Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments

Gabaldon Ponsa, Eloi; Lérida Monsó, Josep Lluís; Guirado Fernández, Fernando; Planes Cid, Jordi

doi:https://doi.org/10.1007/s11227-016-1866-9

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Issue date

2017

Author

Gabaldon Ponsa, Eloi

Lérida Monsó, Josep Lluís

Guirado Fernández, Fernando

Planes Cid, Jordi

Suggested citation

Gabaldon Ponsa, Eloi; Lérida Monsó, Josep Lluís; Guirado Fernández, Fernando; Planes Cid, Jordi; . (2017) . Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments. The Journal of Supercomputing, 2017, vol. 73, núm. 1, p. 354-369. https://doi.org/10.1007/s11227-016-1866-9.

Abstract

Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. Most of the techniques have focused on determining the computing requirements based on load predictions and thus turning unnecessary nodes on and off. Nevertheless, once the available resources have been configured, new opportunities arise for reducing energy consumption by providing optimal matching of parallel applications to the available computing nodes. Current research in scheduling has concentrated on not only optimizing the energy consumed by the processors but also optimizing the makespan, i.e., job completion time. The large number of heterogeneous computing nodes and variability of application-tasks are factors that make the scheduling an NP-Hard problem. Our aim in this paper is a multi-objective genetic algorithm based on a weighted blacklist able to generate scheduling decisions that globally optimizes the energy consumption and the makespan.

URI

http://hdl.handle.net/10459.1/59327

DOI

https://doi.org/10.1007/s11227-016-1866-9

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The Journal of Supercomputing, 2017, vol. 73, núm. 1, p. 354-369

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Except where otherwise noted, this item's license is described as cc-by (c) Gabaldon, et al., 2016