Improved Ant Colony Optimization for Grid Scheduling

Transcription

1 Improved Ant Colony Optimiztion for Grid Scheduling D.Mruthnygm MCA Deprtment, Gnnmni Collge of Technology, Nmkkl, Tmilndu, Indi Dr. R.Um Rni Deprtment of Computer Science, Sri Srd College for Women, Slem,Tmilndu, Indi ABSTRACT: This pper focuses on pplying one of the rpidly growing non-deterministic optimiztion lgorithms, the nt colony lgorithm in Grid computing. It is growing rpidly in the distributed heterogeneous systems for utilizing nd shring lrge-scle resources to solve complex scientific problems. Scheduling is the most recent topic used to chieve high performnce in grid environments with severl conflicting objectives. Within this pper, methods hve been developed nd pplied for scheduling techniques in grid computing. It ims to find suitble lloction of resources for ech job with the comprison of ACO nd proposed ACO. This pper, proposes n improved nt colony scheduling lgorithm combined with the concept of RASA. The first step for this to select set of computers nd network connection (switching, routers, Ethernet, Myrinet Etc.,) for n ppliction. A tsk lgorithm from RASA first estimtes the completion time of the tsks on ech of the vilble grid resources nd then pplies the Mx-min nd Min-min lgorithms. Alloction of resources to lrge number of jobs in grid computing environment presents more difficulty thn in network computtionl environments. This proposed lgorithm is evluted using the simulted execution times for grid environment. Before strting the grid scheduling, the expected execution time for ech tsk on ech mchine must be estimted nd represented by n Expected Time clcultion. The proposed scheduler lloctes dopt the system environment freely t runtime. This resource optimlly nd dptively in the sclble, dynmic nd distribute controlled environment. Conclude of this propose depending upon the performnce of the grid systems. Key words: Grid Computing, Job Scheduling, Heuristic Algorithm, opportunistic Lod Blncing, genetic Algorithms, Simulted Anneling lgorithms nd Mx-Min Ant system. I. INTRODUCTION In the pst few yers nture-inspired techniques hve been widely used for vrious optimiztion problems in design, plnning, scheduling, communiction, etc. One field, which is receiving incresing interest from severl reserchers, is the scheduling problem in Grid Computing Environment. A vriety of heuristic lgorithms hve been designed to schedule the jobs in computtionl Grid. The most commonly used lgorithms re OLB, MET, MCT, Min-Min nd Mx-Min. Reduction of mkespn (mesure the throughput of the grid system) is the prime im of grid scheduling. The ACO becomes very populr heuristic lgorithm to pply for solving grid scheduling problems. Ant colony lgorithm re incresingly being used in vrious rel-world pplictions such s the trvelling slesmn problem (TSP), the qudrtic ssignment problem (QAP), the Job Shop Scheduling Problem (JSP), telecommuniction routing nd lod blncing, etc. nd it hs been shown tht they perform well compred to other non-deterministic lgorithms such s genetic lgorithms(ga), simulted nneling(sa), etc. Scheduling is significnt reserch re in Grid Computing. Mny Scheduling lgorithms hve been designed nd pplied in computtionl grid. However, those lgorithms re not lwys ble to produce efficient scheduling in heterogeneity computing resources. Often, skilled personnel who understnd the requirements, the simultion model representtions nd solve complex computing scheduling problems efficiently nd physicl production issues fill this technicl gp. Scheduling lgorithm is technique of increse the throughput, Qulity of Service (QoS) nd reduces the cost during the job scheduling process for dynmiclly llocte the efficient processors. It needs to be done under multiple objectives nd constrints. Grid Computing enbles ggregtion nd shring of geogrphiclly distributed computtionl, dt nd other resources s single, unified resource for solving lrge scle compute nd dt intensive computing ppliction. Mngement of these resources is n importnt infrstructure in the Grid computing environment. It becomes complex s the resources re geogrphiclly distributed, heterogeneous in nture, owned by different individul or orgniztions with their own policies, hve different ccess nd cost models, nd hve dynmiclly vrying lods nd vilbility. The conventionl resource mngement schemes re bsed on reltively sttic model tht hve centrlized controller tht mnges jobs nd resources ccordingly. These mngement strtegies might work well in those scheduling regimes where resources nd tsks re reltively sttic nd dedicted. However, this fils to work efficiently in mny heterogeneous nd dynmic system domins like Grid where jobs need to be executed by computing resources, nd the requirement of these resources is difficult to predict. Due to highly heterogeneous nd complex computing environments, the 596

