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Swarm approach based on gravity for optimizing energy savings in grid systems

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Abstract

Execution time optimization is one of the most important objectives to accomplish for experiments launched on grid environments. However, the computing community is becoming more conscious about energy savings, seeking their optimization. In this work, both execution time and energy consumption are optimized through two swarm and multi-objective algorithms based on both physics and biology fields. On the one hand, multi-objective gravitational search algorithm (MOGSA) is inspired by the gravity forces between the planet masses. On the other hand, Multi-Objective Firefly Algorithm is based on the light attraction between the fireflies. These swarm algorithms are compared with the standard multi-objective algorithm non-dominated sorting genetic algorithm II to study their efficiency as multi-objective algorithms. Moreover, the best algorithm proposed, MOGSA, is compared with MOHEFT (a multi-objective version of one of the most-used algorithms in workflow scheduling, HEFT), and also with two real grid schedulers: Workload Management System and deadline budget constraint. Results show the superior performance of MOGSA regarding the others.

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Notes

  1. Notice \(cs\) refers to a candidate solution or firefly.

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Authors and Affiliations

  1. Information Technology Department, European Organization for Nuclear Research, CERN, 1211, Geneva 23, Switzerland

    María Arsuaga-Ríos

  2. ARCO Research Group, Department Technologies of Computers and Communications, Escuela Politécnica, University of Extremadura, Campus Universitario s/n, Cáceres , 10003, Spain

    Miguel A. Vega-Rodríguez

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  1. María Arsuaga-Ríos
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  2. Miguel A. Vega-Rodríguez
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Correspondence to María Arsuaga-Ríos.

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Arsuaga-Ríos, M., Vega-Rodríguez, M.A. Swarm approach based on gravity for optimizing energy savings in grid systems. J Heuristics 20, 617–641 (2014). https://doi.org/10.1007/s10732-014-9253-2

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  • DOI: https://doi.org/10.1007/s10732-014-9253-2

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