A Hierarchical Load Balanced Fault tolerant Grid Scheduling Algorithm with User Satisfaction

Transcription

1 A Hierarchical Load Balaced Fault tolerat Grid Schedulig Algorithm with User Satisfactio 1 KEERTHIKA P, 2 SURESH P Assistat Professor (Seior Grade), Departmet o Computer Sciece ad Egieerig Assistat Professor (Seior Grade), Departmet o Iformatio Techology Kogu Egieerig College Perudurai, Erode, Tamiladu INDIA 1 keerthikame@gmail.com, 2 sureshme@gmail.com Abstract: - The huma civilizatio advacemets lead to complicatios i sciece ad egieerig. Dealig with heterogeeous, geographically distributed resources, grid computig acts as a techology to solve these complicated issues. I grid, schedulig is a importat area which eeds more focus. This research proposes a hierarchical schedulig algorithm ad the factors such as load balacig, fault tolerace ad user satisfactio are cosidered. The proactive fault tolerat approach used here achieves better hit rate. The hierarchical schedulig methodology proposed here results i reduced commuicatio overhead ad the user deadlie based schedulig results i better user satisfactio whe compared to the algorithms which are proposed recet based o these factors. The tool used to evaluate the efficiecy of this hierarchical algorithm with other existig algorithms is gridsim. The overall system performace is measured usig makespa ad it proves to be better for the proposed hierarchical approach. Key-Words: - Commuicatio overhead, Resource utilizatio, Load balacig, Fault tolerace, Hierarchical schedulig, User satisfactio. 1 Itroductio Grid computig is a computig paradigm developed to meet the ever icreasig computatioal demads of may applicatios with icreasig umber of processors. Grid ca be of two types based o its fuctioality computatioal grids ad data grids. A computatioal grid is a hardware ad software ifrastructure that provides depedable, pervasive, cosistet ad iexpesive access to high ed computatioal capabilities. Computatioal grids are accessible to their users via sigle iterface. They merge extremely heterogeeous resources ito a sigle virtual resource. Whe data storage is take as the requiremet for establishmet of grid, it is termed as data grid, used for data itesive applicatios which eed access, trasfer ad modificatio of datasets. Some of the characteristics of computatioal grid fall as heterogeeity, dyamicity, scalability ad reliability. The compoets of a grid system are scheduler, load balacer, grid broker ad portals. The scheduler is resposible for maagemet ad allocatio of tasks to the fittest resource, partitioig of tasks i order to schedule parallel executio. The load balacer is aother compoet that is resposible for workload distributio i a balaced maer ad this should always be cosidered to avoid over commitmet of resources. The resource broker pairs services betwee service provider ad service requester. Schedulig ca be static or dyamic. Dyamic schedulig ca be explaied i terms of task executio of resources. Aother way of categorizig the schedulig algorithm based o their resource maagemet are cetralized schedulig, decetralized schedulig ad hierarchical schedulig. I decetralized schedulig, there is o cetral etity cotrol o resources. Local schedulers play a vital role i schedulig. I cetralized schedulig, a cetral etity is resposible for maitaiig ad schedulig the resources. This suffers with sigle poit of failure ad low scalability. This type of schedulig is ot appropriate for large scale grids. Sice the cotrol over the resources is more, this is more efficiet whe compared with decetralized E-ISSN: Volume 14, 2015

