Virtual Machine Placement Based on the VM Performance Models in Cloud

Transcription

1 Vrtual Machne Placement Based on the VM Performance Models n Cloud Hu Zhao, Qnghua Zheng, Member, IEEE, Wezhan Zhang Member, IEEE, Yuxuan Chen, Yunhu Huang SPKLSTN Lab, Department of Computer Scence and Technology, X an Jaotong Unversty No.28, Xannng West Road, X an, Shaanx, 70049, P.R. Chna zhangwzh@mal.xjtu.edu.cn Abstract Cloud servce provders can offer users vrtual machnes (VMs) on-demand as a servce over the Internet. VMs runnng on top of a physcal machne (PM) share physcal resources (CPU, memory, and/or bandwdth), and there may be a great resource contenton among them, whch results n VMs performance degradaton. To prevent ths, cloud provders need to study how to place VMs on PMs effcently. However, the exstng vrtual machne placement (VMP) methods manly tred to optmze the cloud resources nstead of the VM performance. In ths paper, we propose a VMP method based on the VM performance models n cloud. Frstly, wth a real OpenStack cloud platform, we study the vrtualzaton resource schedulng prncple, analyze the nteracton among VMs wth shared hardware, consder the relatonshp between VMs and the host PM, and then we ntroduce the VM performance models to present the VM performance degradaton problem. Secondly, to choose an approprate PM for placng VM, we take nto consderaton the applcaton-aware resource consumpton characterstc, the VM resource requrement and the VM performance models, so as to mnmze the PM performance degradaton and guarantee the VM performance. Fnally, we take the streamng meda servces for examples, and conduct some experments to evaluate our method. The results show t works better than others and guarantees the VM performance sgnfcantly. Keywords cloud; vrtual machne placement; performance degradaton; VM performance models. I. INTRODUCTION The publc cloud servce provders can utlze vrtualzaton technology to offer varous dfferent server nstances (storage, processors, bandwdth, and vrtual machne) as a servce lke electrcty, water, and gas to customers over the Internet. Each of these nstances provdes a certan amount of dedcated resources and charges per nstance-hour. Usng publc cloud, customers can elmnate ther purchasng and mantanng cost, and scale up and down resources dynamcally based on ther needs, whch can help them to focus on ther busness. To ncrease the resource utlzaton n cloud, the cloud servce provders use vrtualzaton technology to execute several VMs concurrently on top of a PM. Each VM employs a partton of resource capacty of PM. To host a VM, a PM must have enough avalable resources, ncludng CPU, memory, storage, and bandwdth. Though VMs n a PM run ndvdually wth ther propretary resources, they share the same physcal resources, whch may result n VMs performance degradaton. If two VMs wth ntense CPU requrements are placed on the same PM, the resource utlzaton n PM may be mbalance, e. g., there s lttle CPU left but a lot of memory and bandwdth remanng. Ths CPU scarcty may block the placement of a new VM on ths PM and degrade the VM performance. To prevent ths, cloud servce provders should study how to place multple VMs wth dfferent requrements on PMs effcently, known as vrtual machne placement (VMP) method. VMP s the process of mappng VMs on to approprate PMs by resolvng constrants of customers and cloud servce provder. Generally, VMP can be dvded nto two dfferent types, that s, statc (ntal) placement and runtme (dynamc) placement. In statc placement, VMs are created for applcatons and placed at approprate PMs, whle n dynamc placement, VMs can be mgrated onlne among PMs when VMs are already s runnng. In ths paper, we focus on the ntal placement of VMs. There are also some exstng works about ntal VMP methods, but they manly focus on optmzng the cloud resources, such as cost [] [3], energy [4] [0], traffc [] [3], network performance [4], [5] or mult-objectves [6] [8]. However, they dd not take the VM performance nto account, whle the VM performance s a very mportant factor to mpact user s experence. In ths paper, we propose a VMP method based on the VM performance models. It ams to offer the VMs wth best performance to users. Frstly, we studed