International Journal on Emerging Technologies 1(2): 48-56(2010) ISSN :

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1 e t Iteratioal Joural o Emergig Techologies (): 48-56(00) ISSN : Dyamic load balacig i distributed ad high performace parallel eterprise computig by embeddig MPI ad ope MP Sadip S. Chauha, Sadip B. Shah ad H.M. Rai Deptt. of Computer Sciece, Gujrat Ist. of Techical Studies, Himat Nagar (GJ) *Deptt. of Electrical Egg. NCC Israa (HR) ABSTRACT : Load balacig ivolves assigig task to each processor to achieve higher performace ad, miimizig the executio time of the applicatio. Although static load balacig ca solve may problems for most regular applicatios but, the trasiet exteral load due to multiple-users o a etwork of workstatios ecessitates a dyamic approach to load balacig. Experimets shows that differet schemes are best for differet applicatios uder varyig program ad system parameters. Therefore, dyamic load balacig schemes become essetial for achievig higher performace. I this study, a cluster-computig eviromet is employed as a computatioal platform. Clusters of SMP (Symmetric Multi-Processors) odes provide support for a wide rage of parallel programmig paradigms. I order to icrease the efficiecy of the system, a dyamic task schedulig algorithm is proposed. The techique is dyamic, adaptive ad, it uses divide ad coquer approach. The algorithm models the cluster as hyper-grids ad the balaces the load amog them. Recursively, the hyper-grids of k- dimesios are divided ito grids of dimesios k?, util the dimesio becomes. The, all the odes of the cluster are almost equally loaded. The optimum dimesio of the hyper-grid is chose i order to achieve the best performace. The simulatio result shows the effectiveess of the algorithm. Keywords : CC - Computatioal Cluster, MPI -Message Passig Iterface, Ope Mp - Ope Message Passig, PSLB - Positioal Scale Load Balacig, NOW - Network of Workstatios. IPC - Iter Process Commuicatio I. INTRODUCTION Moder Scietific computig problems i may domais, e.g., Computatioal Fluid Dyamics (CFD), VLSI simulatios, Ocea Modellig ad, may high performace ecryptio Algorithms ivolve large amout of data ad are based o the cocept of a large-scale spatial grid. A atural ad efficiet way to execute these types of applicatios is to follow the Sigle Program Multiple Data (SPMD) approach, i.e., to distribute the data o the spatial grid ito multiple processors, each of which is assiged with a partitio of the N-Dimesioal grid oto multiple processors, each of which is assiged with partitio of grid. Load balacig is a vital factor i achievig high parallel efficiecy especially o platforms with a large umber of processors. The amout of work assiged to each processor has to be determied such that turaroud time is miimized. For a parallel applicatio ruig o a large umber of processors, the turaroud time is defied as the maximum of all the times take by the idividual processors to complete the task. Static load balacig techiques are frequetly employed to distribute the task o the set of processors to obtai the miimum turaroud time for the applicatio. Total efficiecy of resource utilizatio (costatly usig 00% of all the processors) is rarely achieved, however, due to depedecies amog the work uits o the various processig odes. Exogeous factors could also affect the computatio. For istace, there may be other computatios ruig o the same system, or there may be o-egligible commuicatio time betwee the processig odes. Eve if load-balacig is used, exogeous factors could reder static load-balacig ieffective. I order to achieve effective adaptability, the computatio ought to employ dyamic load-balacig. That is, the computatio must be able to recofigure its processig odes while it rus. A. Computatioal cluster A computatioal cluster (CC) ca be defied as a set of idepedet odes or computers itercoected by a high-speed commuicatio etwork such as Fast or Gigabit Etheret []. The umber of the participatig processig elemets or odes ca rage from tes to hudreds. However, to fully ad effectively exploit ay CC platform, resource maagemet software must be provided to maage the complexity of differet physical architectures for the user. This complexity arises i maagig commuicatio, sychroizatio ad schedulig a large umber