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Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing

Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing

Youssef Harrath, Rashed Bahlool
Copyright: © 2019 |Volume: 9 |Issue: 3 |Pages: 21
ISSN: 2156-1834|EISSN: 2156-1826|EISBN13: 9781522567783|DOI: 10.4018/IJCAC.2019070103
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MLA

Harrath, Youssef, and Rashed Bahlool. "Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing." IJCAC vol.9, no.3 2019: pp.37-57. http://doi.org/10.4018/IJCAC.2019070103

APA

Harrath, Y. & Bahlool, R. (2019). Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing. International Journal of Cloud Applications and Computing (IJCAC), 9(3), 37-57. http://doi.org/10.4018/IJCAC.2019070103

Chicago

Harrath, Youssef, and Rashed Bahlool. "Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing," International Journal of Cloud Applications and Computing (IJCAC) 9, no.3: 37-57. http://doi.org/10.4018/IJCAC.2019070103

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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.

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