how to formulate the VM performance models. We establsh a real cloud platform wth OpenStack. Wth the methods of theoretcal analyss and expermental evaluaton, we study the nature of vrtualzaton resources schedulng prncple on OpenStack cloud platform, nvestgate the nteracton among VMs wth shared hardware, and consder the relatonshp between VM performance and the host PM resource utlzaton, we then ntroduce our models to formulate the VM performance under varous resource consumptons of PM. It can present the performance degradaton problem caused by resource sharng and contenton among VMs n the same host PM. Specfcally, the performance s actually the relatve performance as the rato between the VM performance wth and wthout resource contenton. Based on the VM performance models we can calculate how the performance wll be changed when a new VM placed on a PM, whch s a fundamental base for the followng VMP method. Of course, there are many resources whch affect VMs greatly, but we manly focus on three resources, that s, CPU, memory, and networks n ths paper. Secondly, we apperceve the servce features of VMs wth dfferent applcatons, that s, we analyze and obtan the resource consumpton characterstc of dfferent applcatons. For example, the VMs runnng vdeo lve streamng meda applcatons should requre lots of bandwdth, whle VMs /5/$ IEEE

2 runnng vdeo transcodng need powerful CPU resources. We can know that dfferent applcatons have dfferent demands. To choose an approprate PM as the placed machne, we should take nto consderaton the applcaton-aware resource consumpton characterstc, the VM resource requrement and the VM performance models. Fnally, we conduct experments n our OpenStack cloud platform to evaluate our method and other VMP methods. In our OpenStack cloud platform, we provde some streamng meda servces, ncludng lve streamng, vdeo-on-demand, and vdeo transcodng, so we take these applcatons as examples to evaluate the VM performance. The results show that our VMP method can work better than other methods and can provde VMs wth the better performance. Our man contrbuton lays partcular emphass on the VMP method. To summarze, the contrbutons of ths paper are as follows: ) We propose VM performance models to present the performance degradaton caused by resource sharng and contenton among VMs on the same host PM. Wth the models, we can predct the future VM performance, whch s a fundamental base for the followng placement. 2) Based on VM performance models, we propose a VMP method, whch takes the applcaton-aware resource consumpton characterstc, the VM resource requrement and the VM performance models nto account to place VMs on approprate PMs. 3) We establsh a real cloud platform wth OpenStack, and conduct experments to evaluate our VMP method. The results show t works better than other methods and can sgnfcantly ensure the VM performance. The remander of ths paper s organzed as follows. Secton II summarzes related works. Secton III descrbes our VM performance models. Secton IV gves the VMP method based on VM performance models n detal. We then evaluate our VMP method n Secton V. Secton VI concludes the paper and gves the future works. II. RELATED WORKS There have been some researches about VMP, but they manly focus on how to optmze the cloud resources n dfferent aspects, such as cost, energy, traffc, and network performance. Savng cost [] [3] n cloud s one of the hottest researches n VM placement. Smarro et al. [] proposed a cost-effcent schedulng model for optmzng VMs placement across avalable cloud offers. They used some varables such as average prces or cloud prces trends for an optmal deployment. Chen et al. [2] proposed an optmal VM placement model to capture the ntrnsc trade-off between the electrcty cost and wdearea-network cost. They then ntroduced a cost-aware twophase metaheurstc algorthm, Cut-and-Search, that approxmated the best trade-off between the two costs. Le et al. [3] studed the mpact of load placement polces on coolng and maxmum data center temperatures n cloud servce provders, and they proposed dynamc load dstrbuton polces that consdered all electrcty-related costs as well as transent coolng effects through VMP n hgh performance computng