of tasks, i dealig with portability of libraries facilities used to parallelize/distribute user applicatios, processor speed, available memory, legth of the curret ru queue, percetage of idle time i the recet past, umber of recet etwork iterrupts, etc,. The schedulig of the submitted tasks to processig-odes is a major cocer with regard to performace ad effective use of ay CC. Although Graph Partitioig ad Heuristic methods provide fast but ofte sub-optimal solutios withi a acceptable time, where a optimal solutio caot be obtaied withi reasoable time. She ad Tsai[4] proposed a method where problem of load balacig by optimal task assigmet is viewed as a graph-matchig problem. The task is represeted as a

2 Chauha, Shah ad Rai 49 vertex ad the commuicatio betwee these modules represeted by edges. The weight associated with the vertices represets the commuicatio cost betwee two adjacet vertices of the task graph. The work doe by She ad Tsai uses a heuristic approach based i A* Algorithm [4]. The problem of fidig a optimal solutio to the schedulig problem is NP-complete [, 6, 8] where heuristic methods appear to be a suitable approach to solve this class of problems. B. Cotributio ad orgaizatio This paper presets a method of dyamic load balacig with MPI, OPEN MP [7, 9] which combies methodologies, graph-partitioig ad graph-matchig, to achieve maximum parallel efficiecy o for computig clusters. A locatio based scheme is proposed ad evaluated which is based o previous PSLB algorithm, a pure dyamic load balacig techique. The experimetal result of proposed techique shows that it is highly parallel ad efficiet. II. BASIC CONCEPTS OF LOAD BALANCING The load balacig strategies are classified o three parameters addressig iitiatio (seder or receiver), load balacer locatio (cetralized or distributed) ad decisio makig (local or global). The speed at which a NOW-based parallel applicatio ca be completed depeds o the computatio time of the slowest workstatio; efficiet load balacig ca clearly provide major performace beefits [8, 0]. A. The major categories for load-balacig algorithms are: (I) Static load balacig: Static(Compile-time) load balacig algorithms allocate the tasks of a parallel program to grid based o either the load at the time odes are allocated to some task, or based o a average load of cluster grid. I static schedulig, iformatio regardig tasks executio times ad odes resources is assumed to be kow beforehad. (II) Dyamic load balacig algorithms: Dyamic load balacig algorithms makes chages to the work distributio at ru-time amog cluster. This techique takes ito accout over-loaded ad uder loaded odes, with the assumptio that if the load amog all odes is balaced, the the overall executio time of the applicatio is miimized. It uses curret load iformatio whe makig load balacig distributio decisios. B. Issues to be cosidered for dyamic load balacig (I) Load estimatio policy: Determies how to estimate the workload of a particular ode of the system. (II) Process trasfer policy: Determies whether to execute a process locally or remotely. (III) State iformatio exchage policy: Determies how to exchage the system load iformatio amog the odes. (IV) Priority assigmet policy: Determies the priority of executio of local ad remote processes at a particular ode. (V) Migratio limitig policy: Determies the total umber of times a process, ca migrate from oe ode to aother. C. Load balacig strategies: There are three major load balacig strategies: (I) Seder-Iitiated vs. Receiver-Iitiated Strategies meas-who makes the load balacig decisio. (II) Global vs. Local Strategies meas-what iformatio is used to make the load balacig decisio. (III) Cetralized vs. Distributed Strategies meas- Where the load balacig decisio is made. III. PROBLEM STATEMENT A. Problem ituitio Developig a solutio for Load balacig ito a cluster computig eviromet which icurred a less overhead compare to the prior techiques that have bee proposed. The algorithm ot oly provides a perfect load balaced system at very reasoable time ad, reduces sigificat overhead, but also should miimize the cost of Iter-process commuicatio (IPC) [,, 6, ad 5]. The techique should also be a adaptive based o the curret chages i topology ad, offer high degree of parallelism ad, efficiet utilizatio of