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Adaptive job scheduling with load balancing for workflow application
1.
International Journal of
Computer Engineering and Technology (IJCET), 2, Number and – 6367(Print)© IAEME 0976 – 6367(Print), International Journal of Computer Engineering Technology (IJCET), ISSN ISSN 0976 – 6375(Online) Volume ISSN 0976 ISSN 0976 – 6375(Online) Volume 2 1, Dec - Jan (2011), IJCET Number 1, Dec - Jan (2011), pp. 09-21 ©IAEME © IAEME, http://www.iaeme.com/ijcet.html ADAPTIVE JOB SCHEDULING WITH LOAD BALANCING FOR WORKFLOW APPLICATION IN GRID PLATFORM D.Daniel PG Scholar Karunya University E-Mail: daniel_joen@yahoo.com Mrs.S.P.Jeno Lovesum M.E Asst.Professor Karunya University E-Mail: jenolovesum@karunya.edu D.Asir PG Scholar Karunya University E-Mail: asir.info87@gmail.com A.Catherine Esther Karunya PG Scholar Karunya University E-Mail: catherineesther@karunya.edu.in ABSTRACT Grid computing servers as the globally connected systems which performs high computing in many practical applications. Scheduling plays a key role in providing performance for grid workflow applications. Various scheduling strategies are proposed, including static scheduling strategies which map jobs to resources before execution time, or dynamic alternatives which schedule individual job only when it is ready to execute. Both of the schedules require significantly high scheduling cost and they may not produce good quality of schedule with low cost. This paper proposes a novel semi dynamic algorithm with load balancing concept, which allows the schedule to adapt and schedule the jobs as per the changes in the dynamic grid environment. The proposed novel algorithm schedules the job statically and continues the schedule with dynamic scheduling due to the dynamic nature of the grid. The makespan and the resource usage are the main to objective of this scheduling algorithm. When the resource and 9
2.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME performance fluctuation occur in the grid environment it affects the processing of the jobs which results in the delay in the job completion time. In this algorithm load balancing is incorporated to handle such situation where the jobs are handled after it is dispatched to their respective hosts. When there is resource fluctuation occurs due to the dynamic nature of the grid or over loading of jobs to a processor which delays the makespan, load balancing is done to handle the job execute and to get desired makespan. Index Terms: DAG, Tasks, Makespan, Resource usage, Semidynamic scheduling. I. INTRODUCTION Grids as geographically distributed computing systems has variety of resources often dispersed geographically to be interconnected and shared, for scientific and engineering challenges, in which majority of applications fall into the interdependent task model. These applications are generally known as workflow applications [4]. Due to the growing popularity of grid computing systems, many applications have been attempting to take advantage of these computing environments. Such applications are generally constructed by interweaving interdependent jobs; these applications are called workflow applications. Workflow applications are essentially the same as typical parallel programs, with one exception: a workflow application consists of a set of interdependent applications (not partitioned tasks of a parallel program). Like conventional parallel programs, workflow applications can be represented by a DAG. A DAG, G = (V, E), consists of a set V of v nodes and a set E of e edges. A DAG is also known as a task graph or macro dataflow graph. The nodes usually represent jobs of a workflow application, and the edges usually represent precedence constraints. An edge (i,j)ϵE between job ni and job nj represents the inter job communication. Specifically, the outputs of job ni must be transmitted to job nj for job nj to start its execution. A job with no predecessors is called an entry job, nentry; an exit job, nexit , is one that has no successors. Among the predecessors of a job ni, the predecessor that completes the communication at the latest time is the most influential parent (MIP) of the job denoted as MIP(ni). A job is called a ready job if all of its predecessors have been completed. The longest path of a task graph is the critical path (CP) [1]. Workflow applications can take advantage of a grid computing platform; however, these applications, besides the use of resource heterogeneity and dynamism, 10
3.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME impose a great burden on scheduling. In some systems, this workflow scheduling is left for manual dispatch by users, while other systems employ automated workflow management platforms (WMPs)[1] .These WMPs tend to focus on the minimization of the application’s completion time. However, there are other important performance considerations of WMPs, such as resource usage, load balancing, and fault tolerance. Although some WMPs have facilities to deal with these considerations, they often lack the capability of explicit resource usage control. Rather, for the sake of fault tolerance, resources are overly used (task duplication). The job scheduling has a close relationship with the load balancing. There are two ways load balancing can be made with the given job and