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Multi-objective energy optimization in grid systems from a brain storming strategy

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Abstract

Nowadays, companies are more aware of an environmentally responsible use of computational resources. Terms like Green Computing promote energy savings in large-scale and distributed resource centers. Scheduling in distributed systems, as Grid Computing, is a challenging task in terms of time. Current research is considering energy savings as a new promising objective also for meta-schedulers. In this work, energy consumption and execution time are optimized simultaneously using a Multi-objective brain storm algorithm (MOBSA). This new algorithm is compared with two multi-objective algorithms: a novel algorithm based on the fireflies’ behavior—Multi-objective firefly algorithm (MO-FA)—and the well-known Non-dominated Sorting Genetic Algorithm (NSGA-II). Furthermore, other comparisons with real grid meta-schedulers such as Workload Management System from gLite, and Deadline Budget Constraint from Nimrod-G are carried out. The results show that MOBSA provides the best performance in any of the scenarios studied here.

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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, University of Extremadura, Escuela Politecnica, Campus Universitario s/n, 10003, Cáceres, Spain

    Miguel A. Vega-Rodríguez

Authors
  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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Communicated by V. Loia.

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Arsuaga-Ríos, M., Vega-Rodríguez, M.A. Multi-objective energy optimization in grid systems from a brain storming strategy. Soft Comput 19, 3159–3172 (2015). https://doi.org/10.1007/s00500-014-1474-7

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