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| 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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