clouds. Besdes cost, many approaches amed to reduce energy consumptons n cloud [4] [0]. Dong et al. [4] proposed a VM placement scheme meetng multple resource constrants, such as the physcal server sze (CPU, memory, storage, bandwdth, etc.) and network lnk capacty to mprove resource utlzaton and reduce both the number of actve physcal servers and network elements so as to fnally reduce energy consumpton. Wang et al. [5] proposed a heurstc greedy algorthm, called as CMBFD, to mplement VM deployment and lve mgraton to maxmze total resource utlzaton and mnmze energy consumpton, whch s based on energy-aware and quadratc exponental smoothng method to predct the workloads. Some other works save energy by reducng the quantty of PMs n cloud [6] [8]. Huang et al. [6] explored the balance between server energy consumpton and network energy consumpton to present an energy-aware jont vrtual machne placement, whch can effcently reduce the number of used physcal machnes. L et al. [7] proposed a VMP algorthm, EAGLE, whch can balance the utlzaton of multdmensonal resources, reduce the number of runnng PMs, and thus lower the energy consumpton. Zhang et al. [8] formulated VMP problem as a mult-objectve nonlnear programmng, and proposed an offlne and an onlne algorthms to mnmze the PMs resource used n cloud. Traffc [] [3] and network performance [4], [5] are also two man problems n consoldaton of VMs on physcal servers n cloud. Meng et al. [] proposed a traffcaware VMP algorthm, whch reduced traffc among VMs by optmzng the placement of VMs on host machnes, e.g. VMs wth large mutual bandwdth usage were assgned to the same PM or to PMs n close proxmty. Vu and Hwang [2] presented an algorthm that mproves communcaton performance by reducng overall traffc cost of VMs. L et al. [3] focused on the optmzed placement of VMs to mnmze the network traffcs cost (N-cost) and PMs cost (PM-cost). They also presented an effectve bnary-search-based algorthm to make a trade-off between N-cost and PM-cost. Dong et al. [4] proposed a mult-resource constrants VMP scheme to mprove network performance n cloud. They mnmzed network maxmum lnk utlzaton to balance network traffc dstrbuton and reduce network congeston. Wen et al. [5] presented VrtualKnotter, ncludng an onlne VM placement algorthm and an effcent VM mgraton schedulng algorthm, to reduce network congeston. There are also some works studed how to optmze multobjectves n cloud [6] [8]. Zheng et al. [6], [7] tred to mnmze power consumpton, resource waste, server unevenness, nter-vm traffc, storage traffc and mgraton tme at the same tme. Lu et al. [8] proposed a mult-objectve VMP model to mnmze the number of actve PMs, mnmze communcaton traffc, and balance mult-dmensonal resource n data center smultaneously. In bref, all these studes focused on VMP based on optmzng the cloud resources utlzaton. However, they were not based on performance optmzaton, and they dd not take the VM performance nto account, whle the VM performance s the really one of the man ssues what users wll pay close attenton to.

3 TABLE I. Quantty 4 III. HARDWARE OF FIVE SERVERS Hardware CPU: Xeon E G 4* Memory: 8G Hard Dsk: 250GB Network Cards: 00Mbps*4 CPU: Xeon E G 4*2 Memory: 24G Hard Dsk: 2TB Network Cards: 00Mbps*4 Controller Node Compute Node VM PERFORMANCE MODELS In the Secton I, we showed that the VM performance can be sgnfcantly affected wth other VMs runnng on the same PM because of the resource contenton among VMs. It s mportant to accurately model the VM performance to characterze the resource contenton n PM. There have been some studes about performance modelng n vrtualzed envronment [9] [23]. For example, Watson et al. [9] examned the probablstc relatonshps between vrtualzed CPU allocaton, CPU contenton, and applcaton response tme n vrtualzed envronments. Ramakrshnan et al. [20] examned the performance of varous nterconnect technologes and characterzed the vrtualzaton overhead and ts mpact on performance. In ths paper, we share the same dea on explotng examnng evaluaton, and study the nature of vrtualzaton resource schedulng on cloud platform to model the VM performance. The performance modelng has two man procedures as follows. A. Constructng the