How To Schedule A Cloud Comuting On A Computer (I.E. A Computer)

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1 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 STOCHASTIC MARKOV MODEL APPROACH FOR EFFICIENT VIRTUAL MACHINES SCHEDULING ON PRIVATE CLOUD Hsu Mon Kyi and Thinn Thu Naing 2 Univesity of Comute Studies, Yangon, Myanma hsumonkyi.ucsy@gmail.com 2 Univesity of Comute Studies, Yangon, Myanma ucsy2@most.gov.mm ABSTRACT Cloud comuting is deloyed a lage set of vitualized comuting esouces in diffeent infastuctues and vaious develoment latfoms. One of the significant issues in cloud comuting system is the scheduling and allocation of vitual esouces and vitual machines (s. To addess this issue, this ae oosed an efficient aoach fo vitual machines scheduling in cloud infastuctue esouce management and allocation also called ESA (Efficient Vitual Machines Scheduling Algoithm that ovides the effective and efficient esouce allocation. To analyze the efomance of this scheduling and allocation on cloud infastuctue, an analytical efomance model aoach using Stochastic Makov model is oosed to measue the scalability and tactability fo infastuctue esouce of ivate cloud. This model intends to analyze an academic-oiented ivate cloud system which is imlemented using Eucalytus oen souce system. Accoding to efomance evaluation, the effective mean esonse time of the system to imove the efomance of IaaS sevices in the system. KEYWORDS Cloud Comuting, Vitual Machine, Scheduling, Stochastic Makov Model, Eucalytus Pivate Cloud. INTRODUCTION A Cloud is a tye of aallel and distibuted system consisting of a collection of inteconnected and vitualized comutes that ae dynamically ovisioned and esented as one o moe unified comuting esouces based on Sevice Level Ageements (SLA established though negotiation between the sevice ovides and consumes [0]. Thee ae fou deloyment models of cloud comuting envionment such as Public, Pivate, Community and Hybid cloud. This eseach is only emhasis on the ivate cloud model and data and ocesses ae managed within the oganization that a limited numbe of eole behind a fiewall. Eucalytus oen souce ovide cloud system is configued to ovide IaaS sevices in the system. Some of the classical cloud-based alications include Social Netwoking, Web Hosting, Content Delivey, and Real-Time Instumented data ocessing. It is vey difficult to quantify the efomance of scheduling and allocation olicy on cloud infastuctues fo diffeent alications unde vaying wokload and system size. The eason why esouce allocation and scheduling bings new eseach issues in cloud comuting system is that esouce ovision is moe comlex, since vitual machines and thei host esouces both need to be consideed and comaed with esouce allocation in the taditional aallel and distibuted system, vitual machines contain moe oeties which ae used fo scheduling, fo examle, DOI : 0.52/ijccsa.20.30

