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Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing

Author

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  • Youssef Harrath

    (University of Bahrain, Kingdom of Bahrain)

  • Rashed Bahlool

    (University of Bahrain, Kingdom of Bahrain)

Abstract

The problem of allocating real-time tasks to cloud computing resources minimizing the makespan is defined as a NP-hard problem. This work studies the same problem with two realistic multi-objective criteria; the makespan and the total cost of execution and communication between tasks. A mathematical model including objective functions and constraints is proposed. In addition, a theoretical lower bound for the makespan which served later as a baseline to benchmark the experimental results is theoretically determined and proven. To solve the studied problem, a multi-objective genetic algorithm is proposed in which new crossover and mutation operators are proposed. Pareto-optimal solutions are retrieved using the genetic algorithm. The experimental results show that genetic algorithm provides efficient solutions in term of makespan for different-size problem instances with reference to the lower bound. Moreover, the proposed genetic algorithm produces many Pareto optimal solutions that dominate the solution given by greedy algorithm for both criteria.

Suggested Citation

  • Youssef Harrath & Rashed Bahlool, 2019. "Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 9(3), pages 37-57, July.
    • Handle: RePEc:igg:jcac00:v:9:y:2019:i:3:p:37-57
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