resource in the hand, prediction based and non prediction based. The prediction based load balancing already collects the amount of jobs it have to schedule against the amount of resources, i.e processors. In this case the job scheduling is done keeping in account of the availability of the resource and the load of jobs that is scheduled is scheduled even to all resources based on their status and the scheduled job is dispatched to the hosts [11]. The non prediction based approach does not have any information about the resource and the jobs. Based on the schedule strategy it schedules the job and dispatched to the hosts based on the dynamic changes that happens among the availability of the resource, the load is migrated and jobs are executed, in this case dynamic balancing of the load is done. The rest of the paper is organized as Chapter 2, explains the related works and Chapter 3 gives a detailed presentation about the adaptive scheduling Chapter 4 gives the system model, and chapter 5 tells proposed scheduling with load balancing, chapter 6 gives the comparison and evaluation of the proposed scheduling, which is followed by conclusion and future work on Chapter 7. II. RELATED WORKS Since in many respects, workflow scheduling in grids is similar to the conventional task scheduling problem in tightly coupled heterogeneous computing systems (e.g., clusters), some well-known task scheduling algorithms (e.g., HEFT) have been adopted and modified for grid workflow scheduling. Most of the algorithms are designed in such a way to meet the dynamic nature of the grid. More specifically, rescheduling and advance reservation among other techniques are often used to deal with 11
4.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME uncertainties in resource performance. Most job scheduling approaches adapted from traditional task scheduling algorithms fall into two category look-ahead category and just-in-time category. The major difference between these two categories is whether scheduling decisions are made before the actual job dispatch or at the time any ready jobs are identified, i.e., their predecessors have all been completed. Clearly, for look-ahead approaches, the acquisition of accurate performance information on resources plays a critical role in their decision making [9]. One major drawback of just-in-time approaches is the loss of timely data transfers. For example, provided that a job has three predecessors and they complete at different times, the data transfers from these predecessors to the job start at the time the last predecessor completes its execution. Where, the times the first two predecessors are completed and the time the last predecessor is completed is wasted. The challenge of scheduling grid workflow application with static strategy is discussed many researches, but few research efforts address them. Rescheduling is implemented in the GrADS, where it is normally activated by contract violation. However, the efforts are all conducted for iterative applications, allowing system to perform rescheduling decisions at each iteration. The plan switching approach is to construct a family of activity graphs and investigates the means of switching from one member of the family to another when the execution of one activity graph fails, but the major drawback is all the plans are made without knowledge about the future environment change since the grid does not ensures a stable computing environment [5]. Another rescheduling policy is proposed in, which considers rescheduling at a few, carefully selected points during the execution. The research tackles one of the shortcomings that static scheduling always assumes accurate prediction of job performance. After the initial schedule is made, it selectively reschedules some jobs if the run time performance variance exceeds predefined threshold. However, this approach deals with only the inaccurate estimation and does not consider the change of resource pool [10]. Since the majority of the tasks that grid computing handles are interdependent and most of them are workflow application, the scheduling must concentrate on the resource usage, to have a well organized use of resource; the scheduler must know the information 12
5.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME about the resource, and not just the amount of resource alone. The basic function of the scheduler needs the amount of resource that is available in the grid to schedule the n number of jobs. To have a effective schedule the scheduler needs the status of the resource, its processor speed, how much time it has left before start executing another job, how much jobs it can handle in the given amount of time,etc. Based on which the adaptive scheduling strategy is framed , when the schedule does not produce an optimal performance , or due to the dynamic changes in the availability of the resources the scheduler adapts to the situation and schedules the job to complete its execution. III. ADAPTIVE SCHEDULING STRATEGY Even though static and dynamic scheduling performs near to optimal, its effectiveness in a dynamic grid environment is questioned. The proposed semidynamic strategy based novel adaptive job scheduling with load balancing algorithm by which the workflow scheduler can adapt to the grid dynamics to achieve its strength practically. A. Issues