2 chnces of fults increses [1], nd therefore number of resources vilble to ny given ppliction highly fluctutes over time which reduces the performnce nd the efficiency. Therefore it is necessry to design mechnism for scheduling to improve the efficiency in such infrstructure. This work focuses on scheduling in Grid environment. Grid ppliction performnce is criticl in Grid computing environment. So to chieve high performnce there is need to understnd the fctors tht cn ffect the performnce of n ppliction due to Scheduling, which is one of most importnt fctors tht influence the overll performnce of ppliction. The significnce of this reserch lies in the potentil of the developed nt colony optimiztion lgorithms for job scheduling in Grid Computing nd the ssocited cost nd time. Further, despite the recent dvncements in the field of scheduling lgorithms, the following issues hve not been ddressed by other scheduling lgorithms Opportunistic Lod Blncing (OLB) keeps lmost ll mchines busy ll possible time. Yet the solution is not optiml. Minimum Execution Time (MET) cretes n imblnce condition mong the mchines. Allocting ll the smllest tsks to the sme fstest resources. Hence this solution is sttic. Minimum Completion Time (MCT) tkes clcultion of minimum completion time for job is longer. The drwbck of Min-Min is tht, too mny jobs re ssigned to single grid node. This leds to overloding mong jobs. When compre to Mx-Min, Min-Min is the best one becuse implementtion of Mx-Min is difficult. Most of the lgorithms hve some drwbcks compre thn nt colony optimiztion. All of the lgorithms use clssicl multi-objective technique: weighted sum pproch or single-objective function to obtin the optimum solution. Since there is more thn one optimum solution for multi-objective problem, previous lgorithms re not cpble of generting these conflicting optimum solutions (or trde-off solutions) in single run. If ll the trde-off solutions need to be found, these lgorithms must be run severl times with different prmeter vlues. Normlly these experiments re time consuming nd if they need to be run severl times, they re even more time consuming. Moreover, nt colony optimiztion long with Resource Awre Scheduling Algorithm (RASA) is nother importnt prt of this reserch re nd none of the lgorithms re ble to provide reduce the mkespn of jobs s well s to find out the optiml resource in Grid Computing. In this pper, the bove issues re ddressed. In ddition to these issues, the pper suggests some modifictions to nt colony optimiztion nd new Enhnced Ant Colony System bsed on RASA in Grid Scheduling for heterogeneous computing environment. The proposed lgorithm cn be improved using some form of operting systems, hrdwre, nd softwre, different storge cpcities, CPU speeds, network connectivity s nd technologies needs. In this method we first find the problem resources nd those totl execution times equl to the mkespn of the solution, nd ttempt to move or swp set of jobs from the problem processor to nother resource tht hs the minimum nd mximize of mkespn s compred with ll other resources. As RASA consist of the mx-min nd min-min lgorithms nd both hve no time consuming instructions. ACO nd RASA lgorithms incorporte in which intend to optimize workflow execution times on grids hve been presented here. The comprison of these lgorithms in computing time, pplictions nd resources scenrios hs lso been detiled. In dynmic grid environments this informtion tht cn be retrieved from mny servers includes operting system, processor type nd speed, the number of vilble CPUs nd softwre vilbility s well s their instlltion loctions. The distributed monitoring system is designed to trck nd forecst resource conditions. The n tsks cn obviously intercommunicte. A generl model should tke into considertion tht the communiction phse cn hppen t ny time with I/O phses. To overcome these difficulties our new lgorithm is proposed. II. MATERIALS & METHODS A. Locl vs. Globl The locl Scheduling discipline determines how the processes resident on single CPU re llocted nd executed; globl Scheduling policy uses informtion bout the system to llocte processes to multiple processors to optimize system wide performnce objective. Grid Scheduling flls into the Globl Scheduling [4]. B. Sttic vs. Dynmic The next level in the hierrchy (under the Globl Scheduling) is choice between sttic nd dynmic Scheduling. This choice indictes the time t which the Scheduling decisions re mde. In cse of sttic Scheduling, informtion regrding ll resources in the Grid s well s ll the tsks in n ppliction is ssumed to be vilble by the time the ppliction is scheduled. By contrst, in the cse of dynmic Scheduling, the bsic ide is to perform tsk lloction on the fly s the ppliction executes. C. Optiml vs. Suboptiml In the cse tht ll informtion regrding the stte of resources nd the jobs is known, n optiml ssignment could be mde bsed on some criterion function, such s minimum mkespn nd mximum resource utiliztion. But due to difficulty in Grid scenrios to mke resonble ssumptions which re usully required to prove the optimlity of n lgorithm, current reserch tries to find suboptiml solutions, which cn be further divided into the following two generl ctegories. 597

