Deadlock-free migration for virtual machine consolidation using Chicken Swarm Optimization algorithm

Transcription

1 Deadlock-free mgraton for vrtual machne consoldaton usng Chcken Swarm Optmzaton algorthm Tan, F., Zhang, R., Lewandowsk, J., Chao, K-M., L, L. and Dong, B. Post-prnt deposted n Coventry Unversty repostory January 07 Orgnal ctaton: Tan, F., Zhang, R., Lewandowsk, J., Chao, K-M., L, L. and Dong, B. (06) Deadlock-free mgraton for vrtual machne consoldaton usng Chcken Swarm Optmzaton algorthm. Journal of Intellgent & Fuzzy Systems, volume In press. DOI: 0.333/JIFS IOS Press The fnal publcaton s avalable at IOS Press through Copyrght and Moral Rghts are retaned by the author(s) and/ or other copyrght owners. A copy can be downloaded for personal non-commercal research or study, wthout pror permsson or charge. Ths tem cannot be reproduced or quoted extensvely from wthout frst obtanng permsson n wrtng from the copyrght holder(s). The content must not be changed n any way or sold commercally n any format or medum wthout the formal permsson of the copyrght holders.

2 Deadlock-free mgraton for Vrtual Machne Consoldaton usng Chcken Swarm Optmzaton Algorthm Feng Tan a,b,*, Rong Zhang a,b, Jacek Lewandowsk c,d, Kuo-Mng Chao c, Longzhuang L e and Bo Dong a a The MoE Key Lab for INNS, X an Jaotong Unversty, X an 70049, P.R. Chna b Systems Engneerng Insttute, X an Jaotong Unversty, X an 70049, P.R. Chna c School of Computng, Electroncs and Mathematcs, Coventry Unversty, UK d Department of Genetcs, Wroclaw Unversty of Envronmental and Lfe Scences, Poland e Department of Computer Scence and Technology, Texas A&M Unversty-Corpus Chrst, TX, USA Abstract. Consoldaton of servces s one of the key problems n cloud data centers. It conssts of two separate but related ssues: Vrtual machne (VM) placement and VM mgraton problems. In ths paper, a VM consoldaton scheme s proposed that turns the vrtual machne consoldaton (VMC) problem nto a vector packng optmzaton problem based on deadlockfree mgraton (DFM) to mnmze the energy consumptons. To solve ths NP-hard and computatonally nfeasble for large data centers problem, a novel algorthm named Chcken Swarm Optmzaton based on deadlock-free mgraton (DFM-CSO) algorthm s proposed. The DFM-CSO algorthm s characterzed by the one-step look-ahead wth n-vms mgraton n parallel (OSLA-NVMIP) method, whch carres out the VM mgraton valdaton and the rearrangement of target physcal host, as well as records the mgraton order for each soluton placement, so that VM transfer can be completed accordng to the mgraton sequence. The expermental results, for both real and synthetc datasets, show that the proposed algorthm wth hgher convergence rate s favourable n comparson wth the other deadlock-free mgraton algorthms. Keywords: VM consoldaton, VM placement, Deadlock-free mgraton, Chcken Swarm Optmzaton. Introducton The energy consumed by a data center can be broadly categorzed nto two categores: energy used by IT equpment such as servers, networks, storage, etc., and energy usage by nfrastructure facltes such as coolng and power condtonng systems. The energy consumpton of IT equpments accounts for about half of the total energy consumpton, of whch nearly 40% s consumed by servers. One of the most mportant reasons for energy neffcency n data centers s too much dle tme when servers run at a low load []. One of the man technques to mprove the energy-effcency of servers n data center s called the vrtual machne consoldaton, whch focus on applcaton workloads consoldaton on a smaller amount of physcal hosts (PHs). The research shows that the cost of runnng dle servers wth no tasks assgned accounts for over 50% of the peak power consumpton [-3]. Therefore, consoldaton of vrtual machnes and shuttng down dle servers are an effectve energy-savng strategy. Power modelng s an actve area of research, studyng both lnear and nonlnear correlatons between the system utlzaton such as VM placement or mgraton and power consumpton [4-5]. Whle most of the vrtual machne consoldaton problems focus on VM placement optmzaton, whch s the mappng of vrtual machnes to physcal hosts, yet lttle research concerns how the ntal VM placement can be transformed nto the fnal placement and what the mgraton sequence s. In ths paper we cover both VM placement and mgraton problems whch can help to reduce data center s energy consumpton through effcent VM management. * Correspondng author. E-mal: fengtan@mal.xtu.edu.cn.

