Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

Hongbo Liu*, Ajith Abraham, Václav Snášel, Sean McLoone

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)

Abstract

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications.

Original languageEnglish
Pages (from-to)228-243
Number of pages16
JournalInformation Sciences
Volume192
Issue number1
DOIs
Publication statusPublished - 01 Jun 2012

Keywords

  • Distributed data-intensive computing environments
  • Particle swarm
  • Scheduling problem
  • Security constraints
  • Swarm intelligence
  • Work-flow

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management

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