2 schedulig. I hierarchical schedulig, differet schedulers coordiate at certai level. This type of schedulig lacks i fault tolerace but highly fault tolerat tha cetralized schedulig. The modes of schedulig are batch mode ad immediate mode. I immediate mode schedulig, a task is scheduled as soo as it eters the system. I batch mode schedulig, tasks that eters the system are grouped i batches ad scheduled at certai time itervals. The work proposed i this research is a iitiative to develop a efficiet fault tolerat load balaced algorithm with user satisfactio. Curretly there are may schedulig algorithms that deals with user satisfactio, fault tolerace, load balacig separately. Till ow there is o such algorithm that serves combied for all these factors which are very much essetial for a better schedulig. The proposed algorithm combies all these factors ad proves its efficiecy. 2 Literature Survey The grid eviromet cosists of dyamic ad heterogeeous resources. These resources chages with time i.e., ay ew resource ca joi or ay of the old resources ca exit the grid eviromet at ay time. Due to ueve job arrival patters ad uequal computig capabilities, some resources i the grid eviromet get overloaded or some resources get uder loaded or some resources remai idle. The occurreces of resource failures are high due to the resource characteristics. Both load balacig ad resource failures degrade the system performace ad also user satisfactio. A dyamic, distributed load balacig scheme for a grid eviromet which provides deadlie cotrol for tasks is proposed i [1]. The grid broker assigs gridlets betwee the resources based o the deadlie request. Periodically the resources check their state ad make a request to the Grid Broker accordig to the chage of state i load. The, the Grid Broker assigs Gridlets betwee resources ad schedulig for load balacig uder the deadlie request. A ew groupig based schedulig algorithm that takes user satisfactio ito accout is proposed i [2]. I this approach, groupig of fie graied jobs to coarse graied jobs ad schedulig those coarse graied jobs based o the deadlie is doe. A dyamic load balacig mechaism proposed i [3], provides applicatio level load balacig for idividual parallel jobs. It esures that all loads submitted through the dyamic load balacig eviromet are distributed i such a way that the overall load i the system is balaced ad applicatio programs get maximum beefit from available resources. A layered load balacig algorithm based o the tree model represetatio is proposed i [4]. This model cosists of three mai features: (i) it is layered (ii) It supports heterogeeity ad scalability (iii) It is totally idepedet of ay physical architecture of grid. The eighbourhood load balacig strategy is used to decrease the amout of messages exchaged betwee grid resources. As a cosequece, the commuicatio overhead iduced by task trasfer ad workload iformatio flow is reduced, leadig to a high improvemet i the global throughput of a grid. The load balacig mechaism for optimal load distributio i a o-dedicated cluster or grid computig system with heterogeeous servers processig both geeric ad dedicated applicatios was proposed i [5]. Sice each dedicated task has a desigated server, load distributio is oly applied to geeric tasks. So, the goal of load balacig mechaism is to fid a optimal load distributio strategy for geeric tasks o heterogeeous servers preloaded by differet amout of dedicated tasks such that the overall average respose time of geeric applicatios is miimized. A echo system which creates ats o demad to achieve load balacig durig their adaptive lives is proposed i [6]. They may bear offsprig whe they sese that the system is drastically ubalaced ad commit suicide whe they detect equilibrium i the eviromet. These ats care for every ode visited durig their steps ad record ode specificatios for future decisio makig. A hybrid load balacig algorithm is proposed i [7]. The tasks will be placed i a task queue. The istataeous scheme, the First-Come- First-Served (FCFS) of the hybrid scheduler fuctios to fid the earliest completio time of each task idividually. If the system workload grows heavy, (i.e., more tasks are waitig i the queue) the the scheduler a chace to performs load balacig. The the tasks are shifted to other system so that the overloaded coditio ca be avoided. A system level E-ISSN: Volume 14, 2015

3 load balacig is proposed i [8] which are two folds: First, a distributed load balacig model, trasformig ay grid topology ito a forest structure. Secod, a two level strategy is proposed to balace the load amog resources of computatioal grid. A hybrid load balacig policy is proposed i [9] which itegrate static ad dyamic load balacig techologies. Essetially, a static load balacig policy is applied to select effective ad suitable ode sets. This will lower the ubalaced load probability caused by assigig tasks to ieffective odes. Whe a ode reveals the possible iability to cotiue providig resources, the dyamic load balacig policy will determie whether the ode i questio is ieffective to provide load assigmet. The system will the obtai a ew replacemet ode withi a short time, to maitai system executio performace. A Dyamic Load Balacig Algorithm based o resource type policy [10]. This algorithm makes chages to the distributio of work amog workstatios at ru-time; it uses curret or recet load iformatio whe makig distributio decisios. Multi computers with dyamic load balacig allocate/reallocate resources at rutime based o a priori task iformatio, which may determie whe ad whose tasks ca be migrated. A dyamic ad a distributed protocol is proposed i [11]. The grid is partitioed ito a umber of clusters. Each cluster has a coordiator to perform local load balacig decisios ad also to commuicate with other cluster coordiators across the grid to provide iter-cluster load trasfers. The distributed protocol uses the clusters of the Grid to perform local load balacig decisio withi the clusters ad if this is ot possible, load balacig is performed amog the clusters uder the cotrol of cluster heads called the coordiators. A Load Balaced Mi-Mi algorithm is proposed i [12] which reduces the makespa ad icreases the resource utilizatio. This algorithm cosists of two-phases. I the first phase, the Mi- Mi algorithm is executed ad i the secod phase, the tasks i the overloaded resources are rescheduled to use the uutilized resources. A load balacig approach based o Ehaced GridSim architecture is proposed i [13]. The Machie etity i GridSim 4.0 is treated as a dump etity object i ad is ot able to participate i ay decisio makig activities. I Ehaced GridSim architecture, the Machie Etity is made as active so, it participates i load balacig. The grid eviromet is cosidered as three levels:- Resource Broker level, Machie level, Processig Etity level. Grid Broker is the top maager of a grid eviromet which is resposible for maitaiig the overall grid activities of schedulig ad reschedulig. It gets the iformatio of the work load from grid resources. It seds the tasks to resources for optimizatio of load. Resource is ext to grid Broker i the hierarchy. It is resposible for maitaiig the schedulig ad load balacig of its machies. Also, it seds a evet to grid broker if it is overloaded. Machie is a Processig Etity (PE) maager. It is resposible for task schedulig ad load balacig of its PEs. Also, it seds a evet to resource if it is overloaded. Whe a ew job arrives at a machie, it submits it to a PE, which is lightly loaded. Before submittig the job, the expected status of the PE after the job submissio is predicted. If the submissio of the job turs the uderloaded PE to overloaded PE the the job is assiged to some other uderloaded PE, which may ot become overloaded due to its submissio. If ay of the PE is overloaded, the the few tasks i overloaded PE are shifted to other uderloaded PE to avoid the overloaded coditio. By this way the load is balaced at Machie level. The same procedure is followed at Resource level ad Broker level to balace the load i the grid eviromet. A fault tolerat hybrid load balacig algorithm is proposed i [14]. This algorithm is carried out i two phases: Static load balacig ad dyamic load balacig. I the first phase, a static load balacig policy selects the desired effective sites to carry out the submitted job. If ay of the sites is uable to complete the assiged job, a ew site will be located usig the dyamic load balacig policy. The assigmet of jobs must be adjusted dyamically i accordace with the variatio of site status. A load balacig mechaism, which is proposed i [15], works i 2 phases: I the first phase, job allocatio is doe based o a defied criterio i.e., the heuristic begis with the set of all umapped tasks. The the set of miimum completio times is foud, like Mi-mi heuristic. I secod phase, heuristic algorithm works based o machies workload, which cosists of 2 steps. I the first step, for each task the miimum, secod miimum completio time ad miimum executio time are foud. The the differece betwee these two miimum completio time values is multiplied by the amout of miimum completio time ad the E-ISSN: Volume 14, 2015