performance models In ths paper, we construct the VM performance models based on a real cloud platform bult by OpenStack, whch s an open source software that can be used to deploy and manage cloud nfrastructure n both large-scale and smallscale envronment. OpenStack was ntated by Rackspace and NASA under the Apache 2.0 Lcense n July 200, and t s completely wrtten n Python. Our OpenStack cloud testbed s deployed on fve hgh performance servers wth Icehouse software verson and KVM hypervsor. The nformaton of the fve servers s shown n Table I. One computer s used as Controller Node, and other four computers are treated as Compute Node to provde computng servce and VMs. The controller node conssts of manly sx dfferent components: Nova (compute servce), Neutron (network servce), Horzon (dashboard servce), Glance (mage management servce), Keystone (dentty servce), and Cnder (block storage). The controller node has four network nterfaces. Two nterfaces are used for communcatng wth two publc ISPs. The thrd one s used for nternal communcaton of dfferent components of cloud, and the last one s used for nternal communcaton a- mong the VMs. The compute node has Nova-Compute servce, whch manages the lfecycles of VMs. There are two network nterfaces too, whch are the same as controller node except the publc ISPs communcaton. As we manly focus on CPU, memory, and networks, we study the vrtualzaton resource (CPU, memory, network) schedulng prncple n OpenStack cloud platform, and construct our performance models for CPU, memory, and network. Table II summarzes some mportant symbols n the generalzed system model. Gven a PM, let M j denotes ts workload TABLE II. Symbols V vp M j M j c M j m M j n p j c p j m p j n mp j p c (v) p m (n) p n (b) SYMBOLS USED TO DESCRIBE THE MODELS Meanng The vrtual machne, N The VM performance vector of V The physcal machne j, j M The total CPU resource of M j The total memory resource of M j The total network resource of M j The current VM CPU relatve performance n M j The current VM memory relatve performance n M j The current VM network relatve performance n M j The VM performance vector n M j The CPU relatve performance of V n Mj The memory relatve performance of V n Mj The network relatve performance of V n Mj capacty vector, M j =(Mc,M j m,m j n,p j j c,p j m,p j n). Here, Mc j, Mm, j and Mn j are total physcal CPUs, memory, and network bandwdth of M j. p j c, p j m, and p j n are current VM relatve performance (RP) n M j.ifvmv n runnng on PM M j, then p = p j. The VM relatve performance can be calculated byp = Pvm/P d. Here,P vm s the current VM performance of V nm j, andpd s the desred performance f there s only one VM n M j. In other words, Pd s the VM performance wthout hardware contenton. The performance can be measured as the runtme (e.g., vdeo transcodng tme), throughput (e.g., vdeo streamng), latency (e.g., retreval tme n database), etc. When the VM V runnng on M j, ts CPU relatve performance can be denoted as: f( p k V c k Mc j Mc,r) j c else M j c Mj c,r α V c where Vc k s the number of vcpu kernel of VM V k n PM M j, Mc,r j s the reserved CPUs for runnng tself, α s the CPU performance degradaton parameter. The memory relatve performance can be denoted as: p m Mj m Mj m,r β k V k m where, M j m,r s the reserved memory for runnng tself, β s the memory performance degradaton parameter. The network relatve performance s: p n Mj n γ k V k n where, k V k n <M j n, γ s the network performance degradaton parameter. B. Generatng the performance parameters To test and verfy our performance models, we choose and run some typcal benchmarks to test dfferent physcal resources (CPU, memory, network) n our real OpenStack cloud platform, so as to collect test results and then tran the VM performance models. CPU: Super PI [24] s a computer program that calculates π to a specfed number of dgts after the decmal pont. It s a popular benchmark to test the CPU performance. However, Super PI s sngle threaded, so ts relevance as a performance

