Available online at ScienceDirect. Procedia Computer Science 46 (2015 )

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1 Avalable onlne at ScenceDrect Proceda Computer Scence 46 (2015 ) Internatonal Conference on Informaton and Communcaton Technologes (ICICT 2014) A Novel Famly Genetc Approach for Vrtual Machne Allocaton Chrstna Terese Joseph a,, Chandrasekaran K b, Robn Cyrac a a Department of Computer Scence and Engneerng, Raagr School of Engneerng and Technology, Koch , Kerala, Inda b Department of Computer Scence and Engneerng, Natonal Insttute of Technology, Karnataka, Surathkal , Inda Abstract The concept of vrtualzaton forms the heart of systems lke the Cloud and Grd. Effcency of systems that employ vrtualzaton greatly depends on the effcency of the technque used to allocate the vrtual machnes to sutable hosts. The lterature contans many evolutonary approaches to solve the vrtual machne allocaton problem, a broad category of whch employ Genetc Algorthm. Ths paper proposes a novel technque to allocate vrtual machnes usng the Famly Gene approach. Expermental analyss proves that the proposed approach reduces energy consumpton and the rate of mgratons, and hence offers much scope for future research. c Publshed The Authors. by Elsever Publshed B.V. byths Elsever s an B.V. open access artcle under the CC BY-NC-ND lcense Peer-revew ( under responsblty of organzng commttee of the Internatonal Conference on Informaton and Communcaton Technologes Peer-revew under (ICICT responsblty 2014). of organzng commttee of the Internatonal Conference on Informaton and Communcaton Technologes (ICICT 2014) Keywords: Cloud Computng; Genetc Algorthm; Vrtual machne allocaton; Famly gene; Energy-effcent 1. Introducton The term Cloud Computng s a fuzzy term for whch no concrete defnton exsts. It can be vewed as the delvery of servces over the Internet, as and when the customer demands. The transton of large organzatons from the tradtonal CAPEX model to the OPEX model support the fact that Cloud computng s one of the most promsng technologes n the current IT scenaro. The ncreasng number of users for Cloud Computng ncreases the challenges faced by the Cloud servce provders to provde the requested servces ensurng hgh avalablty and relablty of the servces. The vrtualzaton technque proves functonal n helpng the Cloud servce provders to meet these challenges. In order to employ vrtualzaton, vrtual enttes of the actual versons are created and deployed n the system. Ths technology enables the Cloud servce provder to serve more number of customers than the support provded by the actual hardware resources avalable wth the provder. Generally vrtualzaton s appled at the computer system level. Ths nvolves the creaton and deployment of vrtual machnes. The requests of the customers wll then be processed by these Vrtual Machnes (VMs). Varous VMs wll have dfferent processng and memory requrements. One of the maor factors that needs to be consdered n systems that deploy VMs s the allocaton of the VMs to hosts. Correspondng author. Tel.: E-mal address: xtna 1232@hotmal.com Publshed by Elsever B.V. Ths s an open access artcle under the CC BY-NC-ND lcense ( Peer-revew under responsblty of organzng commttee of the Internatonal Conference on Informaton and Communcaton Technologes (ICICT 2014) do: /.procs

2 Chrstna Terese Joseph et al. / Proceda Computer Scence 46 ( 2015 ) An mproper allocaton can result to the VMs beng executed on unsutable hosts, whch can then lead to unwarranted effects. Ths would affect the credblty of the Cloud servce provder. In order to avod ths, an effcent scheme should be used to correctly allocate the VMs to the hosts that support ther executon. Ths decson problem s called the VM Allocaton Problem. Varous approaches have been appled to solve the NP-Hard problem of VM allocaton. The VM allocaton problem can be consdered as a mult-obectve constraned optmzaton problem. A large number of approaches appled to solve the VM Allocaton problem employ evolutonary technques