CHAPTER 8 DYNAMIC RESOURCE ALLOCATION IN GRID COMPUTING USING FUZZY-GENETIC ALGORITHM

Transcription

1 28 CHAPTER 8 DYNAMIC RESOURCE ALLOCATION IN GRID COMPUTING USING FUZZY-GENETIC ALGORITHM The man aspraton of Grd Computng s to aggregate the maxmum avalable dle computng power of the dstrbuted resources, and provde well-organzed servces to users. An effcent grd resource allocaton s very much essental to acheve ths aspraton. Researchers had proposed many grd schedulng mechansms n the past to fulfll ths aspraton, but, these are mostly based on tradtonal technques. Our proposed new Intellgent Schedulng Algorthm (ISA) uses soft computng technques such as Fuzzy C-mean clusterng, k-mean clusterng and the Genetc Algorthm for effectve job and resource management whch n turn facltates effcent schedulng. In our approach, the Fuzzy C-mean algorthm (overlappng clusterng technque) s employed for resource classfcaton, k-mean clusterng for job categorzaton and the Extended Genetc Algorthm (EGA) for mappng a gven job to the most sutable resource. 8.1 INTRODUCTION The Grd concept Foster and Kesselman (24) s a new generaton technology that combnes physcal resources through a mddleware tool to provde more effectve and low cost solutons to complex problems n the areas of scence, engneerng and busness. Grd Computng s the very mportant framework to meet the growng computatonal demands n the near future by sharng large scale resources through Vrtual Organzaton (VO)

2 29 Foster et al (21). To acheve ths, geographcally dstrbuted resources need to be logcally coupled together to make them work as a unfed resource. The contnuous exponental growth of the nternet and World Wde Web (WWW), the ever mprovng hgh-bandwdth communcatons, the wdespread avalablty of powerful computers and low-cost components wll further boost the transformaton of computatonal grds nto a realty. Under ths prncple, the grd has problems n the effectve mplementaton of flexble, secure, coordnated resource sharng among dynamc collectons of ndvduals and nsttutons. The man causes are that computng resources are geographcally dstrbuted under dfferent ownershps each havng ts own access polcy, cost and varous constrants, and every resource owner wll have a unque way of managng and schedulng hs resources. So, the grd schedulers manly need to ensure that they do not conflct wth the resource owner s polces. In Grd computng, dstrbuted shared resources are utlzed well, only f the resources and jobs are scheduled n the most approprate manner. The optmal scheduler can gve hgher performance n grd computng. Grd schedulng s the allocaton of dstrbuted computatonal resources to user applcatons and s one of the most challengng and complex tasks n Grd computng. The problem of allocatng resources by grd scheduler requres the defnton of a model that allows local and external schedulers to communcate n order to acheve an effcent coordnated management of the resources themselves. To fulfll ths am, some economc / market-based models have been ntroduced n the lterature, where users, external schedulers, and local schedulers negotate to optmze ther objectves. Grd schedulng s bascally responsble for resource dscovery, resource selecton and job assgnment over decentralzed heterogeneous systems, n whch resources belong to multple admnstratve domans. Job to resource mappng task by a grd scheduler s an NP-Complete problem by Garey and Johnson

3 21 (1979). Ths s because, resource nformaton such as CPU usage, the number of CPU and memory avalablty of a sngle computer scheduler are easy to get but a grd envronment s a collecton of dynamc resources that are sharng and dstrbutng n nature. Besdes ths, classfyng job accordng to ts characterstcs s another challengng task n a grd envronment. 8.2 RELATED WORKS A few researchers have attempted to solve grd schedulng problem by applyng the soft computng technques. The Genetc Algorthm was developed for task schedulng by Andrew and Thomas (25) and they proposed ther own encodng scheme. Ths scheduler operates n a grd envronment wth dynamcally changng resources and adapts tself to varable system resources. It also operates n batch fashon and utlzes a genetc algorthm to mnmze the total job completon tme. The authors compared ther scheduler wth sx other standard schedulers; three of them belong to batch-mode and the remanng three, mmedate-mode. Another GA based scheduler was developed for effcently allocatng jobs to resources n a grd system by Javer Carretero et al (27). They presented an extensve study on the usefulness of the GA for desgnng ancent grd schedulers, where makespan and flowtme are mnmzed ether n herarchcal or smultaneous optmzaton mode. Ther scheduler can be used to dynamcally schedule jobs whch arrve n the grd system by runnng t n batch mode over a short tme. The optmal schedulng algorthm was developed for task schedulng by Abraham et al (2). They compared t wth three other natural heurstcs, namely, the Genetc Algorthm (GA), Smulated Annealng (SA) and Tabu Search (TS). They further demonstrated that the hybrdzed

