NDLI: Evaluation of multiobjective swarm algorithms for grid scheduling

Content Provider	IEEE Xplore Digital Library
Author	Arsuaga-Rios, M. Vega-Rodriguez, M.A. Prieto-Castrillo, F.
Copyright Year	2011
Description	Author affiliation: Extremadura Research Center for Advanced Technologies (CETA-CIEMAT), Trujillo, Spain (Arsuaga-Rios, M.; Prieto-Castrillo, F.) \|\| ARCO Research Group, University of Extremadura, Dept. Technologies of Computers and Communications, Escuela Politecnica, Campus Universitario s/n, 10003, Cáceres, Spain (Vega-Rodriguez, M.A.)
Abstract	Often, solutions to complex problems are found in nature. Swarm algorithms are capable of solving such complex problems by implementing patterns from nature. This patterns are found in a variety of scientific fields. In this paper, we discuss two swarm algorithms extracted from Biology and Physics, namely: Multiobjective Artificial Bee Colony (MOABC) and Multiobjective Gravitational Search Algorithm (MOGSA). The first one is based on bees behavior and the other follows the gravity between masses. These algorithms are implemented to solve the grid scheduling problem. Optimization of job scheduling is one of the most challenging tasks in Grid environments because it severely affects the execution time of an experiment (set of jobs). Experiments often are tied up to fulfill deadlines and budgets. One of the main contributions of this work is adding multiobjective processes to these swarm algorithms to minimize those conflictive objectives. Results show that MOABC clearly improves the MOGSA approach when solving the problem. MOABC is also compared with real grid meta-schedulers as Deadline Budget Constraint (DBC) and Workload Management System (WMS) by using the simulator GridSim to prove the improvement that offers this new algorithm.
Starting Page	1104
Ending Page	1109
File Size	339714
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121806
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Processor scheduling grid-sim swarm multiobjective Vectors Resource management Mathematical model Equations grid scheduling Gravity
Content Type	Text
Resource Type	Article

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