ncludng Genetc Algorthm (GA). Some of the lmtatons of the Genetc Algorthm approaches nclude the premature convergence and the hgh processng tme nvolved. Due to premature convergence, many of the Genetc Algorthms converge to a sub-optmal result. These phenomena should be avoded. Another lmtaton s the hgh processng tme. Most of the Genetc Algorthm approaches requre a lot of tme for processng the varous generatons before producng the optmal result. The approach proposed n ths paper attempts to overcome these lmtatons of the Genetc Algorthm approaches to VM Allocaton. In general, the paper ams to: Perform a lterature survey on the varous evolutonary approaches to resource schedulng and allocaton. Propose a novel technque to allocate VMs whch overcomes the lmtatons of the GA-based approaches. Compare the expermental results of the proposed approach wth the results of the exstng approaches. The organzaton of the paper s as follows: Secton 2 gves an outlne of the varous evolutonary approaches used to solve resource schedulng problems n envronments that employ vrtualzaton. Secton 3 defnes the problem. Secton 4 gves the detals of the proposed system. Secton 5 presents the expermental analyss and results and the paper s concluded n Secton Related Works Barbagallo et al. propose a bo-nspred technque that ams at reducng the energy consumpton n data centers through redstrbuton of load among the servers 1. Accordng to the authors, the proposed algorthm can be effcently employed n self-organzng systems. H.Chen et al. propose a method nspred by the foragng behavour of ants, whch globally allocates resources n Cloud 2. An mproved verson of the ant colony optmzaton algorthm that adopts the characterstcs of greedy algorthms as well s presented. The proposed algorthm attans load balancng and mproved schedulng tme. The task schedulng n Cloud s reduced to an optmal matchng problem wth multple obectves by usng a bpartte graph model to represent the tasks. The resource allocaton problem that consders the dependency among VMs as well as the utlzaton of the lnks of the network s consdered by C. Wang et al 3. The authors consder the scenaro where the requests for resources are not ndependent. The proposed algorthm adopts the characterstcs of PSO. A sngle resource request s represented usng an entty called Vrtual Cloud Embeddng (VCE). Dong et al. propose a genetc algorthm approach that works n a dstrbuted manner to place VMs 4. The proposed approach may be used by IaaS Cloud provders to reduce the energy consumpton and thus mprove the effcency. The optmzaton technque- Ant Colony Optmzaton (ACO) may be used to effectvely consoldate VMs n a data center 5. The performance of such algorthm can be enhanced by ncorporatng a dstrbuted and parallel nature. The parallel nature also mproves the scalablty of the algorthm. E. Feller et al. propose a decentralzed schema and propose the use of the ACO-based approach to mprove the effcency of VM consoldaton by reducng the number of mgratons that have to be carred out 6. An approach to consoldate VMs usng ACO to maxmze resource utlzaton and reduce energy consumpton s proposed by Ferdaus et al 7. A Genetc algorthm approach s proposed by Paolo et al. to allocate VMs n dstrbuted systems wth more than one ters 8. A varaton of the Genetc Algorthm called Improved Genetc Algorthm (IGA) s proposed by Zhong et al. to allocate VMs n data centers of IaaS cloud servce provders 9. The Reorderng Groupng Genetc Algorthm Approach (RGGA) was proposed to solve the multdmensonal bnpackng problem of VM allocaton by Wlcox et al. 10 Load balancng and hstory nformaton was also consdered n the Genetc Algorthm approach to allocate VMs by Band et al. 11. The dea of Pareto domnance and smulated annealng are combned to solve the mult-obectve problem of VM allocaton wth the obectves of load balancng and power savng n MOGA-LS 12. The energy consumpton due to communcaton wthn the data center network s one of the parameters consdered n the approach usng Genetc Algorthm to place

