Swarm Optimization for Solving Load Balancing in Cloud Computing

Abstract

© 2020, Springer Nature Switzerland AG. Cloud computing is the new paradigm of representing computing capabilities as a service. With its facility of resource sharing and being cost-effective, it exists in every domain of life, enhancing their functionality and adding new opportunities to it. Accordingly, the focus on solving its dilemmas like load balancing becomes more challenging and the research in swarm-based algorithms to find optimal results has been expanding. This paper discusses the use of two swarm algorithms including Ant-Lion optimizer (ALO) and Grey wolf optimizer (GWO) in task scheduling of the Cloud Computing environment. Additionally, compare the results with commonly known swarm algorithms: Particle Swarm Optimization (PSO) and Firefly Algorithm (FFA). The results show the ALO and GWO are a strong adversary to Particle Swarm Optimization (PSO), and better than Firefly (FFA) and they have potential in load balancing.