2 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 memoy size, softwae ackages, and access to secific devices. Moeove, cloud esouce allocation and sevicing is difficult fo ealistic analyze of cloud sevicing equest. Existing Eucalytus s default schedule is Geedy schedule and Round Robin schedule. The weakness of these aoaches is basic single queuing systems that lead to maximize execution time due to longe jobs necessitating fo the deloyment of a bette scheduling stategy at the cluste level. In ode to imove thoughut and minimize esonse time of system, we enhanced an efficient scheduling algoithm which is called Efficient Vitual Machines Scheduling Algoithm (ESA. In this algoithm, we use multile queues and follow the FCFS incile. This algoithm uses thee effective olicies fo esouce allocation. These olicies ae ( To decease delay time fo lage equest, this system uses thee queues instead of single queue. Waiting equests ae inseted to thei aoiate queue accoding to the use equest tyes. (2 To be fai all equests fom thee queues, the system comae the aival time of fist thee queue fo select queued equest. (3 To be efficient utilize the esouce, if selected equest is not sufficient the esouces, then we choose equest fom anothe queue due to small equest size moe sufficient esouce than lage equest. Accoding to these olicies, the schedule launches the secified (s on hysical esouces accoding to FCFS incile. Howeve, not evey (s equest will be acceted by the schedule since thee may be not enough comuting esouce available in some clouds. Theefoe, if thee is no sufficient esouce fo the equest, the schedule will eject it and lace this equest on the aoiate queue. Then we choose equest fom anothe queue accoding to thee scheduling olicies. To analyze the efomance of efficient scheduling and allocation on cloud infastuctue, we use an analytic modelling aoach using stochastic Makov models. Analytical efomance model allows the system to edict the effects of a ovisioning schedule on taget QoS such as esonse time, thoughut, and esouce utilization. Fist, we constuct seaate sub-models fo esouce allocation and sevicing stes of a cloud sevice and then the oveall solution is obtained by iteation ove individual sub-model solutions. The detailed stes of the model ae descibed in the next section. The two majo contibutions of this system ae to: Develo an efficient scheduling mechanism to ovide heteogeneous equest tyes, fo instances, vaiation in the numbe of coes, sizes of memoy and sizes of stoage. To analyze heteogeneous sevice equest, we use Inteacting Stochastic Makov model aoach. This model esult is to geneate mean esonse time of equest and system availability fo use equest. The next sections will descibe in detail: Section 2 discusses elated wok to this system and the oveview of oosed cloud system is esented in section 3. This ae defines stes of the model aoach in section 4. Then, numeical efomance evaluation esults ae esented in section 5. Finally, Section 6 concludes the ae. 2. RELATED WORK Since Eucalytus [] and Ushe [7] ae the oen souce systems fo cloud infastuctue and develoment, they ovide ceation and esouces allocation acoss a Physical Machine on cluste seves. Howeve, they could not suot the efficient scheduling olicies to consolidate o edistibute s. O.Khalid et al. [9] oosed a dynamic and adative eal-time vitual machine scheduling technique fo HPC wokloads on the Gid. The imay objective is to incease oveall job thoughut in the system. L.Wang et al. [2] esented vgeen design to manage scheduling acoss diffeent PMs with the objective of managing the ove efomance and system level enegy savings. 2

3 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 N.Boboff et al [8] oosed vitual machine lacement algoithm to allocate vitual machines while minimizing the numbe of PMs activated without violating the SLA ageement. Similaity, L.Wangy et al. [6] esented a Multi-Dimensional Scheduling Algoithm (MDSA fo task scheduling in vitual machine based SOA envionments. Each vitual machine is e-installed with some alication level softwae ackages. This algoithm is static based scheduling algoithm. Rodigo N. Calheios et al. [] esented analytical efomance (queuing netwok system model to imove the efficiency of the system. This oosed ovisioning technique detects changes in wokload intensity (aival atten, esouce demands that occus ove time and allocates multile vitualized IT esouces accodingly to achieve alication QoS tagets. Luqun Li [5] discussed an otimistic diffeentiated sevice job scheduling system fo cloud comuting sevice uses and ovides. This system uses non-eemtive ioity M/G/ queuing model fo these job sevices. Hongbin Liang et al. [3] oosed Semi-Makov Decision Pocess model fo esouce allocation on mobile cloud envionment. This system aims to allocate the cloud esouce to maximize the system esouces. To the best of ou knowledge, the oosed efficient vitual machine scheduling (ESA algoithm is aoiate fo esouce allocation and the heteogeneous equest sevicing of IaaS oeties, fo examle, CPU, memoy size, stoage and softwae ackages. And then Stochastic Makov Model aoach is suitable fo analyzing the efomance of scheduling and allocation sevices of IaaS cloud system. 3. PROPOSED SYSTEM OVERVIEW In this section, we esent a comonent of Eucalytus achitectue and system model fo this achitectue. 3.. The Comonents of Eucalytus Achitectue In this section, we will biefly exlain the oveview achitectue of Eucalytus oen souce system. Eucalytus achitectue is deloyed with some comonents: Cloud Contolle (CLC as font-end inteface comonent, the seveal Cluste Contolles (CCs in which Stoage Contolles (SCs ae attached to ovide the EBS block stoage. Then the seveal Node Contolles (NCs ae woking as back-end nodes. Accoding to netwoking achitectue oint of view, the font-end node is configued with two netwok intefaces: one is connected to ublic camus netwok and anothe one is connected to ivate netwoks into Node Contolles (back-end node. The main functions of Eucalytus comonents ae: Cloud Contolle (CLC - The CLC is esonsible fo exosing and managing the undelying vitualized esouces (machines (seves, netwok, and stoage via use-facing APIs. Walus Stoage Contolle (WS3- WS3 imlements scalable ut-get bucket stoage. The cuent imlementation of Walus is inteface comatible with Amazon s S3 (a get/ut inteface fo buckets and objects, oviding a mechanism fo esistent stoage and access contol of vitual machine images and use data. Stoage Contolle (SC - The SC ovides block-level netwok stoage that can be dynamically attached by s. The cuent imlementation of the SC suots the Amazon Elastic Block Stoage (EBS semantics. Cluste Contolle (CC - The CC contols the execution of vitual machines (s unning on the nodes and manages the vitual netwoking between s and extenal uses. Node Contolle (NC - The NC (though the functionality of a hyeviso contols activities, including the execution, insection, and temination of instances. 3

