Affiliations 

  • 1 Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
  • 2 Department of Cyber Security Science, Federal University of Technology, Minna, Niger State, Nigeria
  • 3 Department of Mathematics, Bauchi State University, Gadau, Nigeria
PLoS ONE, 2017;12(5):e0176321.
PMID: 28467505 DOI: 10.1371/journal.pone.0176321

Abstract

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.