4 211 usage of the above algorthms can be appled to a computatonal grd envronment for job schedulng. A Genetc Algorthm scheduler was developed for mult-processor task schedulng by Rcardo et al (1999). They proposed a hybrd approach, where a genetc algorthm can be mproved wth the ntroducton of some knowledge about the past performance of the schedulng problem, represented by the use of a lst heurstcs n the crossover and mutaton genetc operatons. A Genetc Algorthm was developed for multprocessor schedulng by Edwn et al (1994). They developed a GA-based scheduler to solve the multprocessor schedulng problem. A parallel hybrd genetc algorthm was developed to solve a knd of non-dentcal parallel machne schedulng problems by Jaquan Gao (25). He explaned non-dentcal parallel machne schedulng problems for mnmzng the makespan, suggested a parallel hybrd genetc algorthm and mplemented t under the MPI envronment. A hybrd genetc algorthm was developed for flow shop schedulng by Jngjng Wua et al (27). They explaned the concept of hybrd genetc algorthms whch proved to facltate the entre space search, but lacked fne-tunng capablty for obtanng the global optmum. Ths hybrd genetc algorthm, developed for optmzaton and schedulng problems, nvolves more than one crteron and requred mult crtera analyss. A hybrd genetc algorthm was developed to mnmze the makespan by Ln Lu and Yugeng X (26). They proposed a hybrd genetc algorthm for the job shop schedulng problem. They used chromosome representaton based on random keys. They developed the scheduler to mnmze the makespan. Another hybrd genetc algorthm was developed for schedulng by Kamrul Hasan and Ruhul Sarker (27). They presented a Hybrd Genetc Algorthm (HGA) that ncludes a heurstc job orderng wth a Genetc Algorthm. A Hybrd Genetc Algorthm was developed for task

5 212 schedulng by Y-Wen Zhong et al (24). They used a dfferent method to encode a chromosome soluton and used the greedy strategy to mprove the ftness of the ndvduals based on the Lamarckan theory of evoluton. Ther smulaton results are compared wth those of the typcal genetc algorthm and typcal lst heurstc algorthm. From the lterature survey, we conclude that all the researchers developed the GA or Hybrd GA based schedulers to mnmze the makespan for the batch mode. Our scheduler employs applcable soft computng components to solve dfferent sub-tasks n grd schedulng. We have used the K-mean clusterng algorthm for job classfcaton subtask, Fuzzy C-mean clusterng for groupng resources and the GA for selecton of the most sutable resource for the gven task. The man goal of our scheduler s to mnmze the makespan, job watng tme n the job queue, maxmze the throughput and effectve utlzaton of avalable resources n the dynamc envronment. In ths thess, a new knd of dynamc resource allocaton algorthm named ISA s proposed that wll classfy the jobs accordng to the computng requrements; wll classfy the avalable resources accordng to the resource type n the grd and map jobs to the best sutable resources to obtan a superor result. 8.3 CONCEPTUAL MODEL Extended Genetc Algorthm Genetc Algorthms (GA) are a class of stochastc search algorthms whch are based on bologcal evoluton Jann et al (1999). Genetc Algorthms combne the explotaton of past results wth the exploraton of new areas of search space, by usng the survval of the fttest technques combned wth a structure of randomzed nformaton exchange; a GA can mmc some of the

