Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Scheduling in a Grid Computing Environment Using Genetic Algorithms
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Scheduling, Timetabling and Rostering - A Special Relationship?
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
FingerPrint Registration Using Genetic Algorithms
ASSET '00 Proceedings of the 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology (ASSET'00)
A Heuristic on Job Scheduling in Grid Computing Environment
GCC '08 Proceedings of the 2008 Seventh International Conference on Grid and Cooperative Computing
A genetic algorithm for the multiple destination routing problems
IEEE Transactions on Evolutionary Computation
Scheduling multiprocessor job with resource and timing constraintsusing neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Allocation of independent jobs to available resources in a computational grid environment is a combinatorial optimization problem which comes under category of NP hard, NP complete problems. The main objective of this study is to maximize the resource utilization in computational grid environment. The second objective is user satisfaction by minimizing job completion time. This paper presents genetic algorithm with overlapping populations and parallel genetic algorithm to the solution of the problem of job scheduling with resource and timing constraints. The scheduling problem is tackled in preemptive and non-preemptive mode. Two dimensional chromosome representation is proposed for the job assignment problem which reduces the calculation in fitness evaluations. Proposed Genetic algorithms are applied to three problem instances and the results obtained are compared with the results from literature.