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Maximizing business value by optimal assignment of jobs to resources in grid computing

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  • Kumar, Subodha
  • Dutta, Kaushik
  • Mookerjee, Vijay

Abstract

An important problem that arises in the area of grid computing is one of optimally assigning jobs to resources to achieve a business objective. In the grid computing area, however, such scheduling has mostly been done from the perspective of maximizing the utilization of resources. As this form of computing proliferates, the business aspects will become crucial for the overall success of the technology. Hence, we discuss the grid scheduling problem from a business perspective. We show that this problem is not only strongly NP-hard, but it is also non-approximable. Therefore, we propose heuristics for different variants of the problem and show that these heuristics provide near-optimal solution for a wide variety of problem instances. We show that the execution times of proposed heuristics are very low, and hence, they are suitable for solving problems in real-time. We also present several managerial implications and compare the performance of two widely used models in the real-time scheduling of grid computing.

Suggested Citation

  • Kumar, Subodha & Dutta, Kaushik & Mookerjee, Vijay, 2009. "Maximizing business value by optimal assignment of jobs to resources in grid computing," European Journal of Operational Research, Elsevier, vol. 194(3), pages 856-872, May.
    • Handle: RePEc:eee:ejores:v:194:y:2009:i:3:p:856-872
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    References listed on IDEAS

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    1. Han Hoogeveen & Petra Schuurman & Gerhard J. Woeginger, 2001. "Non-Approximability Results for Scheduling Problems with Minsum Criteria," INFORMS Journal on Computing, INFORMS, vol. 13(2), pages 157-168, May.
    2. Leslie A. Hall & Andreas S. Schulz & David B. Shmoys & Joel Wein, 1997. "Scheduling to Minimize Average Completion Time: Off-Line and On-Line Approximation Algorithms," Mathematics of Operations Research, INFORMS, vol. 22(3), pages 513-544, August.
    3. Ravi Bapna & Sanjukta Das & Robert Garfinkel & Jan Stallaert, 2008. "A Market Design for Grid Computing," INFORMS Journal on Computing, INFORMS, vol. 20(1), pages 100-111, February.
    4. Schnizler, Bjorn & Neumann, Dirk & Veit, Daniel & Weinhardt, Christof, 2008. "Trading grid services - a multi-attribute combinatorial approach," European Journal of Operational Research, Elsevier, vol. 187(3), pages 943-961, June.
    5. Chung-Yee Lee & Lei Lei & Michael Pinedo, 1997. "Current trends in deterministic scheduling," Annals of Operations Research, Springer, vol. 70(0), pages 1-41, April.
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    Cited by:

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    2. Anna Ye Du & Sanjukta Das & R. Ramesh, 2013. "Efficient Risk Hedging by Dynamic Forward Pricing: A Study in Cloud Computing," INFORMS Journal on Computing, INFORMS, vol. 25(4), pages 625-642, November.
    3. Sam Ransbotham & Ishwar Murthy & Sabyasachi Mitra & Sridhar Narasimhan, 2011. "Sequential Grid Computing: Models and Computational Experiments," INFORMS Journal on Computing, INFORMS, vol. 23(2), pages 174-188, May.
    4. Dutta, Kaushik & VanderMeer, Debra, 2011. "Cost-based decision-making in middleware virtualization environments," European Journal of Operational Research, Elsevier, vol. 210(2), pages 344-357, April.
    5. Hossein Jahandideh & Julie Ward Drew & Filippo Balestrieri & Kevin McCardle, 2020. "Individualized Pricing for a Cloud Provider Hosting Interactive Applications," Service Science, INFORMS, vol. 12(4), pages 130-147, December.

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