3 D. Approximte vs. Heuristic The pproximte lgorithms use forml computtionl models, but insted of serching the entire solution spce for n optiml solution, they re stisfied when solution tht is sufficiently "good" is found. The fctors, which govern their decision, re: Avilbility of function to evlute solution. The time required evluting solution. The bility to judge the vlue of n optiml solution ccording to some metric. Avilbility of mechnism for intelligently pruning the solution spce. Heuristic represents the clss of lgorithms, which mke the most relistic ssumptions bout priori knowledge concerning process nd system loding chrcteristics. It represents the solutions to the Scheduling problem, which cnnot give optiml nswers nd require the most resonble mount of cost nd other system resources to perform their function. The evlution of this kind of solution is usully bsed on experiments in the rel world or on simultion. Heuristic lgorithms re more dptive to the Grid scenrios [5]. E. Coopertive vs. Non-coopertive If distributed Scheduling lgorithm is dopted, the next issue tht should be considered is whether the nodes involved in job Scheduling re working coopertively or non-coopertively. In the non-coopertive cse, individul schedulers ct lone s utonomous entities nd rrive t decisions regrding their own optimum objects independent of the effects of the decision on the rest of system e.g. ppliction-level schedulers. In the coopertive cse, ech Grid Scheduler hs the responsibility to crry out its own portion of the Scheduling tsk, but ll schedulers re working towrd common system-wide gol. F. Distributed vs. Centrlized In dynmic Scheduling scenrios, the responsibility for mking globl Scheduling decisions my lie with one centrlized Scheduler, or be shred by multiple distributed schedulers. The centrlized strtegy hs the dvntge of ese of implementtion, but suffers from the lck of sclbility, fult tolernce nd the possibility of becoming performnce bottleneck [8]. G. Heuristic Scheduling Algorithms Mny lgorithms were designed for the scheduling of Met tsks in computtionl grids is reviewed in this section. One of the esiest techniques in grid scheduling is Opportunistic Lod Blncing (OLB). It workflow tsks in Grid environments re difficult becuse resource vilbility often chnges during workflow execution. Opportunistic Lod Blncing ttempts to improve the response time of user s submitted pplictions by ensuring mximl utiliztion of vilble resources. A typicl distributed system will hve number of interconnected resources who cn work independently or in coopertion with ech other. Ech resource hs owner worklod, which represents n mount of work to be performed nd every one my hve different processing cpbility. To minimize the time needed to perform ll tsks, the worklod hs to be evenly distributed over ll resources bsed on their processing speed. Heuristic Tsk Scheduling Algorithm in Grid computing environment bsed upon the predictive execution time of tsks. It obtins scheduling strtegy by employing men lod s heuristic informtion nd then selects both the mximum-lod nd the minimum-lod mchines. We ressign tsks between two mchines to rise the lod of the mchine with lower-lod nd reduce tht of the mchine with higher-lod under the men lod heuristic [6]. Ant Colony ws used to solve mny problems such s trveling slesmn problem, vehicle routing problem, grph coloring problem, etc. using ACO in Grid processor scheduling problem leds to finding n optiml or ner optiml solution fter resonble mount of time. A new heuristic function is introduced to led nts to select best processor for executing ech process. Also new fitness function is presented to evlute the fitness of solutions founded by ech itertion's nts. The pheromone updting rule is defined so tht prompt new itertion nts to follow the best solutions found in previous itertions. H. Opportunistic Lod Blncing (OLB) Lod blncing for huge no of system is importnt problems which hve to be solved in order to enble the efficient use of prllel computer systems. This problem cn be compred to problems rising in nturl work distribution processes like tht of scheduling ll ctivities (tsks) needed to construct lrge building. The essentil objective of lod blncing consists primrily in optimizing the verge response time of pplictions, which often mens mintining the worklod proportionlly equivlent on the whole resources of system. The lgorithm dopts n observtionl pproch nd exploits the ide of scheduling job to site tht will probbly run it fster. The opportunistic lgorithm tkes into ccount the dynmic chrcteristics of Grid environments without the need to probe the remote sites. We compred the performnce of the opportunistic lgorithm with different scheduling lgorithms in context of workflow execution running in rel Grid environment. The Opportunistic lgorithm benefits from the dynmic spects of the Grid environment. If site hppens to perform poorly, then the number of jobs ssigned to this site decreses. Similrly, if site process jobs quickly, then more jobs re scheduled to tht site. I. Minimum Execution Time (MET) The first vilble mchine is ssigned job with the smllest execution time. It neither considers the redy time nor the current lod of the mchine nd lso the vilbility of the resources t tht instnt of time is not tken into ccount. The resources in grid system hve different computing power. Allocting ll the smllest tsks to the sme fstest resource redundntly cretes n imblnce condition mong mchines. Hence this solution is sttic. Since the number of resources is much less thn 598

