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A Comparison Between Genetic Algorithm and Cuckoo Search Algorithm to Minimize the Makespan for Grid Job Scheduling

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Advances in Computational Intelligence (ICCI 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 509))

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Abstract

Major subjects like heterogeneity of resources, dynamic and autonomous character of Grid resources are most important challenges for Grid job scheduling. Additionally, there are issues of various strategies being maintained by the resource providers and followed by resource users for execution of their jobs. Thus optimal job scheduling is an NP-complete problem which can easily be solved by using heuristic approaches. This paper compares two heuristic methods: Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA) for job scheduling problem in order to efficiently allocating jobs to resources in a Grid system so that the makespan is minimized. Our empirical results have proved that the CSA performs better than the GA.

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References

  1. Foster, I., Kesselman, C.: The grid 2: blueprint for a new computing infrastructure. Morgan Kaufmann (2003)

    Google Scholar 

  2. Ali, S., Siegel, H.J., Maheswaran, M., Hensgen, D., Ali, S.: Representing task and machine heterogeneities for heterogeneous computing systems. Tamkang J. Sci. Eng. 3(3), 195–207 (2000)

    Google Scholar 

  3. Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. Wiley, New York, NY, USA (2004)

    MATH  Google Scholar 

  4. Yang, X.S., Deb, S.: Cuckoo search via levy flights. In: Proceedings of World Congress on Nature and Biologically Inspired Computing, pp. 210–225 (2009)

    Google Scholar 

  5. Yang, X.S., Deb, S.: engineering optimization by cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Haldia Institute of Technology, Haldia, West Bengal, India

    Tarun Kumar Ghosh & Subhabrata Barman

  2. Department of Engineering & Technological Studies, Kalyani University, Kalyani, West Bengal, India

    Sanjoy Das

  3. Department of Computer Applications, Pailan College of Management and Technology, Kolkata, West Bengal, India

    Rajmohan Goswami

Authors
  1. Tarun Kumar Ghosh
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  2. Sanjoy Das
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  3. Subhabrata Barman
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  4. Rajmohan Goswami
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Corresponding author

Correspondence to Tarun Kumar Ghosh .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India

    Sudip Kumar Sahana

  2. Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India

    Sujan Kumar Saha

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Ghosh, T.K., Das, S., Barman, S., Goswami, R. (2017). A Comparison Between Genetic Algorithm and Cuckoo Search Algorithm to Minimize the Makespan for Grid Job Scheduling. In: Sahana, S.K., Saha, S.K. (eds) Advances in Computational Intelligence. ICCI 2015. Advances in Intelligent Systems and Computing, vol 509. Springer, Singapore. https://doi.org/10.1007/978-981-10-2525-9_14

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  • DOI: https://doi.org/10.1007/978-981-10-2525-9_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2524-2

  • Online ISBN: 978-981-10-2525-9

  • eBook Packages: EngineeringEngineering (R0)

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