Opposition-based learning inspired particle swarm optimization (OPSO) scheme for task scheduling problem in cloud computing

• Published in: Journal of ambient intelligence and humanized computing 2021-10, Vol.12 (10), p.9855-9875
• Main Authors: Agarwal, Mohit, Srivastava, Gur Mauj Saran
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Cloud computing; Completion time; Computational Intelligence; Convergence; Distributed processing; Efficiency; Energy consumption; Engineering; Genetic algorithms; Heuristic; Heuristic methods; Learning; Multiprocessing; Optimization; Original Research; Particle swarm optimization; Robotics and Automation; Scheduling; Sensitivity analysis; Software services; Statistical analysis; Task scheduling; User Interfaces and Human Computer Interaction
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-020-02730-4

The problem of scheduling of tasks in distributed, heterogeneous, and multiprocessing computing environment like grid and cloud computing is considered as one of the most important issue from research perspective. As the performance of such kind of systems is highly depends upon the way, how tasks are allocated among the multiple processing units for their efficient execution. The underlying objective of any task scheduling mechanism is to minimize the overall makespan for the execution of given set of jobs/tasks and computing machines. Scheduling of tasks in cloud computing falls in the class of NP-hard optimization problem. As a result, many meta-heuristic algorithms have been applied and tested to solve this problem but still lot of scope is there for the better strategies. The characteristic of the good algorithm is that it must be adaptable to the dynamic environment. Through this paper, we are proposing task scheduling mechanism based on particle swarm optimization (PSO) in which opposition-based learning technique is used to avoid premature convergence and to accelerate the convergence of standard PSO and compared same with the well-established task scheduling strategies based on PSO, mPSO (modified PSO), genetic algorithm GA, max–min, minimum completion time and minimum execution time. The results obtained for the various class of experiments clearly establish that the proposed opposition-based learning inspired particle swarm optimization based scheduling strategy performs better in comparison to its peers which are taken into the consideration.