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A Hybrid Algorithm Using Genetic Algorithm and Cuckoo Search Algorithm to Solve Job Scheduling Problem in Computational Grid Systems

Author

Listed:
  • Tarun Kumar Ghosh

    (Department of Computer Science and Engineering, Haldia Institute of Technology, West Bengal, India)

  • Sanjoy Das

    (Department of Engineering and Technological Studies, Kalyani University, West Bengal, India)

Abstract

Job scheduling is one of the major challenges in Grid computing systems to efficiently exploit the capabilities of dynamic, autonomous, heterogeneous and distributed resources for execution of different types of jobs. Thus optimal job scheduling is an NP-complete problem which can easily be solved by using heuristic techniques. This paper presents a hybrid algorithm for job scheduling using Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA) for efficiently allocating jobs to resources in a Grid system so that makespan and flowtime are minimized. This proposed algorithm combines the advantages of both GA and CSA. The authors' results have been compared with standard GA, CSA and Ant Colony Optimization (ACO) to show the importance of the proposed algorithm.

Suggested Citation

  • Tarun Kumar Ghosh & Sanjoy Das, 2016. "A Hybrid Algorithm Using Genetic Algorithm and Cuckoo Search Algorithm to Solve Job Scheduling Problem in Computational Grid Systems," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 7(2), pages 1-11, April.
    • Handle: RePEc:igg:jaec00:v:7:y:2016:i:2:p:1-11
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