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Swarm Optimization for Solving Load Balancing in Cloud Computing

  • Aya A. Salah FarragEmail author
  • Safia Abbas Mohamad
  • El Sayed M. El-Horbaty
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)

Abstract

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.

Keywords

Cloud computing Load balancing Task scheduling Ant-Lion optimizer Swarm intelligence 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Aya A. Salah Farrag
    • 1
    Email author
  • Safia Abbas Mohamad
    • 1
  • El Sayed M. El-Horbaty
    • 1
  1. 1.Ain Shams UniversityCairoEgypt

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