3 . Background VM mgraton s a technology whch has attracted consderable nterest from data center researchers n recent years. It allows a vrtual machne to mgrate from one physcal host called source PH to another physcal host called target PH. The VM mgraton to the new placement takes place after the workload optmzaton. When there are m such vrtual machnes that need to be mgrated to the new placements, the new VM placement has m mgraton tasks. If there are nsuffcent resources on the target physcal host for a VM that needs to be mgrated, then the mgraton of the VM s called nfeasble mgraton, otherwse, the mgraton of the VM s called transferable mgraton or transferable. If there s at least one nfeasble mgraton task among the m mgraton tasks, then the mgraton of the whole VM cannot be successfully completed, and the new placement s called nfeasble placement. In practce, deadlock may occur durng vrtual machne mgratons, whch transform the ntal placement nto the new placement soluton. There are four condtons for the deadlock occurrence: mutual excluson, hold whle watng, no preempton and crcular wat [6]. These condtons on both drect and ndrect deadlock examples llustrated n Fgure and below are further dscussed. Note that VM -M notaton, denotes that the -th VM needs to take up M unts of host s CPU. VM-4 PH VM-3 (a) Intal placement VM3- PH VM-4 VM3- PH Fg.. Drect deadlock (b) Fnal placement In the frst example of drect deadlock, also known as nfeasble mgraton, let s assume that the data center s made of two physcal hosts: PH and PH. As shown n Fg., the PH has 8 CPUs and PH has 3 CPUs avalable. In ths case, when the ntal VM needs to be transformed nto the fnal placement, there are mgraton tasks to do. The frst task s the mgraton of VM requrng 3 CPUs from ts ntal placement on PH to the new placement on PH.The second task n turn s the mgraton of VM3 requrng CPUs from PM to PM. In ths case each VM mgraton requres the other VM to release ts resources, what results n the deadlock, f and only VM-3 PH f there ext two physcal hosts. Although resources requrement n the fnal placement wll not exceed the PM s maxmum resources, the mgraton wll not be completed wthout resources from other servers. VM- VM- VM3-3 PH PH (a) Intal placement VM3-3 Fg.. Indrect deadlock. PH PH (b) Fnal placement In the second example of ndrect deadlock, mproper mgraton order leads to the deadlock. As shown n Fg., assume that the data center s made of two physcal hosts: PH and PH and that PH has 4 CPUs and PH has 5 avalable. In ths case there are mgraton tasks that need to be done, and f the mgraton order s: VM- PH, VM3-3 PH, and VM- PH, then the mgraton can be completed. However f the order s dfferent and the frst mgraton s VM- PH, then there are only avalable CPUs left n PH, and ths PH cannot longer meet the requrements for the VM3 mgraton. Meanwhle, smlar problem exsts on PH where only two CPUs are avalable, and also PH cannot meet the requrement for the VM mgraton to that host. Consequently a crcular wat s formed accordng to the mgraton order VM3-3 PH and VM- PH, that results n deadlock whch does not allow the VM mgraton to complete. Although t s rare to break frst three condtons of deadlock s occurrence, breakng the fourth condton s relatvely common. When these happen, the VM mgraton may lead to deadlock and n consequence may requre redundant servers to be added n order to resolve the deadlock problem. Consderng constraned resources and heavly loaded data centers, t s almost mpossble to nclude addtonal physcal servers, especally for some prvate clouds composed of a really small scale of physcal servers. Ths paper helps to solve ths problem by proposng the novel algorthm named Chcken Swarm Optmzaton based on deadlock-free mgraton (DFM- CSO). It ams to fnd an optmal vrtual machne placement and the mgraton sequence, whch wll not requre redundant servers to mtgate the deadlock problem. The DFM-CSO algorthm s characterzed by the OSLA-NVMIP method, whch carres out the VM mgraton valdaton and the rearrangement of target physcal hosts, as well as records the mgraton order for each soluton placement, VM- VM-

