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Cluster Computing

, Volume 22, Supplement 5, pp 11975–11988 | Cite as

Ranging and tuning based particle swarm optimization with bat algorithm for task scheduling in cloud computing

  • R. ValarmathiEmail author
  • T. Sheela
Article

Abstract

Cloud computing is the new technology offering services to build new application through virtualization. Virtualization improves the usage of resource utilization in cloud environment. Recently research in Task Scheduling problem has received more attention because the customers want to maximize the utilization of resources in a cheaper way. In this paper an enhanced particle swarm optimization (PSO) algorithm for improving the efficiency in the task scheduling has been proposed. A ranging function and tuning function based PSO (RTPSO) based on data locality is introduced in this paper for solving the inertia weight assignment problem in existing PSO algorithm for task scheduling. The large inertia weight and small inertia weight will assist a global search and local search respectively. In addition, we have combined the RTPSO with Bat algorithm (RTPSO-B) to improve the optimization. Cloudsim is used in this paper to simulate the task scheduling in cloud environment. The proposed RTPSO-B based task scheduling is compared with various existing task scheduling algorithms such as GA, ACO, ordinary PSO. Simulation results proved proposed RTPSO-B based task scheduling method reduces makespan, cost and increases the utilization of resources.

Keywords

Cloud computing Task scheduling Particle swarm optimization Bat algorithm 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Faculty of CSESathyabama Institute of Science and TechnologyChennaiIndia
  2. 2.Department of Computer Science and EngineeringSri Sairam Engineering CollegeChennaiIndia
  3. 3.Department of Information TechnologySri Sairam Engineering CollegeChennaiIndia

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