4 The CPU relatve performance (%) decmal places 4000 decmal places 6000 decmal places 0.5 Average value Ftted value The number of vcpus of all VMs The memory relatve performance (%) M 500M G Average value Ftted value The total memory used by VMs n PM (G) Fg.. The test results of CPU performance. Fg. 2. The test results of memory performance. measure of the mult-core machnes s dmnshng quckly. To test stablty on mult-core CPU, Hyper PI [25] s developed to support multple threads of Super PI to be run at the same tme. The test steps are as follows. Step: Choose a compute node (e.g., Compute Node 3) n cloud, and place one VM (e.g., vcpu, 2G memory). Run Hyper PI benchmark on the VM, and get the runtme as the desred performance (P d ); Step2: Contnue to place 2, 3,..., n VMs, then run Hyper PI benchmark on the VMs several tmes, and calculate the average runtme P vm. Step3: Calculate the VM CPU relatve performance byp = P vm/p d. In ths experment, we get the relatve performance of calculatng π to 2000, 4000, 6000 dgts after the decmal pont respectvely, and then we calculate ther average value. The results are shown n Fg.. From the results, wth the ncreasng of number of vcpus, the VM CPU relatve performance decreases slowly at frst. And then, the VM CPU relatve performance decreased rapdly after a certan value of the threshold. In the fgure, the dvdng pont s at 8, whch s the number of CPU cores of PM. When the number of vcpus of all VMs s over 8, there s a great resource contenton among these VMs, whch results n a great performance degradaton. For example, when the number of vcpus of all VMs s 20, the VM CPU relatve performance s just around 0.5. We also ft our CPU performance model usng the results to get the parameters, and then we get the theoretcal value (lne Ftted value shown n Fg. too) usng the ftted model. We can see the fttng result s proper, and the fttng error s about 0.27%, whch shows that the CPU model can descrbe the CPU performance degradaton trend very well. The ftted model s: { 0.055v +.03 f(v = p k c(v)= V c k Mc j Mcr) j 5.96/v else Memory: We use memtester [26], [27] to test memory performance. When there are multple VMs runnng on the PM, they need to read and wrte the same memory at the same tme frequently. As the memory s shared and competed, ths leads to performance degradaton n readng and wrtng speed. We run memtester benchmark and get the runtme to test the memory performance. The test steps are smlar to those of testng CPU, and they are as follows. Step: Choose a compute node (e.g., Compute Node 3) n cloud, and place one VM (e.g., vcpu, 2G memory). Run memtester benchmark on the VM, and get the runtme as the desred performance (Pd ); Step2: Contnue to place 2, 3,..., n VMs, then run memtester benchmark on the VMs several tmes, and calculate the average runtme Pvm. Step3: Calculate the VM memory relatve performance by p = Pvm/P d. We test memtester to read and wrte dfferent memory szes (e.g., 50M, 500M, G) under dfferent memory utlzatons of PM, and the results are shown n Fg. 2. We can see that the memory performance decreases wth the ncreasng of memory used n PM, whch shows that the memory consumpton (the sum of all VMs memory) greatly affects the VM memory performance. Meantme, we ft the memory performance model usng the results wth a fttng error 0.277%. The fttng model s: p m(n)= Mj m Mj m,r 9.78+n (n = k V k m) Network: We use Netperf [28], [29] to test the maxmum bandwdth of vrtual network nterface of a sngle VM. Netperf s a software applcaton that provdes network bandwdth testng between two hosts on a network. It contans two components, netserver for recevng data and netperf for sendng data. The test steps are as follows. Step: Choose a compute node (e.g., Compute Node 3) n cloud, and place one VM (e.g., vcpu, 2G memory). Deploy netserver. Run netperf deployed on a computer to send data to netserver, and get the maxmum throughput as the desred performance (Pd ); Step2: Contnue to place 2, 4,..., 2*n VMs, and deploy netserver on them. Then send data to these VMs at the same tme, and get the maxmum throughput Pvm. Step3: Calculate the VM network relatve performance by p = Pvm/P d. The results are shown n Fg. 3. We can see that the network performance decreases wth the ncreasng of VMs. We ft the