4 so that VM transfer can be completed accordng to the mgraton sequence. It can help to obtan an optmal placement and a specfc mgraton order whch ensures that the optmal placement s transferable. Moreover, the OSLA-NVMIP method takes the dea of parallel prorty to reduce the mgraton tme. The expermental results, for both real and synthetc datasets, show that the proposed algorthm wth hgher convergence rate s favorable n comparson wth the other deadlock-free mgraton algorthms. Ths paper s organzed as follows. Secton 3 focuses on problem formulaton and presents the proposed methods. Secton 4 ntroduces the DFM-SCO algorthm and the OSLA-NVMIP deadlock avodance strategy. Secton 5 presents the experments and dscusses the results. The paper s concluded n Secton Problem formulaton The study n ths paper s presented under one assumpton that servers share the same hard dsks pool, bandwdth, CPUs, and memory taken as the computng resources. Furthermore, redundant servers n data center are not allowed. Such presented VM consoldaton problem s descrbed as a vector packng problem and uses mnmzaton of the energy consumpton of the placement as the obectve functon. 3.. Power consumpton model One of the most popular power consumpton models s lnearly proportonal to the CPU utlzaton [7-9]. However, wth rapd development of computer hardware technologes, the predcton performance of lnear model s not accurate enough. Lterature revew [0] shows that the cubc polynomal power consumpton model s sgnfcantly better than the lnear model. Let s assume there are M servers and N VMs n a data center. The power consumpton, accordng to polynomal model, for the -th server can be defned as: C( ) C U ( U ) ( U ) () dle 3, wth the total power consumpton model defned as: M Mnmze : C x C( ) () dle C a, U b,,where, represents the power consumed when the -th server s n dle state., are three regresson coeffcents, g whch descrbe the -th server s power consumpton. mem U represent the CPU-utlzaton and memory-utlzaton of -th server, respectvely. The constrants condtons for such defned model are as follows: The x 0, - th server del x (3), other mem 0 U (4) 0 U,,,, M. (5) n Eq. (), s used to descrbe whether the -th server s shutdown or not. Eq. (4) and Eq. (5) constran the physcal machne resource occupancy upper lmtaton of VMs memory and CPU respectvely. Under such defned constrants the goal of ths study s to mnmze the energy functon C presented n equaton Eq. (). For ths purpose the experments were conducted on IBM 3850 X5 severs located n the data center at the Dstance Learnng College of the X an Jaotong Unversty n Chna, whch provdes educatonal courses for over 69,000 students. For the purpose of ths experment the performance data was collected by Veeam Montor [] every hours from 0//04 to //05 []. 4. Methods To solve the NP-hard and computatonally nfeasble for large data centers problem of VM mgraton, a novel algorthm named Chcken Swarm Optmzaton based on deadlock-free mgraton (DFM-CSO) s proposed. In ths secton, the man steps of the DFM-CSO algorthm wll be ntroduced, and several key optmzaton strateges wll be dscussed. 4.. Introducton to the framework of DFM-CSO algorthm DFM-CSO s an optmzaton algorthm whch adds deadlock avodance strategy named OSLA-

