Welcome to the InfoSci Platform
Reference Hub2
Binary Self-Adaptive Salp Swarm Optimization-Based Dynamic Load Balancing in Cloud Computing

Binary Self-Adaptive Salp Swarm Optimization-Based Dynamic Load Balancing in Cloud Computing

Bivasa Ranjan Parida, Amiya Kumar Rath, Hitesh Mohapatra
Copyright: © 2022 |Volume: 17 |Issue: 1 |Pages: 25
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781799894001|DOI: 10.4018/IJITWE.295964
Cite Article Cite Article

MLA

Parida, Bivasa Ranjan, et al. "Binary Self-Adaptive Salp Swarm Optimization-Based Dynamic Load Balancing in Cloud Computing." IJITWE vol.17, no.1 2022: pp.1-25. http://doi.org/10.4018/IJITWE.295964

APA

Parida, B. R., Rath, A. K., & Mohapatra, H. (2022). Binary Self-Adaptive Salp Swarm Optimization-Based Dynamic Load Balancing in Cloud Computing. International Journal of Information Technology and Web Engineering (IJITWE), 17(1), 1-25. http://doi.org/10.4018/IJITWE.295964

Chicago

Parida, Bivasa Ranjan, Amiya Kumar Rath, and Hitesh Mohapatra. "Binary Self-Adaptive Salp Swarm Optimization-Based Dynamic Load Balancing in Cloud Computing," International Journal of Information Technology and Web Engineering (IJITWE) 17, no.1: 1-25. http://doi.org/10.4018/IJITWE.295964

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

In the recent era of cloud computing, the huge demand for virtual resource provisioning requires mitigating the challenges of uniform load distribution as well as efficient resource utilization among the virtual machines in cloud datacenters. Salp swarm optimization is one of the simplest, yet efficient metaheuristic techniques to balance the load among the VMs. The proposed methodology has incorporated self-adaptive procedures to deal with the unpredictable population of the tasks being executed in cloud datacenters. Moreover, a sigmoid transfer function has been integrated to solve the discrete problem of tasks assigned to the appropriate VMs. Thus, the proposed algorithm binary self-adaptive salp swarm optimization has been simulated and compared with some of the recent metaheuristic approaches, like BSO, MPSO, and SSO for conflicting scheduling quality of service parameters. It has been observed from the result analysis that the proposed algorithm outperforms in terms of makespan, response time, and degree of load imbalance while maximizing the resource utilization.