An Adaptive Load Balancing using Particle Swarm Optimization for Cloud Task Scheduling
An Adaptive Load Balancing using Particle Swarm Optimization for Cloud Task Scheduling |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-9 |
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| Year of Publication : 2023 | ||
| Author : Chaitanya Udatha, Gondi Lakshmeeswari |
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| DOI : 10.14445/22315381/IJETT-V71I9P204 | ||
How to Cite?
Chaitanya Udatha, Gondi Lakshmeeswari, "An Adaptive Load Balancing using Particle Swarm Optimization for Cloud Task Scheduling," International Journal of Engineering Trends and Technology, vol. 71, no. 9, pp. 36-45, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I9P204
Abstract
With cutting-edge services available via subscription, the usage of cloud technology is rapidly increasing in daily life using advanced services. Efficient management of services and delivering them on demand require proper scheduling of resources and requests. Load balancing aware scheduling techniques are employed to accomplish this, which distribute requests uniformly among resources and optimize resource utilization. In cloud computing, load balancers are essential for balancing workloads on resources. They ensure even workload distribution across all resources by transferring workloads from overloaded to underloaded resources. The proposed Load Balancing Improved Multi-Objective Particle Swarm Optimization (LBIMOPSO) technique aims to manage load uniformly and allocate tasks to the best-suited virtual machines. It is a robust optimization technique that considers multiple objective functions simultaneously and effectively balances workloads in a cloud computing environment. However, according to an existing survey, there is an improvement in makespan performance compared to proper load balancing across virtual machines. Therefore, the proposed LBIMOPSO algorithm improves resource utilization, makespan, and load balance deviation compared to traditional swarm-intelligence-based ant colony and particle swarm optimization algorithms.
Keywords
Cloud computing, Improved particle swarm optimization, Load balance deviation, Makespan, Resource utilization.
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