6 213 nnovatve ntellgence of human search. A generaton s a collecton of artfcal creatures (strngs). In every new generaton a set of strngs s created usng nformaton from the prevous tmes. Occasonally, a new part s tred for good measure. GAs s randomzed, but they are not smple random walks. They effcently explot hstorcal nformaton to speculate on new search ponts wth expected mprovement. The approach used n ths work generates a set of ntal schedulng, evaluates the schedulng to obtan a measure of ftness, selects the most approprate and combnes them together usng operators (crossover and mutaton) to formulate a new set of solutons. The basc type of GA, known as the Smple GA (SGA), uses a populaton of bnary strngs, sngle pont crossover and proportonal selecton Jann et al (1999). Some of the modfcatons, such as, new type of crossover operator called Cut and Paste Cross over (CPX) to the SGA s ncorporated n Extended GA and t wll reduce the ftness value of the chromosome sets; these are adopted n our work descrbed n chapter 7. (a) Intal Populaton The populaton sze depends on the nature of the problem. Tradtonally, the populaton s generated randomly, coverng the entre range of possble solutons. The selecton of a new populaton wth respect to the probablty dstrbuton s based on ftness values, and recombnes the chromosomes n the new populaton by mutaton and crossover operators. After some number of generatons, no further mprovement s observed and the best chromosome represents an optmal soluton. The selecton process, e, selecton of a new populaton wth respect to the probablty dstrbuton based on ftness value s represented n the followng steps:

7 Calculate the ftness value eval(v ) for each chromosome v (=1,ps) 2. Fnd the total ftness of the populaton F ps 1 eval( v ) (8.1) 3. Calculate the probablty of a selecton chromosome v (=1,ps); ps eval( v ) / F p s for each (8.2) 4. Calculate a cumulatve probablty v (=1,ps) p cum for each chromosome p cum p j1 j s (8.3) The selecton process s mplemented ps tmes: each tme, we select a sngle chromosome for a new populaton n the followng steps: Generate a random (float) number r from the range [,1]. 1 If r p cum, then select the frst chromosome (v ); otherwse, select the -th chromosome v (2 ps) such that p r p. 1 cum cum Some chromosomes would be selected more than once, the best chromosome wll get more copes; the average ones wll reman, and the worst de off.

8 215 (b) Crossover The crossover operators are the most mportant ngredents of any evolutonary algorthm. Indeed, by selectng ndvduals from the parental generaton and nterchangng ther genes, new ndvduals (descendants) are obtaned. The am s to obtan descendants of better qualty that wll feed the next generaton and enable the search to explore new regons of soluton space not yet explored. There exst many types of crossover operators explored n the evolutonary computng lterature, whch depend on chromosome representaton. We used our prevous work cross over operator represented n chapter 7, whch gves the best ftness value to the chromosome and always mantans the chromosome length n all generatons as the same Clusterng algorthms A cluster s a collecton of objects whch are smlar to one another and dssmlar to objects whch belong to other clusters. In ISA, the Clusterng algorthm s used to reduce the tme for the selecton of a sutable cluster of resources for the partcular group of jobs. We used the K-mean clusterng technque whch s the exclusve clusterng algorthm for formng job groups, and the Fuzzy C-mean clusterng algorthm, whch s the overlappng clusterng algorthm for creatng resource clusters. (a) K-mean clusterng algorthm K-mean s one of the smplest unsupervsed learnng algorthms that solve the well known clusterng problems. The procedure follows a smple and easy way to classfy a gven data set through a certan number of clusters (assume k clusters) fxed a pror.

9 216 Ths algorthm ams at mnmzng an objectve functon; n ths case a squared error functon s used. The objectve functon k n ( j) 2 j (8.4) j1 1 J x c where x ( j) j 2 c s a chosen dstance measure between a data pont x (j) and the cluster centre c j, s an ndcator of the dstance of the n data ponts from ther respectve cluster centres. The algorthm s composed of the followng steps: Step 1: Place K ponts n the space represented by the objects that are beng clustered. These ponts represent the ntal group centrods. Step 2: Assgn each object to the group that has the closest centrod. Step 3: When all the objects have been assgned, recalculate the postons of K centrods. Step 4: Repeat Steps 2 and 3 untl the centrods no longer move. Ths produces a separaton of the objects nto groups from whch the metrc to be mnmzed can be calculated. It can be proved that the procedure wll always termnate, the K-means algorthm does not necessarly fnd the most optmal confguraton, correspondng to the global objectve functon mnmum. The algorthm s also sgnfcantly senstve to the ntal randomly selected cluster centres. The K-means algorthm can be run multple tmes to reduce ths effect.

