A Simulation Study of Multi-criteria Scheduling in Grid Based on Genetic Algorithms

Job scheduling is a critical mechanism which can affect significantly the performance of complex heterogeneous distributed systems, such as Grids. Since, computational Grids are multi-criteria environments by nature, modern allocation algorithms should concern more than one criterion to produce sche...

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Vydané v:2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications s. 317 - 324
Hlavní autori: Gkoutioudi, K., Karatza, H. D.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2012
Predmet:
Acceleration
Genetic Algorithm
Genetic algorithms
Grid Computing
Job Scheduling
Multi-crteria
Processor scheduling
Program processors
Scheduling
Sociology
Statistics
ISBN:1467316318, 9781467316316
ISSN:2158-9178
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Popis
Shrnutí:Job scheduling is a critical mechanism which can affect significantly the performance of complex heterogeneous distributed systems, such as Grids. Since, computational Grids are multi-criteria environments by nature, modern allocation algorithms should concern more than one criterion to produce scheduling solutions. In this paper, we propose a multi-criteria Genetic Algorithm, which tries to eliminate the security risks and power consumption of the system, apart from the completion time of jobs. Although Genetic Algorithms are suitable for large search space problems such as job scheduling, they are too slow to be executed online. Hence, we changed the implementation of a traditional genetic algorithm, introducing the Accelerated Genetic Algorithm. In addition, Accelerated Genetic Algorithm with Overhead is also presented, which concerns the extra overhead caused by the application of Accelerated Genetic Algorithm. We propose the best combination of weights of each criterion of our Genetic Algorithm in order to maximize the performance of the system. Afterwards, Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead are compared with three well-known heuristics. Simulation results indicate a substantial performance advantage of both Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead.
ISBN:1467316318
9781467316316
ISSN:2158-9178
DOI:10.1109/ISPA.2012.48