the system resources i geeral. B. Related work I this sectio we will look at some of the load balacig schemes which have bee proposed i the literature. A large umber of load balacig ad task assigmet techiques have bee proposed the classical example of this is the work doe by She ad Tsai which uses the well-kow A* algorithm to fid optimal task assigmet, a Positioal Sca Load Balacig algorithm (PSLB) [,7] which is origially the parallel versio of a actual A* algorithm, Optimal Load Balacig by Issac [4], Customized Dyamic Load Balacig for a Network of Workstatios by Mohammed Wei Li, ad may more techiques have bee proposed i the literature which motivates us to work further i this area of dyamic load balacig. Moreover, differet schemes are best for differet applicatios uder varyig program ad system parameters

3 50 Chauha, Shah ad Rai such as the umber of processors, data size, iteratio cost, commuicatio cost, etc., therefore, dyamic load balacig becomes essetial for good performace. C. Dyamic schedulig Predictig the Future: A commo approach take for load balacig o a etwork of workstatio is to predict future performace based o past iformatio. The mai cotributio of this paper is the methodology for automatic geeratio of etwork topology with dyamic load balacig. CHARM implemets a local distributed receiveriitiated scheme. If the work-load falls below a Threshold [, 8], the ode requests a eighbour with higher workload for more work. Much past work has explored the broad problem of dyamic load balacig. Various studies have examied sychroizatio, task migratio, ad cost for IPC [9], Processig Power, umber of CPU cycles, available memory ad, may other issues. However, the cosesus is that there is o oe silver bullet ad various techiques are best implemeted o a applicatio-specific basis. To cosider some state-of-the-art techiques, oe could apply optimistic techique to icrease parallel performace. IV. SYSTEM MODEL & HYPER-GRIDS A Computatioal Cluster (CC) is a collectio of idepedet processig-odes itercoected by a etwork. Each ode vi is autoomous, has full iformatio o its ow resources, ad maages its work load. Each ode vi has a processig power ti which represets the umber of work uits that ca be executed per uit of time. The etwork uses a packet switched protocol ad let w be the size i bits of a packet, which is costat. The etwork s flow bij, which is the effective data rate i bits per secod o the lik that coect the odes vi to vj. The tasks are idepedet ad ca be executed o ay ode regardless its iitial placemet. There are two parameters associated with each task (ti:) the umber of work uits (i terms of computatios) withi the task (ßi ), ad ) the umber of packets required to trasfer the task (µi). A. Hyper-Grid Usually, a computatioal cluster has a irregular topology. This topology ca be described by o-orieted graph G (V, E) [, 7], where V represets the cluster odes ad E the set of liks betwee odes. The first phase of this techique is to map the graph G (V, E) ito a multidimesioal grid, called hyper-grid. The resultig grid is usually icomplete, i the sese that some of the liks betwee eighbours ad/or odes are missig. The missig liks ad odes are called virtual liks ad virtual odes respectively. The secod phase is to recursively dividig the origial hyper-grid ito hyper-grids of smaller dimesios. The idea is to balace the load amog the hyper-grids dimesios startig from -dimesio. A -dimesioal grid (G) ca be defied as a set of -dimesioal parallel hyper-grids as follows: Gi = (Gi, Gi, Gi..., Gi pi) i =...() The hyper-grids of dimesio represet the odes alog oe dimesio (e.g. odes coected by bus) ad from the equatio oe ca deduce that the umber of odes of G is N = i Π pi therefore, we ca defie a hyper-grid recursively as follows : Defiitio : A -dimesioal hyper-grid is a set of parallel ( )-dimesioal hyper-grids. Zero-dimesioal hyper-grids are the odes of the system which are coected by liks with the followig properties: Liks are either pair-wise vertical or parallel (liks which lie o the same lie or are parallel lies), ad The legth of liks that coect direct eighbour odes is the uity ad it is costat. Ay ode of the system ca be represeted as Vi, i, , i, for clarity reaso, let deote by I the vector [I, I, , I]. The dyamic task schedulig techique