3 560 Chrstna Terese Joseph et al. / Proceda Computer Scence 46 ( 2015 ) VMs by Grant et al. 13 An extenson to ths work uses a reparng procedure 14. A Hybrd Genetc Algorthm approach s used to effcently allocate VMs by Tang et al. 15. A maor class of the bo-nspred methods for VM placement employs Genetc Algorthm. The proposed approach uses a varaton of the Genetc Algorthm approach, called Famly Genetc Algorthm (FGA), whch tres to overcome the lmtatons of the Genetc Algorthm approaches. 3. Problem Defnton The VM Placement problem s a mult-obectve optmzaton problem. Our am s to fnd an optmal placement, whch s a mappng from VMs to hosts. Consder a system wth m host machnes and n VMs. Each VM s represented as v and each host s represented as p. We have a sngle decson varable for the problem denoted by y. The value of ths decson varable s 1 when the th VM s allocated to the th host machne and 0 otherwse. The set of all hosts and all VMs n the system are represented by P and V respectvely. In our system, each host machne can be represented by the vector: p = (d, cpu, mem, bw ) (1) where d provdes an dentfcaton number for the host, cpu gves the processng power of the host, mem gves the amount of memory the host has and bw gves the amount of the bandwdth that the host supports. Each VM s also represented by a smlar vector, gven by: v = (d, cpu, mem, bw ) (2) where d gves the dentfcaton of the VM, cpu gves the processng power requred by the VM, mem gves the amount of memory requested by the VM and bw gves the amount of bandwdth requested by the VM. The problem can be formally defned as follows: Fnd a mappng from the set of VMs, V, to the set of PMs, P, such that the physcal resource utlzaton s maxmzed. In the envronment consdered, the obectve of maxmzng physcal resource utlzaton can be decomposed nto three obectves: Maxmze v cpu p cpu, vmem p mem subect to the constrants m y = 1 =1 n =1 n =1 n =1 y v cpu y v mem y v bw p cpu p mem p bw, vbw p bw The constrants ensure that each VM s allocated to only one host, though one host may be mapped to more than one VMs. They also ensure that the load on each host machne s not greater than ts capacty. We can also defne upper and lower thresholds for the utlzaton on a host. The utlzaton of th host machne by th VM can be gven as: p () u = v cpu p cpu vmem p mem p bw 100, f y = 1 0, otherwse vbw The total utlzaton of host can then be calculated as p u = n =1 p () u (3) (4) (5) (6)

4 Chrstna Terese Joseph et al. / Proceda Computer Scence 46 ( 2015 ) Fg. 1. Archtecture of the proposed system ntegrated nto CloudSm. 4. Proposed System The archtecture of the proposed system s as shown n Fg. 1. The Famly Genetc Algorthm (FGA) module s ntegrated nto CloudSm. In CloudSm, we have Data centers that comprse of hosts. Each of the hosts has one or more Processng Elements (PE). On these hosts, we have varous VMs runnng. These VMs have one or more cloudlets runnng on them. In CloudSm, user obs are drectly represented as Cloudlets. The cloudlets have varous requrements. The processng power requrement of each Cloudlet s represented usng Mllon Instructons Per Second (MIPS). In the proposed archtecture, the FGA module takes the host lst and the VM lst and produces an optmal mappng. The FGA module dvdes the entre processng among the varous famles that run n parallel n the module. The Famly Genetc Algorthm (FGA) attempts to overcome the lmtatons of the Genetc Algorthm approaches. Accordng to Jan et al. 16, the maor contrbutng factor towards premature convergence s the mutaton operator. The authors attempt to reduce the chances of premature convergence by usng a self-adustng mutaton operator. Generally, n GA approaches, the mutaton rate, that s, the probablty of mutaton s statc. The value of ths parameter of GA s defned at the begnnng of the GA and remans constant throughout. As a varaton to ths tradtonal GA, Jan et al. vary the rate of mutaton. Thus here, the mutaton probablty s dynamc. It s defned to be dependent on a parameter called populaton