4 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 The Eucalytus ivate cloud system achitectue is shown in below. Client Client 2 Client n Stoage Seve Web Intefaces CLC,WS3 Cloud Contolle(CLC Walus Stoage Contolle(WS3 Schedule Cluste Contolle(CC Elastic Block Stoage(EBS Cluste CC,EBS Vitual Machines( Node Contolles(NC Vitual Machine Monito (M CC,EBS Cluste 2 M M M M NC NC2 NC3 NC4 Figue. The Eucalytus system achitectue 3.2. System Model fo Eucalytus Achitectue This system model is constucted based on Eucalytus ivate cloud infastuctue achitectue. In such system, seveal tyes ae offeed accoding to the uses equiements. These tyes with secific CPU, RAM and stoage caacity ae ovisioned afte ceation an instance. These use equest ae deloyed on node contolle (NCs each of which may be shaed by multile s. The Eucalytus infastuctue offes two tyes of esouce ools. These ool ae unning (tun on and ending (tun on, but not eady ool. Use equests seveal tyes ae submitted to a esouce allocation decision module that ocesses equest on a fist-come fist-seve (FCFS basis as follows. The equest at the head of the queue is ovisioned on a unning seve if thee is caacity to un a on one of the unning seves. If no unning NC is available, a NC fom ending ool is used fo ovisioning the equested. If none of these seves ae available, the equest is ejected and laced this equest on aoiate queue. This system model uses the efficient vitual machine scheduling algoithm (ESA using oosed effective scheduling olicies to enhance FCFS scheduling olicy. Using the ESA algoithm, the instances will be scheduled to un on oe hysical machines so that it will have a highe efomance. 4. STEPS OF PROPOSED MODEL APPROACH This section descibes hieachical stes of the develoed models and inteaction among the models. Ste by ste ocesses of equest in Eucalytus cloud is shown in Figue 2 and the detail analysis of stes ae descibed in below. 4

5 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 Use Request Scheduling Queue Phase Resouce Allocation Decision Phase (RADP Instance Ceation Waiting fo Resouce Allocation and Sevicing Stes (iresouce Allocation Decision (ii usage (ceation and deloyment and (iii Run Time Execution Tanslate these stes into analytical model Deloyment Running Finishing Figue 2. The Pocess of esouce allocation and sevicing Resouce allocation and sevicing stes descibe in figue tanslate thee inteactive sub models fo analyze the efomance of system. These stes based on Makov model; ( esouce allocation decision model, (2 usage model and (3 execution model esectively. These models ae descibed below. 4.. Resouce Allocation Decision Model To calculate the esouce allocation decision ocess, we design a continuous time Makov chain (CTMC shown on Figue λ λ λ λ 0,0 0,, n-, P P P P ( P ( 0, P P ( P ( P ( P P P λ λ λ, P ( P P ( n-, u,s u=numbe of use equest in queue s=ool (unning and ending Figue 3. Resouce allocation decision model 5

