An effective multi-objective task scheduling and resource optimization in cloud environment using hybridized metaheuristic algorithm

Article type: Research Article

Authors: Kalimuthu, Raj Kumar | Thomas, Brindha

Affiliations: Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, TamilNadu, India

Correspondence: [*] Corresponding author. Raj Kumar Kalimuthu, Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, TamilNadu, India. E-mail: [email protected].

Abstract: In today’s world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms.

Keywords: Metaheuristic algorithm, Internet of Things, cloud computing, resource optimization, scheduling algorithms

DOI: 10.3233/JIFS-212370

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4051-4063, 2022