itroduced i this paper has a phase that computes a oe dimesioal vector of loads of task hyper-grids. These loads are the balaced across the liear array of processor hypergrids. Defiitio 4. A hyper-grid load Wx is the umber of active tasks stored i odes that are withi hyper-grid of dimesio x, called x-hyper grid. This value is calculated by each processig-ode for each hyper-grid that itersects it. B. Task Schedulig ad allocatio Positioal Sca Load Balacig algorithm (PSLB) [], leads to a perfect load balaced system at a very reasoable time. Algorithm preserves the locality decompositio ad it is based o the parallel prefix operatio, or sca [4], which ca be defied as follows: Defiitio : The prefix sum operatio (+, A) takes the biary associative operator +, ad a ordered set of elemets A = {a0, a,..., a }, returs the ordered set {0, a0, (a0 + a), (a0 + a + a),..., (a0 + a... + a )} C. PSLB algorithm The basic PSLB algorithm applies to -dimesioal data grids. PSLB algorithm is a very powerful dyamic load

4 Chauha, Shah ad Rai 5 balacig algorithm, operatig at the fie grai level. The geeralizatio of the algorithm to -dimesioal data-grids is also itroduced i []. A brief descriptio of the PSLB algorithm for grids of oe dimesio (lie or bus) of system etworks is give i algorithm. (a) PSTS Algorithm I order to schedule more geeral applicatios (tasks) executig o irregular etwork topologies, we propose a techique based o PSLB, called the Positioal Sca Task Schedulig (PSTS). PSTS [] approaches the task schedulig by applyig the same techique as PSLB. PSTS uses the additive sca operatio i order to fid out the destiatio ode for each work uit withi each task. Firstly the algorithm idexes the work uits (ot the tasks), the uses the sca operator to collect iformatio about the load i the system ad processig powers, ad fially for each ode calculates locally the destiatio of each work uit. The key issue here is that, istead of cosiderig a work uit as a basic processig uit, a task, cosistig of may work uits, is cosidered as a basic elemet. I other words, a task is a o-divisible load however, the algorithm uses the work uits to decide whether a task has to be migrated or ot. START Idex the work uits Use SCAN operator to collect iformatio Broadcast the collected iformatio Calculate locally the destiatio of each work Perform the migratios of the work uits END Algorithm PSLB Algorithm - Let T = {t, t,..., tm}, represets the set of active tasks i the system, which cosists of odes {v, v,..., v}. The total load i the system is W = m βi...() i= 0 As the system is heterogeeous, each ode vi has differet processig power, ƒñi, ad is give as the umber of work uits ca be executed per uit of time. So, the total processig power of the system is π = m τi...() i= 0 I the algorithm, we utilize the ormalized quatities. m Let π = Σ τi the total processig power of the system, i= 0 the ormalized processig of a ode vi,? i = ti/π. Therefore i a perfect load balaced system, the load of each ode, accordig to the equatios, ad is give by Wi = W * τi Π = Wγi...(4) Vi The goal of the PSTS algorithm is to move each task t i, from its curret locatio v i, to a ode v j, v j = F(t i ), so that the whole system is well balaced, ad therefore the respose time of differet active tasks i the system is miimized R(t i, v i v j ). PSTS algorithm, firstly calculates the exclusive additive sca of the work uits of all tasks o the hyper-grids G of dimesio equal to. Sr = (+ Lr), r,, , p...(5) Where Lr, is a vector of elemets represetig the umber of work uits of a ode belogig to the hypergrid G x. Thus, (+, Lr) is performed cocurretly for all the hypergrids of a dimesio equal to oe. I the same way, this operatio is performed cocurretly o hypergrids of the same dimesio, Spr = (+, Lpr), r =,, , pp...(6) The resultig vector S p*. Represet the exclusive additive scas for all hyper-grids of the same dimesio p. The total work load W i the system is calculated by the operatio (+, S r ) o the -dimesioal hyper-grid. The sca operatio is also used for determiig the relative processig powers of the hyper-grids of differet dimesios: λpr = (+, λpr), r =,,......, pp...(7) This implies that each hyper-grid kows the ormalized processig power of its hyper-odes. The ext step is to calculate the destiatio of each task withi the hypergrids of the same dimesio. Each task t i, accordig to its iitial placemet, is assiged to a ode v i, v i = Fiit (t i ), ad let F(t i ) = v J. The, the problem cosists of calculatig the idex of the destiatio ode vj, J = [ j, j,..., j]. The calculatios start from the highest dimesio ad cotiues util the dimesio is. I order to balace the load i the system, algorithm calculates the least idex λ <= i/w. This meas that the algorithm works accordig