4 the number of tsks, the tsks need to be scheduled on the resources in certin order. Mny of the btch mode lgorithms intend to provide strtegy to order nd mp these prllel tsks on the resources, in order to complete the execution of these mny processor tsks t erliest time. They cn lso be pplied to optimize the execution time of workflow ppliction which consists of lot of independent prllel tsks with limited number of resources [3]. J. Minimum Completion Time (MCT) It uses the redy time of the mchine to clculte the job s completion time (redy time of the mchine + execution time of the job). It clcultes the completion time of current job in the erliest vilble mchines. From the list, the job with smllest completion time is selected nd is ssigned to tht mchine. This mens the ssigned job my hve higher execution time thn ny other job. This lgorithm clcultes the completion time of current unfinished job in only one erliest vilble node. But, the sme job my be completed in lesser time in some other mchine which is vilble t tht time. K. Min-Min It strts with set of unmpped tsks. The minimum completion time of ech job in the unmpped set is clculted. This lgorithm selects the tsk tht hs the overll minimum completion time nd ssigns it to the corresponding mchine. Then the mpped tsk is removed from the unmpped set [4]. The bove process is repeted until ll the tsks re mpped. When compred with MCT, Min-Min considers ll the unmpped tsks during their mpping decision. The smller mkespn cn be obtined when more tsks re ssigned to mchines tht complete them the erliest nd lso execute them the fstest. L. Mx-Min First it strts with set of unmpped tsks. The minimum completion time of ech job in the unmpped set is found. This lgorithm selects the tsk tht hs the overll mximum completion time from the minimum completion time vlue nd ssigns it to the corresponding mchine. The mpped tsk is removed from the unmpped set. The bove process is repeted until ll the tsks re mpped. On comprison with MCT, Mx-Min considers ll unmpped tsks during their mpping decision. The Mx-Min my produce blnced lod cross the mchine. When compre to Mx-Min, Min-Min is the best one. M. Ant Colony Optimiztion (ACO) Ant colony optimiztion (ACO) ws first introduced by Mrco Dorigo s his Ph.D. thesis nd ws used to solve the TSP problem [2]. ACO ws inspired by nt s behvior in finding the shortest pth between their nests to food source. Mny vrieties of nts deposit chemicl pheromone tril s they move bout their environment, nd they re lso ble to detect nd follow pheromone trils tht they my encounter. With time, s the mount of pheromone in the shortest pth between the nest nd food source increses, the number of nts ttrcted to the shortest pth lso increses. This cycle continues until most of the nts choose the shortest pth. As this work is coopertive one nd none of the nts could find the shortest pth seprtely, Mx-Min Ant System is bsed on the bsic ACO lgorithm but considers low nd upper bound vlues nd limits the pheromone rnge to be between these vlues. Defining those vlues, lets MMAS void nts to converge too soon in some rnges. In ACO one nt prticipte in ech itertion serch nd lso there is no pheromone evportion rule. Hence the nt lgorithm is suited for usge in Grid computing tsk scheduling.?? Figure-1: Ants try to move from one plce to nother.?? Figure-2: Ants re reinforcing the tril with its own pheromone.?? Figure-3: Ants choose the shortest pth. The bove figures 1, 2, nd 3 re shown the nt s behvior. In the grid environment, the lgorithm cn crry out new tsk scheduling by experience, depending on the result in the previous tsk scheduling. In the grid computing environment, this type of scheduling is very much helpful. Hence this pper proposes the nt lgorithm for tsk scheduling in Grid Computing. N. Resource Awre Scheduling Algorithm (RASA) The lgorithm builds mtrix C where Cij represents the completion time of the tsk Ti on the resource Rj. If the number of vilble resources is odd, the min-min strtegy is pplied to ssign the first tsk, otherwise the mx-min strtegy is pplied. The remining tsks re ssigned to their pproprite resources by one of the two strtegies, lterntively. For instnce, if the first tsk is ssigned to resource by the min-min strtegy, the next tsk will be ssigned by the mx-min strtegy. In the next 599

