Resource Sharing Models and Heuristic Load Balancing Methods for

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1 Resource Sharng Models and Heurstc Load Balancng Methods for Grd Schedulng Problems Wanneng Shu 1,2, Lxn Dng 2,3,*, Shenwen Wang 2,3 1 College of Computer Scence, South-Central Unversty for Natonaltes, Wuhan , Chna shuwanneng@yahoo.com.cn 2 State Key Lab of Software Engneerng, Wuhan Unversty, Wuhan , Chna lxdng@whu.edu.cn 3 Computer School, Wuhan Unversty, Wuhan , Chna dlxc2010@yahoo.com.cn Abstract Grd computng utlzes dstrbuted heterogeneous resources to support large-scale or complcated computng tasks, and an approprate resource schedulng algorthm s fundamentally mportant for the success of grd applcatons. Due to the complex and dynamc propertes of grd envronments, tradtonal model-based methods may result n poor schedulng performance n practce. In ths paper, we propose a heurstc genetc load balancng algorthm. The mplementaton and smulaton results ndcate that our approaches can allocate jobs effcently and effectvely. Keywords: Load Balancng, Resource Sharng, Grd Envronments, Job Schedulng, Heurstc Genetc Load Balancng Algorthm 1. Introducton Grd computng has emerged as a potental next generaton platform for solvng large-scale problems n scence, engneerng, and commerce. It s expected to nvolve mllons of resources scatterng across multple organzatons, admnstratve domans, and polces. Grd computng provdes a dstrbuted computng nfrastructure for solvng large-scale advanced scentfc and engneerng problems through sharng of resources, usually over hgh-speed communcaton networks [1]. It can be sad that the Internet was born n the 1980s, the World Wde Web technology n the early 1990s, the emergence and rapd spread of the early 21st century, the grd computng technology, the formaton of the three major mlestone n the hstory of the development of the Internet. An mportant purpose n grd computng s the mplement of unfed descrpton method for the geographcally dstrbuted, heterogeneous resource, so that the grd system can gve users the vrtual unfed resource nterface and execute the task schedulng by the users on the fttest computatonal resource node dynamcally. Grd computng enables the sharng, selecton, and aggregaton of geographcally dstrbuted heterogeneous resources and becomes an mportant soluton paradgm for supportng complcated computng problems. Grds of computatonal resources usually nclude heterogeneous processors [2], and heterogeneous network lnks that are orders of magntude slower than n a parallel computer. Therefore, the executon on grds of applcatons desgned for parallel computers usually leads to poor performance as the dstrbuton of workload does not take the heterogenety nto account [3]. An mportant ssue of such grd computng systems s the effcent assgnment of jobs and utlzaton of resources of unused devces, commonly referred to as the load balancng or job schedulng problem. Due to the randomness of task arrval and each node on the ablty dfference n grd envronment wll cause some of the nodes assgned task s too heavy, and some nodes are dle, that s load mbalances. Therefore, load balancng s also an mportant ssue n grd envronment, whch the schedulng algorthm should consder the load balancng problem. Load balancng has two meanngs: Frst, a large number of concurrent access to data traffc share to be treated separately n the multple node devces, reduce the tme users wat for a response; Secondly, computng a sngle heavy load sharng to multple nodes on the devce to do parallel processng, after the end of each node devce processng, summary of the results returned to the user, the system processng capacty has been greatly mproved. The man purpose of load balancng s to balance the load of each resource n order Internatonal Journal of Advancements n Computng Technology(IJACT) Volume4, Number9, May 2012 do: /jact.vol4.ssue