4 divided by miimum executio time. I the secod step, if the umber of the remaiig tasks is ot less tha threshold, the the heuristic algorithm is executed to balace the load. Fially, the task which has the criteria value as maximum will be selected ad removed from the set of umapped tasks. A Augmetig Hierarchal Load Balacig algorithm is proposed i [16]. To evaluate the load of the cluster, probability of deviatio of average system load from average load of cluster is calculated ad checked for the cofiemet withi a defied rage of 0 to 1. The fittest resources are allocated to the jobs by comparig the expected computig power of the jobs with the average computig power of the clusters. A Failure Detectio Service (FDS) mechaism ad a flexible failure hadlig framework is proposed i [17]. The FDS eables the detectio of both task crashes ad user-defied exceptios. The Grid-WFS is built o top of FDS, which allows users to achieve failure recovery i a variety of ways depedig o the requiremets ad costraits of their applicatios. The resources are modeled based o the system reliability. Reliability of a grid computig resource is measured by mea time to failure (MTTF), the average time that the grid resource operates without failure. Mea time to repair (MTTR) is the average time it takes to repair the Grid computig resource after failure. The MTTR measures the dowtime of the computig resource. Various fault recovery mechaisms such as checkpoitig, replicatio ad reschedulig are discussed i [18]. Takig checkpoits is the process of periodically savig the state of a ruig process to durable storage. This allows a process that fails to be restarted from the poit its state was last saved, or its checkpoit o a differet resource. Replicatio: Replicatio meas maitaiig a sufficiet umber of replicas, or copies, of a process executig i parallel o differet resources so that at least oe replica succeeds. I [19], it is described that the fault tolerace is a importat property i order to achieve reliability. Reliability idicates that a system ca ru cotiuously without failure. A highly reliable system is the oe that cotiues to work without ay iterruptio over a relatively log period of time. The fault tolerace is closely related to Mea Time to Failure (MTTF) ad Mea Time betwee Failures (MTBF). MTTF is the average time the system operates util a failure occurs, whereas the MTBF is the average time betwee two cosecutive failures. The differece betwee the two is due to the time eeded to repair the system followig the first failure. Deotig the Mea Time to Repair by MTTR, the MBTF ca be obtaied as MTBF=MTTF + MTTR. A check poitig mechaism is proposed i [20] to achieve fault tolerace. The check poitig process periodically saves the state of a process ruig o a computig resource so that, i the evet of resource failure, it ca resume o a differet resource. If ay resource failure happes, it ivokes the ecessary replicas i order to meet the user applicatio reliability requiremets. I our previous work [21], we have proposed a efficiet fault tolerat schedulig algorithm (FTMM) which is based o data trasfer time ad failure rate. System performace is also achieved by reducig the idle time of the resources ad distributig the umapped tasks equally amog the available resources. A schedulig strategy that cosiders user deadlie ad commuicatio time for data itesive tasks with reduced makespa, high hit rate ad reduced commuicatio overhead is itroduced i [22]. This strategy does ot cosider the occurrece of resource failure. I our previous work [23], we have proposed a ew Bicriteria schedulig algorithm that cosiders both user satisfactio ad fault tolerace. The proactive fault tolerat techique is adopted ad the schedulig is carried out by cosiderig the deadlie of gridlets submitted. The mai cotributio of this paper icludes achievig user satisfactio alog with fault tolerace ad miimizig the makespa of jobs. I our previous work [24], we have proposed a multicriteria schedulig algorithm that cosiders load balacig, fault tolerace ad user satisfactio as a cetralized approach. A Prioritized user demad algorithm is proposed i [25] that cosiders user deadlie for allocatig jobs to differet heterogeeous resources from differet admiistrative domais. It produces better makespa ad more user satisfactio but data requiremet is ot cosidered. While schedulig the jobs, failure rate is ot cosidered. So the scheduled jobs may be failed durig executio. A work based o user satisfactio ad hierarchical load balacig is proposed i [26] that cosiders user demads ad load balacig. It miimizes the respose time of the jobs ad improves the utilizatio of the resources i grid eviromet. By cosiderig the user demad of E-ISSN: Volume 14, 2015