10 217 (b) Fuzzy C- Mean clusterng algorthm Fuzzy C-Means (FCM) s a method of clusterng whch allows one pece of data to belong to two or more clusters. Ths method s frequently used n pattern recognton. It s based on the mnmzaton of the followng objectve functon: J m N C j1 1 u m j x c j 2, 1 m (8.5) where m s any real number greater than 1, u j s the degree of membershp of x n the cluster j, x s the th of d-dmensonal measured data, c j s the d-dmenson center of the cluster, and * s any norm expressng the smlarty between any measured data and the center. The algorthm s composed of the followng steps: Step 1: Intalze U=[u j ] matrx, U() Step 2: At k-step: calculate the center s vectors C(k)=[c j ] wth U(k) c j N 1 N u 1 m j u. x m j (8.6) Step 3: Update U(k), U(k+1) u j C k 1 1 x c j x c k 2 m1 (8.7) Step 4: If U(k+1) - U(k) < then STOP; otherwse, return to step 2.

11 PROPOSED ARCHITECTURE AND ALGORITHM (a) Archtecture The proposed dynamc resource allocaton algorthm called ISA archtecture s shown n Fgure 8.1. The resources present n the grd envronment are heterogeneous. The avalable network resources n the grd envronment may vary over tme. Jobs are ndvsble, ndependent of all other jobs, arrve randomly over tme and have to be processed by the most approprate resources n the grd. Job Manager Resource Manager Job Pool Grd Informaton Servce Job Groupng usng K-Mean Cluster algorthm Resource classfcaton usng Fuzzy C-Means Algorthm Scheduler Extended Genetc Algorthm Fgure 8.1 Archtecture of the Intellgent Schedulng Algorthm Two man components of the ISA are: the Job Manager and the Resource Manager. The job manager s responsble for collectng all the ncomng jobs and group them based on the defned characterstcs such as job length and computng power, needed to run on machne usng the K-mean clusterng algorthm and the jobs n each cluster are mantaned n separate watng queues. The resource manager montors and mantans all the avalable resources n such a way that the most approprate resource can be selected for any watng job. The resource manager uses the Fuzzy C-mean

12 219 clusterng algorthm to classfy nto dfferent clusters to meet the requrements of varous types of watng jobs. Now, the grd scheduler selects the group of resource clusters that meet the requrements for runnng any gven job and then selects the best resource cluster from the group for that job. Fnally, we apply the Extended Genetc Algorthm to select the best resource for the jobs to schedule. The ISA uses the followng steps to schedule the jobs by selectng the approprate resource for effcent schedulng. (b) Proposed algorthm () K-mean job clusterng th job. Step 1: Let J be the total number of jobs and JL be the length of the J = {j1, j2, j3.., J m }. (8.8) Step 2: In order to dvde the jobs nto three groups, we set the threshold value T by, T mean(jl) (8.9) Step 3: Place 3 ponts n the space represented by the jobs that are beng grouped. These ponts represent the ntal Job Group centrods (JG) based on the followng condtons. add J to Group1 JG add J to Group2 add J togroup3 ff JL T ff JL T ff JL T (8.1) Step 4: Repeat step 2 n each group to fnd new centrod

13 22 Step 5: Use the k-mean objectve functon for the next generaton. J 3 m j1 1 ( j) J c j 2 (8.11) () Fuzzy C-mean resource clusterng Let X be the total number of resource set. X = {x 1,x 2,.,x n} (8.12) To defne the membershp functon, frst calculate the Turn Around Tme (TAT) of every resource n the resource set. The Turn Around Tme of each resource s defned as: TAT k =T proc +T wat + T comm where k=1,2,.n (8.13) T proc = Processng tme, T wat = Watng tme, T comm = Communcaton tme follows: The degree of membershp s defned n the frst teraton as Let u k be the membershp functon u k mn( TAT ) such that < u k 1 (8.14) TAT k n k1 mn( TAT ) 1 TATk (8.15)

14 221 Let J m (p) be the Fuzzy C-mean objectve functon, defned as J m N C ( p) [ uk ] k 1 1 m x k c 2, 1 m (8.16) where m s any real number greater than 1, u k s the degree of membershp of x k n the cluster, x k s the k th of d-dmensonal measured data, c s the d- dmenson center of the cluster, and * s any norm expressng the smlarty between any measured data and the center. Fuzzy parttonng s carred out through an teratve optmzaton of the objectve functon shown above, wth the update of membershp u k and the cluster centers c by: u next k C 1 x x k k 1 c c 2 m1, c j N 1 N u 1 m k u. x m k k ( 1) Ths teraton wll stop when max k k k u u, where s a termnaton crteron between and 1, where as s the teraton steps. Ths procedure converges to a local mnmum or saddle pont of J m (p). () Schedule the job and resource usng EGA The jobs n the job groups wll search for the best resource cluster wth the help of ISA and fnd the best resource wthn the cluster usng the Extended Genetc Algorthm. The Expected Tme to Compute (ETC) matrx ETC (nb_jobs nb_machnes), n whch the component ETC[][j] s the expected executon tme of job n machne j. ETC[][j]=(load of job t) / ( computng speed of machne m)