6 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 The system uses aive at the system with the Poisson ate λ. In this model, aival use is u (u {,,n}. States in the model in Figue 3 ae labelled as (u,s, whee u denotes the numbe of uses cuently waiting in the queue and s denotes the tye of ool that the use equested is undegoing allocation decision. In this model, state (0,0 indicate a use has not aived at the system. Fom state (0,0 model tansits to state (0, with ate λ, due to aival of a use. State (0, descibe the RADP is deciding if at least one unning NC can accet the use equested fo allocation. Similaity, state (0, indicate the RADP is deciding if any ending NC can accet the equest fo allocation. This system assumes that is the mean seaching delay to fine a NC fo allocation in RADP. In state (0,, thee ossible outgoing events can occu: (a job is acceted fo allocation on one of the unning NCs, and the model goes to state (0,0 with ate, P. (b use equest cannot be acceted fo allocation on any unning NC, and the model goes to state (0, with ate ( P,(c aival of new equest and the model goes to state (, with ate λ. If no unning NC is available, a tansition occus fom state (0, to state (0,.In state (0,, thee ossible outgoing event ae same tansaction with the state (0,. Next State (, eesents the condition that one equest is waiting in the decision queue and equest job is undegoing allocation decision. In this model, inut and out aametes discussed in the following Model Inut and Outut Inut aametes in this model, cloud use in accoding to the Poisson distibution ate λ is assumed to be given, the delay aametes, can be measue fom Geedy seach and ae comute fom usage model. Oututs of this model ae P, P (i Aveage equest sevice unavailable obability (Sevice unavailabe that a use equest will be ejecting due to insufficient caacity. Sevice unavailabl e = n u= 0 ( P π λ ( u, (iimeasue of sevice availability that use equest will be available Sevice = (2 available Sevice unavailabl e (iii Aveage waiting time in esouce allocation decision hase E[W RADP ] = E[W q_dec ](queuing delay fo esouce allocation decision+e[w dec ] (decision delay ( E[ W RADP ] = n i= 0 i( π ( i, + π ( i, + λ( λ( Sevice unavailabl e ( P + ( Vitual Machines Usage Model usage models catue the instantiation ceation and deloyment of a on a NC. In this model, we design seaate usage models accoding to use equest tyes. Thee kind of equest tyes ae tye (one CPU coe fo each equest, tye2 (two CPU coes fo each equest and tye3 (fou CPU coes fo each equest. Figue 4 shows usage model fo each use equest fo one which uns on one CPU coe of a seve in the unning ool. We assume that all event times (e.g., equest inte-aival time, sevice time, ovisioning time etc. consideed in this model ae exonentially distibuted. Sevice time fo 6

7 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 each equest tye: µ obtained fom un time model. We design seaate usage models fo unning, ending ool of NCs. States of the model in Figue 4 ae indexed by (i,j,k, whee, i denotes numbe of equest in the queue, j denotes numbe of s cuently being ovisioned, k denotes the numbe of CPU coes on a NC which have aleady been deloyed. In Figue 4, Q is buffe size in each PM and M is maximum numbe of s that can be deloyed on a NC fo the tye equest. In this model, Fom state (0,0,0, afte a job aival, model goes to state (0,,0, with ate λ. In state (0,,0, a instance is ceated. Mean time to ceation a on a unning PM, is β and the model moves fom (0,,0 to (0,0, with ate β. Uon sevice comletion, instance is emoved and the model moves fom (0,0, to (0,0,0 with ate µ ; this ate is comuted as an outut fom the execution model. When a is being ovisioned in state (0,,0, aival of a new job will take the model to state (,,0, whee the new job is waiting in the queue. In this usage model, inut and outut aametes ae discussed in the following Model Inut and Outut Figue 4. Vitual Machines usage model This model assumes total H NCs in the unning ool, the aival ate λ to each unning NC is given by: λ λ = (4 H the mean time to ceation a on the unning NC is β and sevice ate µ ae obtained fom the un time model. Oututs of this model ae (i the steady state obability ( π that a unning NC cannot accet a job fo all equest tye ovisioning: = M π π ( + π i= 0 Q,, i ( Q,0, M (5 (ii Pobability fo all equest tye that a use equest can be acceted in the unning ool 7

8 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 P ( π H = (6 Fo a ending NC is simila to the unning NC model, with few diffeences: (i the aival ate λ to each ending NC is given by: P = λ( λ (7 H (ii the ending NC equies some additional stat-u time to make it eady to use. Time to make a ending NC eady fo use, is assumed to be exonentially distibuted with mean. (iii Mean time to ovision a on a ending NC is β fo the fist to be deloyed on this PM; mean time to ovision s fo subsequent jobs is the same as that fo a unning NC, i.e., β. Afte solving the ending NC, we can comute the steady state obability ( π that a ending NC can not accet a equest fo ovisioning and oveall ool model is a set of H. The obability of ending ool can accet the equest is given by: γ H P = ( π (8 Fom usage models, we can also comute aveage waiting time in usage E([W usage ] =(E[W vm_q ](queuing delay + (E[W ov ](ovision delay. Accoding to thei Resouce Allocation Decision Model and usage Model, we can comute aveage esonse time fo a equest. This is given by: E[T es ]= E([W usage ] + E[W RADP ] ( Vitual Machine Execution Model Once a equest is successfully allocated, it utilizes the esouces until its execution is comleted. CPU P 0 P I/O oeation P Waiting fo I/O oeation P 0 Finish Figue 5. Vitual Machines execution model fo each Vitual Machine equest execution model is used to detemine the mean time fo a sevice comletion. We use a Discete Time Makov Chain (DTMC to catue the details of execution. Fom the initial state labelled CPU, a can finish its execution with a obability P 0 o go fo some I/O oeations with obability (- P 0.A tansition can occu fom local I/O to waiting I/O with a obability (- P o fom local I/O to CPU with obability. Assuming the mean sevice 8

