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Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment

Author

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  • Mohit Agarwal

    (Department of Physics & Computer Science, Dayalbagh Educational Institute, Agra, Uttar Pradesh 282002, India)

  • Gur Mauj Saran Srivastava

    (Department of Physics & Computer Science, Dayalbagh Educational Institute, Agra, Uttar Pradesh 282002, India)

Abstract

Task scheduling is one of the most difficult problems which is associated with cloud computing. Due to its nature, as it belongs to nondeterministic polynomial time (NP)-hard class of problem. Various heuristic as well as meta-heuristic approaches have been used to find the optimal solution. Task scheduling basically deals with the allocation of the task to the most efficient machine for optimal utilization of the computing resources and results in better makespan. As per literature, various meta-heuristic algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and their other hybrid techniques have been applied. Through this paper, we are presenting a novel meta-heuristic technique — genetic algorithm enabled particle swarm optimization (PSOGA), a hybrid version of PSO and GA algorithm. PSOGA uses the diversification property of PSO and intensification property of the GA. The proposed algorithm shows its supremacy over other techniques which are taken into consideration by presenting less makespan time in majority of the cases which leads up to 22.2% improvement in performance of the system and also establishes that proposed PSOGA algorithm converges faster than the others.

Suggested Citation

  • Mohit Agarwal & Gur Mauj Saran Srivastava, 2018. "Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1237-1267, July.
    • Handle: RePEc:wsi:ijitdm:v:17:y:2018:i:04:n:s0219622018500244
    DOI: 10.1142/S0219622018500244
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    References listed on IDEAS

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    1. Mohammed Abdullahi & Md Asri Ngadi, 2016. "Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-29, June.
    2. Jian-xia Chang & Tao Bai & Qiang Huang & Da-wen Yang, 2013. "Optimization of Water Resources Utilization by PSO-GA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3525-3540, August.
    3. Zahra Pooranian & Mohammad Shojafar & Jemal H. Abawajy & Ajith Abraham, 2015. "An efficient meta-heuristic algorithm for grid computing," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 413-434, October.
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    5. Wenshuai Wu & Gang Kou, 2016. "A group consensus model for evaluating real estate investment alternatives," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-10, December.
    6. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
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    Cited by:

    1. Kaiwei Liu & Xingcheng Wang & Zhihui Qu, 2019. "Train Operation Strategy Optimization Based on a Double-Population Genetic Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 12(13), pages 1-26, June.
    2. Huang, Yuming & Ge, Bingfeng & Hipel, Keith W. & Fang, Liping & Zhao, Bin & Yang, Kewei, 2023. "Solving the inverse graph model for conflict resolution using a hybrid metaheuristic algorithm," European Journal of Operational Research, Elsevier, vol. 305(2), pages 806-819.
    3. Amit Chhabra & Sudip Kumar Sahana & Nor Samsiah Sani & Ali Mohammadzadeh & Hasmila Amirah Omar, 2022. "Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm," Energies, MDPI, vol. 15(13), pages 1-36, June.

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