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A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids

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Abstract

The sensitivity analysis of a Cellular Genetic Algorithm (CGA) with local search is used to design a new and faster heuristic for the problem of mapping independent tasks to a distributed system (such as a computer cluster or grid) in order to minimize makespan (the time when the last task finishes). The proposed heuristic improves the previously known Min-Min heuristic. Moreover, the heuristic finds mappings of similar quality to the original CGA but in a significantly reduced runtime (1,000 faster). The proposed heuristic is evaluated across twelve different classes of scheduling instances. In addition, a proof of the energy-efficiency of the algorithm is provided. This convergence study suggests how additional energy reduction can be achieved by inserting low power computing nodes to the distributed computer system. Simulation results show that this approach reduces both energy consumption and makespan.

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Authors and Affiliations

  1. Computer Science and Communications, University of Luxembourg, 6, rue Coudenhove-Kalergi, 1359, Luxembourg, Luxembourg

    Frédéric Pinel, Johnatan E. Pecero & Pascal Bouvry

  2. Interdisciplinary Centre for Security Reliability and Trust, University of Luxembourg, 6, rue Coudenhove-Kalergi, 1359, Luxembourg, Luxembourg

    Bernabé Dorronsoro

  3. NDSU-CIIT Green Computing and Communications Laboratory, Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND, 58108-6050, USA

    Samee U. Khan

Authors
  1. Frédéric Pinel
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  2. Bernabé Dorronsoro
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  3. Johnatan E. Pecero
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  4. Pascal Bouvry
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  5. Samee U. Khan
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Corresponding author

Correspondence to Frédéric Pinel.

Additional information

This work is supported by the Fonds National de la Recherche Luxembourg, CORE Project Green-IT (C09 IS/05).

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Pinel, F., Dorronsoro, B., Pecero, J.E. et al. A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids. Cluster Comput 16, 421–433 (2013). https://doi.org/10.1007/s10586-012-0207-x

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