5 the jobs, the schedulig algorithm also improves the user satisfactio. The mai cotributio i this work is that a hierarchical schedulig algorithm is proposed which cosiders multiple costraits such as user deadlie, failure rate, load of resources ad commuicatio overhead at the time of schedulig. 3 Materials ad Methods 3.1 Problem Formulatio We have proposed a schedulig architecture i our previous work which is for cetralized schedulig of resources. I this work, a hierarchical schedulig architecture give below i figure 1 is followed. The proposed hierarchical schedulig algorithm is static followig a batch mode of schedulig. 3.2 Proposed Hierarchical Algorithm I this work, a hierarchical schedulig methodology is proposed. The tasks are expected to be submitted by the user at differet levels of hierarchy such as machie, resource, grid broker. A machie is a collectio of processig elemets (PE s). A resource is a collectio of machies ad a grid broker is a collectio of resources that has all the iformatio about the resources such as capacity, availability etc. The hierarchy followed here is similar to the hierarchy followed by gridsim which is give below i figure 2. Fig.2 GridSim Architecture The schedulig algorithm takes load of PE s, average load of machies, average load of resources ad average load of the system as factors i decidig the applicable resource/machie/pe i order to balace the load of the grid system. The factors such as user deadlie of tasks submitted by the user are cosidered at the time of schedulig i order to achieve user satisfactio. The calculatio of load of each PE, machie ad resource becomes essetial sice schedulig is carried out at three levels. Load of each PE is calculated usig the formula Fig.1 Hierarchical Schedulig Architecture m j =0 MI j Load PE i = (1) MIPS i AT i E-ISSN: Volume 14, 2015

6 where m is the umber of tasks allocated to PE i ad AT i is the availability time of PE i. Load of each Machie is calculated usig the formula Load M i = j =0 Load PE j (2) where j is the umber of tasks submitted to PEs uder that machie. Load of each resource is calculated usig the formula Load R i = j =0 Load M j (3) where j is the umber of tasks submitted to PEs uder that resource. The average load of each machie is calculated by, AL M i = k=1 Load PE k (4) The average load of each resource is calculated by, AL R i = k=1 AL M k (5) ad the average load of grid broker is calculated by, AL GB i = k=1 AL R k (6) After calculatig the load ad average load, the balace threshold is calculated i order to categorize the resources as overloaded, uderloaded ad ormally loaded. The balace threshold is calculated at machie level by usig the formula, Ω M = AL M i + σ M (7) At resource level, the balace threshold is calculated as, Ω R = AL R i + σ R (8) The balace threshold at grid broker level is calculated as, Ω GB = AL GB i + σ GB (9) where σ M, σ R, σ GB are the deviatio factors at machie, resource ad grid broker levels respectively. At machie level, the deviatio factor ca be calculated by, N Load PE i AL M 2 i=1 i σ M = N (10) At resource level, the deviatio factor is calculated by, N Load M i AL R 2 i=1 i σ R = N (11) ad at the grid broker level, the deviatio factor is give by, N Load R i AL GB 2 i=1 i σ GB = N (12) The hierarchical schedulig algorithm is give i Algorithm 1. It works as follows. If a task is submitted by the user at machie level which is the lower level i the hierarchy, the the schedulig algorithm works for the PE s uder that machie. Wheever a task is submitted at resource level, they are scheduled to the PE s uder the machies which are uder that particular resource. Whe a task is submitted at the grid broker level which is the higher level of hierarchy, they are scheduled to the resources uder that grid broker. If the tasks at ay level is failed to be scheduled, the it is set to its higher level of the hierarchy ad scheduled. The balace threshold is very importat i decidig whether a resource is over loaded, uder loaded or ormally loaded. After categorizig the resources, the resources which are uderloaded are cosidered for schedulig. This is illustrated with equatios 7, 8, 9, 10, 11 ad 12. E-ISSN: Volume 14, 2015