with Traditional Scheduling Planning is a onetime activity in the traditional static scheduling. The static scheduling does not consider the future change of grid environment after the resource mapping is made. On the other hand, rescheduling in execution phase is proposed but mainly used to support fault tolerance. Overall, the issues with traditional static scheduling are: (1) Accuracy of estimation of communication and computation costs, (2) Adaptation to dynamic environment, Figure 3.1 Classification of Static scheduling and (3) Separation of workflow scheduler from executor. Fundamentally the above two issues are related to the lack of collaboration between the workflow scheduler and executor. With collaboration, a scheduler will be aware of the grid environment change, including the job performance variance and resource availability, and is able to 13
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME adaptively reschedule based on the increasingly accurate estimations. This approach can both continuously improve performance by considering the new resources and minimize the impact caused by unexpected resource downgrade or unavailability [9]. The main issue with dynamic scheduling in workflow application is the execution procedure of the interdependent tasks. The output of one job could be the input of another job. In the dynamic scheduling jobs are executed in all possible ways where the resources are taken into account for scheduling B. Adaptive Scheduling The basic idea of adaptive scheduling for a given DAG and a set of currently available resources, the scheduler makes the initial resource mapping as any other traditional static approaches do [5]. Along with scheduler gets updation from the executor about the resources information, such as: Resource Pool Change: If new resource is discovered after the current plan is made, rescheduling may reduce the makespan of a DAG by considering the resource addition. When resource fails, fault tolerant mechanism is triggered and it is taken care of by Executor. However, if the failure is predictable, rescheduling can minimize the failure impact on overall performance. Resource Performance Variance: The performance estimation accuracy is largely dependent on history data, and inaccurate estimation leads to a bad schedule. If the run time Performance Monitor can notify the scheduler of any significant performance variance, the scheduler along with predictor will evaluate its impact and reschedule if necessary. The scheduler reacts to event by evaluating if makespan can be reduced by rescheduling. For example, if a new resource becomes available, the scheduler will evaluate if a new schedule with the extra resource in consideration can produce smaller makespan [7]. If so, the scheduler will replace the current one with new one by submitting it to the Executor. IV. SYSTEM MODEL This paper proposes Adaptive Rescheduling approach that can both continuously improve performance by considering the new resources and minimize the impact caused by unexpected resource downgrade or unavailability. 14
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME Grid consists of number of sites. Each site is autonomous in nature; it has its own users and global users, hosts are time and resource shared. The hosts of same group or organizations are clustered together and termed to be sites. The resources of the same cluster or site can access by the hosts of the same site as their own. When the resource that has to be accessed from another site the complexity arises. There are administrators allocated for every site. Depending on which they deploy access polices and access rights and processors allocation etc. These this differs for every organization. The workflow application has n number of interrelated jobs in one task. The start time of first job of the task to the finish time of nth job in the task is termed as makespan. These jobs are denoted by DAGs(Directed Acyclic Graph) each node is job and the edges denote the relation between jobs. The cluster has load scheduler which makes the load balancer and also reports status of the resource to the job scheduler which maintains in a history repository for future job scheduling. Figure 4.1 System Design V. ADAPTVE SCHEDULING WITH LOAD BALANCING FOR WORKFLOW APPLICATIONS While the task scheduling problem in heterogeneous computing systems with perfectly accurate performance information on resources and applications still remains very difficult, uncertainties on resource performance and the lack of control over grid resources make workflow scheduling even more complex. Unlike many other workflow scheduling schemes, we consider both makespan and resource usage to be equally important and take this into account in our scheduling model. Efficient resource usage is crucial in grid scheduling because 1) a grid consists of multiple sites administered by 15
