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Workflow Scheduling Algorithms for Grid Computing
"... Workflow scheduling is one of the key issues in the management of workflow execution. Scheduling is a process that maps and manages execution of interdependent tasks on distributed resources. It introduces allocating suitable resources to workflow tasks so that the execution can be completed to sat ..."
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Cited by 18 (4 self)
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Workflow scheduling is one of the key issues in the management of workflow execution. Scheduling is a process that maps and manages execution of interdependent tasks on distributed resources. It introduces allocating suitable resources to workflow tasks so that the execution can be completed to satisfy objective functions specified by users. Proper scheduling can have significant impact on the performance of the system. In this chapter, we investigate existing workflow scheduling algorithms developed and deployed by various Grid projects.
Robust task scheduling in nondeterministic heterogeneous computing systems
 in Proc. IEEE Intl. Conf. Cluster Computing
, 2006
"... The paper addresses the problem of matching and scheduling of DAGstructured application to both minimize the makespan and maximize the robustness in a heterogeneous computing system. Due to the conflict of the two objectives, it is usually impossible to achieve both goals at the same time. We give ..."
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Cited by 12 (3 self)
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The paper addresses the problem of matching and scheduling of DAGstructured application to both minimize the makespan and maximize the robustness in a heterogeneous computing system. Due to the conflict of the two objectives, it is usually impossible to achieve both goals at the same time. We give two definitions of robustness of a schedule based on tardiness and miss rate. Slack is proved to be an effective metric to be used to adjust the robustness. We employ ǫconstraint method to solve the biobjective optimization problem where minimizing the makespan and maximizing the slack are the two objectives. Overall performance of a schedule considering both makespan and robustness is defined such that user have the flexibility to put emphasis on either objective. Experiment results are presented to validate the performance of the proposed algorithm.
Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments
"... Abstract: A heterogeneous computing environment is a suite of heterogeneous processors interconnected by highspeed networks, thereby promising high speed processing of computationally intensive applications with diverse computing needs. Scheduling of an application modeled by Directed Acyclic Graph ..."
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Cited by 7 (1 self)
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Abstract: A heterogeneous computing environment is a suite of heterogeneous processors interconnected by highspeed networks, thereby promising high speed processing of computationally intensive applications with diverse computing needs. Scheduling of an application modeled by Directed Acyclic Graph (DAG) is a key issue when aiming at high performance in this kind of environment. The problem is generally addressed in terms of task scheduling, where tasks are the schedulable units of a program. The task scheduling problems have been shown to be NPcomplete in general as well as several restricted cases. In this study we present a simple scheduling algorithm based on list scheduling, namely, low complexity Performance Effective Task Scheduling (PETS) algorithm for heterogeneous computing systems with complexity O (e) (p+ log v), which provides effective results for applications represented by DAGs. The analysis and experiments based on both randomly generated graphs and graphs of some real applications show that the PETS algorithm substantially outperforms the existing scheduling algorithms such as Heterogeneous Earliest Finish Time (HEFT), CriticalPathOn a Processor (CPOP) and Levelized Min Time (LMT), in terms of schedule length ratio, speedup, efficiency, running time and frequency of best results. Key words: DAG, task graph, task scheduling, heterogeneous computing system, schedule length,
Heuristics Based Genetic Algorithm for Scheduling Static Tasks in Homogeneous Parallel System
"... Multiprocessor task scheduling is an important and computationally difficult problem. Multiprocessors have emerged as a powerful computing means for running realtime applications, especially that a uniprocessor system would not be sufficient enough to execute all the tasks. That computing environm ..."
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Cited by 6 (0 self)
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Multiprocessor task scheduling is an important and computationally difficult problem. Multiprocessors have emerged as a powerful computing means for running realtime applications, especially that a uniprocessor system would not be sufficient enough to execute all the tasks. That computing environment requires an efficient algorithm to determine when and on which processor a given task should execute. A task can be partitioned into a group of subtasks and represented as a DAG (Directed Acyclic Graph), that problem can be stated as finding a schedule for a DAG to be executed in a parallel multiprocessor system. The problem of mapping metatasks to a machine is shown to be NPcomplete. The NPcomplete problem can be solved only using heuristic approach. The execution time requirements of the applications ’ tasks are assumed to be stochastic. In multiprocessor scheduling problem, a given program is to be scheduled in a given multiprocessor system such that the program’s execution time should be minimized. The last job must be completed as early as possible.
Task Scheduling For Multiprocessor Systems Using Memetic Algorithms
"... Abstract: In multiprocessor systems, an efficient scheduling of a parallel program onto the processors that minimizes the entire execution time is vital for achieving a high performance. The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be exec ..."