5 5 Chauha, Shah ad Rai to the relative power of each hyper-grid ad the sum of the work load of the etire system. After these scas each - dimesioal hyper-grid kows whether is a receiver or a seder. A receiver (resp. seder) meas that a hypergrid is uder-loaded (resp. over-loaded). If, for istace, a -dimesioal hyper-grid has to receive additioal tasks the its ow work cosists of balacig its ow workload accordig to the PSLB algorithm ad just wait to receive more tasks which will go to the appropriate odes. O the other had, if it is over-loaded, the it uses the PSLB algorithm to balace its ow workload (oly for the tasks that have to remai i the hyper-grid), kowig that the hyper-grids of higher dimesios will balace the tasks amog their elemets, ad therefore will migrate the extra tasks to their appropriate hyper-grids of lower dimesio. This procedure guaratees that after its completio the etire system will be as close as possible to the perfect load balaced state. The descriptio of the PSTS algorithm is give i algorithm. Algorithm Positioal Sca Task Schedulig Algorithm. repeat. r =. for all r-dimesioal hyper-grids i parallel do 4. Srq (+, L rq ), q =,,..., pr 5.?rq (+, t pr ), q =,,..., pr 6. ed for 7. W r S r + i, p, i.., ir. ( times) 8.? r?r + t i, p, i,..., ir. ( times) 9. r = r + 0. util (r = ). W i= Σ Wi + p, p, p,..., p( times).?r i= Σ? i + t p, p, p,..., p ( times). for all -dimesioal grids i parallel do 4. Calculate, usig the PSLB algorithm, if the -dimesioal grid is a seder or a receiver 5. if ay -dimesioal grid has to migrate tasks the 6. Apply the PSLB algorithm for the destiatio -dimesioal grid, ad 7. Migrate the tasks to the appropriate odes. 8. else 9. Apply the PSLB algorithm for its ow workload 0. ed if. ed for. Ed. D. PSTS Performace model Let d deote the dimesioality of the grid. If d = (bus topology) the umber of commuicatio steps eeded is comm S = ( ), where is the umber of the participatig odes, (see Figure ). The umber of computatio steps is comp S = ( ) Fig.. d = Comm. Ad Comp. Steps. Let p ad q be the costs i time uits of a commuicatio ad a computatio step respectively, the the total cost of the algorithm ca be expressed as follows : S = Scomm + Scomp = ( )p + ( )q = ( ) (p + q)...(8) For d =, the etwork topology is a grid cosistig of lies ad colums where =. (see Figure ) Fig.. d = Comm. ad Comp. Steps The umber of commuicatio ad computatio steps eeded for each lie (of odes) is give by the equatio 8. ( )p + ( )q. This correspods to balacig the load alog each lie of the grid (or hyper-grid of oe dimesio). Balacig the load alog the colums ca be doe by performig the algorithm o hyper-grid of dimesio (by cosiderig each lie as a hyper-ode of that hyper-grid). Therefore, the total cost for a -D grid is: S = S comm + S comp = ( )p + ( )q + ( )p + ( )q...(9) Fially, for d = (figure 4.), the total cost of computatio ad commuicatio steps eeded is:

6 Chauha, Shah ad Rai Fig.. d =, comm.. ad comp. steps S= S comm + S comp = ( )p + ( )q + ( ) + ( )q + ( )p + ( )q...(0) or S= S comm + S comp = ( + + ) (p + q) ad cosequetly for d = k Sk = Sk comm + Sk comp = ( k k) (p + q) E. Embeddig Irregular Network ito N-Dimesioal Grid There are may ways of embeddig a irregular etwork topology G(V, E) ito a -D grid. The resultig grid is called icomplete ad cotais two types of odes ad liks. Nodes (resp. liks) which are mapped oto V(resp. E) elemets are called active odes (resp. liks). The odes (resp. liks) which are ot assiged to ay elemet of V (resp. E) are called virtual odes (resp. virtual liks). I order to esure that the algorithm described above works o a icomplete grid, the virtual odes are cosidered as active ode with zero processig power. I the same way we ca cosider the virtual liks as active liks with zero badwidth. I order to miimize the cost of the