5 NVMIP to the CSO algorthm. CSO was frst proposed by Meng et al. [3] n 04, as an swarm ntellgence algorthm. It s a stochastc optmzaton algorthm whch mtates the behavor of a group of chckens searchng for food. Ths algorthm classfes chckens nto three categores, namely: rooster, hen and chck accordng to ther ftness level. In ths model each type of chcken carres out dfferent searchng strategy and the chcken swarm updates tself after several. What characterzes ths algorthm s ts ablty to avod local optma and quckly fnd the global optmal value, when solvng the optmzaton problem. The OSLA-NVMIP deadlock avodance strategy means that, n each step of transferrng VMs, all transferable mgraton of the VMs are moved nto target PHs n parallel, whle the transferablty of each soluton placement s verfed and modfed accordng to whether there exst suffcent resources on the target physcal host for each VM that needs to be mgrated, whch ensures that every soluton placement can be transferred. In prncple f one soluton placement s unable to be transferred, then the target PHs s rearranged untl the placement becomes transferable. Ths strategy wll be dscussed n detal further n ths secton. Start t= Intalzaton OSLA-NVMIP Calculate the populaton ftness t>? Update the local optmal and global optmal Termnaton crtera met? Yes Stop Locaton update mod( t, G) =? Yes NO NO t=t+ Chcken swarm ntalzaton Fg. 3. Flowchart dagram of DFM-CSO algorthm. Outlned n Fgure 3 s the flowchart of the DFM-CSO algorthm, proposed n ths paper, whch s made of the followng eleven man steps: Step : Set t=. Step : Intalzaton. Intalze servers and vrtual machnes, create placements, and set the swarm populaton sze and other parameters. Each chcken n the pool s encoded to represent a placement. Step 3: OSLA-NVMIP. one-step look-ahead wth n-vms mgraton n parallel method carres out the VM mgraton valdaton and the rearrangement of target PH, as well as records the mgraton order for each soluton placement. Step 4: Calculate the populaton ftness. Calculate the ftness for each placement. Step 5: f t s greater than 0, then go to Step 0, otherwse go to Step 6. Step 6: t=t+. Step 7: Detect the udgement condtons. If the condtons are met then go to Step 8, else, go to Step 9. Step 8: Chcken swarm ntalzaton. Classfy chckens nto three categores accordng to ther ftness. Step 9: Locaton update. Update the locaton of dfferent chcken groups accordng to predefned locaton model and encode them to represent ther placement. Go to Step 3. Step 0: Update the local optmal and global optmal values. Step : Detect the termnaton condtons. If the termnaton condtons are met then ext the loop, else, go to Step Core models and strategy 4.. Swarm locaton update model The rooster s locaton update model s defned as follows: chrom chrom N,, ( (0, )), f f fk fk f exp( ), otherwse f k [, CN], k, where CN s the number of chcken swarms, chrom depcts the poston of the -th chcken; chrom, s the -th element of the -th chrom at tme step. N(0, s ) s the normal dstrbuton wth means 0 and standard devaton s. e

6 s the smallest constant used to avod zero-dvsonerror. k s a rooster s ndex randomly selected from the roosters group, s the ftness value of the correspondng chrom. f In turn, the hen s locaton update formula s defned as follows: chrom chrom,, p* Rand *( chrom chrom ) c,, p* Rand *( chrom chrom ), p f - f f + e c = exp( - ), p = exp( - f - f ), c c. c c,, Where Rand s a unform random number from [0,]. c [,,CN] s the rooster s ndex, whch s the -th hen s group-mate, whle ndex of the chcken (rooster or hen), whch s randomly chosen from the swarm. p s a nfluence factor that the chrom s affected by the rooster, whch s the hen s group-mate, whle p s the nfluence factor that the chrom s affected by other hens and roosters. Smlar to nature, where chcks move around