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

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2017Suggested citation
Gabaldon Ponsa, Eloi;
Lérida Monsó, Josep Lluís;
Guirado Fernández, Fernando;
Planes Cid, Jordi;
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(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.
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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.