5 round the tsk ssignment begins with strtegy different from the lst round. For instnce if the first round begins with the mx-min strtegy, the second round will begin with the min-min strtegy. Jobs cn be frmed out to idle servers or even idle processors. Mny of these resources sit idle especilly during off business hours. Policies cn be in plces tht llow jobs to only go to servers tht re lightly loded or hve the pproprite mount of memory/processors chrcteristics for the prticulr ppliction. In this experimentl results show tht if the number of vilble resources is odd it is preferred to pply the min-min strtegy the first in the first round otherwise it is better to pply the mx-min strtegy the first. Alterntive exchnge of the min-min nd mx-min strtegies results in consecutive execution of smll nd lrge tsk on different resources nd hereby, the witing time of the smll tsks in Mx-Min lgorithm nd the witing time of the lrge tsks in min-min lgorithm re ignored. As RASA consist of the mx-min nd min-min lgorithms nd hve no time consuming instruction, the time complexity of RASA is O( ) where m is the number of resources nd n is the number of tsks (similr to Mx-Min nd Min-Min lgorithms) [1]. III. PROPOSED WORK The grid scheduler finds out the better resource for prticulr job nd submits tht job to the selected systems. The grid scheduler does not hve control over the resources nd lso on the submitted jobs. Any mchine in grid cn execute ny job, but the execution time differs. The resources re dynmic in nture. As compred with the expected execution time, the ctul time my vry when running the jobs in the llocted resources. So, the job plcement hs been determined ccording to the scheduling intension nd then dt move opertions hve been initited for necessry tsk to trnsfer relevnt mchines. Processors re climed fter ll job components hve been plced. In between the job plcement time nd job climing time the processors could be llocted to some other job nd if this hppens the job component cn be re-plced on nother tsk. The time between job plcement time nd job climing time is decresed by fixed mount fter every climing filure. A job cn fil for vrious resons, e.g., bdly configured or fulty nodes, hrdwre, nd softwre errors. During this scheduling filed, job nd counts the number of filures of the supposedly fulty node. When job fils previously set number of times then the job is removed nd not rescheduled [6]. If the error count of node exceeds fixed number then tht node is not considered by the co-lloctor nymore. The sttes t the bottom depict the hppy flow, i.e., the sttes job goes through if nothing fils. Different errors occur t vrious sttes of job. Depending on the kind of error, this system will chooses to end the job ltogether or to retry the job. The resubmit the job immeditely done too quickly from new tsk of mchine, due to filure cnnot clim its network. We lso wit for job to finish so it cn properly execute its clen up phse in which it removes the temporrily creted works. When job request with n incomplete or incorrect network specifiction is submitted the job will nturlly, not be resubmitted nd will exit immeditely. Once ll components re plced the climing phse strts. In contrst to other jobs this is done once for the whole job, i.e., the components do not get climed independently. The climing is done s job submission request nd cn fil for mny different resons, e.g., misspelled or non-existent executble nme, input jobs not present, locl resource mnger unvilble, etc. Some of these errors could be cused by the system itself nd could be locl phenomenon. In this cse the job cn be retried. When new component is successfully submitted, it is merged into the job component list of the mlleble job. The first step of resource discovery in job scheduling is to determine the set of resources tht the user submitting the job hs ccess to, in this regrd, computing over the grid is no different from remotely submitting job to single tsk: without uthoriztion to run on resource the job will not run. At the end of this step the user will hve list of mchines or resources to which he or she hs ccess. The min difference tht grid computing lends to this resources tht re not uthorized for use. Problem is sheer numbers [7]. It is now esier to get ccess to more resources, lthough eqully difficult to keep trck of them. Also, with current stge implementtions, user cn often find out the sttus of mny more mchines thn wht he or she resources tht re not uthorized for use. When user is performing scheduling t the Grid level, the most common solution to this problem is to simply hve list of ccount nmes, mchines, nd psswords written down somewhere nd kept secure. While the informtion is generlly vilble when needed, this method hs problems with fult tolernce nd sclbility for few stges, to proceed in resource discovery, the user must be ble to specify some miniml set of job requirements in order to further filter the set of fesible resources. The set of possible job requirements cn be very brod nd will vry significntly between jobs. It my include sttic detils (the operting system or hrdwre for which binry of the code is vilble, or the specific rchitecture for which the code is best suited) s well s dynmic detils (for exmple, minimum RAM requirement, connectivity needed, time spce needed). Some schedulers re t lest llowing for better corse-grined informtion bout the pplictions fulfills. The grid scheduler s im is to llocte the jobs to the vilble nodes. The best mtch must be found from the list of vilble jobs to the list of vilble resources. The selection is bsed on the prediction of the computing power of the resource. The nt bsed lgorithm is evluted using the simulted execution times for grid environment. Before strting the grid scheduling, the 600