2 to enhance the resource utlzaton and ncrease the system throughput [4]. The assessment methods s load balancng s the rato of the maxmum executon tme of each schedulng program host wth the shortest executon tme, the smaller the value, the host load balancng, the better the schedulng scheme. Our problem s thus to load balancng the executon by computng a data dstrbuton dependng on the processors speeds and network lnks bandwdths. In grd computng, an effectve and effcent schedulng algorthm s a crtcal problem for effcent job executon [5, 6]. The avalablty of the selected resources for job executon s a prmary factor to determne computng performance. Therefore, t s necessary to develop more robust and adaptve schedulng algorthms, whch s one of the man motvatons of ths paper. An mportant ssue here s how to formally defne the grd schedulng problem. Grd users submt computng tasks to share grd resources, and grd scheduler accordng to some strateges assgns these tasks to the approprate resources. Effcent schedulng polcy or algorthm can take advantage of the processng capacty of the grd system so as to mprove applcaton performance. The goal of grd schedulng algorthm s manly to ncrease the throughput and utlzaton of the system, to grd task completon tme s shortest n the whole system. In the followng, the job schedulng problem, whch s the key ssue for balancng the entre system load whle completng all the jobs at hand as soon as possble, s studed. Then, we focus on the desgn of effcent grd schedulers usng heurstc load balancng methods. The rest of the paper s organzed as follows. Sectons 2 descrbe the related works. Resource sharng models for grd computng are gven n secton 3.In secton 4, we show the mplementaton and smulaton results. Fnally, we conclude the paper n Secton Related works A grd system s formed usng many heterogeneous or homogeneous resources to deal wth largescale scentfc problems. How to approprately and effcently assgn resources to tasks, generally called job schedulng, s one of the mportant ssues. The man purpose of job schedulng s to shorten the job completon tme and enhance the system throughput. In ths secton, we gve lots of algorthms have been studed for job schedulng problems n grd computng. D. Paranhos et al. [7] developed a dynamc FPLTF (Fastest Processor to Largest Task Frst) algorthm that schedules tasks to resources accordng to the workload of tasks n the grd system. Casanova et al. [8] developed a general adaptve job schedulng algorthm for a class of grd applcatons wth large numbers of ndependent tasks n grd. Kwang Mong Sm et al [9] use multple knds of ant to fnd multple avalable resources to balance resources utlzaton n job schedulng. In [10], the authors presented an approach for grd system adaptaton, n whch grd jobs are mantaned, usng an adaptable resource broker. Huedo et al. [11] reported a job schedulng algorthm based on top of the grd framework, whch uses nternally adaptve job schedulng. T. Phan et al. [12] proposed a proxy-based clustered archtecture for moble grds, whch the job schedulng polces n moble grds need to manage resources and applcaton executon dependng on the requrements of resource consumers. K.S. Chatrapat et al. [13] proposed a FCFS algorthm (Frst come frst served schedulng algorthm) that jobs are executed accordng to the order of job arrvng tme and the next job wll be executed n turn. Plar Herrero et al. [14] proposed a RR algorthm (Round robn schedulng algorthm) manly focuses on the farness problem, whch defnes a rng as ts queue and also defnes a fxed tme quantum. Each job n queue has the same executon tme and t wll be executed n turn. The major advantage of RR algorthm s that jobs are executed n turn and do not need to wat for the prevous job completon. Thomas Rngs et al. [15] proposed a Mn-mn schedulng algorthm. It set the jobs that can be completed earlest wth the hghest prorty, and each job wll always be assgned to the resource that can complete t earlest. The man dea of Mn-mn s that t assgns tasks to resources whch can execute tasks the fastest. Max-mn algorthm sets the hghest prorty to the job wth the maxmum earlest completon tme. In [16], the authors proposed a MFTF algorthm (Most ft task schedulng algorthm), whch manly attempts to dscover the ftness between tasks and resources for user. It assgns resources to tasks accordng to a ftness value.p.k. Sur et al. [17] proposed a dynamc load balancng algorthm (DLBA) whch performs an ntra-cluster and nter-cluster load balancng. It consders load ndex and other conventonal nfluental parameters at each node n dynamcally schedulng the tasks. Ruay-Shung et al. [18] proposed a balanced ant colony optmzaton (BACO) 316

