Attitudinal data based server job scheduling using genetic algorithms: Client-centric job scheduling for single threaded servers

In this paper we present the implementation of Genetic Algorithms (GA) for job scheduling on computational grids that optimizes the makespan and the total flowtime. Job scheduling on computational grids is a key problem in large scale grid‐based applications for solving complex problems. The aim is to obtain an efficient scheduler able to allocate a large number of jobs originated from large scale applications to grid resources. Several variations for GA operators are examined in order to identify which works best for the problem. To this end we have developed a grid simulator package to generate large and very large size instances of the problem and have used them to study the performance of GA implementation. Through extensive experimenting and fine tuning of parameters we have identified the configuration of operators and parameters that outperforms the existing implementations in the literature for static instances of the problem. The experimental results show the robustness of the implementation, improved performance of static instances compared to reported results in the literature and, finally, a fast reduction of the makespan making thus the scheduler of practical interest for grid environments.

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Job scheduling with forbidden setups and two objectives using genetic algorithms and penalties

Central European Journal of Operations Research ◽

10.1007/s10100-010-0162-7 ◽

2010 ◽

Vol 19 (3) ◽

pp. 285-298 ◽

Cited By ~ 3

Author(s):

Karsten Hentsch ◽

Peter Köchel

Keyword(s):

Genetic Algorithms ◽

Job Scheduling

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Optimization of Genetic Operators for Scheduling Problems

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2007.p1092 ◽

2007 ◽

Vol 11 (9) ◽

pp. 1092-1098 ◽

Cited By ~ 1

Author(s):

António Ferrolho ◽

◽

Manuel Crisóstomo ◽

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Job Scheduling ◽

Software Tool ◽

Scheduling Problems ◽

Genetic Operators ◽

Mutation Operators ◽

Crossover And Mutation

Genetic algorithms (GA) can provide good solutions for scheduling problems. But, when a GA is applied to scheduling problems various crossovers and mutations operators can be applicable. This paper presents and examines a new concept of genetic operators for scheduling problems. A software tool called hybrid and flexible genetic algorithm (HybFlexGA) was developed to examine the performance of various crossover and mutation operators by computing simulations of job scheduling problems.

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