6 expected execution time for ech tsk on ech mchine must be estimted nd represented by n ET mtrix. Ech row of ET mtrix consists of the estimted execution time for job on ech resource nd every column of the ET mtrix is the estimted execution time for prticulr resource of list of ll jobs in job pool. Here the lgorithm, rj denotes the expected time which resource Rj will become redy to execute tsk fter finishing the execution of ll tsks ssigned to it. First, the Cij entries re computed using the ETij (the estimted execution time of tsk Ti on resource Rj) nd rj vlues. For ech tsk Ti, the resource tht gives the erliest expected completion time is determined by scnning the ith row of the C mtrix (composed of the Cij vlues). The tsk Tk tht hs the minimum erliest expected completion time is determined nd then ssigned to the corresponding resource from ACO lgorithm. Cij=ETj+rj (1) Specifiction of the resources is ccording to resources speed (MIPS) nd bndwidth (Mbps), specifiction of the tsks depends on instructions nd dt (MIPS) completion time of the tsks on ech of the resources.tsks/resources R1, R2 nd R3 four tsks T1, T2, T3 nd T4 re in the met-tsk Mv nd the grid mnger is supposed to schedule ll the tsks within Mv on three resources R1, R2 nd R3. Tble 1 is shown the specifiction of the resources nd tsks. 1. procedure ACO 2. begin 3. Initilize the pheromone 4. While stopping criterion not stisfied do 5. Position ech nt in strting node 6. Repet 7. for ech nt do 8. Chose next node by pplying the stte trnsition rte 9. end for 10. until every nt hs build solution 11. Updte the pheromone 12. end while 13. end Figure-4: Pseudo code for Existing Ant colony Algorithm. TABLE 1: SPECIFICATION OF THE RESOURCES AND TASKS. Tsks R1 (redy time MIPS) R2 (redy time MIPS) R3 (redy time MIPS) R1 (Executed time MIPS) R2 (Executed time MIPS) R3 (Executed time MIPS) T T Job scheduling system is the most importnt prt of grid resource mngement system [9]. The scheduler receives the job request, nd chooses pproprite resource to run tht job. In this pper, the formultion of job scheduling is bsed on the expected time to compute (ETC) mtrix. Met-tsk is defined s collection of independent tsk (i.e. tsk doesn t require ny communiction with other tsks). Tsks derive mpping stticlly. For sttic mpping, the number of tsks, t nd the number of mchines, m is known priori. ETC (i, j) represents the estimted execution time for tsk ti on mchine mj. The expected completion time of the tsk ti on mchine mj is CT (ti, mj) = redy (i) + ETC(ti, mj) redy (i) is the mchine vilbility time, i.e. the time t which mchine mj completes ny previously ssigned tsks [10]. The new lgorithm is proposed nd compre with existing lgorithm lso presented here. It is strt from mechnism for defining the grid nodes s well s the input dt sources nd output dt loctions lod blncing scheme to improve the scling efficiency of the prllel computtion nd ctivity of ech node in the grid. To collection of prtil result sets from the nodes in the grid nd then bck to centrlized loction. In this method, we chieve the optionl dditionl nlysis from the collected results. The result of the lgorithm will hve four vlues (tsk, mchine, strting time, executed completion time). Then the new vlue of free(j) is the strting time plus ETij. A heuristic function is used to find out the best resource. Ŋ ij =1 / free (j) (2) Using the formul 3 the highest priority mchine is found which is free erlier. Here four nts re used. Ech nt strts from rndom resource nd tsk (they select ETij rndomly jth resource nd ith job). All the nts mintin seprte list. Whenever they select next tsk nd resource, they re dded into the list. At ech itertion, the nts clculte the new pheromone level of the elements of the solutions is chnged by pplying following updting rule Tij = 1 / Etij (3) The scheduling lgorithm is executed periodiclly. At the time of execution, it finds out the list of vilble resources (processors) in the grid environment, form the ET mtrix nd strt scheduling. When ll the scheduled jobs re disptched to the corresponding resources, the scheduler strts scheduling over the unscheduled tsk mtrix ET. This is gurnteed tht the mchines will be fully loded t mximum time. The Pij s vlue hs been modified to include the ETij is modified to the following eqution T3 T Pij = Tij Ŋ ij(1/etij) / Tij Ŋ ij(1/ ETij) (4) Furthermore, insted of dding ETij, execution time of the ith job by the jth mchine (predicted), in the clcultion of probbility Pij = Tij Ŋ ij / Tij Ŋ ij (5) 601

