ISSN (Onlne): 169-081 95 Optmal Provsonng of Resource n a Cloud Servce Yee Mng Chen 1 Shn-Yng Tsa Department of Industral Engneerng and Management Yuan Ze Unversty 135 Yuan-Tung Rd. Chung-L Tao-Yuan Tawan ROC. Abstract Cloud servce allows enterprse class and ndvual users to acqure computng resources from large scale data centers of servce provers. Ths cloud servce s more nvolved n purchasng and consumng manners between provers and users than others. However Cloud servce provers charge users for these servces. Specfcally to access data from ther globally dstrbuted storage edge servers provers charge users depng on the user s locaton and the amount of data transferred. User applcatons may ncur large data retreval and executon costs. Therefore optmzng executon tme the cost arsng from data transfers between resources as well as executon costs should be taen nto account. In ths paper we present a dscrete Partcle Swarm Optmzaton (DPSO) approach for tass allocaton. We construct applcaton Amazon EC as an example and smulaton wth Cloud based compute and transmsson resources. Expermental studes llustrate that the proposed method s more effcent and surpasses those of mathematcal programmng and reflectng the actual beneft of savng wth the total cost as well as tass allocaton. Keywords: Partcle Swarm Optmzaton Resource Allocaton Cloud servce prover. 1. Introducton Cloud computng s a modalty of computng characterzed by on demand avalablty of resources n a dynamc and scalable fashon. The term resource here could be used to represent nfrastructure platforms software servces or storage. Cloud computng servces allow users to lease computng resources from large scale data centers operated by servce provers. Usng cloud servces cloud users can deploy a we varety of applcatons dynamcally and on-demand. Most cloud servce provers use machne vrtualzaton to prove flexble and cost effectve resource sharng. The cloud servce prover s responsble to mae the needed resources avalable on demand to the cloud users. It s the responsblty of the cloud servce prover to manage ts resources n an effcent way so that the cloud user needs can be met when needed at the desred Qualty of Servce (QoS) level[1]. Recently many companes such as Amazon Google and Mcrosoft have launched ther cloud servce busnesses. Most cloud servce provers use machne vrtualzaton technques to prove flexble and cost-effectve resource sharng among users. Vrtual machne(vm)nstances normally share physcal processors and I/O nterfaces wth other nstances. It s expected that vrtualzaton can mpact the computaton and communcaton performance of cloud servces. Although most commercal provers present VM performance crtera to customers t s dffcult for management systems to assure VMs of ther mnmze executon cost or maxmum assgned resources. If the tass of VMs for example suddenly change from le to actve the locatons of VMs cannot be optmzed agan to meet the change[]. In ths paper we propose meta-heurstc optmzaton approach based on Partcle Swarm Optmzaton (PSO) for fndng the near optmal tass allocaton wth reasonable tme. The approach s to dynamcally generate an optmal tas allocaton so as to complete the tass n a mnmum perod of tme as well as utlzng the resources n an effcent way. The rest of the paper s organzed as follows. Secton deals wth some theoretcal foundatons related to tass allocaton model. In Secton 3 we descrbe the proposed DPSO based algorthm n detal. Expermental results are presented n Secton and some conclusons and future wors are proved towards the.. Provsonng of Resources n a Cloud Envronment Cloud computng servces are often roughly classfed nto a herarchy of as a servce terms as followng[3]: Infrastructure a s a Serv ce (IaaS) s provng general on-demand computng resources such as vrtualzed servers or varous forms of storage (bloc ey/value database etc.) as metered resources. Ths can often be seen as a drect evoluton of shared hostng wth added ondemand scalng va resource vrtualzaton and use-based bllng.
