Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
425742 | Future Generation Computer Systems | 2006 | 12 Pages |
Abstract
This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is two-fold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records and (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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Maozhen Li, Bin Yu, Man Qi,