7 for ech tsks Ti nd resources Rj lloctions Compute pproximte Cij=Ej+rj to nt s resource llocte end for do until ll tsks in Mv re mpped for ech tsksti nd Rj if the number of resources is even then find the resource free times for ech tsk in Mv find the erliest completion time nd the resource tht obtins it find the tsk Tk with the minimum erliest completion time find the tsk Tk with the mximum erliest completion time ssign tsk Tk to the resource Rl tht gives the better completion time from min nd mx Choose plce p rndomly from set the resources Suitble for event e, ccording to probbilities end for for ech no of resource & tsks (nts) best of C nd Citertion best with Tmin nd Tmx end for end for end while Figure-5: Proposed Algorithm. This lgorithm [10] cn be improved using some form of operting systems, hrdwre, nd softwre, different storge cpcities, CPU speeds, network connectivity s nd technologies needs. In this method we first find the problem resources nd those totl execution times equl to the mkespn of the solution, nd ttempt to move or swp set of jobs from the problem processor to nother resource tht hs the minimum nd mximize of mkespn s compred with ll other resources [11]. After pplying the bove locl optimum technique, find out the problem resource reduce time gin, swp or move some of the jobs from the resource for relevnt jobs. The serch is performed on ech problem processor nd continues until there is no further improvement in the fitness vlue of the solution. Using the Mv re mpped model, the scheduling problems re number of independent jobs to be llocted to the vilble grid resources. Becuse of no preemptive scheduling, ech job hs to be processed completely in single mchine [12]. Number of mchines is vilble to prticipte in the lloction of tsks. The worklod of ech job the computing cpcity of ech resources (in MIPS), m- represents the redy time of the mchine fter completing the previously ssigned jobs of minimum erliest completion time find the tsk Tk with the mximum erliest completion time, where the executed mchines represents the n-number of jobs nd m-represents the number of mchines.[13]. IV. RESULTS & DISCUSSION The proposed lgorithm trget on grids if the number of vilble resources is odd, the min-min strtegy is pplied to ssign the first tsk, otherwise the mx-min strtegy is pplied. The remining tsks re ssigned to their pproprite resources by one of the two strtegies, lterntively. For instnce, if the first tsk is ssigned to resource by the min-min strtegy, the next tsk will be ssigned by the mx-min strtegy. Alterntive exchnge of the min-min nd mx-min strtegies results in consecutive execution of smll nd lrge tsk on different resources nd hereby, the witing time of the smll tsks in mx-min lgorithm nd the witing time of the lrge tsks in min-min lgorithm re ignored. As RASA consist of the mx-min nd min-min lgorithms nd both hve no time consuming instructions. ACO nd RASA lgorithms incorporte in which intend to optimize workflow execution times on grids hve been presented here. The comprison of these lgorithms in computing time, pplictions nd resources scenrios hs lso been detiled. In dynmic grid environments this informtion tht cn be retrieved from mny servers includes operting system, processor type nd speed, the number of vilble CPUs nd softwre vilbility s well s their instlltion loctions. The distributed monitoring system is designed to trck nd forecst resource conditions. The n tsks cn obviously intercommunicte. A generl model should tke into considertion tht the communiction phse cn hppen t ny time with I/O phses. To overcome these difficulties our new lgorithm is proposed. In this method four nts re used. The number of nts used is less thn or equl to the number of tsks. From ll the possible scheduling lists find the one hving minimum mkespn nd uses the corresponding scheduling list. Here three kinds of ET mtrices re formed, first one consists of currently scheduled jobs nd the next consists of jobs which hve rrived but not scheduled. The scheduling lgorithm is executed periodiclly. At the time of execution, it finds out the list of vilble resources (processors) in the grid environment, form the ET mtrix nd strt scheduling. When ll the scheduled jobs re disptched to the corresponding resources, the scheduler strts scheduling over the unscheduled tsk mtrix ET. This gurntees tht the mchines re fully loded t mximum time. M k e s p n Exiting ACO CTij Itertion Exiting ACO CTij Figure-6: The Completion time of mkespn for Exiting ACO Algorithm. 602

8 M k e s p n Figure-7: The Completion time of mkespn for proposed Algorithm M k e s p n Proposed ACO CTij Itertion Figure-8: Compre the Completion time of mkespns for Existing ACO &Proposed Algorithms.. Proposed ACO CTij Existing ACO CTij Proposed ACO CTij Itertion These executions minimize the overll completion time of the tsks by finding the most suitble resources to be llocted to the tsks. It should be noticed tht minimizing the overll completion time of the tsks does not necessrily result in the minimiztion of execution time of ech individul tsk. The completion time of mkespn for both ACO nd proposed lgorithms re illustrted in fig-6 nd fig-7 respectively. Tsk is ssigned to resource by the min-min strtegy; the next tsk will be ssigned by the mx-min strtegy. In the next round the tsk ssignment begins with strtegy different from the lst round. For instnce if the first round begins with the mx-min strtegy, the second round will begin with the min-min strtegy. Jobs cn be frmed out to idle servers or even idle processors. Mny of these resources sit idle especilly