ISSN (Onlne): 169-081 96 Platform as a Serv ce (P aas) s provng an exstent managed hgher-level software nfrastructure for buldng partcular classes of applcatons and servces. The platform ncludes the use of underlyng computng resources typcally blled smlar to IaaS products although the nfrastructure s abstracted away below the platform. Software as a Servce (SaaS) s provng specfc already-created applcatons as fully or partally remote servces. Sometmes t s n the form of web-based applcatons and other tmes t conssts of standard nonremote applcatons wth Internet-based storage or other networ nteractons. EC and other server clouds follow an IaaS model n whch the cloud users rent vrtual servers and selects or controls the software for each vrtual server tass[]. Every cloud servce provers mght have a unque way of managng and tass allocaton must ensure that they do not conflct wth the resource owner's polces. In the worst-case stuaton the cloud servce provers mght charge dfferent prces to dfferent cloud users for ther resource usage and ths mght vary from tme to tme. Mathematcal programmng approaches [5] usng column generaton or branch-and-bound technques can solve the tass allocaton problem[6]. However the general n- processor tass allocaton has been found to be NPcomplete[7]. Therefore fndng exact optmum solutons to large-scaled tass allocaton problem s computatonally prohbtve. The development of meta-heurstc optmzaton theory has been flourshng durng the last decade. Partcularly wth ts sound exploraton ablty of both global and local optmal solutons some search technques nvolvng nature-nspred meta-heurstcs have become the focus of resource allocaton research. As mentoned n [8] schedulng s NP-complete. Metaheurstc methods have been used to solve well-nown NP-complete problems. Effcent Meta-heurstc methods whch are used frequently are smulated annealng (SA) [9] genetc algorthm (GA) [10] ant colony optmzaton (ACO) [11] and partcle swarm optmzaton (PSO)[1]. In ths study we conser the tass allocaton wth the followng scenaros(fgure 1). The processors n the system are heterogeneous and they are capactated wth varous unts of memory and processng resources. Hence a tas wll ncur dfferent executon cost f t s executed on dfferent processors. On the other hand all of the communcaton lns are assumed to be entcal and some communcaton cost between two tass wll be ncurred f there s a communcaton need between them and they are executed on dfferent processors. Fgure 1 The framewor of tass allocaton process In ths paper a verson of dscrete partcle swarm optmzaton (DPSO) s proposed for cloud servce prover s tass allocaton and the goal of allocaton s to mnmze the executon cost and communcaton cost mentoned above smultaneously..1 Tass allocaton Model The Tass allocaton model [131]s an nteger program wth a quadratc objectve functon (1) whch represents the total executon cost and communcaton cost respectvely. t p t 1 t p Mn C( X) ec cc (1 x x ) 1 1 x 1j 1 j 1 j (1) Constrants: n 1 t 1 t 1 x 1 1 t () r x R 1 p (3) m x M 1 p () x (01) (5) Constrant () states that each tas should be allocated to exactly one processor. Constrants (3) and () ensure that processng resource and the memory capacty of each processor s no less than the total amount of resource demands of all of ts allocated tass. The last constrant (5) guarantees that x are bnary decson varables. As
ISSN (Onlne): 169-081 97 mentoned n the prevous secton the goal of the tass allocaton s to mnmze the total executon cost and communcaton cost smultaneously. 3. Proposed Dscrete Partcle Swarm Optmzaton Algorthm In ths secton we propose a verson of dscrete partcle swarm optmzaton for tass allocaton. Partcle needs to be desgned to present a sequence of tass n avalable cloud servce provers. Also the velocty has to be redefned. Detals are gven what follows. In our method solutons are encoded n a t p matrx called poston matrx n whch p s the number of avalable processors at the tme of allocaton and t s the number of tass. The poston matrx of each partcle has the two followng propertes: 1) All the elements of the matrces have ether the value of 0 or 1. In other words f X s the poston matrx of -th partcles n a d-dmensonal space then: X t p (01) ) In each row of these matrces only one element s 1 and others are 0. In poston matrx each row