On a multi-objective evolutionary algorithm for optimizing end-to-end performance of scientific workflows in distributed environments

Yi Gu, Shwu Ling Shenq, Qishi Wu, Dipankar Dasgupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Large-scale distributed scientific workflows demand various system resources that are geographically scattered and typically shared by many users through wide-area network connections. These domain-specific workflows have different end-to-end performance requirements, which necessitate optimizing multiple objectives when mapped to heterogeneous network environments. We construct mathematical models for workflow mapping and formulate it as a multi-objective optimization problem to minimize latency and maximize throughput. We propose a workflow mapping solution based on a multi-objective genetic algorithm that uses a chromosome scheme to represent a set of possible workflow mapping schemes and employs genetic operators including selection, mutation and crossover to steer the evolution process. The performance superiority of the proposed mapping solution is illustrated through extensive simulations in comparison with existing workflow mapping methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 Spring Simulation Multiconference, SpringSim 2012 - 45th Annual Simulation Symposium 2012, ANSS 2012
Pages69-77
Number of pages9
Edition2 BOOK
StatePublished - Dec 1 2012
Externally publishedYes
Event45th Annual Simulation Symposium 2012, ANSS 2012, Part of the 2012 Spring Simulation Multiconference, SpringSim 2012 - Orlando, FL, United States
Duration: Mar 26 2012Mar 30 2012

Publication series

NameSimulation Series
Number2 BOOK
Volume44
ISSN (Print)0735-9276

Other

Other45th Annual Simulation Symposium 2012, ANSS 2012, Part of the 2012 Spring Simulation Multiconference, SpringSim 2012
Country/TerritoryUnited States
CityOrlando, FL
Period3/26/123/30/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Keywords

  • End-to-end performance
  • Genetic algorithm
  • Multi-objective optimization
  • Scientific workflow

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