Title: A hybrid algorithm for efficient task scheduling in cloud computing environment

Authors: M. Roshni Thanka; P. Uma Maheswari; E. Bijolin Edwin

Addresses: Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of Computer Science and Engineering, College of Engineering, Guindy Campus, Anna University, Chennai, India ' Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

Abstract: Cloud is a boon to the generation which provides services that can reduce the overhead in maintenance and computational complexities. Scheduling the user's job in the cloud resources plays an important role for the better performance. Task scheduling is an NP-hard problem, since it may have more than one solution to fit in. In this paper a hybrid algorithm is proposed by the amalgamation of artificial bee colony Algorithm and particle swarm optimisation named as ABPS algorithm. The proposed ABPS algorithm optimises the task scheduling on the cloud environment by providing minimised makespan, cost, and maximised resource utilisation and to balance the load. The proposed ABPS algorithm compared with ABC and PSO algorithm have been simulated in the CloudSim simulation tool. The proposed ABPS algorithm based on makespan outperforms ABC and PSO algorithms by 22.07% and 28.12%, respectively, also when compared with cost outperforms ABC and PSO algorithms by 32.41% and 44.49% respectively. ABPS algorithm based on resource utilisation outperforms ABC and PSO algorithms by 49.37% and 48.88% respectively and based on degree of imbalance outperforms ABC and PSO algorithms by 16.21% and 20.51% respectively.

Keywords: cloud computing; task scheduling; artificial bee colony; ABC algorithm; particle swarm optimisation; PSO algorithm; makespan; cost; load balancing; resource utilisation.

DOI: 10.1504/IJRIS.2019.099850

International Journal of Reasoning-based Intelligent Systems, 2019 Vol.11 No.2, pp.134 - 140

Received: 30 Aug 2017
Accepted: 19 Feb 2018
Published online: 24 May 2019 *

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