Title
Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments.
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.
Year
DOI
Venue
2017
10.1007/s11227-016-1866-9
The Journal of Supercomputing
Keywords
Field
DocType
Weighted blacklist, Energy aware scheduling, Multi-objective optimization, Genetic algorithm, Federated clusters, Co-allocation
Job shop scheduling,Optimal matching,Computer science,Scheduling (computing),Parallel computing,Blacklist,Symmetric multiprocessor system,Multi-objective optimization,Energy consumption,Genetic algorithm,Distributed computing
Journal
Volume
Issue
ISSN
73
1
1573-0484
Citations 
PageRank 
References 
3
0.37
22
Authors
4
Name
Order
Citations
PageRank
Eloi Gabaldon151.05
Josep Lluis Lérida2293.28
Fernando Guirado38912.69
Jordi Planes448631.38