7 For all tasks MT i submitted at Machie level, Perform possible allocatio to the tasks i the list of PE s uder that machie usig algorithm 2. If MT i is ot empty, Submit the tasks i MT i list to the ext upper level of the hierarchy RT i For all tasks RT i submitted at Resource level, Perform possible allocatio to the tasks i the list of PE s uder that resource usig algorithm 3. If RT i is ot empty, Submit the tasks i RT i list to the ext upper level of the hierarchy GBT i For all tasks GBT i submitted at Grid Broker level, Perform possible allocatio to the tasks i GBT i to the list of PE s uder that Grid Broker usig algorithm 4. If GBT i is ot empty, Icremet J f which is the umber of tasks ot scheduled. Algorithm 1: Hierarchical Schedulig Algorithm 1. Get the list of tasks MT i with their user deadlie UD i. 2. Get the list of PE s uder that machie from GIS. 3. Costruct ETC T i, R j matrix of size m where m is the umber of tasks ad is the umber of PE s uder that machie where the tasks are submitted. 4. For all PE j i the list, 4.1 Calculate failure rate FR R j where R j represets PE j 4.2 Calculate RT R j is the umber of tasks submitted to R j. 4.3 Calculate Load of PE s, Average load of machie. 5. Calculate balace threshold at machie level 6. Create a list of uderloaded PE s (UP) which has Load PE i < Ω M. 7. For each task T i i MT i i queue ad for each PE j, Costruct CT T i, R j, DT T i, R j, TCT T i, R j matrix of size m 8. For all task T i i MT i 8.1 Create list UT i1 ad UT i2 with PE s that has TCT T i, R j UD Ti ad TCT T i, R j > UD Ti respectively. 8.2 Sort UT i1 ad UT i2 based o FR R j of resources i ascedig order 8.3 Create lists ULT i1 ad ULT i2 with the set of uderloaded resources from UT i1 ad UT i2 respectively i order. 8.4 If etries i ULT i1, Select the first resource i the list for task T i ad dispatch T i to resource R j ad Icremet Deadlie Hit Cout ad Hit Cout. else if etries i ULT i2, Select the first resource i the list for task T i ad dispatch T i to resource R j ad Icremet Hit Cout. 8.5 Remove task T i from Task_list MT i. 8.6 Update RT R j ad FR R j where j is the PE to which the task T i is dispatched. 9. If there are tasks i Task_list T, Repeat steps from 4.3. Edif Algorithm 2: Schedulig at Machie Level E-ISSN: Volume 14, 2015

8 The schedulig algorithm at the machie level is give i algorithm 2. The algorithm works as follows. The machie receives the tasks with user deadlie UD T i. The task s iformatio such as its legth i MI is used to calculate the executio time ETC T i, R j of each task i each of the available resources. With the ready time iformatio RT R j available for each resource at GIS, the algorithm calculates the completio time CT T i, R j. The failure iformatio of resources such as umber of tasks submitted to a resource T sub ad umber of tasks successfully completed T succ ad umber of tasks ot completed successfully T f is also available i GIS which helps i calculatig the failure rate FR R j. The list of resource i which the task gets completed withi user deadlie is collected for each task ad they are sorted based o their failure rate. Based o the balace threshold, the resources are categorized as overloaded ad uderloaded ad fially the load is balaced by submittig the task to the uderloaded resource. Whe a resource is assiged a task, the load of each resource ad system, balace threshold, failure rate ad ready time are recalculated. The same procedure is repeated for all tasks till the task list becomes empty. The schedulig algorithm at resource level ad grid broker level is give i algorithm 3 ad 4 respectively. 4 Results ad Discussio 4.1 Experimetal Eviromet The simulatio is based o the schedulig architecture i Figure 1. The umber of PE s ad tasks cosidered is 16 ad 512 respectively. The umber of machies rages from 1 to 4 ad umber of PEs per machie rages from 1 to 2. The 16 PE s are grouped to form umber of machies ad machies are grouped to form resources which are agai grouped ad cotrolled by resource/grid broker. The factors cosidered i desigig this algorithm are user satisfactio, which ca be evaluated usig deadlie hit cout, fault tolerace, which ca be evaluated usig hit cout, load balacig, which ca be evaluated usig average resource utilizatio ad whe these are applied i a hierarchical maer, it is evaluated usig commuicatio time ad as a whole the system performace is improved, which ca be evaluated usig makespa. The performace metrics such as makespa, hit cout, deadlie hit cout ad average resource utilizatio are defied below. Makespa: Makespa is oe of the most importat stadard metric of grid schedulig to measure its performace. It is defied as the overall completio time of a batch of tasks ad is give by, Makespa = max RT R j, j (13) It is used to measure the ability of grid to accommodate gridlets i less time. Hit cout: Hit cout is a ew metric itroduced i this chapter. It represets the umber of tasks successfully completed i a batch of tasks. Here, each batch is assumed to have 512 tasks ad the hit cout gives the umber of tasks successfully completed out of 512. Deadlie Hit Cout: This is a ew metric itroduced i this chapter which represets the umber of tasks successfully completed withi the give user deadlie. Average resource utilizatio: This metric is ewly itroduced i order to measure the load balacig which ca be calculated as follows. The utilizatio of each resource RU R j ca be calculated by the Equatio (14). m i=0 MI i RU R j = 100 (14) MIPS j AT j The average resource utilizatio ARU of the system ca be calculated usig Equatio (15). ARU = 1 N N j =1 RU R j where N is the umber of resources. (15) Commuicatio Time: I additio to these metrics, this chapter itroduces a ew metric amed commuicatio time which is the time take for trasferrig the tasks betwee differet levels of hierarchy. E-ISSN: Volume 14, 2015