hens to forage for food, the CSO model has ts chcks whch move around hens to search for optma. Ths feature s defned as: chrom chrom,, m,, c [,,CN] s an L*( chrom chrom ),where chrom stands for the poston m, (L (0, )) s a of the -th hen( m [, CN ] ) and L parameter, whch ensures that chck follows ts hen to search for an optma. The parameter L value for each chck s randomly chosen between 0 and. 4.. OSLA-NVMIP It s very dffcult to estmate placement transferablty wthout the vrtual machne mgraton sequence. To estmate the gven placement weather t s transferable or not from ntal placement s not a trval task. Moreover, t s the NP-hard problem to search the VM mgraton sequence knowng only the ntal and the gven placement. Snce tracng whether a soluton can be transferred or not requres the VM mgraton sequence, the VM mgraton sequence becomes the key to transferablty detecton. Xng et al. [4] adopted one-step look-ahead method to solve the deadlock problem n flexble manufacturng system. The dea behnd ths method s that f one step forward enters the unsafe state, then the method returns ths deadlock path and takes other path nstead. In turn, Sarker and Tang [5] proposed an algorthm, whch s smlar to one-step look-ahead wth n-vm mgraton n parallel method to deal wth mgraton deadlock problem. Ths paper adopts the OSLA-NVMIP strategy, whch can rearrange the target PHs for n-number of VMs whch are falng to be successfully mgrated. The framework of OSLA-NVMIP strategy proposed below, takes the length of the vector as the amount of VM s, and each vector component value s the correspondng physcal host number assgned to each VM. For example, chrom=[4 ] represents that No. VM s placed n the No.4 PH, and that No. VM and No.3 VM are placed n the No. PH. The man steps of OSLA-NVMIP strategy are as follows: Framework of OSLA-NVMIP strategy: Step : Fnd out all VMs whch need to be mgrated. Step :For every PH, fnd all transferable VMs, and record them (see Algorthm for more detals). Then mmgrate these VMs nto the correspondng PHs. Step 3: Detect f the termnaton condton s met. That s, udge whether the number of VMs need to be mgrated before the transferrng n Step s equal to the number of VMs need to be mgrated after the transferrng n Step. If these two numbers are zero, then stop ths procedure; If the two values are equal but not zero, then go to Step 4; otherwse, return to Step. Step 4: Calculate dle vrtual machnes. Step 5: If there stll exst VMs that need to be mgrated, then contnue to Step 6, else stop the procedure. Step 6: Rearrange the target PH for the VM whch needs to be mgrated. The target PH s selected from the lst of currently used PHs. Calculate avalable resources and the number of dle PMs after each mgraton. Step 7: Detect whether the termnaton condton s met or not. Compare the number of mgrated VMs wth the number of VMs whch needed mgraton n Step. If the two values are

7 equal, stop the procedure; otherwse, reset the soluton to the ntal state and set the mgraton sequence to null. For all the VMs that are dentfed n Step, the mgraton process to dfferent target PHs can be done n parallel. In ths way, the mgraton tme can be shortened due to mult-vm mgraton wthn one step. The pseudo code for Step s showed n Algorthm lstng below. Algorthm The pseudo codes for Step for every physcal host PH do fnd all the VMs that need to be mgrated to PH as a set, named vmposton 3 f length(vmposton)>0 then 4 Cc_cost=0; 5 Cm_cost=0; 6 for = to length(vmposton) do 7 Cc_cost= Cc_cost + VM.Cc(vmposton()); 8 Cm_cost= Cm_cost + VM.Cm(vmposton()); 9 If (Cc_cost <= PMuseable.Cc()) (Cm_cost <= PMuseable.Cm()) then 0 mgratenum=mgratenum+; mgratonsequence(mgratenum)= vmposton(); else Cc_cost= Cc_cost VM.Cc(vmposton()); 3 