dfferenta. Populaton dfferenta s a rato that s used to ndcate the rate at whch the dfferent ndvduals dffer from each other. Ths parameter gudes the probablty of mutaton. The use of ths self-adustng probablty of mutaton ensures that no premature convergence takes place. In ther approach the degree at whch 2 ndvduals, say A and B dffer from each other s gven by: l 1 d (A, B) = A B =0 where l gves the length o f the chromosome. Thus, the total rate at whch each of the ndvduals dffer from the rest of the populaton can be defned as: Populaton d f f erenta = N =0 where N s the populaton sze. N =0 d(a, B k ) (N 1) (N 1) l 100 The outlne of the Famly Gene Algorthm s descrbed n Algorthm 1. The basc dea n FGA s that we dvde the entre populaton nto famles. In tradtonal GA, we take an entre populaton. The varous operators of GA, selecton, (7) (8)

5 562 Chrstna Terese Joseph et al. / Proceda Computer Scence 46 ( 2015 ) crossover and mutaton are appled at once to the entre populaton across all the generatons. Researchers have proved that these steps are the most tme-consumng steps n GA. In FGA, by dvdng the populaton nto famles and then processng each of these famles n parallel, we attempt to enhance the speed of GA. When employed n a dstrbuted parallel system, the processng of each famly may be carred out n parallel, thus greatly reducng the total runtme. Ths approach was frst proposed by Janhua et al 17. In ther approach the famles were constructed consderng the neghbourng solutons. Our problem of VM allocaton does not defne such neghbours. So here, n order to construct the famles, we perform smple mutatons. The resultng chromosomes whch vary, though only slghtly, from each other, are placed n the same famly. The processng tme s further reduced by destroyng the famles whch do not offer any hope of obtanng better ndvduals. Each famly s processed k tmes. If no better ndvdual has been encountered tll then, we destroy the famly and take the next famly. If atleast one better ndvdual has been generated from the processng of the current famly, then we contnue processng the famly for W teratons. The values of k and W are determned through expermental evaluatons. For each ndvdual n the populaton, we assess the qualty of the ndvdual by calculatng the ftness value assocated wth t. In GA, the ftness value s generally a functon of the obectves that we take nto consderaton. In the proposed approach, the obectve that we take nto consderaton s the physcal resource utlzaton. The algorthm that we used to calculate the ftness value of each ndvdual s outlned n Algorthm 2. An addtonal precauton has to be taken whle employng famly gene approach to the VM allocaton problem. It should be ensured that all the chromosomes satsfy the constrants. To ensure ths, we mplement a separate functon where each ndvdual s checked for feasblty. In case the ndvdual s found to be nfeasble, an attempt s made to transform the nfeasble soluton nto a feasble one. Algorthm 3 takes as nput an ndvdual and returns a chromosome representng a feasble assgnment. Algorthm 1 Outlne of the Famly Gene algorthm Input: Lsts of hosts and VMs Intalze the lst of hosts and Vms. Intalze the values of parameters of GA and the number of famles to be constructed. Randomly ntalze the populaton. Compute the ftness values of each chromosome n the populaton. Calculate the populaton dfferenta. Perform crossover and mutaton. Select the famly heads as the best ndvduals from the current populaton. for all Famly Populaton do repeat Perform mutaton on the famly head and nsert mutated chromosome nto famly. Compute the ftness value of the chromosome obtaned after mutaton. f ftness of the mutated chromosome s greater then add the mutated chromosome to the populaton. Set flag as true end f untl famly sze repeat Perform crossover and mutaton on the current famly to get the next generaton of the current famly. untl k tmes f flag=true else W tmes f flag=false Select the fttest ndvdual from the populaton to get the best soluton. 5. Expermental Analyss and Results The expermental analyss was done usng the CloudSm toolkt, whch was developed by Rakumar Buyya et al. 18 Ths s an open source tool used by maorty of the researchers to smulate the Cloud envronment. The toolkt

