Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Adapting scientific workflow structures using multi-objective optimization strategies

Adapting scientific workflow structures using multi-objective optimization strategies Adapting Scientific Workflow Structures Using Multi-Objective Optimization Strategies IRFAN HABIB, FET, University of the West of England ASHIQ ANJUM, University of Derby RICHARD MCCLATCHEY, FET, University of the West of England OMER RANA, Cardiff University Scientific workflows have become the primary mechanism for conducting analyses on distributed computing infrastructures such as grids and clouds. In recent years, the focus of optimization within scientific workflows has primarily been on computational tasks and workflow makespan. However, as workflow-based analysis becomes ever more data intensive, data optimization is becoming a prime concern. Moreover, scientific workflows can scale along several dimensions: (i) number of computational tasks, (ii) heterogeneity of computational resources, and the (iii) size and type (static versus streamed) of data involved. Adapting workflow structure in response to these scalability challenges remains an important research objective. Understanding how a workflow graph can be restructured in an automated manner (through task merge, for instance), to address constraints of a particular execution environment is explored in this work, using a multi-objective evolutionary approach. Our approach attempts to adapt the workflow structure to achieve both compute and data optimization. The question of when to terminate the evolutionary search in order to conserve computations is http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

Adapting scientific workflow structures using multi-objective optimization strategies

Download PDF
21 pages

Loading next page...
 
/lp/association-for-computing-machinery/adapting-scientific-workflow-structures-using-multi-objective-7fHAJ0mLnx

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1556-4665
DOI
http://dx.doi.org/10.1145/2451248.2451252
Publisher site
See Article on Publisher Site

Abstract

Adapting Scientific Workflow Structures Using Multi-Objective Optimization Strategies IRFAN HABIB, FET, University of the West of England ASHIQ ANJUM, University of Derby RICHARD MCCLATCHEY, FET, University of the West of England OMER RANA, Cardiff University Scientific workflows have become the primary mechanism for conducting analyses on distributed computing infrastructures such as grids and clouds. In recent years, the focus of optimization within scientific workflows has primarily been on computational tasks and workflow makespan. However, as workflow-based analysis becomes ever more data intensive, data optimization is becoming a prime concern. Moreover, scientific workflows can scale along several dimensions: (i) number of computational tasks, (ii) heterogeneity of computational resources, and the (iii) size and type (static versus streamed) of data involved. Adapting workflow structure in response to these scalability challenges remains an important research objective. Understanding how a workflow graph can be restructured in an automated manner (through task merge, for instance), to address constraints of a particular execution environment is explored in this work, using a multi-objective evolutionary approach. Our approach attempts to adapt the workflow structure to achieve both compute and data optimization. The question of when to terminate the evolutionary search in order to conserve computations is

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS) – Association for Computing Machinery

Published: Apr 1, 2013

There are no references for this article.