Cm_cost= Cm_cost VM.Cm(vmposton()); 4 end f 5 end for 6 end f 8 end for In the above lstng, Cc_cost s an occupancy rate of the sum of CPU utlzaton of all transferable VMs that mgrated to a specfc PH n a step; and Cm_cost s an occupancy rate of memory utlzaton of all transferable VMs that mgrated to the specfc PH n a step. VM.Cc s an occupancy rate of CPU utlzaton of sngle transferable VM that mgrated to a specfc PH; and VM.Cm s an occupancy rate of memory utlzaton of sngle transferable VMs that mgrated to the specfc PH. PMuseable.Cc() and PMuseable.Cm() are the percentage of the resdual CPU and Memory capacty of the -th PH 5. Experment and analyss Performance of the proposed DFM-CSO and other four mproved mgraton algorthms: DFM-PSO, DFM-GA, DFM-IGA(mproved DFM-GA algorthm) and DFM-BBO/DE, were compared and evaluated n experments on both real and synthetc datasets. Synthetc VM nstances have been generated usng method proposed by Gao et al. [6]. In turn, for real dataset generaton 0 types of Amazon EC [7] nstances have been used. In the experment scenaro, descrbed below, the ntal placement s what the placement state (locaton, CPU, Memory, etc.) of all VMs consdered are n a moment. To smulate ths, Matlab software has been used. The results obtaned on both datasets show that the proposed algorthm wth hgher convergence rate s favourable n comparson wth the other mproved deadlock-free mgraton algorthms. Note that, after ntroducng the OSLA-NVMIP deadlock avodance strategy nto PSO [8], GA [8], IGA [9] and BBD/DE [0], we mplemented and obtaned four mproved algorthms, DFM-PSO, DFM-GA, DFM-IGA and DFM- BBO/DE. Ref. [0] and [] had gven the convergence proof of the PSO algorthm, whch shows that the orgnal PSO s nether wth local convergence nor wth global convergence. So, the same thng happens n the convergence of DFM-PSO. It s proved by means of homogenous fnte Markov chan analyss that a generc GA wll never converge to the global optmum regardless of the ntalzaton, crosser, operator and obectve functon. However, varants of canoncal GA s that always mantan the best soluton n the populaton, ether before or after selecton, are shown to converge to the global optmum []. As the same theory, both of the GA and IGA n ths paper adopt the method whch mantan the best soluton after selecton, so the DFM-GA and DFM-IGA whch proposed n ths paper are wth global convergence under the deadlock avodng strategy. Ref. [3] gves the convergence proof of the BBO algorthm based on the assumpton that the teraton tme tends to be nfnte. So, the BBO/DE have the same convergence property under the deadlock avodng strategy. Ref. [3] ndcates that, for the CSO, the approprate choose of parameter G s problem-based. If the value of G s very bg, t's not conducve for the algorthm to converge to the global optmal quckly. Whle f the value of G s very small, the algorthm may trap nto local optmal [3]. Ths prncple also works on DFM-CSO.

8 5. Scenaro There are 4 PHs and 8 VMs wth the same confguraton. Intal VM placement s VM_ntal = [,,,, 3, 3, 4, 4], where each VM s CPU and memory occupancy rate demands are: VM.Cc = [/4, /4, /4, /4, /4, /4, /4, /4] and VM.Cm = [/0, /0, /0, /0, /0, /0, /0, /0] respectvely. Snce memory utlzaton demand of each VM s relatvely low and the requred resources of PH are adequate, hence ths scenaro can be regarded as sngleresource case. Fgure.4 shows the CPU resources of the ntal placement. VM- VM6- VM- PH VM5- PH3 VM4- VM8- VM3- PH VM7- PH4 Fg.4. Scenaro VM s ntal placement There s a varety of canddate fnal placements wth the same mnmum power consumpton, because the method presented n ths paper consders the fnal placement energy consumpton as the only cost functon that needs to be optmzed wthout