Multiobjective Small-World Optimization for Energy Saving in Grid Environments

被引：3
作者：
Arsuaga-Rios, Maria [1 ]
Vega-Rodriguez, Miguel A. [2 ]
机构：
[1] CERN, European Org Nucl Res, Beams Dept, CH-1211 Geneva 23, Switzerland
[2] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, ARCO Res Grp, Caceres 10003, Spain
来源：
COMPUTER JOURNAL | 2015年 / 58卷 / 03期
关键词：
multiobjective optimization; grid environments; swarm algorithms; energy saving; execution time; small-world; JOINT OPTIMIZATION; RESPONSE-TIME; ALGORITHMS; CONSUMPTION; MODEL;
D O I：
10.1093/comjnl/bxu045
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Job scheduling is a challenging task in grid environments and reducing execution time is one of the most important ways to tackle this problem. Nowadays, society is also more conscious about energy saving, making this fact a really important issue to pursue. In this paper, a new multiobjective algorithm is presented based on the small-world phenomenon: Multiobjective Small-World Optimization (MOSWO), to optimize both objectives: energy consumption and execution time. MOSWO is compared with another recent and swarm algorithm based on the firefly's behaviour: Multiobjective Firefly Algorithm (MO-FA). Both algorithms are compared with the standard multiobjective algorithm Non-dominated Sorting Genetic Algorithm II to show their efficiency as multiobjective approaches. Moreover, the best algorithm proposed, MOSWO, is compared with MOHEFT (multiobjective version of one of the most used algorithms in workflow-scheduling, HEFT) and also with two real grid schedulers: Workload Management System and Deadline Budget Constraint. The results show the advantages of our proposal.
引用
收藏
页码：432 / 447
页数：16
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