research-article

Adapting scientific workflow structures using multi-objective optimization strategies

Authors:

Richard Mcclatchey,

Omer RanaAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 8, Issue 1

Article No.: 4, Pages 1 - 21

https://doi.org/10.1145/2451248.2451252

Published: 19 April 2013 Publication History

Abstract

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 tackled with a novel termination criterion. The results presented in this article demonstrate the feasibility of the termination criterion and demonstrate that significant optimization can be achieved with a multi-objective approach.

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Index Terms

Adapting scientific workflow structures using multi-objective optimization strategies
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 8, Issue 1

April 2013

126 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/2451248

Issue’s Table of Contents

Copyright © 2013 ACM.

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Publication History

Published: 19 April 2013

Accepted: 01 August 2012

Revised: 01 August 2012

Received: 01 November 2011

Published in TAAS Volume 8, Issue 1

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