lookng for example at the number of VMs to be mgrated. The optmal soluton calculated by DFM-CSO algorthm s shown below: Optmal placement: bestchrom=[, 3, 3,, 3,,, ] ; VM mgraton sequence: VM_Mgratesequence=[, 3, 6, 7,, 8] ; Parallel nodes: parallernode=[, 4, 5, 6]. The -th element of parallelnode denotes the aggregated number of VM mgratons untl the -th step. There are 4 parallel mgratons accordng to the results: The frst parallel mgraton: VM- PH; VM3- PH3; The second parallel mgraton: VM6- PH; The thrd parallel mgraton: The fourth parallel mgraton: VM7- PH; VM- PH3; VM8- PH. The results ndcate that accordng to the mgraton sequence, all mgratons are deadlock-free. Thanks to parallel mgratons, the tme spent for these mgratons s shorter than the tme requred to mgrate each VM separately. The optmal placement best chrom shows that no VM s placed to PH4, therefore PH4 wll be dle after mgratons and the number of physcal hosts wth workload s three. Ths wll save the energy consumpton. 5. Synthetc dataset The method used to generate Synthetc nstances s showed n Algorthm. Algorthm Generaton of Synthetc Instances for = to n do Cc = * rand ( Cc) ; 3 Cm = rand ( Cm) ; 4 r = rand() ; 5 f ( r P Cc Cc) ( r P Cc Cc) then Cm = Cm + Cm ; 6 7 end f 8 end for, where Cc and Cm are parameters used to control the utlzaton of CPU and memory respectvely. s correspondng to the correlatons between CPU and Memory utlzaton. The algorthm s ntroduced from Ref. [6] nto ths paper. P 5.3 Real dataset As outlned n Table, the real dataset has been made of 0 general purpose T and C3 nstances from Amazon EC [7]. TABLE Instance Types from Amazon EC Instance Memory Physcal vcpu Type (GB) Processor t.nano 3 Intel Xeon famly t.mcro 6 Intel Xeon famly t.small Intel Xeon famly t.medum 4 Intel Xeon famly t.large 36 Intel Xeon famly c3.large 3.75 Intel Xeon E5-680 v c3.xlarge Intel Xeon E5-680 v c3.xlarge 8 5 Intel Xeon E5-680 v c3.4xlarge 6 30 Intel Xeon E5-680 v c3.8xlarge 3 60 Intel Xeon E5-680 v

9 5.4 Synthetc datasetresult and analyss Several scenaros are used to compare the performance of the DFM-CSO algorthm wth that of DFM-PSO, DFM-GA, DFM-IGA and DFM- BBO/DE. For a far comparson, all of the common parameters of these methods are set to be the same. We set the populaton sze as 50 and the maxmum number of as 500 and 00 physcal hosts as servers. The related parameter values of these algorthms are showed n TABLE 3. TABLE 3 The related parameter values Algorthm Paramerters DFM-PSO DFM-GA DFM-IGA DFM- MBBO DFM-CSO c=c=.49445,w=0.79 pmutaton=0.3,pcrossover=0.7 pmutaton=0.3,pcrossover=0.7 Pmutaton=0.,I=,E=, F=0.6,pcrossover=0. CN r=0.*cn,cn h=0.6*cn, CN c=cn-cn r-cn h, CN m=0.*cn c,g=3, L [0.5,0.9] 5.5 Experment result based on synthetc dataset Scenaro sets parameters Cc = Cm = 0.5, P = to general 00 VMs synthetc nstances, and generate ntal placement randomly. Fg.5. Comparson of DFM-CSO wth four algorthms on Synthetc dataset The expermental results shown n Fg.5 and TA- BLE 4, show that the proposed algorthm s characterzed by the hghest convergence rate n comparson wth other four mgraton algorthms wth the soluton beng close to the optmum after about 5 teratons. The algorthms whch optmum soluton s closest to the one obtaned wth the DFM-CSO are the DFM-IGA and DFM-BBO/DE. The convergence rates of these two algorthms are very smlar. The other two algorthms performed sgnfcantly worst n terms of the optmal soluton accuracy as well as the convergence rate. TABLE 4 Comparson of DFM-CSO wth four algorthms on Synthetc dataset Algorthms Cost(W) Idle servers Save Cost % DFM-PSO % DFM-GA % Intal placement DFM-IGA % DFM- BBO/DE % DFM-CSO % The DFM-GA algorthm characterzed by slow search rate n the earler stages of operaton has