The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of APPLIED MATHEMATICS AND INFORMATICS
Transactions ID Number: 20-329
Full Name: Yezid Donoso
Position: Associate Professor
Age: ON
Sex: Male
Address: Cra 1 No. 18A- 12
Country: COLOMBIA
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E-mail address: ydonoso@uniandes.edu.co
Other E-mails: yedm2@hotmail.com
Title of the Paper: MAGS – An Approach Using Multi-Objective Evolutionary Algorithms for Grid Task Scheduling
Authors as they appear in the Paper: Miguel Camelo, Yezid Donoso, Harold Castro
Email addresses of all the authors: miguelhdo@hotmail.com,ydonoso@uniandes.edu.co,hcastro@uniandes.edu.co
Number of paper pages: 10
Abstract: Grid task scheduling problem has been a research focus in grid computing for the past years. Some Deterministic, Heuristics or Metaheuristic scheduling approaches have been proposed to solve this NP-complete problem. However, these algorithms do not take the Multi-Objective nature of Grid Computing performance into account. In this paper we present a Multi-Objective approach using Evolutionary Algorithm (MOEA) to efficiently solve such kind of scheduling problems. Our proposal is based on NSGA-II MOEA algorithm combined with a set of Heuristics in different evolutionary operators which allow a fast convergence to optimal (or near-optimal) solutions. The results obtained by our proposed algorithm were compared and evaluated against Mono-Objective and Multi-Objective algorithms used for Grid task scheduling. The main contributions of this paper are the proposed mathematical model, the optimization model and the algorithm to solve it. Additionally we show the effectiv!
eness and robustness of the proposed algorithm.
Keywords: Evolutionary Algorithms, Grid, Task Scheduling, Heuristics, Metaheuristics, Meta-Scheduler, Multi-Objective Optimization, NP-Complete, NSGA-II, Pareto Front.
EXTENSION of the file: .pdf
Special (Invited) Session: A Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms
Organizer of the Session: 150-118
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