, the application may require the

additional memory back and this may present anpenalty performance. At last,

these systems can be further customized for a particular on-premises Cloud

International Journal of Pure and Applied Mathematics Special Issue

1439

Page 18: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

depending upon the workload and application qualities.

Table 1: Virtual Machines Memory Utilization

Figure 6: Memory Utilization by VMs

5. Conclusion

This paper has presented CloudVAMP, a customizable framework to securely

empower transient memory oversubscription in onpremises Clouds by means of

vertical flexibility without VM downtime and including live movement to

counteract oversubscription situations. By utilizing the memory swelling

systems and live relocation abilities accessible in the KVM hypervisor,

CloudVAMP incorporates with Cloud Management Platforms to powerfully

reduce and expand the allocated memory to the VMs so they fit the memory

prerequisites of the applications running in the VMs.

We have presented a bland engineering that can be sent for various CMPs, and

we have executed a completely utilitarian open-source evidence of-idea taking

into account OpenNebula which is as of now being utilized as a part of creation

at our examination centre. The advantages of CloudVAMP have been evaluated

through a contextual analysis that utilizations even and vertical versatile virtual

groups that run occupations from a generation Grid framework and a multi-

inhabitant situation taking into account Docker containers.

The capacity of CloudVAMP to recover unused memory from the VMs to

empower brief oversubscription for the CMPs has brought about expanded VM-

per-host solidification proportion with a diminished effect for the running

applications. The utilization of live relocation has been advantageous to

reestablish the level of service in memory overload situations.

International Journal of Pure and Applied Mathematics Special Issue

1440

Page 19: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

References

[1] Mell P., Grance T., The NIST definition of cloud computing, National Institute of Standards and Technology 53(6) (2009).

[2] Baranwal G., Vidyarthi D.P., A fair multi-attribute combinatorial double auction model for resource allocation in cloud computing, Journal of Systems and Software 108 (2015), 60-76.

[3] Beaumont O., Eyraud-Dubois L., Lorenzo-del-Castillo J.A., Analyzing real cluster data for formulating allocation algorithms in cloud platforms, Parallel Computing 54 (2016), 83-96.

[4] Yan L., Development and application of desktop virtualization technology, Proc. of IEEE International Conference on Communication Software and Networks (2011).

[5] Fiedler M., Hossfeld T., Tran-Gia P., A generic quantitative relationship between quality of experience and quality of service, Proc. of IEEE Network (2010).

[6] Calyam P., Rajagopalan S., Selvadhurai A., Venkataraman A., Berryman A., Mohan S., Ramnath R., Leveraging OpenFlow for resource placement of virtual desktop cloud applications, IEEE/IFIP IM (2013).

[7] Deboosere L., Vankeirsbilck B., Simoens, P. De Turck F., Dhoedt B., Demeester P., Cloud-based desktop services for thin clients, Proc. Of IEEE Internet Computing (2012).

[8] Beaty K., Kochut A., Shaikh H., Desktop to cloud transformation planning, Proc. of IEEE International Symposium on Parallel Distributed Processing (2009).

[9] Calyam P., Rajagopalan S., Seetharam S., Selvadhurai A., Salah K., Ramnath R., VDC-analyst: design and verification of virtual desktop cloud resource allocations, Computer Networks 68 (2014), 110-122.

[10] Weiwei Lin, BaoyunPeng, Chen Liang, Bo Liu, Novel resource allocation model and algorithms for cloud computing, Proc. of the 4th Int. Conf. on Emerging Intelligent Data and Web Technologies (2013), 77–82.

[11] Ficco M., Esposito C., Chang H., Raymond Choo K.K., Live migration in emerging cloud paradigms, IEEE Cloud Comput. (2016), 12–19.

[12] Badger L., Patt-Corner R., Voas J., Draft cloud computing synopsis and recommendations of the national institute of standards and technology, NIST Spec. Publ. 146 (2011).

International Journal of Pure and Applied Mathematics Special Issue

1441

Page 20: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

[13] Galante G., Bona L.C.E.D., A survey on cloud computing elasticity, Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing (2012), 263-270.

[14] Ficco M., Esposito C., Palmieri F., Castiglione A., A coral-reefs and game theory-based approach for optimizing elastic cloud resource allocation, Future Generation Computer Systems(2016).

[15] Maguluri S.T., Srikant R., Ying L., Heavy traffic optimal resource allocation algorithms for cloud computing clusters, Performance Evaluation 81 (2014), 20-39.

[16] Moltó G., Caballer M., de Alfonso C., Automatic memory-based vertical elasticity and oversubscription on cloud platforms, Future Generation Computer Systems 56 (2016), 1-10.

[17] Beltrán M., BECloud: A new approach to analyse elasticity enablers of cloud services, Future Generation Computer Systems 64 (2016), 39-49.

[18] Naskos A., Stachtiari E., Gounaris A., Katsaros P., Tsoumakos D., Konstantinou I., Sioutas S., Cloud elasticity using probabilistic model checking, arXiv preprint arXiv:1405.4699 (2014).

[19] Barba-Jimenez C., Ramirez-Velarde R., Tchernykh A., Rodríguez-Dagnino R., Nolazco-Flores J., Perez-Cazares R., Cloud Based Video-on-Demand Service Model Ensuring Quality of Service and Scalability, Journal of Network and Computer Applications (2016).

[20] Coutinho R.D.C., Drummond L.M., Frota Y., de Oliveira D., Optimizing virtual machine allocation for parallel scientific workflows in federated clouds, Future Generation Computer Systems 46 (2015), 51-68.

[21] Waldspurger C.A., Memory resource management in VMware ESX server, ACM SIGOPS Operating Systems Review 36(SI) (2002), 181-194.

[22] Efficient Memory Management Techniques In Cloud Computing. (n.d.).

http://cloudtweaks.com/2013/07/efficient-memory-management-techniques-in-cloud-computing/

[23] Kundu A., Banerjee C., Guha S.K., Mitra A., Chakraborty S., Pal C., Roy R., Memory utilization in cloud computing using transparency, 5th International Conference on Computer Sciences and Convergence Information Technology (2010), 22-27.

[24] Son D.O., Choi H.J., Park J.H., Kim C.H., Analysis of Memory Management Policies for Heterogeneous Cloud Computing,

International Journal of Pure and Applied Mathematics Special Issue

1442

Page 21: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

International Conference on Information Science and Applications (2013), 1-3.

[25] Denning P.J., Thrashing: Its causes and prevention, Proceedings of the fall joint computer conference, part I (1968), 915-922.

[26] Kwak B.J., Song N.O., Miller L.E., Performance analysis of exponential backoff, IEEE/ACM transactions on networking 13(2) (2005), 343-355.

[27] Baset S.A., Wang L., Tang C., Towards an understanding of oversubscription in cloud, 2nd USENIX Workshop on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services (2012).

[28] Moreno-Vozmediano R., Montero R.S., Llorente I.M., Iaas cloud architecture: From virtualized datacenters to federated cloud infrastructures, Computer 45(12) (2012), 65-72.

[29] Moltó G., Caballer M., Romero E., de Alfonso C., Elastic memory management of virtualized infrastructures for applications with dynamic memory requirements, Procedia Computer Science 18 (2013), 159-168.

International Journal of Pure and Applied Mathematics Special Issue

1443

Page 22: A Dynamic Memory Allocation Strategy for Virtual …A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform 1F. Antony Xavier Bronson , 2S.P. Rajagopalan and 3V.

1444