3 algorthm for job schedulng n the grd envronment; the man purposes of the proposed algorthm are to balance the entre system load whle tryng to mnmze the makespan of a gven set of jobs. Jun Wu et al. [19] proposed an ordnal sharng learnng (OSL) method for job schedulng problems. The algorthm crcumvents the scalablty problem by usng an ordnal dstrbuted learnng strategy, and realzes mult-agent coordnaton based on an nformaton sharng mechansm wth lmted communcaton. Preetam Ghosh et al. [20] proposed a prcng strategy for job allocaton n moble grds usng a non-cooperatve barganng theory framework. 3. Resource sharng models It s well known that the problem of fndng optmum schedulng n heterogeneous systems s n general NP-hard. Due to the NP-Complete nature and the dffculty to prove the optmalty of schedulng algorthms n grd scenaros, current research always tres to fnd suboptmal solutons. Tradtonal schedulng algorthm doesn t well adapt to the characterstcs of grd resources n resource heterogenety, and the resource allocaton decsons, parallelsm and dstrbuton, etc. of effcent schedulng algorthm. Therefore, how ratonal allocaton and management of grd resources to meet the servce needs of a wde range of applcatons to acheve optmal use of resources, has become the areas of grd research focus and hotspots. A typcal grd scenaro s as follows: an applcaton can generate several jobs, whch n turn can be composed of subtasks; the grd system s responsble for sendng each subtask to a resource to be solved. Each of the schedulers can submt jobs to any of the computng resources, and fnally generate job-to-resource mappngs. Our resource manager automatcally selects the set of optmal resources among the set of canddate resources usng a heurstc algorthm and requests resource allocaton, so t provdes convenence for a user to execute a job. It also guarantees effcent and relable job executon through a fault tolerance servce. In general, the decentralzed job schedulng problem n Grds can be denoted as a 3-tuple { R, J, F }, the set of avalable resources s denoted by R { r1, r2,..., r m }, the computng power r of resource are denoted by Te (, k) and Tr (, k ), Te (, k) s the expected tme to compute job jk r n resource,s assumed to be known n ths model, Tr (, k) s the receved tme to job jk from the J { j root resource. 1, j2,..., j n } denote a set of jobs FJ : R[0,1] s a job submsson functon. Thus, we look for a dstrbuton n of these jobs over the m resources that mnmzes the overall r computaton tme. Therefore, resource ends ts processng at tme: T T (, k) T (, k) e r k 1 (1) Several performance requrements and optmzaton crtera can be consdered for grd job schedulng problem, whch s mult-objectve n ts general formulaton. Job schedulng optmzaton crtera nclude makespan, resource utlzaton, load balancng, matchng proxmty, turnaround tme, total weghted completon tme, weghted response tme, etc. One of the most popular and extensvely studed optmzaton crtera s the mnmzaton of the makespan. Makespan s an ndcator of the general productvty of the grd system: small values of makespan mean that the scheduler s provdng good and effcent plannng of tasks to resources. The tme, Makespan,taken by grd system to compute the set of n jobs s therefore: Eq.(2) can be wrtten as follows: Makespan max T max( T (, k) T (, k)) m e r 1 m 1 m k 1 Makespan T (1,1) max{ T (1,1), max[ T (, k) T (, k)]} r e e r 2 m k 2 The job schedulng problem s formulated as follow: (2) (3) 317

4 Mnmze Makespan (4) Numbers of GA (Genetc Algorthms) [21] methods have been presented for mnmzng the total job completon tme n grd envronments. By makng use of stochastc or autocorrelaton models of grd workloads, t s possble to study job schedulng algorthms for dealng wth the dynamcty, randomness, heterogenety of grd job schedulng problems. GA s probablstc meta-heurstc methods nspred by the survval of the fttest prncple. GA s a popular evolutonary optmzaton approach that can fnd the global optmal soluton n complex multdmensonal search spaces. R.Subra et al. [22] proposed a GA based schedulng algorthm for grd computng envronments. Ye et al. [23] dscussed the use of a GA for allocatng network resources to fnd the best possble soluton wthout any constrans, such as cost or tme. Selects the set of optmal resources usng a GA, t provdes a user wth the convenence of job executon and ensures that the job s executed effcently. GA has the advantage of smple, versatle, and robustness n solvng optmzaton problems, premature convergence but later n the search because of the blndness and randomness of the algorthm. The man research of task schedulng s how to sheld the underlyng computng resources n homogeneous or heterogeneous grd computng system, to provde users wth a unform grd system wthn the resource vew, to fnd the best match strategy between tasks and resources, management and schedulng tasks executed n parallel mplementaton, so that system resources to ratonal use of the fnal system of computng resources utlzaton. In the followng secton, amng at the condtons and characterstc of applcaton of task schedulng a heurstc genetc load balancng algorthm (HGLBA) s proposed to solve the problem of selectng optmal resources that mnmze the total executon tme of a task. Applcaton of the HGLBA dramatcally reduces the computng complexty n resource allocatng meetng the requests of real tme schedule. Here, optmal resources are those that mnmze the longest executon tme of jobs that are runnng n grds. The HGLBA uses the followng components: (ⅰ) Representaton of a gene a In the HGLBA, each chromosome represents a schedule of ndependent jobs. { 0,..., n 1}. a n. (ⅱ) Populaton: Generate an ntal populaton A() t, A( t) PopSze. (ⅲ) Ftness functon: a A() t Ft( a ) exp( ), Makespan (ⅳ) Selecton( A( t)) P P { b, b 1,..., b j1 gn p Ftness( b ) (, Ft b 1)... Ft( b j1)} (ⅴ) Crossover: a, a A( t) j, k : crossover pont ' a j ' ' a Crossover( a, a ) ' j, a { 0,..., ka, rk 1,..., rn 1 aj} a, j { 0,..., kaj, k 1,..., n 1 a}, a { 0,..., n 1} aj { r,..., rn } (ⅵ) Mutaton: A() t a, 0 1, k : mutaton pont '' '' ' ' Mutaton( a ) a a, { 0,..., k 1, k,..., n 1}, k k 1 1, a { 0,..., n 1} 318