9 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 times on the CPU, local I/O and waiting I/O to be mean sevice time: 0 0 µ 0µ c P0 P µ l P0 P w, c ul µ and µ esectively, we comute the w ( P ( P ( P = + + (0 P µ 5. NUMERICAL PERFORMANCE EVALUATION RESULT We evaluated cloud use equest sevices ae two solutions- ( Sevice equest available obability and (2 mean esonse time fo esouce allocation and sevicing. In this system model, we show the effect of changing job aival ates, job sevice time and system caacity (numbe of seves in each ool. We assumed exonential distibution fo inte-aival times and sevice times. An examle two scenaios ae consideed fo this model outut. Fist is maximum of one on each NC, buffe size in font of RADP to be 20, and buffe size within each NC to be zeo. In this stochastic model, esouce allocation decision model (in ou examle, 4 states and usage models (fo each PM model esective numbe of states ae 3and 4 ae solved in this system. Next scenaio is simila to the fist excet eight on each NC. All models wee solving using SHARPE [4] softwae ackage. Assume values of key aametes ae shown in Table. Table. Values of key aametes Symbol Meaning Value Mean seach delays fo esouce allocation decision hase: fom a 4 seconds, aticula ool (unning and ending Mean time to fo instantiation and deloyment a on a β unning seve Mean time to fo instantiation and deloyment a on a β ending seve 8 minutes 2 minutes Mean time to eae on ending state fo eady to use 20 seconds γ µ Mean sevice time 5-30 minutes λ Cloud use equest aival time equest/h H Numbe of unning NC in unning ool 8-6 NCs H Numbe of ending NC in ending ool 8-6 NCs Table 2 shows numeical esult of a fixed mean sevice time (5 minutes fo each equest and diffeent aival ate at diffeent numbe of NCs. In this exeiment, decease use equest sevice available obability at inceasing aival ate. Obseve aival ate at 300, 350, 400, 450 and 500 use equest an hou, fo an incease in caacity fom 8 to 6 NCs in each ool Table2. Use equest sevice available obability at diffeent aival ate Comaison of sevice availability esult in unning and ending ools Aival (8,8 (2,2 (6,6 ate (s Sevice available Sevice available Sevice available Sevice available Sevice available Sevice available equest/h ( (8s ( (8s ( (8s

10 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe Diffeent sevice time and fixed aival ate (350 use equest /h esults ae shown in Table 3. In these tables esult, sevice available obability incease at inceasing numbe of in each ool. Table3. Use equest sevice available obability at diffeent sevice time Sevice time fo each equest(min Sevice available ( Comaison of sevice availability esult in unning and ending ools (8,8 (2,2 (6,6 Sevice available (8s Sevice available ( Sevice available (8s Sevice available ( Sevice available (8s In ou exeiment fo fist scenaio, figue 6(a shows, at a fixed aival ate (350 cloud use equest /h and mean esonse time incease at inceasing sevice time. Figue 6(b shows that with inceasing aival ate, mean esonse time inceases fo a fixed numbe of NCs in each ool. In Figue 6(b, obseve aival ate at 300, 350, 400, 450 and 500 use equest an hou, fo an incease in caacity fom 8 to 6 NCs in each ool. Figue 6(a. Mean esonse time fo diffeent sevice time and fixed aival ate (350 use equest /h at diffeent numbe of NCs 0