PSTS algorithm o a icomplete grid, oe eed to miimize the umber of virtual odes or the dimesio of the correspodig gird. Propositio 4.. Cosider a etwork G(V, E) cosistig of odes. The best performace of PSTS is achieved whe G(V, E) is mapped oto a [log()]-d grid [proof []]. Coquer approach for load balacig. Geerally the problem of load balacig is NP-Complete problem. This techique first recursively partitios ad N - Dimesioal grid ito a -Dimetioal grid the applies SCAN - Operator [0,0] to which keeps tracks of curret load iformatio of grid ad trasfers this iformatio to the broadcaster which is resposible for the load balacig i a grid of cluster. The broadcaster also computes locally the trasfer of the load of grid to aother grid based o the Threshold values if load exceeds the Threshold [] the that ode becomes heavily loaded ad requires to be off-load ad if the ode has a work load below threshold value the that ode becomes lightly loaded ode ad this iformatio is oted by the broadcaster usig the SCAN operator ad the broadcaster will trasfer the load to this lightly loaded odes. The techique is Adaptive, fully dyamic ad oper-emptive which icurred a small amout of overhead to achieve fully dyamic load balaced cluster. A. Results To test the methodology, experimets are implemeted i Sequetial ad parallel with the use of P-THREAD, MPI ad Ope MP o cluster of grid. I the followig text, descriptio of test cases is give, ad the based o data from the give test case follows the result test, which iclude compariso betwee sequetial ad parallel with the use of Positioal Sca Load Balacig (PSLB) algorithm i cluster of two-way grids ad also the experimets aalyzed with V-Tue Performace Aalyzer by Itel (R). (a) Test case A ode SMP cluster is used for testig. Every ode is equipped with Itel (R) Core (TM) Duo Processor clocked at.8 GHz. Each ode has GB memory resultig i a total of GB RAM for whole cluster. Both odes are coected usig shared memory ad a high speed switch i (LAN). (b) Tests with sequetial techique The algorithm which is proposed is implemeted o a sigle machie with the Dual Core machie with the umber of work uit as show i Table ad, the estimated program executio time is also listed out for both Sequetial ad parallel executio usig P-Threads. V. EXPERIMENTAL RESULTS AND DESCUSSION The algorithm that s proposed ad discussed i previous chapter is implemeted o cluster eviromet which is actually a modificatio of origial Positioal Sca Task Schedulig (PSTS) Algorithm which uses Divide ad Fig. 4. Performace Graph for Sequetial ad P-Thread implemetatio

7 54 Chauha, Shah ad Rai Table. Executio time i umber of secods for both sequetial ad parallel usig (P-Thread) implemetatio. Executio Time Sr. No. Number of (secods) work Uits Sequetial P-Thread 00, , , , , , , , , ,00, C. Tests with proposed algorithm ad MPI ad Ope MP Now, it is attempted to use of graph matchig to obtai load balacig across a cluster comprisig of a two-way SMPs. This amouts to re-assigig of tasks to each ode i such a maer that the sum of the computatio ad commuicatio time for all processor i the ode remais same. First, proposed algorithm is applied with P-THREAD. Two threads are created ad task modules are assiged to each thread, tha both threads are executed parallel to get optimal executio time, which are preseted i Table. Fially, the same the techique is used with MPI ad Ope MP to parallelize dyamically with processors for achievig a load balacig ad higher performace [9, 8]. Performace (Executio Time/No. od jobs) Performace Graph For MPI vs. Ope MP Number of Work Uits = Value X 05 Fig. 5. Performace Graph For MPI ad OpeMP implemetatio MPI Ope MP Table. Executio time i MPI ad Ope MP Sr. No. Number of Executio Time (secods) work Uits MPI Ope MP Performace (Executio Time/No. od jobs) 00, , , , , , , , , ,00, Number of Work Uits = Value X Sequetial P-Thread MPI Ope MP Fig. 6. Combied Performace Graph For Sequetial ad P-Thread MPI AND OpeMP implemetatio Table. Executio time i Sequetial, P-Therad, MPI ad Ope MP Sr. No. Number of Executio Time (secods) work Uits Sequ. P-Thread MPI OpeMP 00, , , , , , , , , ,00, A Speed up factor deoted by µ is itroduced to quatify the result. The optimality idex is defied as : Speed up = ExecutioTime(Sequetial) ExecutioTime(Parallel)...(5.)