5 The network relatve performance (%) Average value Ftted value The resource utlzaton (%) Network CPU Memory The number of VMs n PM The concurrency users n lve streamng system Fg. 3. The test results of network performance. Fg. 4. The resource consumpton of lve streamng. network performance model usng the results wth a fttng error 0.029%, and then we get the followng formula. C. The sum p n(b)=0.986/b (b = k V k n) The above descrbes our performance models for CPU, memory, and network of VMs. They can descrbe the performance degradaton caused by resource contenton among VMs placed on the same PM. The proposed modelng method can be generalzable to a generc applcaton, because the only knowledges needed for the models are some performance parameters for cloud platform, all of whch can be retreved from prevously learnng. Note that, the performance parameters are some dependence on the hardware. As long as the hardware of cloud platforms s kept the same, those parameters can be appled drectly. For other cloud platforms, those parameters should be traned agan. IV. VM PLACEMENT METHOD In ths secton, we present our VMP method based on VM performance models. We consder the resource consumpton characterstcs of applcatons, the resource requrement of VMs, and the server capacty of PMs n the process. The applcaton-aware resource consumpton characterstc s ntroduced frstly. The VMP algorthm wll be presented n detal n the followng subsecton. A. Applcaton-aware resource consumpton characterstc The applcatons runnng on VMs are dfferent, so the VM resource requrements are dfferent. For example, for vdeo lve streamng applcatons, they need to support large-scale users watchng vdeos smultaneously, so they are bandwdthntensve and requre lots of bandwdth to transmt data to users. Another example s vdeo process, such as vdeo transcodng. It s a computaton ntensve and tme-consumng task, whch requres lots of computaton (CPU) resources. So we need to study the resource consumpton characterstcs of dfferent applcatons. Defne W =(w c,w m,w n) as the resource consumpton characterstc vector of applcaton runnng n VM V. Here, w c, w m, and w n are the CPU, memory, and network utlzatons, and they can be obtaned by montorng the VM performance. For example, Fg. 4 shows the resource (CPU, memory, bandwdth) utlzatons of lve streamng servce, whch proves that t s a bandwdth-ntensve applcaton. Wth the ncrease of users, the bandwdth has been draned, whle the memory and CPU usages are around 20% and 30%, respectvely. So we can use W =(0.3,0.2,) for lve streamng applcatons. Of course, we can also get the resource consumpton characterstcs of other applcatons usng the smlar way. B. Problem formulaton Generally, we assume that there are N VMs and M PMs (N M). No VM requests more resource than a sngle PM can offer. For each VM V ( =,2,...,N), ts resource requrement vector s denoted as Y =(vc,v m,v n). Here, vc, vm, and vn are vcpu, memory, and bandwdth requrements. Meantme, the resource consumpton vector of applcaton runnng on V s W =(wc,w m,w n). Accordng to the resource requrement vector Y and the resource consumpton vectorw ofv, we can calculate ts real resources requrement vector R = Y W =(vc wc,v m wm,v n wn). For each PM M j (j =,2,...,M), ts workload capacty vector M j =(Mc,M j m,m j n,p j j c,p j m,p j n), mpcurr j =(p j c,p j m,p j n) s ts current VM relatve performance vector before placement, and ts current resource consumed by all VMs C j before = (v,n,b) can calculated by v = k V c k, n = k V m, k and b = k V n k. Let matrx X NM = {x j x j =0,} ndcates the VMP soluton, where x j =means VM V s placed on M j, and x j =0mples V s not placed on M j. After VMs placement, all VMs resource consumed of M j wll be C j after =(v,n,b ), here v = v + x j= v c wc, n = n+ x j= v m wm, and b = b+ x j= v n wn. Then we calculate the new relatve performance vector mp j after wth C j after usng our VM performance models n last secton. Let vpafter denotes the VM performance of V after placement. If x j =, then vpafter = mpj after. On the one hand, to offer the best VMs, we should maxmze the total performance of all VMs, e.g., max( N = vp after ). Onthe other hand, to maxmze the cloud resource utlzaton, we should mnmze the performance degradaton of all PMs, e.g.,

