Scheduling parameter sweep workflow in the Grid based on resource competition

Smanchat, Sucha; Indrawan, Maria; Ling, Sea; Enticott, Colin; Abramson, David

doi:10.1016/j.future.2013.01.005

Cited by 10 publications

(19 citation statements)

References 39 publications

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“…Although, many design frameworks [35] have been developed, they are not accessible for scientists working with scientific workflows and workflow management systems. Instead, parameter sweeps and manual trial and error are still the methods of choice for life science researcher today [36,37], which was therefore used as comparison here. To our knowledge there is no other comprehensive approach for general workflow optimization freely available today, except Nimrod/OK [7], which is a more specialized workflow parameter optimization tool from the engineering domain and apparently not available for download.…”

Section: Discussionmentioning

confidence: 99%

A new optimization phase for scientific workflow management systems

Holl

Zimmermann

Palmblad

et al. 2014

Future Generation Computer Systems

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Section: Discussionmentioning

confidence: 99%

A new optimization phase for scientific workflow management systems

Holl

Zimmermann

Palmblad

et al. 2014

Future Generation Computer Systems

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“…The disparate processing units share no system resources, they have their own operating system, and communicate through high-speed network. The main computing models within the distributed parallel computing systems include cluster [26,89], grid [13,32,82,86], and cloud computing [33,59,82].…”

Section: Parallel Computing Systemsmentioning

confidence: 99%

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

et al. 2018

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While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

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“…To execute a Grid workflow, the tasks in the workflow must be scheduled to Grid resources. The scheduling of Grid workflow has been a prominent research area and has attracted much attention from researchers [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…By parallelizing the execution of multiple instances of a parameter sweep workflow, the overall execution time can be reduced [8]. However, a bottleneck may occur as the tasks in multiple instances of the parameter sweep workflow require the same set of Grid resources [5,9]. Although some algorithms are designed to handle parameter sweep workflows, they do not specifically address the bottleneck issue [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Grid Workflow Scheduling Based on Bottleneck Detection and Execution Context

Smanchat

Sritawathon

Ngamsantivong

2013

Proceedings of International Conference on Information Integration and Web-Based Applications &Amp; Services

Self Cite

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Scheduling of Grid workflows has been a prevalent research area as it is the mechanism that helps improving the efficiency of the Grid workflow execution. The most common goal of scheduling workflow in the Grid is to minimize execution makespan and many algorithms have been proposed to address this issue. However, most of the algorithms so far usually focus and are implemented based on a single principle and act according to that principle. Only few algorithms consider adaptive scheduling process that acts differently in different situations.In this paper, we propose an adaptive Grid workflow scheduling that reacts to the presence of bottlenecks and different execution context. With this adaptive approach, the proposed algorithm can achieve better overall makespan. Simulations of parameter sweep workflows with single instance and multiple instances executed in parallel are used to evaluate the algorithm against four existing algorithms.

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Scheduling parameter sweep workflow in the Grid based on resource competition

Cited by 10 publications

References 39 publications

A new optimization phase for scientific workflow management systems

A new optimization phase for scientific workflow management systems

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

An Adaptive Grid Workflow Scheduling Based on Bottleneck Detection and Execution Context

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