5 4. Smulaton results In ths secton, the goals of ths evaluaton are to measure the performance of our proposed algorthm. We use the smulaton tool whch s called GrdSm [24]. The GrdSm toolkt allows modelng and smulaton of enttes n parallel and dstrbuted computng systems-users, applcatons, resources, and resource brokers (schedulers) for desgn and evaluaton of schedulng algorthms. It provdes a comprehensve faclty for creatng dfferent classes of heterogeneous resources that can be aggregated usng resource brokers. GrdSm provdes some applcatons for the grd envronment, such as VM (vsual modeler), job schedulng. Fg.1. shows the declnaton of the executon tme as the number of generatons.fg.2 s the ascendng response tme as the number of jobs, and Fg. 10 shows the comparson of the average completon tme for four dfferent approaches to select resources:.e. GA, DLBA, HGLBA, and FPLTF. By the expermental results, we can easly fnd out the HGLBA can acheve grd system load balance n any stuaton and take less tme to execute jobs. It means that HGLBA can handle dfferent sze jobs and also keeps the grd system load more balanced and have better performances. In FPLTF algorthm, a job s always assgned to the resource wth hghest ftness value. It means that the resource wth hgher ftness value s the resource wth hgher computng power. However, FPLTF algorthm always assgns job to the fastest resource, t does not consder the load of the resource. Therefore, the load of the resource become heavy and many assgned jobs wll wat n the queue. Therefore, the makespan s larger than HGLBA. GA and DLBA algorthm do not consder any status of resource. Therefore, those performance are poor than HGLBA algorthm GA DLBA HGLBA FPLTF 450 Tme(s) Number of Generatons(g) Fgure.1 The declnaton of the executon tme as the number of generatons 319

6 9 8 GA DLBA HGLBA FPLTF Response tme(s) Number of jobs x 10 4 Fgure.2 The ascendng response tme as the number of jobs GA DLBA HGLBA FPLTF tme(s) Number of jobs x 10 4 Fgure 3.The average completon tme n dfferent number of jobs 5. Conclusons Grd computng and grd technologes prmarly emerged for satsfyng the ncreasng demand of the scentfc computng communty for more computng power. In grd computng, the key technology s effcent allocaton and use of grd resources. A good schedulng algorthm can assgn jobs to resources effcently and can balance the system load. In ths paper, we propose a HGLBA algorthm to choose sutable resources to execute jobs accordng to resources status and the sze of gven job n the Grd envronment. 6. Acknowledgments The Project was supported by the Specal Fund for Basc Scentfc Research of Central Colleges, South-Central Unversty for Natonaltes (Grant No. CZY11005), Natural Scence Foundaton of South-Central Unversty for Natonaltes (Grant No. YZY10004), Natonal Natural Scence Foundaton of Chna (Grant No and ), the Research Fund for the Doctoral Program of Hgher Educaton (Grant No ), and the Fundamental Research Funds for the 320

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