6 mn( M j= mpj curr mp j after ). Therefore, we can defne our optmzaton problem as follows (denoted as Π): N Π:max( vpafter ) = M and mn( mpcurr j mp j after ) j= s.t. x j = {0,},0 vp,mp j M N x j =,0 x j N j= C. Problem solvng The proposed VM placement attempts to obtan two objectves. Many methods convert the orgnal problem wth multple objectves nto a sngle-objectve optmzaton problem, whch s called a scalarzed problem. In ths paper, we use a lnear scalarzaton to formulate t as a sngle-objectve optmzaton problem. Let R = max( N = vp after ) and Q = mn( M j= mpj curr mp j after ), then we convert problem Π as max(ωr ( ω)q), here ω s the scalarzaton parameter. If ω =, then the VM performance s the man objectve. ω =0means the cloud resource utlzaton s the man objectve. When 0 <ω<, there s a trade-off between the two objectves. To obtan the optmal soluton, we have to test every possble solutons. If there are N VMs and M PMs, then gettng the optmal result demands a tme-complexty of O(M N ). We can see that t s mpractcal n real-lfe stuatons. In ths paper, we use a greedy algorthm to get an approxmate optmal soluton wth O(M N). The problem can be dvded nto N sub-problems. For each VM placement, we choose a PM M j, whch can get the maxmum value, that s, max(ω vpafter ( ω) mpj curr mp j after ), to place the VM. At last, we can get the global approxmate optmal soluton for all VMs. Algorthm presents the algorthm n the pseudo code form. A. Expermental settng = V. EXPERIMENTS We use our OpenStack cloud platform as the testbed, and we set the parameter ω to, 0.5, and 0 respectvely. To evaluate our VMP method based on performance models, the exstng schedulng methods n schedulng module of Nova (Nova-VMPs), such as ChanceScheduler, SmpleScheduler, ZoneScheduler, and AbsractScheduler, are consdered n our experments, respectvely. The man task of schedulng module of Nova s to choose a PM for placng a VM nstance. ChanceScheduler (Random) s a random schedulng algorthm, and SmpleScheduler (Smple) realzes the mnmum load schedulng algorthm. ZoneScheduler (Zone) s used to select a random PM n an avalable zone. Dfferent from the ChanceScheduler, ZoneScheduler frst selects an avalable zone, then gets the PMs n the avalable zone, then randomly selects a PM. AbsractScheduler (Weghted) gves dfferent Algorthm Our VMP algorthm Input: VMs V = {V }, PMs M = {M j }, ω Output: The VMP soluton X NM : Set X NM wth zeros; 2: for each VM V n V do 3: Get ts resource requrement Y =(vc,v m,v n); 4: Get ts resource consumpton characterstc W = (wc,w m,w n); 5: Calculate the real resources requrement vector R = Y W =(vc wc,v m wm,v n wn); 6: max =0; pm = ; 7: for each PM M j n M do 8: Get relatve performance vector mpcurr; j 9: 0: Get resource consumed by all VMs Ccurr j =(v,n,b); Calculate the new resource consumed C j after = Ccurr j +R =(v,n,b ); // f place V on M j : Calculate the new relatve performance vector mp j after wth Cj after usng our performance models; 2: Calculate =(ω vpafter ( ω) mpj curr mp j after ); 3: f >maxthen 4: max = ; 5: pm = j; 6: end f 7: end for 8: f pm then 9: X,pm =; // Place V on M pm ; 20: end f 2: end for 22: return X NM weghts for dfferent PMs, and chooses a PM wth sutable weght for placng VMs. Besdes the VMP methods n Nova of OpenStack, we also compare our VMP method wth CMBFD [5]. There may be a lttle unfar f competng our method wth CMBFD, because the latter tred to maxmze total resource utlzaton and mnmze energy consumpton, nstead of mprovng VM performance. We take two applcatons, lve streamng and vdeo transcodng, as our test applcatons. We randomly generate two VMs request sequences (TestCase and TestCase2), both of whch have 32 random VMs requrements wth dfferent confguratons and applcatons respectvely. Once a VM request s comng, we place t usng our VMP and other VMPs respectvely. If the new VM s for lve streamng, we add t nto lve streamng server cluster, and count the maxmum concurrency for lve streamng of the cluster. If the new VM s for vdeo transcodng, we run the same transcodng task on all transcodng VMs smultaneously, and then calculate the average transcodng tme of all transcodng VMs. We repeat the above processes untl all 32 VMs are place on PMs. We use the maxmum concurrency and the average transcodng tme as the VM performance metrcs for lve streamng and transcodng servces. B. Expermental results The detaled results are shown n Fg. 5. From the Fg. 5(a) and 5(b), we can see that our VMP methods (ω =,0.5,0)

