Title: A new throttled adapted load balancing strategy for dynamic VM allocations in cloud datacentres

Authors: S. Shanmugapriya; N. Priya

Addresses: PG Department of Computer Science, Dwaraka Doss Goverdhan Doss Vaishnav College, University of Madras, Chennai, India ' Research Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, University of Madras, Chennai, India

Abstract: Infrastructure as a service is a crucial service offered by cloud computing (CC) that delivers on-demand virtual machines (VMs). As the cloud is growing continuously and millions of users are requesting the IaaS cloud simultaneously for accomplishing their tasks, scheduling the VM resources is one of the major challenges. Highly faced improper allocation of VMs leads to server overloading, low resource utilisation, and maximises the loads response time (RT). These issues can be resolved with the load balancing (LB) strategy by selecting the suitable VM grounded on the changing needs. In this study, we enhanced the existing throttled load balancing (TLB) strategy and proposed a new throttled adapted load balancing (TALB) approach, with the main goals of decreasing the VM's searching time (ST) and RT, datacentre processing time (DPT), cost, and workload balancing among VMs. The TALB algorithm is evaluated using the CloudAnalyst simulation tool with the existing RR, ESCE, and TLB algorithms.

Keywords: cloud computing; CC; infrastructure as a service; IaaS; virtual machine; load balancing; LB; throttled load balancing; TLB; CloudAnalyst; throttled adapted load balancing; TALB.

DOI: 10.1504/IJCC.2024.139600

International Journal of Cloud Computing, 2024 Vol.13 No.3, pp.191 - 213

Received: 30 Jun 2022
Accepted: 19 May 2023
Published online: 04 Jul 2024 *

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