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

  • Authors:

  • Hongbo Liu;Ajith Abraham;Václav Snášel;Seán McLoone

  • Affiliations:

  • School of Information, Dalian Maritime University, 116026 Dalian, China and School of Computer, Dalian University of Technology, 116023 Dalian, China and Machine Intelligence Research Labs, Auburn ...;Machine Intelligence Research Labs, Auburn, WA 98071, USA and Department of Computer Science, VŠB-Technical University of Ostrava,708 33 Ostrava-Poruba, Czech Republic;Machine Intelligence Research Labs, Auburn, WA 98071, USA and Department of Computer Science, VŠB-Technical University of Ostrava,708 33 Ostrava-Poruba, Czech Republic;Department of Electronic Engineering, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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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.