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An improved load balanced metaheuristic scheduling in cloud

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Abstract

Cloud computing refers to on-demand delivery of service over internet and has application in various domains like media, research, business, bigdata analysis etc. Task scheduling is one of the prime issues in this type of environment. Various metaheuristic algorithms and hard optimization problems have been proposed for solving cloud task scheduling which is a non-deterministic polynomial or an NP. Adaptation of the scheduling strategy to the changes taking place in the environment has to be done by a good scheduler. A proposal for cloud scheduling by means of a balanced load using both firefly algorithm (FA) and particle swarm optimization (PSO) heuristics has been made. The aim is to balance the load of the entire system while at the same time bring down the makespan of a set of tasks. This new strategy for scheduling has been simulated with CloudSim tool kit package. The results of this experiment proved that the proposed FA performed better than min–min scheduling, PSO, and also the first come first serve methods.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Surya Engineering College, Erode, India

    M. Aruna

  2. Department of Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore, India

    D. Bhanu

  3. Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, India

    S. Karthik

Authors
  1. M. Aruna
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  2. D. Bhanu
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  3. S. Karthik
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Correspondence to M. Aruna.

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Aruna, M., Bhanu, D. & Karthik, S. An improved load balanced metaheuristic scheduling in cloud. Cluster Comput 22 (Suppl 5), 10873–10881 (2019). https://doi.org/10.1007/s10586-017-1213-9

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  • DOI: https://doi.org/10.1007/s10586-017-1213-9

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