Title: A proactive population dynamics load balancing algorithm in cloud computing for QoS optimisation

Authors: Shahbaz Afzal; G. Kavitha

Addresses: Department of Information Technology, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India ' Department of Information Technology, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India

Abstract: Load unbalancing is a grim concern among cloud service providers that has adverse consequences on Quality of Service (QoS) and profit turnout. Load balancing tries to overcome load imbalances by ensuring proper task deployment among cloud resources, yielding productive resource utilisation, throughput, and other QoS metrics. A proactive load balancing approach featuring population dynamics model called as PPDLB model is proposed to limit the tasks within the maximum carrying capacity of VM. The PPDLB algorithm was compared with the existing Improved Weighted Round Robin (IWRR), Harris Hawk Optimisation (HHO) and Spider Monkey Optimisation (SMO) algorithms. The PPDLB algorithm was found to be efficient than existing algorithms in terms of makespan, resource utilisation, degree of balance and number of task migrations. The dominance of the proposed algorithm over existing load balancing techniques lies in the fact that it eliminates the need to solve migration associated metrics. Moreover, it has greater convergence rate when search space becomes large which makes it feasible for large scale scheduling problems.

Keywords: cloud computing; scheduling; load unbalancing; proactive load balancing; set partitioning; parallel queues; population dynamics; quality of service.

DOI: 10.1504/IJGUC.2021.120095

International Journal of Grid and Utility Computing, 2021 Vol.12 No.5/6, pp.554 - 572

Received: 05 Dec 2019
Accepted: 27 Sep 2020
Published online: 07 Jan 2022 *

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