9 1. Get the list of tasks RT i with their user deadlie UD i. 2. Get the list of PE s uder that resource from GIS. 3. Costruct ETC T i, R j matrix of size m where m is the umber of tasks ad is the umber of PE s uder that resource where the tasks are submitted. 4. For all PE j i the list, Do 4.1 Calculate failure rate FR R j, where R j represets PE j 4.2 Calculate RT R j where is the umber of tasks submitted to R j. 4.3 Calculate Load of machie, Average load of resource. Doe 5. Calculate balace threshold at resource level 6. Create a list of uderloaded machie s (UM) which has Load M i < Ω R. 7. For each task T i i RT i i queue ad for each PE j, Do Costruct CT T i, R j, DT T i, R j, TCT T i, R j matrix of size m Doe 8. For all task T i i RT i Do 8.1 Create list UT i1 ad UT i2 with PE s that has TCT T i, R j UD Ti ad TCT T i, R j > UD Ti respectively. 8.2 Sort UT i1 ad UT i2 based o FR R j of resources i ascedig order 8.3 Create lists ULT i1 ad ULT i2 with the set of uderloaded resources from UT i1 ad UT i2 respectively i order. 8.4 If etries i ULT i1, Select the first resource i the list for task T i ad dispatch T i to resource R j ad Icremet Deadlie Hit Cout ad Hit Cout. else if etries i ULT i2, Select the first resource i the list for task T i ad dispatch T i to resource R j ad Icremet Hit Cout. 8.5 Remove task T i from Task_list RT i. 8.6 Update RT R j ad FR R j where j is the resource to which the task T i is dispatched. Doe 9. If there are tasks i Task_list T, Repeat steps from 4.3. Edif Algorithm 3: Schedulig at Resource Level E-ISSN: Volume 14, 2015

10 1. Get the list of tasks GBT i with their user deadlie UD i. 2. Get the list of PE s uder that grid broker from GIS 3. Costruct ETC T i, R j matrix of size m where m is the umber of tasks ad is the umber of PE s uder that grid broker where the tasks are submitted. 4. For all PE j i the list, Do 4.1 Calculate failure rate FR R j, where R j represets PE j 4.2 Calculate RT R j where is the umber of tasks submitted to R j. 4.3 Calculate Load of resource, Average load of grid broker Doe 5. Calculate balace threshold at grid broker level 6. Create a list of uderloaded resource s (UR) which has Load R i < Ω GB. 7. For each task T i i GBT i i queue ad for each PE j, Do Costruct CT T i, R j, DT T i, R j, TCT T i, R j matrix of size m Doe 8. For all task T i i GBT i Do 8.1 Create list UT i1 ad UT i2 with PE s that has TCT T i, R j UD Ti ad TCT T i, R j > UD Ti respectively. 8.2 Sort UT i1 ad UT i2 based o FR R j of resources i ascedig order 8.3 Create lists ULT i1 ad ULT i2 with the set of uderloaded resources from UT i1 ad UT i2 respectively i order. 8.4 If etries i ULT i1, Select the first resource i the list for task T i ad dispatch T i to resource R j ad Icremet Deadlie Hit Cout ad Hit Cout. else if etries i ULT i2, Select the first resource i the list for task T i ad dispatch T i to resource R j ad Icremet Hit Cout. 8.5 Remove task T i from Task_list GBT i. 8.6 Update RT R j ad FR R j where j is the resource to which the task T i is dispatched. Doe 9. If there are tasks i Task_list T, Repeat steps from 4.3. Edif Algorithm 4: Schedulig at Grid Broker Level E-ISSN: Volume 14, 2015

