Research Article

Cloud Computing Task Scheduling using Genetic Algorithm: A Survey

Authors

  • Esraa Masadeh Engineering and Artificial Intelligence Department, Al-Salt Technical College, Al-Balqa Applied University, Al-Salt, Jordan
  • Raja Masadeh Computer Science Department, the World Islamic Sciences and Education University, Amman, Jordan
  • Omar Almomani Department of Networks and Cybersecurity, Al-Ahliyya Amman University, Amman, Jordan
  • Kholoud Alshqurat Academic Services Department, The World Islamic Sciences and Education University, Jordan
  • Shaymaa Masadeh Academic Services Department, The World Islamic Sciences and Education University, Jordan

Abstract

Cloud computing has become a hot research topic due to the robust development and migration of many services to this cloud environment. The main problem that appears is regarded with both management's efficiency and the large amount of resources utilization. These resources are managed by data centers as well as distributed to internet users dynamically depending on their availability, request and quality parameters that are in request to be usable. Thus, task scheduling is a major concern that can affect system performance. This study presents a survey of employing genetic algorithm for task scheduling in cloud environments. This survey provides a comprehensive overview of task scheduling approaches in cloud environments, with a particular focus on the application of genetic algorithms. It discusses fundamental cloud computing concepts, scheduling criteria, and classification of scheduling methods. Furthermore, it analyses a wide range of GA-based scheduling algorithms, comparing their performance, task characteristics, and simulation tools.

Article information

Journal

Journal of Humanities and Social Sciences Studies

Volume (Issue)

7 (6)

Pages

74-87

Published

29-06-2025

How to Cite

Esraa Masadeh, Raja Masadeh, Omar Almomani, Kholoud Alshqurat, & Shaymaa Masadeh. (2025). Cloud Computing Task Scheduling using Genetic Algorithm: A Survey. Journal of Humanities and Social Sciences Studies, 7(6), 74-87. https://doi.org/10.32996/jhsss.2025.7.6.8

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Keywords:

Task scheduling, Cloud computing, Genetic Algorithm, Optimizations