8 B. Aalysis usig Itel (R) V-Tue Performace Aalyzer Fially, the parallel implemetatio is tested with Itel (R) V-Tue performace Aalyzer which provides the iformatio about behavior of differet fuctio used. This iformatio is used to fid the maximum Speed-Up that ca be achieved usig proposed algorithm ad usig parallel implemetatio. Performace gai is: As the Fig 4 shows that the oly.88% of process ruig sequetially so Scalar fractio a ca be calculated as follows: α =.88/00 = 0.88 α = 0.88 Now, Accordig to Amdahl s Law, Speed-up ca be calculated usig followig equatio: Speed up = α α p Chauha, Shah ad Rai 55...(5.) Where a = 0.88 ad p = No. of Nodes i SMP =, so Speed-up C. Discussio Speed up = Speed up = (5.) The result preseted i Table- is based o sigle processor ad i Table- is based o a two-way SMP ode. I the result, the advatage of the POSITIONAL SCAN TASK SCHEDULING (PSTS) Algorithm methodology is clearly evidet. The program calculates the actual time usig timeval, timezoe structure ad usig gettimeofday() fuctio built i UNIX <time.h> library header file, this represets the actual time take from lauch of the parallel job to its completio. The parallel tasks are programmed usig P-thread, MPI ad, Ope MPI (Ope Message Passig Iterface) ad the utility mpiru p # Output_ fileame [0] was used to lauch the jobs. p parameter idicates the umber of processes. # sig idicates the umber of odes o which the job will executes. Here we ca also provide a file ame amed as.profile which cotais all the iformatio about odes ad there port addresses if more umber of odes are participatig i task executio. Fially the file ame to be executed o differet ode. Though the size of the state-space reduces drastically because of parallel implemetatios, the overheads icurred i commuicatio over the etwork ad mpiru to lauch ad termiate processes actually icrease the turaroud time. This implies that the methodology shall be effective for cases with large umber of task modules where the speedup due to reductio is state-space size makes up for these overheads. VI. CONCLUSION I this project, the load balacig ad, parallel task assigmet policies for cluster computig eviromet ad distributed etwork are discussed. Also, some existig load balacig techiques ad their drawbacks ad possible kids of solutios are discussed. A perfect ad low-iter Process Commuicatio(IPC) cost based load balacig techique which is based o Positioal Sca Load Balacig (PSLB) ad its modificatio Positioal Sca Task Schedulig (PSTS) is implemeted, which makes use Divide ad coquer approach to partitio the N-Dimesioal grid recursively i -Dimetioal grid ad the applies SCAN operator to collect recet etwork load iformatio. The performace evaluatio shows that proposed scheme offers high performace ad low commuicatio overhead which icrease throughput ad sigificatly improves system respose time. Till ow may techiques ad solutios have bee proposed ad they works correctly with the selectio of suitable parameters o specific applicatio. To ehace the performace of the parallel ad distributed system load balacig is vital parameter. To esure the reliability ad fault-tolerace to further improvemet of system performace proposed techique ca be implemeted by itroducig some fault tolerat mechaism with that. The techique which is proposed is fully dyamic ad adaptive accordig to the chages ito the topology of etwork. This solutio achieves high performace by providig perfect load balacig. As the fault-tolerace ad reliability is also a importat factor i order to improve the overall system performace. This ca be ehaced by itroducig a feature of fault tolerace i the techique which we show above. REFERENCES [] Ilias K. Savvas ad M-Tahar Kechadi, Dyamic Task Schedulig i Computig Cluster Eviromets, IEEE Proceedigs of the ISPDC/HeteroPar 04. [] Virgiia Mary Lo. Heuristic Algorithms for Task Assigmet i Distributed Systems IEEE Trasactio o Advaced i Parallel ad Distributed computig Proceedig, 7(): November, (988). [] She ad Tsai. A Graph Matchig Approach to Optimal Task Assigmet i Distributed Computig Systems Usig a Miimax Criterio, IEEE Trasactio o Advaced i Parallel ad Distributed computig Proceedig, C-4(): March, (985). [4] Isfaq Ahmad ad Muhammad Kafil, A Parallel Algorithm for Optimal Task Asigmet i Distributed System IEEE Trasactio o Advaced i Parallel ad Distributed computig Proceedig, March, (997). [5] Babak Taati & Michael Greespa. A Dyamic Load- Balacig Parallel Search for Eumerative Robot Path Plaig, Spriger joural for Distributed ad Parallel computig September, (006).

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