Title: Improved grey wolf optimisation algorithm for heterogeneous cloud environment task scheduling

Authors: V. Vignesh; R. Santhosh

Addresses: Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, India ' Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, India

Abstract: The attraction towards cloud computing by industry and individuals increases everyday as the benefits and advantages are much reliable and convenient to user to make the process simple. Software and data giants like Google, Microsoft, and Apple are efficiently utilising the cloud features and the research towards improving its efficiency and utilisation is going on worldwide. Cloud computing has large computational data intensive task and by reducing the complexity of task scheduling the efficiency could be improved. This research identifies the issues the existing task scheduling model and provides an optimised scheduling algorithm. Conventional models such as particle swarm optimisation and PBEES algorithm are compared with proposed improved grey wolf optimisation model experimentally to achieve 96% of utilisation efficiency. This reduces the computation cost and provides high performance computing with reliability among the clients and service providers.

Keywords: cloud computing; learning-based grey wolf; reliability.

DOI: 10.1504/IJNVO.2021.115817

International Journal of Networking and Virtual Organisations, 2021 Vol.24 No.3, pp.250 - 266

Received: 02 Mar 2020
Accepted: 23 Aug 2020
Published online: 24 Jun 2021 *

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