15 222 To formulate the problem, we consder user jobs J= {j 1,j 2,j 3.,j } on heterogeneous resources R={r 1,r 2,r 3,.,r j } wth an objectve of mnmzng the completon tme and maxmzng the utlzaton of resources. The ftness of each ndvdual n the populaton s calculated wth the help of makespan and flow tme. The ftness value s calculated as: Ftness= λ*makespan+(1 λ)*mean flowtme where λ s a mnmzng factor. Here, we set λ=.75. The completon tme of a job s the addton of watng tme of a partcular job and the ETC value of that job at a partcular machne. C[] = W[]+ETC[][j]. =job_d, j=machne_d where C[] = Completon tme of job, W[] = Watng tme of job Makespan =max{completon tme of all job } The smplest rule to mnmze makespan s to schedule the Longest Job on the Fastest Resource (LJFR). Flow tme = completon tme w[] Mean flowtme= Flowtme / Number of jobs (v) Pseudo code for Intellgent Schedulng Algorthm ISA ( ) { Generate ntal populaton of ndvdual jobs n the job cluster wth ndvdual resources n the resource cluster;

16 223 Evaluate ndvdual jobs n the job cluster and resources n the resource cluster, accordng to the ftness functon; Whle stoppng condton s satsfed. { Count from 1 to amount generaton; Select two parents from ntal populaton (Populaton Parent1 and Parent2); Crossover (Parent1 and Parent2) Chld; Mutaton (Parent p, Parent q, Chld); Ftness (Chld c, Best Chromosome bc); Improvement (Chld c); Replace (Chromosome chrom, Chld c); Schedulng (best chromosome); } } return set of the best chromosome n the populaton for job schedulng. 8.5 PERFORMANCE EVALUATION Expermental setup GrdSm toolkt s used to smulate a grd envronment wth 5 concurrent users support, who submt jobs to the job brokers and ranges from 25 to 15. Grds wth resources as small as 2 nodes to as large as 5 resources are generated. We smulated the grd wth heterogeneous resources wth dfferent MIPS ratngs and each resource wth dfferent Processng Elements (PEs). The cost of usng a resource s kept the same for all the users. The jobs are also smulated wth varyng QoS requrements. Jobs wth average PE requrements of 4 wth 1-5% varatons are consdered and all the jobs are submtted wthn 2 seconds of smulaton start tme. In ths experment, one of the assumptons s that each of the jobs s allowed to run n each node by usng space-sharng mechansm.

17 224 The GrdSm smulator s used to mplement ths ntellgent dynamc resource allocaton algorthm called ISA for dynamc resource allocaton n grd, and dfferent experments are conducted to evaluate the performance of ISA wth respect to other resource allocaton technques for dfferent performance crtera. In the proposed ISA, we can select the best ndvdual resource from the cluster after the 15 th generaton from an ntal populaton. There are a number of crossover operators that are avalable for generatng a new canddate, and we consdered the crossover operator Cut and Paste whch gves the best behavor after evaluatng ther relatve performance. ISA s compared wth Fuzzy C-mean genetc algorthm Scheduler (FCS), Effcent Genetc Algorthm (EGA), the Shortest Job Frst (SJF) and the Frst Come Frst Served (FCFS) resource allocaton methods and experments are conducted usng dfferent metrcs: makespan, average watng tme of job n job pool, total cost for grd resource utlzaton, and servce throughput by varyng the batch sze for the above mentoned resource allocaton algorthms Results and Observatons (a) Makespan analyss Frst, we conducted an experment to evaluate the relatve performance of dfferent resource allocaton technques by consderng makespan tme for varyng batch and populaton szes. The makespan values of the best solutons throughout the optmzaton run were recorded and the averages were calculated from the dfferent trals. In a grd envronment, the man emphass was to allocate the job and resource as fast as possble by usng approprate resource allocaton algorthm. The followng Fgures 8.2 to 8.5 shows the makespan tme of jobs run n each node of grd envronment.