represents a tas allocaton and each column represents allocated tass n a processor. VeloctyV of each partcle s consered as a t p matrx whose elements are n range[ V max V max]. Also Pbest and nbest are t p matrces and ther elements are 0 or 1 as poston matrces. p represents the best poston that - th partcle has vsted snce the frst tme step and p gd represents the best poston that -th partcle and ts neghbors have vsted from the begnnng of the algorthm. In ths paper we used star neghborhood topology for p gd. In each tme step p and p gd should be updated: V X weght V old 1 ( t 0 C rand1 ( p X ) C rand ( p X 1 gd f V ( t max{ V otherwse ( t } In (6) ( t s the element n t-th row and p-th column of the -th velocty matrx n the updated tme step of the algorthm and X ( t denotes the element n t- th row and p-th column of the -th poston matrx n the updated tme step. C 1 and C are postve acceleraton V (6) ) (7) constants whch control the nfluence of P and P gd on the search process. Also rand 1 and rand are random values n range [0 1] sampled from a unform dstrbuton. weght whch s called nerta weght was ntroduced by Sh and Eberhart [7] as a mechansm to control the exploraton and explotaton abltes of the swarm. Usually w starts wth large values (e.g. 0.9) whch decreases over tme to smaller values so that n the last teraton t s to a small value (e.g. 0.1). Eq. (7) means that n each row of poston matrx value 1 s assgned to the element whose correspondng element n velocty matrx has the max value n ts correspondng row. If n a row of velocty matrx there s more than one element wth max value then one of these elements s selected randomly and 1 assgned to ts correspondng element n the poston matrx. The pseudo code of the proposed DPSO algorthm s stated as follows: Create and ntalze a t p -dmensonal swarm wth P partcles repeat for each partcle =1 P do f f ( X ) f ( p ) then // f( ) represent the ftness P X ; functon of Eq.(1) f f ( P ) f ( Pgd ) then Pgd P ; for each partcle =1 P do update the velocty matrx usng Eq. (6) update the poston matrx usng Eq. (7) untl stoppng condton s true;. Expermental results In ths secton we wll present the expermental results and comparatve the computatonal performance. The platform for conductng the experments n a PC wth Dual Core Processor 00+.9 GHz CPU and 1.75G RAM. All programs are coded n Java programmng language n orland Julder 006. We gve a formal descrpton of our tass allocaton model. We start wth a descrpton of a cloud nfrastructure. Then we formalze user tass and allocaton of tass on the cloud nfrastructure. In our
ISSN (Onlne): 169-081 98 model we represent a cloud as a connected graph of networed computaton nodes. We assume that there exsts a communcaton ln between each par of nodes. We also assume that each ln has an ndvual bandwth and the data transfer on one ln does not affect the other lns. A node n corresponds to a computng entty le a physcal or a vrtual machne. An edge e s a communcaton ln between two nodes. Fgure shows an example of a cloud. The cloud s depcted by the drected acyclc graph (DAG). The nodes contan tass by users submt to be executed on the cloud. The upper part of the node ec represent tas executon cost. The numbers on the edges represent the communcaton cost of bandwth lns. ec 1 ec ec 6 Processors EC Standard Instance P1 P P3 P P5 P6 P7 P8 Table1. Amazon EC Standard Instance Memory(M) 1.7G 1.7G 15G 7.5G 7.5G 1.7G 1.7G 1.7G CPU(G) 8.0G~9.6G 8.0G~9.6G 8.0G~9.6G 5.0G~6.0G 5.0G~6.0G.0G~.8G.0G~.8G.0G~.8G Executed cost(ec) $0.96~$1.1 1 $0.8~$0.5 $0.8~$0.5 ec ec 3 ec 5 ec 7 Fgure the drected acyclc graph of tass To smulate our proposed DPSO algorthm for nterconnecton tass graph n fgure we have used the data set of Amazon EC Standard Instance are shown n Table 1. The stoppng crteron n DPSO s the number of generatons such that no mprovement s obtaned n the value of ftness functon (fgure 3).The acheve results of eght tass allocaton are shown n Table. ec 8.1 Comparatve performances In ths secton we present the comparatve performances between the proposed DPSO and mathematcal programmng(table 3). The parameter values used n both of DPSO and mathematcal programmng LINGO are optmally tuned by ntensve prelmnary experments to let the competng algorthms perform at the best level. To be specfc the parameter settng used by DPSO s (number of partcles=15 c1=1c=3) and cc 0. 00008. j Table 3. Comparson of the performance for varous tass allocaton Quantty Heurstcs Math. programmng DPSO LINGO Processors ftness Tme Mn Cost Tme (sec) (sec) 0.507 0 0.507 0 Tass 8 8 1.013 0.69 1.011 1 1 1.9 1.3.936 16 Fgure 3 The convergence of DPSO for eght tass allocaton. Table The eght tass allocaton solutons through DPSO Optmal Tas 1 Tas Tas 3 Tas Tas 5 Tas 6 Tas 7 Tas 8 Allocaton Processor 8 Processor 7 Processor 7 Processor 1 Processor Processor Processor 7 Processor 6 Cost Total executon cost and communcaton cost $1.011