11 4.2 Simulatio Results The HRL_LBFT algorithm is evaluated for the above defied metrics. The results are compared with Mimi, FTMM, BSA, LBFT ad LBEGS algorithms. The makespa values of the algorithms such as HRL_LBFT, Mi-mi, FTMM, BSA, LBFT ad LBEGS are show i figure 3. The results show that the proposed HRL_LBFT has miimized makespa tha the other algorithms. The Mi-mi is a bechmark algorithm for measurig the schedulig algorithm s performace based o makespa. But it is oted that the makespa of proposed HRL_LBFT has a otable improvemet over Mi-mi. The deadlie hit cout values of the algorithms such as HRL_LBFT, Mi-mi, FTMM, BSA, LBFT ad LBEGS are show i figure 5. The results show that the proposed HRL_LBFT relatively has a high umber of deadlie hits tha the other algorithms. Sice LBEGS does t cocetrate o user satisfactio, its deadlie hit cout is low whe compared to BSA ad HRL_LBFT which cosiders user satisfactio. Fig. 5 Performace based o Deadlie Hit Cout Fig. 3 Performace based o Makespa (sec) The hit cout values of the algorithms such as HRL_LBFT, Mi-mi, FTMM, BSA, LBFT ad LBEGS are show i figure 4. The results show that the proposed HRL_LBFT has highest hit cout tha the other algorithms. Sice LBEGS oly cocetrates o load balacig, the hit cout is very low whe compared with other algorithms. Fig. 4 Performace based o Hit Cout The average resource utilizatio of the algorithms such as HRL_LBFT, Mi-mi, FTMM, BSA, LBFT ad LBEGS are show i figure 6 ad the results shows that the proposed HRL_LBFT relatively has high resource utilizatio tha Mi-mi, FTMM ad BSA, but early same utilizatio as LBFT algorithm. Commuicatio time is a measure of overhead due to trasfer of tasks from oe level of hierarchy to aother. The cetralized LBFT algorithm ad the proposed hierarchical HRL_LBFT algorithms are compared i figure 7 based o this metric i order to prove the efficiecy of hierarchical approach. The results show that the hierarchical approach reduces the commuicatio time i a remarkable way. The average percetage improvemet of HRL_LBFT based o makespa towards Mi-mi is 34.8%. With FTMM, the percetage improvemet is 28.3 % ad towards BSA, a improvemet of 17% is achieved. Whe compared with LBFT, it shows a average percetage improvemet of 12.4% ad with LBEGS, it shows 16.4% improvemet. The average percetage improvemet of HRL_LBFT based o hit cout towards Mi-mi is 28.2%. With FTMM, the percetage improvemet is 13.2 % ad towards BSA, a improvemet of 3.5% is achieved. Whe E-ISSN: Volume 14, 2015

12 compared with LBFT, it shows a average percetage improvemet of 1% ad with LBEGS, it shows 18.2% improvemet. Fig. 6 Performace based o Average Resource Utilizatio HRL_LBFT performs better with a percetage improvemet of 12.4% over LBFT. 5 Coclusios ad Future Work I this work, a schedulig algorithm takig user satisfactio, load balacig, ad fault tolerace ito accout is proposed. Schedulig is doe at three levels such as machie level, resource level ad grid broker level. Because of this hierarchy cosidered, the commuicatio time is miimized that i tur miimizes the makespa. Curretly may works are carried out separately for load balacig, fault tolerace ad user satisfactio. But this proposed work gives a combied solutio with all these factors. The efficiecy of this algorithm is proved with the evaluatio parameters such as makespa, deadlie hit cout, hit rate, resource utilizatio, average resource utilizatio ad commuicatio overhead. I this work, the tasks cosidered are computatio itesive tasks. I future, data itesive tasks ca also be cosidered for schedulig ad this ca be agai exteded with the factor of security i grid which would lead to a efficiet work that ca be used i computatioal grid. Also, like user deadlie, the budget for task executio ca also be obtaied as a task requiremet from user ad schedulig ca be doe cosiderig it. Fig. 7 Performace based o Commuicatio Time (sec) The average percetage improvemet of HRL_LBFT based o deadlie hit cout towards Mi-mi is 33.4%. With FTMM, the percetage improvemet is 25.6 % ad towards BSA, a improvemet of 3% is achieved. Whe compared with LBFT, it shows a average percetage improvemet of 1.8% ad with LBEGS, it shows 34.2% improvemet. Based o resource utilizatio, the HRL_LBFT algorithm has a percetage improvemet of 19.5% over Mi-mi, 13.3% over FTMM, 11% over BSA, 0.8% over LBFT ad 0.5% over LBEGS. Based o commuicatio time, Refereces [1] Hao Y, Liu G. ad Wec N, A ehaced load balacig mechaism based o deadlie cotrol o GridSim, Future Geeratio Computer Systems, Vol.28, 2012, pp [2] Suresh P, Balasubramaie P, Groupig based User Demad Aware job schedulig Approach for computatioal Grid, Iteratioal Joural of Egieerig Sciece ad Techology, Vol.4, No.12, 2012, pp [3] Payli R.U., Yilmaz E., Ecer A., Akay H.U. ad Chie S., DLB- A Dyamic load balacig tool for grid computig, The Joural of scalable computig, Vol.7, No.2, 2006, pp E-ISSN: Volume 14, 2015

