PGGA: a predictable and grouped genetic algorithm for job scheduling

Authors:
Maozhen Li;Bin Yu;Man Qi
Affiliations:
Electronic and Computer Engineering, School of Engineering and Design, Brunel University, Uxbridge, UK;Electronic and Computer Engineering, School of Engineering and Design, Brunel University, Uxbridge, UK;Department of Computing, Canterbury Christ Church University, Canterbury, Kent, UK
Venue:
Future Generation Computer Systems - Parallel input/output management techniques (PIOMT) in cluster and grid computing
Year:
2006

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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.