A Comparative Analysis and Evaluation of Swarm-Adaptive Scheduling Methods in Cloud Environment

A Comparative Analysis and Evaluation of Swarm-Adaptive Scheduling Methods in Cloud Environment
  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-7
Year of Publication : 2024
Author : Anu Kadian, Kamna Solanki, Amita Dhankhar
DOI : 10.14445/22315381/IJETT-V72I7P102

How to Cite?

Anu Kadian, Kamna Solanki, Amita Dhankhar, "A Comparative Analysis and Evaluation of Swarm-Adaptive Scheduling Methods in Cloud Environment," International Journal of Engineering Trends and Technology, vol. 72, no. 7, pp. 11-23, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I7P102

Abstract
Cloud computing provides a distributed environment to share resources and optimise task processing. Load balancing and makespan are the primary challenges of task scheduling that affect performance and user satisfaction in the cloud environment. An effective task scheduling method in a cloud environment can optimise resource utilisation and generate an effective sequence of task execution. The optimisation algorithms can be integrated within the task scheduler to map and execute the tasks effectively. In this paper, four swarm-based scheduling algorithms are implemented and compared in the cloud environment. The algorithms included in this work are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) algorithm, Ant Colony Optimization (ACO), and Grey Wolf Optimization (GWO) algorithms. These algorithms are simulated in different scenarios with 10 to 100 tasks. The comparative evaluation is conducted against Response Time, Makespan, Resource Utilization, and migration count parameters. The analysis results identified that the GWO achieved more effective results than ACO, PSO, and ABC algorithms.

Keywords
Cloud computing, Resource scheduling, Task scheduling, Resource allocation, Cloud environment.

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