ISSN (Onlne): 169-081 99 5. Conclusons Ths paper presented a verson of Dscrete Partcle Swarm Optmzaton (DPSO) algorthm for tass allocaton. We used the heurstc to mnmze the total cost of applcaton tass excuton on Cloud computng envronments. The performance of the proposed algorthm was compared wth the mathematcal programmng method through carryng out exhaustve smulaton tests and dfferent settngs. Expermental results show that the advantage of the DPSO algorthm s ts speed of convergence and the ablty to obtan faster and feasble allocaton. As future wor the authors of the paper plan to carry out exted smulaton studes that conser not only CPU tme and memory space share but also networ bandwth as resources. Acnowledgments Ths research wor was sponsored by the Natonal Scence Councl R.O.C. under project number NSC99-1-E- 155-0. References [1] W. Chung R. Chang A mechansm for resource montorng n Gr computng Future Generaton Computer Systems Vol. 5. No.1.009pp. 1-7. [] M. Armbrust A. Fox R. Grffth A. D. Joseph R. H. Katz A. Konwns G. Lee D. A. Patterson A. Rabn I. Stoca and M. Zahara. Above the Clouds: A ereley Vew of Cloud Computng. Techncal Report UC/EECS-009-8 EECS Department Unversty of Calforna ereley Feb 009. [3] I. Foster Y. ZhaoI. Racu S. Lu S. Cloud computng and gr computng 360-degree compared Gr Computng Envronments Worshop008 pp. 1 10. [] Amazon Elastc Compute Cloud http://aws.amazon.com/ec [5] A. Ernst H. Hang M. Krshnamoorthy Mathematcal programmng approaches for solvng tas allocaton problems Proc. of the 16th Natonal Conf. Of Australan Socety of Operatons Research 001. [6] G.H. Chen J.S. Yur A branch-and-bound-wth-derestmates algorthm for the tas assgnment problem wth precedence constrant Proc. of the 10th Internatonal Conf. on Dstrbuted Computng Systems 1990 pp. 9 501. [7] Zs. Németh V. Sunderam Characterzng grs: Attrbutes defntons and formalsms Journal of Gr ComputngVol. 1. No.1003pp. 9-3. [8] A. Abraham H. Lu M. Zhao Partcle swarm schedulng for wor-flow applcatons n dstrbuted computng envronments n: Metaheurstcs for Schedulng: Industral and Manufacturng Applcatons n: Studes n Computatonal Intellgence Sprnger Verlag Germany 008 pp. 37-3. [9] A. Abraham R. uyya. Nath Nature's heurstcs for schedulng jobs on computatonal Grs n: Proceedngs of the 8th Internatonal Conference on Advanced Computng and Communcatons Tata McGraw-Hll Inda 000 pp. 5-5. [10] Y. Gao H.Q. Rong J.Z. Huang Adaptve Gr job schedulng wth genetc algorthms Future Generaton Computer SystemsVol. 1No. 1005 pp. 151-161. [11] A. Abraham R. uyya and. Nath Nature s heurstcs for schedulng jobs on computatonal grs Proc. of the 8th IEEE Internatonal Conference on Advanced Computng and Communcatons Inda 000pp.5-5. [1] H. Lu A. Abraham An hybr fuzzy varable neghborhood partcle swarm optmzaton algorthm for solvng quadratc assgnment problems Journal of Unversal Computer Scence Vol.13 No.7 007 pp. 103-105. [13] P.Y.Yn S.S. YuP.P. Wang and Y.T. Wang A Hybr Partcle Swarm Optmzaton Algorthm for Optmal Tas Assgnment n Dstrbuted System Computer Standards & Interfaces Vol. 8 006 pp. 1-50. [1] P. Ruth X. Jang D. Xu and S. Goasguen. Vrtual dstrbuted envronments n a shared nfrastructure. Computer Vol. 38 No. 5 005pp.63 69. Yee Mng Chen s a professor n the Department of Industral Engneerng and Management at Yuan Ze Unversty where he carres out basc and appled research n agent-based computng. Hs current research nterests nclude soft computng supply chan management and system dagnoss/prognoss. Shn-Yng Tsa was a graduated student n the Department of Industral Engneerng and Management at Yuan Ze Unversty where she was studyng basc and appled research n Cloud computng and heurstc algorthms. She now wors n Gold Crcut Electroncs as a Desgn Engneerng.