Open Access

ARTICLE

Hybrid Task Scheduling Algorithm for Makespan Optimisation in Cloud Computing: A Performance Evaluation

by Abdulrahman M. Abdulghani^*

Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, 43400, Malaysia

* Corresponding Author: Abdulrahman M. Abdulghani. Email:

Journal on Artificial Intelligence 2024, 6, 241-259. https://doi.org/10.32604/jai.2024.056259

Received 18 July 2024; Accepted 04 September 2024; Issue published 16 October 2024

Abstract

Cloud computing has rapidly evolved into a critical technology, seamlessly integrating into various aspects of daily life. As user demand for cloud services continues to surge, the need for efficient virtualization and resource management becomes paramount. At the core of this efficiency lies task scheduling, a complex process that determines how tasks are allocated and executed across cloud resources. While extensive research has been conducted in the area of task scheduling, optimizing multiple objectives simultaneously remains a significant challenge due to the NP (Non-deterministic Polynomial) Complete nature of the problem. This study aims to address these challenges by providing a comprehensive review and experimental analysis of task scheduling approaches, with a particular focus on hybrid techniques that offer promising solutions. Utilizing the CloudSim simulation toolkit, we evaluated the performance of three hybrid algorithms: Estimation of Distribution Algorithm-Genetic Algorithm (EDA-GA), Hybrid Genetic Algorithm-Ant Colony Optimization (HGA-ACO), and Improved Discrete Particle Swarm Optimization (IDPSO). Our experimental results demonstrate that these hybrid methods significantly outperform traditional standalone algorithms in reducing Makespan, which is a critical measure of task completion time. Notably, the IDPSO algorithm exhibited superior performance, achieving a Makespan of just 0.64 milliseconds for a set of 150 tasks. These findings underscore the potential of hybrid algorithms to enhance task scheduling efficiency in cloud computing environments. This paper concludes with a discussion of the implications of our findings and offers recommendations for future research aimed at further improving task scheduling strategies, particularly in the context of increasingly complex and dynamic cloud environments.

---

Keywords

---