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Cited by 3 (0 self)
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Abstract: In multiprocessor systems, an efficient scheduling of a parallel program onto the processors that minimizes the entire execution time is vital for achieving a high performance. The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimize. This scheduling problem is known to be NP Hard. In multiprocessor scheduling problem, a given program is to be scheduled in a given multiprocessor system such that the program’s execution time is minimized. The objective is makespan minimization, i.e. we want the last job to complete as early as possible. The tasks scheduling problem is a key factor for a parallel multiprocessor system to gain better performance. A task can be partitioned into a group of subtasks and represented as a DAG ( Directed Acyclic Graph), so the problem can be stated as finding a schedule for a DAG to be executed in a parallel multiprocessor system so that the schedule can e minimized. This helps to reduce processing time and increase processor utilization. Genetic algorithm (GA) is one of the widely used technique for this optimization. But there are some shortcomings which can be reduced by using GA with another optimization technique, such as simulated annealing (SA). This combination of GA and SA is called memetic algorithms. This paper proposes a new algorithm by using this memetic algorithm technique.
Artificial Immune Systems Applied to Multiprocessor Scheduling
"... Abstract. We propose an efficient method of extracting knowledge when scheduling parallel programs onto processors using an artificial immune system (AIS). We consider programs defined by Directed Acyclic Graphs (DAGs). Our approach reorders the nodes of the program according to the optimal executio ..."
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Abstract. We propose an efficient method of extracting knowledge when scheduling parallel programs onto processors using an artificial immune system (AIS). We consider programs defined by Directed Acyclic Graphs (DAGs). Our approach reorders the nodes of the program according to the optimal execution order on one processor. The system works in either learning or production mode. In the learning mode we use an immune system to optimize the allocation of the tasks to individual processors. Best allocations are stored in the knowledge base. In the production mode the optimization module is not invoked, only the stored allocations are used. This approach gives similar results to the optimization by a genetic algorithm (GA) but requires only a fraction of function evaluations. 1
A Method for Distributing Scheduling Heuristics Inside Service Oriented Environments Using a NatureInspired Approach
"... Abstract—As Distributed Systems begin to rely more and more on Service Oriented Architectures there is an increasingly need to store information remotely and to access to it by means of services. In this frame scheduling heuristics play an important role as they help reduce task execution costs. We ..."
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Cited by 1 (1 self)
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Abstract—As Distributed Systems begin to rely more and more on Service Oriented Architectures there is an increasingly need to store information remotely and to access to it by means of services. In this frame scheduling heuristics play an important role as they help reduce task execution costs. We propose a model that follows a nature inspired paradigm to represent the scheduling heuristics itself. Services are used to access remotely available data required by the algorithm. Furthermore a model to share the schedule data among multiple distributed scheduling algorithms that run in parallel is devised. Keywordsscheduling algorithms; distributed computing; nature inspired scheduling I.
Static Task Scheduling with a Unified Objective on Time and Resource Domains
, 2006
"... Task scheduling for parallel and distributed systems is an NPcomplete problem, which is well documented and studied in the literature. A large set of proposed heuristics for this problem mainly target to minimize the completion time or the schedule length of the output schedule for a given task gra ..."
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Cited by 1 (0 self)
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Task scheduling for parallel and distributed systems is an NPcomplete problem, which is well documented and studied in the literature. A large set of proposed heuristics for this problem mainly target to minimize the completion time or the schedule length of the output schedule for a given task graph. An additional objective, which is not much studied, is the minimization of number of processors allocated for the schedule. These two objectives are both conflicting and complementary, where the former is on the time domain targeting to improve task utilization and the latter is on the resource domain targeting to improve processor utilization. In this paper, we unify these two objectives with a weighting scheme that allows to personalize the importance of the objectives. In this paper, we present a new genetic search framework for task scheduling problem by considering the new objective. The performance of our genetic algorithm is compared with the scheduling algorithms in the literature that consider the heterogeneous processors. The results of the synthetic benchmarks and task graphs that are extracted from wellknown applications clearly show that our genetic algorithmbased framework outperforms the related work with respect to normalized cost values, for various task graph characteristics.
Hybrid Algorithm for Multiprocessor Task Scheduling
"... Multiprocessors have become powerful computing means for running realtime applications and their high performance depends greatly on parallel and distributed network environment system. Consequently, several methods have been developed to optimally tackle the multiprocessor task scheduling problem ..."
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Cited by 1 (0 self)
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Multiprocessors have become powerful computing means for running realtime applications and their high performance depends greatly on parallel and distributed network environment system. Consequently, several methods have been developed to optimally tackle the multiprocessor task scheduling problem which is called NPhard problem. To address this issue, this paper presents two approaches, Modified List Scheduling Heuristic (MLSH) and hybrid approach composed of Genetic Algorithm (GA) and MLSH for task scheduling in multiprocessor system. Furthermore, this paper proposes three different representations for the chromosomes of genetic algorithm: task list (TL), processor list (PL) and combination of both (TLPLC). Intensive simulation experiments have been conducted on different random and realworld application graphs such as GaussJordan, LU decomposition, Gaussian elimination and Laplace equation solver problems. Comparisons have been done with the most related algorithms like: list scheduling heuristics algorithm LSHs, Bipartite GA (BGA) [1] and Priority based MultiChromosome (PMC) [2]. The achieved results show that the proposed approaches significantly surpass the other approaches in terms of task execution time (makespan) and processor efficiency.
CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. (2009) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cpe.1417
, 2009
"... evolution for scheduling workflow applications on global Grids ..."