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME different entities that use their own resources for other tasks beside the grid jobs and 2) due to the fluctuations and uncertainty surrounding sites in a grid system, lower resource usage not necessarily the minimization of the number of resources used, rather the minimization of resource time means lower overall variance in the expected completion time (makespan) of an application [1]. A. Job Scheduling To start with, the schedule is made static with the predefine tasks and its execution procedures. The tasks are scheduled statically based on their priority, the priority of each task to be set with upward rank value, ranku, which is based on the mean computation and communication cost. The task list is created by sorting the tasks in ascending order of the ranku. Tie-breaking is done randomly. There can be alternative policies for tie-breaking, such as selecting the task whose immediate successor tasks has higher upward rank. Since these alternate policies increase time complexity, random selection strategy is preferred [7]. The task list that is made based on the priority is taken as S* (current schedule). At the end of each iteration, mutation is considered if no improvement on S* is made during the current iteration. Schedule randomly chooses a mutation method between point and swap mutations and mutates each job in S* with a probability of 0.5 sufficient to generate substantially different schedules. The mutated schedule is then used as the current schedule (S*). If there have been some improvements on S* in the current iteration, it is passed onto the next iteration for further improvements. This schedule manipulation process repeats for a predefined number of iterations. Now, jobs in the current schedule S* are dispatched to their assigned hosts, as they become ready, i.e., their predecessor jobs have finished [1]. B. Cluster Based Dynamic Load Balancing In the grid computing the resources are globally distributed and the geographically located resources are termed as clusters. The clusters are group of processors in an organization or a LAN (Local Area Network). Number of clusters group together and perform the computation. The user can access any number of resources from anywhere any time. Each cluster has a scheduler which has the details of the resources or processor information, about their current computation. Amount of jobs they executed 16
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME amount of resource they hold .etc. the scheduler also has the details of the neighboring clusters. The cluster communicates between them to make the load dynamically balance and to execute the job effectively [11]. When the scheduled jobs are dispatched to the hosts. The next step is the load balancing. When a job is getting delayed to execute, the Actual Latest Finish time of the job is calculated. When the delay is less than the ALFT, then the load is balanced. If the delay is greater than the ALFT, the scheduler communicates with the neighboring cluster and the load is dynamically allocated to processor that is optimal to execute the remaining job. Migration is used for allocating the job from one resource to another. VI. EVALUATION AND COMPARISION Adaptive scheduling strategy based HEFT-based adaptive rescheduling algorithm (AHEFT) has the advantage of continuously improves the performance, considering the new resource and minimizes the impact caused by unexpected resource down grade or unavailability [7]. Drawbacks of adaptive rescheduling technique are it takes time in rescheduling the jobs and to implement the collaboration model, rescheduling has to be integrated with advance resource reservation and resource availability prediction model. The gridflow gives the Advantage of grid performance service comprises performance prediction capability with a new application response measurement technique[2], which can be used to enable prediction-based scheduling as well as response-based scheduling. But the disadvantage such as the process of a grid workflow encompasses multiple administrative domains (organizations) [7]. The lack of central ownership and control results in incomplete information and Computational and networking capabilities can vary significantly over time in the grid environment. Application performance prediction becomes difficult and real-time resource information update within a large-scale global grid becomes impossible, which lower its performance 17
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME Figure 5.1 The structure of adaptive semidynamic scheduling strategy with Load Balancing Critical Path-on-a processor (CPOP) Algorithm and Heterogeneous Earliest First Time (HEFT) Algorithm, both gives more or less same performance measures as high performance and fast schedule time. But has slight disadvantage of high schedule cost [4]. Duplication-based Bottom-Up Scheduling Algorithm (DBUS) gives uses both task insertion and task duplication. It also gives the facility to minimize the schedule length [6]. It does not impose any restriction on number of task duplication and Task duplication mainly causes an increase of resource usage which causes disadvantage to the algorithm. The ADOS algorithm has the highest possibility for reducing the makespan of the task, which is the total amount of time required from the start of the first job to the end of the last job. It also reduces the resource usage. The disadvantage is the algorithm itself is a complicated which makes iteration more complex in selecting the scheduling best schedule strategy. The Adaptive scheduling with load balancing strategy provides best performance on both parameters of the grid work, it reduces the makespan of the jobs and it makes effective use of resource. The concept provide less complex static and dynamic 18
