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Meta-heuristic based reliable and green workflow scheduling in cloud computing

Author

Listed:
  • Nidhi Rehani

    (National Institute of Technology)

  • Ritu Garg

    (National Institute of Technology)

Abstract

Efficient workflow scheduling in modern cloud environment involves optimization of various conflictive objectives like execution performance (time), reliability, energy consumption etc. Despite this trend, numerous heuristics have been devoted to workflow scheduling mainly focused on the optimization of makespan (execution time) only without giving much attention on other important objectives. Reducing energy consumption is the major concern as it brings several important benefits like reduction in the operating costs, increase in the system reliability and environmental protection. Moreover, the compute processors in cloud are not failure free. Any kind of failure can be critical for an application. Hence in this paper, we proposed the multi-objective NSGA-II based scheduling algorithm for workflow applications with the aim to optimize three conflicting criterion simultaneously: makespan\execution time, reliability and energy consumption for executing the workflow application in cloud environment. In order to reduce the computation complexity of the algorithm, we used the efficient non-domination level update mechanism rather than applying the non-domination sorting from the scratch each time. The simulation analysis of the proposed algorithm on CloudSim toolkit shows that the Pareto optimal solutions obtained have good convergence, uniform diversity and computational efficiency.

Suggested Citation

  • Nidhi Rehani & Ritu Garg, 2018. "Meta-heuristic based reliable and green workflow scheduling in cloud computing," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 811-820, August.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:4:d:10.1007_s13198-017-0659-8
    DOI: 10.1007/s13198-017-0659-8
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    References listed on IDEAS

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    1. Harish Sharma & Jagdish Chand Bansal & K. V. Arya & Xin-She Yang, 2016. "Lévy flight artificial bee colony algorithm," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(11), pages 2652-2670, August.
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