Scheduling Tasks Sharing Files on Heterogeneous Clusters

Giersch, Arnaud; Robert, Yves; Vivien, Frédéric

doi:10.1007/978-3-540-39924-7_88

Cited by 13 publications

(69 citation statements)

References 2 publications

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“…As this decision is made once for each task, the total time complexity of these heuristics is Oðpn 2 þ pnjAjÞ. After Casanova et al [8], [9], Giersch et al [15], [16] proposed several different heuristics. These heuristics have better time complexity and their solution quality is comparable with those of the previous heuristics.…”

Section: Structurementioning

confidence: 99%

“…' . The additional policies readiness, shared, and locality proposed by Giersch et al [15], [16] are also explained in Table 2. As seen in Fig.…”

Section: Structurementioning

confidence: 99%

“…We prefer this approach to a direct task-to-processor assignment heuristic because the proposed refinement heuristics are developed by taking the flaws of existing constructive scheduling heuristics into account. For this purpose, we use the heuristics proposed by Giersch et al [15], [16] because of their short runtimes. The additional policies are not used, but all five of the heuristics, each having a different objective function, are used since their relative performances vary with the computation-to-communication ratio characteristics of applications.…”

Section: Initial Task Assignment Phasementioning

confidence: 99%

“…This framework has recently been studied in [7], [8], [9], [15], [16] for adaptive scheduling of parameter-sweep-like applications in Grid environments. Such applications arise in the Application Level Scheduling (AppLeS) project [7].…”

Section: Introductionmentioning

confidence: 99%

“…They also proposed a new heuristic XSufferage exclusively for APST. After this work, Giersch et al [15], [16] proposed several different heuristics which reduce the time complexity while preserving the quality of schedules. All these scheduling heuristics are based on the greedy choices that depend on the momentary completion time values of tasks.…”

Section: Introductionmentioning

confidence: 99%

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Iterative-Improvement-Based Heuristics for Adaptive Scheduling of Tasks Sharing Files on Heterogeneous Master-Slave Environments

Kaya

Aykanat

2006

IEEE Trans. Parallel Distrib. Syst.

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Abstract-The scheduling of independent but file-sharing tasks on heterogeneous master-slave platforms has recently found important applications in Grid environments. The scheduling heuristics recently proposed for this problem are all constructive in nature and based on a common greedy criterion which depends on the momentary completion time values of the tasks. We show that this greedy decision criterion has shortcomings in exploiting the file-sharing interaction among tasks since completion time values are inadequate to extract the global view of this interaction. We propose a three-phase scheduling approach which involves initial task assignment, refinement, and execution ordering phases. For the refinement phase, we model the target application as a hypergraph and, with an elegant hypergraph-partitioning-like formulation, we propose using iterative-improvement-based heuristics for refining the task assignments according to two novel objective functions. Unlike the turnaround time, which is the actual schedule cost, the smoothness of proposed objective functions enables the use of iterative-improvement-based heuristics successfully since their effectiveness and efficiency depend on the smoothness of the objective function. Experimental results on a wide range of synthetically generated heterogeneous master-slave frameworks show that the proposed three-phase scheduling approach performs much better than the greedy constructive approach.

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Section: Structurementioning

confidence: 99%

“…' . The additional policies readiness, shared, and locality proposed by Giersch et al [15], [16] are also explained in Table 2. As seen in Fig.…”

Section: Structurementioning

confidence: 99%

Section: Initial Task Assignment Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Iterative-Improvement-Based Heuristics for Adaptive Scheduling of Tasks Sharing Files on Heterogeneous Master-Slave Environments

Kaya

Aykanat

2006

IEEE Trans. Parallel Distrib. Syst.

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Scheduling Tasks Sharing Files on Heterogeneous Clusters

Giersch

Robert

Vivien

2003

Recent Advances in Parallel Virtual Machine and Message Passing Interface

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Metaheuristics for Scheduling in Distributed Computing Environments

2008

Studies in Computational Intelligence

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We consider the problem of scheduling an application on a computing system consisting of heterogeneous processors and one or more file repositories. The application consists of a large number of file-sharing, otherwise independent tasks. The files initially reside on the repositories. The interconnection network is heterogeneous. We focus on two disjoint problem cases. In the first case, there is only one file repository which is called as the master processor. In the second case, there are two or more repositories, each holding a distinct set of files. The problem is to assign the tasks to the processors, to schedule the file transfers from the repositories, and to order the executions of tasks on each processor in such a way that the turnaround time is minimized. This chapter surveys several solution techniques; but the stress is on our two recent works [22, 23]. At the first glance, iterative-improvement-based heuristics do not seem to be suitable for the aforementioned scheduling problems. This is because their immediate application suggests iteratively improving a complete schedule, and hence building and exploring a complex neighborhood around the current schedule. Such complex neighborhood structures usually render the heuristics time-consuming and make them stuck to a part of the search space. However, in both of the our recent works, we show that these issues can be solved by using a three-phase approach: initial task assignment, refinement, and execution ordering. The main thrust of these two works is that iterative-improve-based heuristics can efficiently deliver effective solutions, implying that iterative-improve-based heuristics can provide highly competitive solutions to the similar scheduling problems.

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Scheduling Tasks Sharing Files on Heterogeneous Clusters

Cited by 13 publications

References 2 publications

Iterative-Improvement-Based Heuristics for Adaptive Scheduling of Tasks Sharing Files on Heterogeneous Master-Slave Environments

Iterative-Improvement-Based Heuristics for Adaptive Scheduling of Tasks Sharing Files on Heterogeneous Master-Slave Environments

Scheduling Tasks Sharing Files on Heterogeneous Clusters

Metaheuristics for Scheduling in Distributed Computing Environments

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