6 Chrstna Terese Joseph et al. / Proceda Computer Scence 46 ( 2015 ) Algorthm 2 Calculaton of the ftness value of each chromosome Input: Chromosome Output: Ftness of the chromosome for all p P do Intalze utlzaton values for all v V do f VM s assgned to current host then Update the utlzaton values end f return vcpu p cpu vmem p mem vbw p bw Algorthm 3 Check the feasblty of an ndvdual Input: Chromosome Output: FeasbleS oluton Intalze lst o f f ree hosts to contan all hosts Intalze the capactes and the number o f pes for all v ɛv do Remove from free host the host assgned n chromosome. for all p ɛp do for all v ɛv do f v s assgned to p then Calculate the utlzaton. Update the remanng capacty of the host end f f the host s overutlzed then Assgn any non-free host that can accept the VM. f no allocated hosts can accept the VM then Assgn a sutable host from the free hosts. Update the capactes of the assgned host. end f end f Update the chromosome wth the new assgnments. return Updated chromosome provdes smple allocaton polces and 6 power-aware allocaton polces. The proposed allocaton polcy usng FGA was mplemented, run and compared wth the exstng polces. Fg. 2(a). shows the placement of hosts by the default allocaton polcy n CloudSm. Fg. 2(b). gves the placement that results from usng the proposed approach. Whle allocatng VMs usng the proposed approach the number of hosts n use s reduced. The proposed approach performs the allocaton usng ust the hosts that are requred to satsfy the VM requrements. The remanng hosts whch are not mapped to any VM may be swtched off to further reduce energy consumpton. An mportant parameter that characterzes the performance of an allocaton polcy s the energy consumpton. There s a growng concern nowadays for the ncreasng power consumpton of data centers. Nevertheless, an allocaton polcy that reduces energy consumpton s much more favourable. For our analyss, we allocate varyng number of VMs usng the proposed approach and the exstng approaches. On analyss, t s found

7 564 Chrstna Terese Joseph et al. / Proceda Computer Scence 46 ( 2015 ) (a) (b) Fg. 2. (a) the no. of VMs on each host n the ntal placement; (b) the number of VMs on each host n the placement by the proposed approach. (a) (b) Fg. 3. (a) the energy consumpton; (b) the number of VM mgratons; (c) the SLA tme per actve host for the exstng and proposed approaches. that the energy consumpton s greatly reduced by allocatng VMs usng the proposed approach. Fg. 3(a). supports ths observaton. For any allocaton polcy, f the resultng allocaton s unable to meet any of the resource requrements, the VMs have to be mgrated from the allocated host to some other sutable host. The mgraton of VMs ncurs an overhead on the system. So, an allocaton polcy that keeps the number of VM mgratons at a mnmum s preferred.

8 Chrstna Terese Joseph et al. / Proceda Computer Scence 46 ( 2015 ) Fg. 3(b). compares the number of mgratons for varous approaches for varyng number of VMs. It can be observed that the number of mgratons s lesser for the proposed approach. When users submt obs to be executed n the Cloud envronment, they specfy certan condtons that should be met by the Cloud servce provder. Ths set of user requrements s called the Servce Level Agreement (SLA). The maor obectve of the Cloud servce provder should be to attan a hgher level of SLA. A parameter related to SLA that depends on the allocaton polcy used s the SLA tme per host. Ths parameter gves the tme n percentage where each host follows the SLA. Fg. 3(c). shows that the SLA tme per actve host s greater for the proposed approach, ensurng a hgher SLA level n the proposed approach. In summary, t can be observed that the proposed approach reduces energy consumpton and the number of VM mgratons and ncreases the SLA level, whle keepng the number of actve hosts at a mnmal level. 