A proactive population dynamics load balancing algorithm in cloud computing for QoS optimisation. (24th December 2021)

• Record Type: Journal Article
• Authors: Afzal, Shahbaz; Kavitha, G.
• Is Part Of: International journal of grid and utility computing. Volume 12:Number 5/6(2021)
• Page Start: 554
• Page End: 572
• Publication Date: 2021-12-24
• Subjects: cloud computing -- scheduling -- load unbalancing -- proactive load balancing -- set partitioning -- parallel queues -- population dynamics -- quality of service; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

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.