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

Mohit Agarwal () and Gur Mauj Saran Srivastava
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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

International Journal of Information Technology & Decision Making (IJITDM), 2018, vol. 17, issue 04, 1237-1267

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.

Keywords: Cloud computing; distributed computing; makespan; meta-heuristic algorithm; particle swarm optimization (PSO); genetic algorithm (GA) (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0219622018500244

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