7 The maxmum concurrency users Our (ω=) Our (ω=0.5) 600 Our (ω=0) Random 500 Smple Zone 400 Weghted CMBFD The number of lve streamng VM servers The maxmum concurrency users Our (ω=) Our (ω=0.5) 600 Our (ω=0) Random 500 Smple Zone 400 Weghted CMBFD The number of lve streamng VM servers (a) (b) The transcodng tme (s) Our (ω=) Our (ω=0.5) Our (ω=0) Random Smple Zone Weghted CMBFD The transcodng tme (s) Our (ω=) Our (ω=0.5) Our (ω=0) Random Smple Zone Weghted CMBFD The number of transcodng VMs placed on PMs (c) The number of transcodng VMs placed on PMs (d) Fg. 5. The results comparson wth varous methods. (a) and (b) are the maxmum concurrency results for lve streamng n TestCase and TestCase2; (c) and (d) are the transcodng results for vdeo transcodng n TestCase and TestCase2. perform better than others n lve streamng tests. For example, our VMP methods only needs 4 VMs that can acheve the maxmum concurrency (200) for lve streamng, whle CMBFD needs 5 VMs and Nova-VMPs need more than 6 VMs to reach the same result. Although CMBFD s better than Nova-VMPs, t s a lttle worse than our VMPs. CMBFD ams to optmze energy consumpton n cloud, so t can t guarantee VM performance. Our VMP methods take the VM performance models nto account, whch can reflect the VM performance loss caused by resource sharng and contenton on a PM, so as to provde VMs wth best performance to users. Of course, all methods stop ncreasng when the maxmum concurrency for lve streamng s over 200, whch s lmted by the resource constrant of our cloud platform, so the maxmum number of users supported by the streamng meda servers wll not ncrease wth the ncrease of VMs. Meanwhle, from the vdeo transcodng test results shown n Fg. 5(c) and 5(d), we can see that the performance dfference among all VMP methods s not clear wth the small scale of VMs schedulng. However, wth the ncrease of the number of transcodng servers, especally when there are more than 0 transcodng servers, our VMP methods work much better than others. At frst, when there are several VMs on the cloud platform, there s no resource competton problem among VMs, so the transcodng tmes of all VMP methods are almost the same. When the number of VMs ncreases, the more VMs there are, the more resource competton there wll be, so the VM performance degrades clearly. However, our VMP methods can place VMs wth the best performance, so they outperform the others n average transcodng tme. VI. CONCLUSION Ths paper addresses the ssue of VM performance degradaton when placng VMs on PMs. After analyzng the shortcomngs of exstng VMP method, our applcaton-aware VMP method based on the VM performance models s proposed. It ntroduces VM performance models, whch are the fundamental bases for the followng placement. Then t takes the applcaton-aware resource consumpton characterstc nto consderaton to place VMs on approprate PMs, whch can guarantee the VM performances and ensure customers QoE. Lastly, we take the streamng meda servces for examples and conduct some experments to evaluate our VMP method n our

8 real OpenStack cloud platform. The results show that t works better than other methods. As for future work, we wll study performance models of other resources, such as dsk I/O, whch also affects the VM performance greatly. Besdes, our method s a statc placement. Once the VMs are placed n a PM, t can t place the VMs agan n another PM. Next, we ntend to study how to dynamcally place VMs. ACKNOWLEDGMENT Ths research was partally supported by the Natonal Scence Foundaton of Chna under Grant Nos , , , , 98005, 92830, the MOE Innovaton Research Team No.IRT3035, the Natonal Key Technologes R&D Program of Chna under Grant No.203BAK09B0, the Co-ordnator Innovaton Project for the Key Lab of Shaanx Provnce under Grant No.203SZS05- Z0, Changjang Scholars Program of MOE, the Fundamental Research Funds for the Central Unverstes, and the Chna Scholarshp Councl. REFERENCES [] J.L.L. Smarro, R. Moreno-Vozmedano, R.S. Montero, and I.M. 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