6. Concluson The problem of VM Allocaton s one of the most mportant decson problems present n all systems that nvolve vrtualzaton, such as, Clouds and Grds. The paper proposes an approach to enhance the effcency of the tradtonal GA approaches to VM allocaton. It has been seen that the energy consumpton has been greatly reduced. The number of VM mgratons s also reduced, whle at the same tme ncreasng the SLA tme per host. The promsng results obtaned from the proposed approach show that the famly genetc algorthm may be employed effcently n real data centers. As the energy consumpton s reduced, t can also be used n green data centers. Though the proposed approach has been tested n the Cloud smulaton envronment, ths approach may be extended to any of the other systems that nvolve vrtualzaton. References 1. Barbagallo, D., D Ntto, E., Dubos, D.J., Mrandola, R.. A bo-nspred algorthm for energy optmzaton n a self-organzng data center. In: Self-Organzng Archtectures. Sprnger; 2010, p Hongwe Chen Le Xong, C.W.. Cloud task schedulng smulaton va mproved ant colony optmzaton algorthm. Journal of Convergence Informaton Technology(JCIT) 2013;8. 3. Wang, C., Wu, Q., Tan, Y., Guo, D., Wu, Q.. Vce-pso: Vrtual cloud embeddng through a meta-heurstc approach. In: Hgh Performance Computng and Communcatons & 2013 IEEE Internatonal Conference on Embedded and Ubqutous Computng (HPCC EUC), 2013 IEEE 10th Internatonal Conference on. IEEE; 2013, p Dong, Y.S., Xu, G.C., Fu, X.D.. A dstrbuted parallel genetc algorthm of placement strategy for vrtual machnes deployment on cloud platform. The Scentfc World Journal 2014;2. 5. Esnault, A., Feller, E., Morn, C.. Energy-aware dstrbuted ant colony based vrtual machne consoldaton n aas clouds bblographc study. Informatcs Mathematcs (INRIA) 2012;: Feller, E., Morn, C., Esnault, A., et al. A case for fully decentralzed dynamc vm consoldaton n clouds 2012;: Ferdaus, M.H., Murshed, M., Calheros, R.N., Buyya, R.. Vrtual machne consoldaton n cloud data centers usng aco metaheurstc. In: Euro-Par 2014 Parallel Processng. Sprnger; 2014, p Campegan, P.. A genetc algorthm to solve the vrtual machnes resources allocaton problem n mult-ter dstrbuted systems. In: Second Internatonal Workshop on Vrtualzaton Performance: Analyss, Characterzaton, and Tools (VPACT 2009), Boston, Massachusett. 2009,. 9. Zhong, H., Tao, K., Zhang, X.. An approach to optmzed resource schedulng algorthm for open-source cloud systems. In: ChnaGrd Conference (ChnaGrd), 2010 Ffth Annual. IEEE; 2010, p Wlcox, D., McNabb, A., Sepp, K.. Solvng vrtual machne packng wth a reorderng groupng genetc algorthm. In: Evolutonary Computaton (CEC), 2011 IEEE Congress on. IEEE; 2011, p Madhusudhan, B., Sekaran, K.C.. A genetc algorthm approach for vrtual machne placement n cloud 2013;. 12. Zhao, J., Dng, Y., Xu, G., Hu, L., Dong, Y., Fu, X.. A locaton selecton polcy of lve vrtual machne mgraton for power savng and load balancng. The Scentfc World Journal 2013;. 13. Wu, G., Tang, M., Tan, Y.C., L, W.. Energy-effcent vrtual machne placement n data centers by genetc algorthm. In: Neural Informaton Processng. Sprnger; 2012, p Quang-Hung, N., Nen, P.D., Nam, N.H., Tuong, N.H., Thoa, N.. A genetc algorthm for power-aware vrtual machne allocaton n prvate cloud. In: Informaton and Communcaton Technology. Sprnger; 2013, p Tang, M., Pan, S.. A hybrd genetc algorthm for the energy-effcent vrtual machne placement problem n data centers. Neural Processng Letters 2014;: Jan, Z., Wang, S.. Study on self-adustng of gene mgraton genetc algorthm. Journal of X an Jaotong Unversty 2002; Janhua, L., Xangqan, D., Sun an, W., Qng, Y.. Famly genetc algorthms based on gene exchange and ts applcaton. Systems Engneerng and Electroncs, Journal of 2006;17(4): Calheros, R.N., Ranan, R., Beloglazov, A., De Rose, C.A., Buyya, R.. Cloudsm: a toolkt for modelng and smulaton of cloud computng envronments and evaluaton of resource provsonng algorthms. Software: Practce and Experence 2011;41(1):23 50.