during off business hours. Fig-8 is shown the compre the completion times of mkespn of ACO s well s proposed lgorithm. Policies cn be in plces tht llow jobs to only go to servers tht re lightly loded or hve the pproprite mount of memory/processors chrcteristics for the prticulr ppliction. V. CONCLUSION This pper investigtes chosen job hd been llocted to the best selected nt of ech itertion. This process is repeted until ll jobs hve been scheduled nd complete solution hs been built. Ech nt in the colony builds solution, in this mnner in ech itertion the serching of proper resource lloction on ech processing jobs. This lgorithm cn find n optiml processor nd network for ech mchine to llocte job tht minimizes the trdiness time of job when the job is scheduled in the system. The proposed scheduling lgorithm is designed to chieve high throughput computing in grid environment. Min-min nd Mx-min lgorithms re pplicble in smll scle distributed systems. When the numbers of the lrge tsks re more thn the number of the tsks in met-tsk, the Min-min lgorithm cnnot schedule tsks, ppropritely nd the mkespn of the system gets reltively lrge. It will be unlike the Min-min lgorithm, the Mx-min lgorithm ttempts to chieve lod blncing with in resources by scheduling the lrge tsks prior to the smll ones. However, within computtionl grid environment high throughput is of gret enhncement of resource lloction ccording to (CPU, network nd operting system) system existing scheduling lgorithms in lrge scle distributed system s cost of the communiction nd mny other cses open problem in this re here we concentrte throughout mechnism of entire system needs. REFERENCES [1] Seed Prs nd Rez Entezri-Mleki RASA: A New Tsk Scheduling Algorithm in Grid Environment World Applied Sciences Journl 7 (Specil Issue of Computer & IT): , 2009,ISSN [2] K. Kously nd P. Blsubrmnie, To Improve Ant Algorithm s Grid Scheduling Using Locl Serch. ( VOL. 7, NO. 4, DECEMBER [3] Fngpeng Dong nd Selim G. Akl, "Scheduling Algorithms for Grid Computing:Stte of the Art nd Open Problems", School of Computing, Queen's UniversityKingston, Ontrio, Technicl Report No , Jnury [4]. L. Wng, H. J. Siegel, V. P. Roychowdhury, nd A. A. Mciejewski, "Tsk Mpping nd Scheduling in Heterogeneous Computing Environments Genetic-Algorithm-Bsed Approch," Journl of Prllel nd Distributed Computing, vol. 47, pp. 8-22, [12] Szjd, D., Lwson, B., nd Owen, J. Hrdening Functions for Lrge-Scle DistributedComputtions. IEEE Symposium on Security nd Privcy, 2003, pp Vnderbei, R. Liner Progrmming: Foundtions nd Extensions, Second Edition.Norwell: Kluwer, 2001, pp < 17 Mrch [5] J.Brevik, D.Nurmi, nd R.Wolski, Automtic Methods for Predicting Mchine Avilbility in Desktop Grid nd Peerto- Peer Systems, In Proceedings of CCGRID 04, pp , [6] A Heuristic Algorithm for Tsk Scheduling Bsed on Men Lod 1 Lin Ni1,2, Jinqun Zhng1,2, Chungng Yn1, Chngjun Jing1 1 Deprtment of Computer Science, Tongji University, Shnghi, , Chin. [7] P. Cremonesi nd C. Gennro. Integrted performnce models for SPMD pplictions nd MIMD rchitectures. IEEE Trns. on Prllel nd Distributed Systems, 13(12): ,

9 [8] E. Tsikkouri et l., Scheduling Workflows with Budget Constrints, In the CoreGRID Workshop on Integrted reserch in Grid Computing, S. Gorltch nd M. Dnelutto (Eds.), Technicl Report TR-05-22, University of Pis, Diprtimento Di Informtic, Pis, Itly, Nov , 2005, pges [9]. J. D. Ullmn, "NP-complete Scheduling Problems," Journl of Computer nd System Sciences, vol. 10, pp , [10] D.Mruthnygm nd Dr.R.Um Rni, Enhnced Ant Colony System bsed on RASA lgorithm in Grid Scheduling, (IJCSIT) Interntionl Journl of Computer Science nd Informtion Technologies, Vol. 2 (4), 2011, ,ISSN: [11]. L. Wng, H. J. Siegel, V. P. Roychowdhury, nd A. A. Mciejewski, "Tsk Mpping nd Scheduling in Heterogeneous Computing Environments Using Genetic-Algorithm-Bsed Approch," Journl of Prllel nd Distributed Computing, vol. 47, pp. 8-22, [13] E. Rosti, G. Serzzi, E. Smirni, nd M. S. Squillnte. Models of prllel pplictions with lrge computtion nd I/O requirements. IEEE Trns. on Softwre Engineering, 28: , Mrch 2002 Authors Profile D.Mruthnygm received his M.Phil, Degree from Bhrthidsn University, Trichy in the yer He hs received his M.C.A Degree from Mdrs University, Chenni in the yer He is working s Associte Professor in Mster of Computer Applictions Deprtment, Gnnmni College of Technology, Pchl, Nmkkl, Tmilndu, Indi. His res of interest include Computer Networks, Grid Computing nd Mobile Computing. Dr.R.Um Rni received her Ph.D., Degree from Periyr University, Slem in the yer She is rnk holder in M.C.A., from NIT, Trichy. She hs published round 40 ppers in reputed journls nd ntionl nd interntionl conferences. She hs received the best pper wrd from VIT, Vellore, Tmil Ndu in n interntionl conference. She hs done one MRP funded by UGC. She hs cted s resource person in vrious ntionl nd interntionl conferences. She is currently guiding 5 Ph.D., scholrs. She hs guided 20 M.Phil, scholrs nd currently guiding 4 M.Phil, Scholrs. Her res of interest include informtion security, dt mining, fuzzy logic nd mobile computing. 604