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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/2482

Title: PGGA: A predictable and grouped genetic algorithm for job scheduling
Authors: Li, M
Yu, B
Qi, M
Keywords: Grid computing
Job scheduling
Divisible load theory
Genetic algorithm
Load balancing
Performance modelling
Publication Date: 2006
Publisher: Elsevier
Citation: Future Generation Computer Systems: The International Journal of Grid Computing: Theory, Methods and Applications. 22(5) 588-599, Apr 2006
Abstract: This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is twofold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records, (2) the divisible load theory (DLT) is employed to predict an optimal fitness value by which the PGGA speeds up the convergence process in searching a large scheduling space. Comparison with traditional scheduling methods such as first-come-first-serve (FCFS) and random scheduling, heuristics such as a typical genetic algorithm, Min-Min and Max-Min indicates that the PGGA is more effective and efficient in finding optimal scheduling solutions.
URI: http://bura.brunel.ac.uk/handle/2438/2482
ISSN: 0167-739X
Appears in Collections:School of Engineering and Design Research papers
Electronic and Computer Engineering

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