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME scheduling strategy and clear condition of the occurrence of load balancing, which makes the scheduling to have less schedule time. VII. CONCLUSION In this paper, the scheduling of workflow applications in grids is addressed. Unlike many previous scheduling approaches for such a class of applications, the semidynamic scheduling strategy takes into account both makespan and resource usage. The schedule achieves the two objectives effectively combining a static heuristic scheduling scheme with a dynamic scheduling technique with load balancing. Based on the research and study conducted, the results obtained, the resource-usage-conscious scheduling scheme significantly improves resource utilization without sacrificing too much of makespan. The load balancing strategy incorporated into scheduling helps in ensuring the quality of schedules against performance fluctuations of grid resources. The future work would be the implementation of the proposed Adaptive Scheduling with load balancing concept for the workflow application carried on the grid environment. The result should be taken and compared and a complete performance evaluation study will be conducted to determine the promising performance of the schedule on the grid platform. REFERENCE 1. Young Choon Lee, Member, IEEE, Riky Subrata, and Albert Y. Zomaya, Fellow, IEEE “On the Performance of a Dual-Objective Optimization Model for Workflow Applications on Grid Platforms,” Proc, IEEE Transactions On Parallel And Distributed systems, Vol. 20, N0. 9, September 2009. 2. J. Cao, S.A. Jarvis, S. Saini, and G.R. Nudd, “GridFlow: Workflow Management for Grid Computing,” Proc. Third IEEE/ACM Int’l Symp. Cluster Computing and the Grid (CCGrid ’03), pp. 198-205, 2003. 3. H. Casanova, “Simgrid: A Toolkit for the Simulation of Application Scheduling,” Proc. First IEEE/ACM Int’l Symp. Cluster Computing and the Grid (CCGrid ’01), pp. 430-437, 2001. 4. R. Wolski, “Dynamically Forecasting Network Performance Using the Network Weather Service,” Proc. Sixth IEEE Int’l Symp. High Performance Distributed Computing (HPDC ’97), pp. 316-325, 1997. 19
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Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME 5. H. Topcuoglu, S. Hariri, and M. Wu, “Performance-Effective and Low- Complexity Task Scheduling for Heterogeneous Computing,” IEEE Trans. Parallel and Distributed Systems, vol. 13, no. 3, pp. 260-274, Mar. 2002. 6. D. Bozdag, U. Catalyurek, and F. Ozguner, “A Task Duplication Based Bottom- Up Scheduling Algorithm for Heterogeneous Environments,” Proc. 19th Int’l Parallel and Distributed Processing Symp. (IPDPS ’05), Apr. 2005. 7. Z.Yu and W.Shi, “An Adaptive Rescheduling Strategy for Grid Workflow Applications,” Proc. 21st Int’l Parallel and Distributed Processing Symp. (IPDPS), 2007. 8. Y. Gil, V. Ratnakar, E. Deelman, G. Mehta, and J. Kim, “Wings for Pegasus: Creating Large-Scale Scientific Applications Using Semantic Representations of Computational Workflows,” Proc.19th Conf. Innovative Applications of Artificial Intelligence (IAAI ’07),pp. 1767-1774, 2007. 9. M. Wieczorek, R. Prodan, and T. Fahringer, “Scheduling of Scientific Workflows in the ASKALON Grid Environment,” ACMSIGMOD Record, vol. 34, no. 3, pp. 56-62, Sept. 2005. 10. G. Singh, E. Deelman, G. Mehta, K. Vahi, M.-H. Su, G.B. Berriman,J. Good, J.C. Jacob, D.S. Katz, A. Lazzarini, K. Blackburn, andS. Koranda, “The Pegasus Portal: Web Based Grid Computing,”Proc. 20th Ann. ACM Symp. Applied Computing (SAC ’05),pp. 680-686, 2005. 11. A.Mondal, K. Goda, and M. Kitsuregawa, Effective Load Balancing via Migration and Replication in Spatial Grids, LNCS 2736, pp. 201-211, 2003. 12. B.A. Shirazi, A.R. Hurson, and K.M. Kavi, Scheduling and Load Balancing in Parallel and Distributed Systems. IEEE CS Press, 1995. 13. A.M. Dobber, G.M. Koole, and R.D. van der Mei, “Dynamic Load Balancing Experiments in a Grid,” Proc. Fifth IEEE Int’l Symp. Cluster Computing and the Grid (CCGrid ’05), pp. 123-130, 2005. 20
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International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 2, Number 1, Dec - Jan (2011), © IAEME D.Daniel received the B.E degree in Information Technology from Karunya University in 2009. He is currently doing his Post graduate in Karunya University,now works on the project in parallel and distributed Systems, and Continues research on adaptive scheduling techniques in grid computing. D. Asir received the B.E degree in Information Technology from Anna University in 2009. He is currently doing his Post graduate in Karunya University,now works on the project in parallel and distributed Systems, and Continues research on Dynamic load balancing techniques in grid computing. Mrs.S.P.Jeno Lovesum ,Asst professor, has completed her Master of Engineering (CSE) in Annamalai University, Chidambaram and doing her research in Cloud Computing. A.Catherine Esther Karunya received the B.E degree in Information Technology from Karunya University in 2009. She is currently doing her Post graduate in Karunya University,now works on the project in Computing Security and continues her research in Networking. 21
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