been mproved after the number of teraton. DFM-PSP algorthm characterzed by the general slow search rate was prone to trap nto local optma. When lookng at the dle physcal servers consoldaton solutons obtaned wth dfferent algorthms, the DFM- PSO and DFM-GA ncreased from 0 dle servers n the ntal placement up to 3 and 38 dle servers, respectvely, after 500 consoldaton teratons. The DFM-IGA, DFM-BBO/DE and DFM-CSO all reached up to 4 dle physcal servers. Comparng the energy consumpton of the ntal placement wth the energy consumpton of the optmum placement of varous methods, we observed that the optmum placement obtaned by DFM-CSO algorthm saves 4.9% energy and beats DFM-PSO and DFM-BBO/DE. The proposed algorthm, DFM-CSO, has an outstandng feature that the speed of approachng optmum placement s faster than others, seen n Fg. 5. TABLE 5 Comparson of DFM-CSO wth three algorthms on Synthetc dataset Algorthms Average Cost(W) DFM-PSO Standard devaton(w) DFM-IGA DFM- BBO/DE DFM-CSO

10 Usng the same parameter values and runnng the DFM-PSO, DFM-IGA, DFM-BBO/DE and DFM- CSO for 0 tmes, respectvely. The results are shown n TABLE 5. The DFM-CSO has the mnmal standard devaton as compare wth other three algorthms, whch mean that DFM-CSO has batter stablty than others. The convergence rate of DFM- CSO s outstandng, because ts results approached the optmum placement after 5 teratons. Moreover, the experment has been carred out 5 tmes when set the number of teratons as 30 and set G as, 3, 4, 5, 6, 7, 8, 9, 0, respectvely, n scenaro. The experment results are shown n Fgure 6. Fg.6. Comparson of DFM-CSO wth dfferent G In Fgure 6, we can observe that, wth the decrease of the value of G, the convergence rate of DFM-PSO becomes fast, whle the optmum values that the algorthm obtaned are very close. Ths can conclude that, wth the nfluence of locaton update strategy, the faster the speed of chcken grows and smaller the value of G s, the faster the convergence rate of the proposed algorthm s. 5.6 Experment result based on Real-world dataset 5.6. Real data scenaro In the frst real data scenaro, 00 vrtual machnes were generated wth 5 types of C3 nstances and ther ntal placement allocatons were random. There were 00 PHs wth the same specfcaton and each PH had 40 CPUs and 8 GB of memory. The experment results are shown n Fgure 7 and TABLE 6. TABLE 6 Comparson of DFM-CSO wth four algorthms on real dataset of C3 nstances Algorthms Cost(W) Idle servers Save Cost Intal placement % DFM-PSO % DFM-GA % DFM-IGA % DFM- BBO/DE % DFM-SCO % Comparng Fgure 5 wth Fgure 7 we can notce certan smlarty between these two graphs. We can also note that the convergence rate s smaller when the vrtual machne resources are larger Real data scenaro In the second real data scenaro, the total of 00 vrtual machne nstances were generated and randomly ntalzed wth 5 types of T nstances. There were 00 physcal hosts wth the same specfcaton and each physcal host had 40 CPUs and 8 GB of memory. Fg.8. Comparson of DFM-CSO wth four algorthms on real dataset made of T nstances. Comparson of Fgure 7 and Fgure 8, reveals that the convergence rate of DFM-CSO algorthm s relatvely hgh compared wth DFM-IGA and DFM- BBO/DE, but much less effectve than the other two. The resource requrement of vrtual machnes n scene 3 s much less than n scene, and DFM-CSO, whch s more applcable to vrtual machnes that request more resources, may not have obvous energy-savng effect for correcton of all the possble solutons. Besdes, t also shows that the vrtual machne whch have more resources leads to teraton of convergence reducng relatvely. Fg.7. Comparson of DFM-CSO wth four algorthms on real dataset of C3 nstances

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