11 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 Figue 6(b. Mean esonse time fo diffeent aival ate and fixed mean sevice time (5 minutes at diffeent numbe of NCs In second scenaio, figue 7(a shows that a fixed aival ate (350 cloud use equest /h and, inceases mean esonse time at inceasing mean sevice time. And also, Figue 7(b shows that with inceasing aival ate, mean esonse time inceases fo a fixed numbe of NCs in each ool. Accoding to these scenaios, the moe numbe of in each NC the moe efomance fo this system. Figue 7(a. Mean esonse time fo diffeent sevice time and fixed aival ate (350 use equest /h at diffeent numbe of NCs

12 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 Figue 7(b. Mean esonse time fo diffeent aival ate and fixed mean sevice time (5 minutes at diffeent numbe of NCs 6. CONCLUSIONS It has been widely acceted that vitual machines can be emloyed as comuting esouces fo high efomance comuting. Thus, vitual machine scheduling and esouce allocation is essential in cloud comuting envionment. Theefoe, we esent Stochastic Makov model fo evaluate the efomance of esouce scheduling and allocation on Eucalytus ivate cloud system. In this ae, we quantify the effects of vaiations in wokload (e.g., use equest aival ate, sevice ate of and system caacity (NCs in each ool on cloud sevice quality. Ou aoach is tactable and catues many ealistic featues of a lage sized cloud, with educed comlexity of analysis. REFERENCES [] D. Numi, R.Wolski, C.Gzegoczyk, G. Obetelli, S.Soman, L.Youseff, and D. Zagoodnov. The Eucalytus oen-souce cloud-comuting system, In Poceedings of Cloud Comuting and Its Alications, [2] G. Dhiman, G.Machetti, T. Rosing. vgeen: A System fo Enegy Efficient Comuting in Vitualized Envionments, ACM, In Poc.ISLPED,2009. [3] Hongbin Liang, Dijiang Huang, LinX.Cai, Xuemin (Sheman Shen and Daiyuan Peng, Resouce Allocation fo Secuity Sevices in Mobile Cloud Comuting, IEEE,. 9-95,May 20. [4] K. S. Tivedi and R. Sahne, SHARPE at the age of twenty two, ACM Sigmetics Pefomance Evaluation Review, vol. 36, no. 4, , Mach [5] L.Li An Otimistic Diffeentiated Sevice Job Scheduling System fo Cloud Comuting Sevice Uses and Povides, IEEE Thid Intenational Confeence on Multimedia and Ubiquitous Engineeing, ,

13 Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 [6] L.Wangy, G.V.Laszewskiy, M.Kunzez and J.Taoz Schedule Distibuted Vitual Machines in a Sevice Oientedent Envionment, [7] M. McNett, D. Guta, A. Vahdat and G. M. Voelke. Ushe: an extensible famewok fo managing custes of vitual machines. In Poc. LISA, [8] N.Boboff, A.Kochut and K.Beaty Dynamic lacement of vitual machines fo managing SLA violations,2009 [9] O.Khalid,I.Maljevic and R.Anthony. Dynamic Scheduling of Vitual Machines Running HPC Wokloads in Scientific Gids IEEE,2009. [0] R.Buyya,, C. S. Yeo and Venugoal, Maket oiented cloud comuting: Vision, hye, and eality fo deliveing IT sevices as comuting utilities. Poceeding of the 0 th IEEE Intenational Confeence on the High Pefomance Comuting and Communications,2008. [] Rodigo N. Calheios, Rajiv Ranjany, and Rajkuma Buyya, Vitual Machine Povisioning Based on Analytical Pefomance and QoS in Cloud Comuting Envionments, 20. Authos Hsu Mon Kyi. She is eceived the Bachelo of Comute Science degee and Maste of Comute Science degee fom Univesity of Comute Studies, Yangon in 2003 and 2007 esectively. Cuently she is a Ph.D student in Univesity of Comute Studies, Yangon (Myanma as well as cuently, she is an assistant lectue in Comute Science at Univesity of Comute Studies, Yangon (Myanma. He eseach inteests ae Distibuted Comuting, Cloud Comuting and Vitualization Technology. Thinn Thu Naing. She obtained he Ph.D degee in Comute Science fom Univesity of Comute Studies, Yangon in 2004, Bachelo of Comute Science degee and Maste of Comute Science degees in 994 and 997 esectively fom Univesity of Comute Studies, Yangon. Cuently, she is a Pofesso in Comute Science at Univesity of Comute Studies, Yangon (Myanma. He secialization includes Cluste comuting, Gid comuting, Cloud comuting and Distibuted comuting. 3