13 [4] Yagoubi B. ad Slimai Y., Load balacig strategy i grid eviromet, Joural of Iformatio Techology ad Applicatios, Vol.1, No.4, 2007, pp [5] Li K, Optimal load distributio i o dedicated heterogeeous cluster ad grid computig eviromets, Joural of Systems Architecture, Vol.54, 2008, pp [6] Salehi M.A., Deldari H. ad Dorri B.M, Balacig Load i a Computatioal Grid Applyig Adaptive, Itelliget Coloies of Ats, Iformatics, Vol.32, 2008, pp [7] Li Y., Yag Y. ad Zhou L, A hybrid load balacig strategy of sequetial tasks for grid computig Eviromets, Future Geeratio Computer Systems, Vol.25, 2009, pp [8] Yagoubi B. ad Meddeber M, Distributed Load Balacig Model for Grid Computig, Joural of Computer Sciece, Vol.12, 2010, pp [9] Ya K.Q., Wag S.S., Wag S.C. ad Chag C.P, Towards a hybrid load balacig policy i grid computig system, Expert Systems with Applicatios, Vol.36, 2009, pp [10] Kumar K, A Dyamic Load Balacig Algorithm i Computatioal Grid usig Fair Schedulig, Iteratioal Joural of Computer Sciece, Vol.8, No.1, 2011, pp [11] Payli R.U., Erciyes K. ad Dagdevire O, Cluster-Based Load Balacig Algorithms for Grids, Iteratioal Joural of Computer Networks & Commuicatios, Vol.3, No.5, 2011, pp [12] Kokilavai T, Load Balaced Mi-Mi Algorithm for Static Meta-Task Schedulig i Grid Computig, Iteratioal Joural of Computer Applicatios, Vol.20, No.2, 2011, pp [13] Qureshi K., Rema A. ad Mal P, Ehaced GridSim architecture with load balacig, The Joural of Supercomputig, Vol.57, No. 3, 2011, pp [14] Balasagameshwara J. ad Raju N, A hybrid policy for fault tolerat load balacig i grid computig eviromets, Joural of Network ad Computer Applicatios, Vol.35, 2012, pp [15] Bardsiri A.K. ad Rafsajai M.K, A New Heuristic Approach Based o Load Balacig for Grid Schedulig Problem, Joural of Covergece Iformatio Techology, Vol.7, No.1, 2012, pp [16] Raj J.S., Hridya K.S. ad Vasudeva V, Augmetig Hierarchical Load Balacig with Itelligece i Grid Eviromet, Iteratioal Joural of Grid ad Distributed Computig, Vol. 5, No. 2, 2012, pp [17] Hwag S. ad Kesselma C, A Flexible Framework for Fault Tolerace i the Grid, Joural of Grid Computig, Vol.1, 2003, pp [18] Dabrowski C, Reliability i grid computig, Iteratioal Joural of Computer Sciece, Vol.8, No.1, 2009, pp [19] Latchoumy P. ad Khader S.A.P, Survey o Fault Tolerace i GridComputig, Iteratioal Joural of Computer Sciece & Egieerig Survey (IJCSES), Vol.2, No.4, 2011, pp [20] Priya B.S, Fault Tolerace ad Recovery for Grid Applicatio Reliability usig Check Poitig Mechaism, Iteratioal Joural of Computer Applicatios, Vol.26, No.5, 2011, pp [21] Keerthika P, Kasthuri N, A Efficiet Fault Tolerat Schedulig Approach for Computatioal Grid, America Joural of Applied Scieces, Vol. 9, Issue 12, 2013, pp doi: /ajassp [22] Suresh P, Balasubramaie P, User Demad Aware Schedulig Algorithm for Data Itesive Tasks i Grid Eviromet, Europea Joural of Scietific Research, Vol.74, No.4, 2012, pp [23] Keerthika P, Kasthuri N, A Efficiet Grid Schedulig Algorithm with Fault Tolerace ad User Satisfactio, Mathematical Problems i Egieerig, Volume 2013, Article ID , [24] Keerthika P, Kasthuri N, A Hybrid Schedulig Algorithm with Load Balacig for Computatioal Grid, Iteratioal Joural of Advaced Sciece ad Techology, Vol. 58, 2013, pp E-ISSN: Volume 14, 2015

14 [25] Suresh P, Balasubramaie P ad Keerthika P, Prioritized User Demad Approach for Schedulig Meta Tasks o Heterogeeous Grid Eviromet, Iteratioal Joural of Computer Applicatios, Volume 23, No.1, [26] Suresh P, Balasubramaie P, User Demad Aware Grid Schedulig Model with Hierarchical Load Balacig, Mathematical Problems i Egieerig, Volume 2013,Article ID , E-ISSN: Volume 14, 2015