18 225 These developments ndcate the fastest of each node by usng our resource allocaton algorthm ISA. 6 P opula ton S z e = 2 Makes pan n m s B a tch S z e Fgure 8.2 Batch Sze Vs Makespan Populaton sze=2 6 5 P opula ton S z e = 4 Makes pan n ms B atc h S z e Fgure 8.3 Batch Sze Vs Makespan Populaton sze=4

19 226 Fgures 8.2 and 8.3 show the bar charts for the experments of batch sze versus makespan for selected fve dfferent resource allocaton mechansms where the populaton sze s fxed as 2 and 4. The makespan s reduced for ISA compared wth other algorthms. Fgures 8.4 and 8.5 show the bar chart for the experments of populaton sze versus makespan for dfferent resource allocaton mechansm and fxed the batch sze as 75 and B a tc h S z e = 75 Makes pan n m s P opula ton S z e Fgure 8.4 Populaton Sze Vs Makespan Batchsze=75

20 227 B a tc h S z e = 15 Makes pan n m s P opula ton S z e Fgure 8.5 Populaton Sze Vs Makespan Batchsze=15 From the above mentoned bar charts, t s evdent that the dynamc resource allocaton algorthm ISA provded the least makespan tme when compared to others. Ths s due to the selecton of the best resource for the specfc job requrement from the best selected cluster wth mnmal communcaton delay, maxmum CPU capacty and memory to meet the ndvdual job s requrement. In Fgures 8.4 and 8.5, the batch sze s fxed as 75 and 15, the populaton sze s vared and the makespan s measured. Ths shows that whle the populaton sze ncreases, the makespan s mnmzed when compared to other schedulers. (b) Average watng tme analyss Fgures 8.6 to 8.9, show the watng tme for each job n Grd envronment, and the comparsons of watng tme of dfferent resource allocaton algorthms (FCFS, SJF, EGA, and FCS). It can be seen from the fgures that our resource allocaton algorthm ISA allocates the jobs to

21 228 resources much faster than the tradtonal algorthms and reduces the average watng tme of jobs n job queue. 2 P opula ton S z e = 2 A v g W a tn g T m e n m s B atc h S z e Fgure 8.6 Batch sze Vs Average watng Tme Populaton sze=2 Fgures 8.6 and 8.7 show the average watng tme analyss for selected fve resource allocaton algorthms whch we have used for our algorthm comparson. In ths analyss, we vared the batch szes from 25 to 15 and calculated the average watng tme for dfferent populaton szes lke 2 and 4. Fgures 8.8 and 8.9 show the average watng tme analyss for dfferent resource allocaton algorthms. In ths experment, we vared the populaton sze from 1 to 5 and average watng tme s calculated for dfferent batch szes of 75 and 15.

22 229 P opula ton S z e = 4 2 A vg Watn g T m e n m s B a tc h S z e Fgure 8.7 Batch sze Vs Average watng Tme Populaton sze=4 2 B a tc h S z e = 75 A v g Watn g T m e n m s P opula ton S z e Fgure 8.8 Populaton sze Vs Average watng Tme Batchsze=75 Fgures 8.6 to 8.9 show the average watng tme performance evaluaton of dynamc resource allocaton algorthm ISA wth other resource allocaton algorthms. In the ISA, we mantaned a dfferent job cluster n the job pool based on ts characterstcs.

23 23 B a tc h S z e = 15 2 A vg Watn g T m e n m s P opula ton S z e Fgure 8.9 Populaton sze Vs Average watng Tme Batch sze=15 The ISA chooses the best resource from the resource clusters for the approprate job from the job cluster. So, the average watng tme of the wated jobs n the job pool s mnmzed when varyng the batch sze. In Fgure 8.8 and 8.9, we fxed the batch sze as 75 and 15, and vared the populaton sze. Ths also shows that the average watng tme of the job n the job pool s mnmzed compared to other algorthms. (c) Total cost analyss Fgures 8.1 and 8.11 show the cost analyss of dfferent resource allocaton algorthms compared to our proposed resource allocaton algorthm ISA. The cost analyss shows the batch sze versus total cost for the fxed populaton sze of 2 and 4.

