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Optimization of resources in parallel systems using a multiobjective artificial bee colony algorithm

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Abstract

Most of the approaches to achieve exascale computing heavily rely on designing power efficient hardware, but experts usually forget that the usage of efficient middlewares, like resource managers or job schedulers, can also play an important role in optimizing power and performance of supercomputing infrastructures. For the optimization of both, power and performance, we propose the implementation of a multiobjective version of artificial bee colony algorithm (MOABC). We have compared our algorithm with other deterministic (first-fit and MOHEFT) and stochastic (NSGA-II) resource selection approaches. The results of our simulations show that, in real computing environments, MOABC is more likely to obtain better optimizations of response times and power consumption.

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Acknowledgements

This work was partially Funded by the AEI (State Research Agency, Spain) and the ERDF (European Regional Development Fund, EU), under the contract TIN2016-76259-P (PROTEIN Project).

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Authors and Affiliations

  1. Escuela Politécnica de Cáceres, University of Extremadura, Avda. Universidad s/n, 10003, Cáceres, Spain

    César Gómez-Martín & Miguel A. Vega-Rodríguez

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  1. César Gómez-Martín
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  2. Miguel A. Vega-Rodríguez
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Correspondence to César Gómez-Martín.

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Gómez-Martín, C., Vega-Rodríguez, M.A. Optimization of resources in parallel systems using a multiobjective artificial bee colony algorithm. J Supercomput 74, 4019–4036 (2018). https://doi.org/10.1007/s11227-018-2407-5

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