24 231 P opula ton S z e = 2 2 T o a ta l C o s t B a tc h S z e Fgure 8.1 Batch sze Vs Total Cost Populaton sze=2 2 P opula ton S z e = 4 T otal C os t Ba tc h S z e Fgure 8.11 Batch sze Vs Total Cost Populaton sze=4

25 232 B atc h S z e = 75 2 T o tal C o s t P opula ton S z e Fgure 8.12 Populaton sze Vs Total Cost Batch sze=75 2 B a tch S z e = 15 Total C os t P opula ton S z e Fgure 8.13 Populaton sze Vs Total Cost Batch sze=15 Fgures 8.1 to 8.13 show the cost based comparatve study of the ISA wth other resource allocaton algorthms. In the grd envronment, resources are not avalable free of cost. So, grd users need to pay and use the partcular resources for runnng ther jobs based on how much tme the jobs

26 233 run on t. In ISA, we chose the best resource for the specfc jobs based on ther CPU capacty and memory requrement. So, the executon tme of jobs on selected resources of the resource pool s mnmzed. Hence, the overall resource utlzaton cost s maxmzed. In Fgure 8.12 and 8.13, we fxed the batch sze as 75 and 15 and vared the populaton sze from 1 to 5 n the resource pool. Ths also shows that the cost for the ISA s reduced compared to other resource allocaton algorthms. (d) Servce throughput The followng Fgures 8.14 to 8.17 show the servce throughput analyss for all resource allocaton algorthms. Here, we measured the servce throughput n terms of resource utlzaton. P opula ton S z e = 2 S ervc e T h ro u g h p u t B a tch S z e Fgure 8.14 Batch sze Vs Servce Throughput Populaton sze=2

27 234 P opula ton S z e = 4 S ervc e T hroug hput Ba tc h S z e Fgure 8.15 Batch sze Vs Servce Throughput Populaton sze=4 The proposed dynamc resource allocaton algorthm ISA can dynamcally schedule the heterogeneous jobs on to heterogeneous resources n a grd envronment. The expermental result shows that the scheduler maxmzes the throughput by effcently schedulng all the job requests from the grd users. B atch S z e = 75 S ervc e T hroug hput P opulaton S z e Fgure 8.16 Populaton sze Vs Servce Throughput Batchsze=75

28 235 Ba tc h S z e = 15 S ervc e T hroug hput P opulaton S z e Fgure 8.17 Populaton sze Vs Servce Throughput Batch sze=15 Fgures 8.14 to 8.17 show the servce throughput analyss of the ISA wth other resource allocaton algorthms. In ISA, the jobs are scheduled by the best performance resources whch are avalable n the resource clusters. So, the servce throughput,.e, the number of jobs completed by the partcular resource per second s maxmzed. We analyzed the performance of the ISA by fxng the batch sze and varyng the populaton sze and ths s shown n Fgures 8.14 and We vared the populaton sze from 1 to 5 and fxed the batch sze as 75 and 15, whch are shown n Fgures 8.16 and 8.17 and the servce throughput s measured. Ths also shows that the proposed ISA servce throughput s ncreased compared to other resource allocaton algorthms. 8.6 SUMMARY OF CONTRIBUTIONS We have studed the dynamc resource allocaton algorthm for a grd envronment as a combnatoral predcton and optmzaton, based on k- mean, Fuzzy C-mean clusterng algorthm and GA technques. Our approach,

29 236 the ISA, automatcally generates batch mode schedulng systems for a heterogeneous envronment, by defnng the objectve functon. We used batch mode jobs for smulatons wth workload traces from the exstng systems. The expermental result shows that ISA mnmzes the makespan, watng tme, and cost, and maxmzes the servce throughput. Our ISA resource allocaton algorthm maps the jobs and resources n a better way and produces the best results when compared to the tradtonal schedulng algorthms such as FCFS and SJF. The ISA provdes near-optmal resource selecton n the resource cluster and s well adapted to heterogeneous resources. Extended Genetc Algorthms are powerful, but usually suffer from longer schedulng tme, whch s reduced n our algorthm due to ftness evaluaton, hybrdzng wth K-mean, Fuzzy C-mean clusterng technques whch are ncorporated n the ISA.