Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Hill Climbing-Based Decentralized Job Scheduling on Computational Grids
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
MUE '09 Proceedings of the 2009 Third International Conference on Multimedia and Ubiquitous Engineering
Mathematical Model of Cloud Computing Framework Using Fuzzy Bee Colony Optimization Technique
ACT '09 Proceedings of the 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies
Independent tasks scheduling based on genetic algorithm in cloud computing
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
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The complex business strategies of cloud services make Job scheduling a challenging issue. The mapping of user jobs onto a computing resource to achieve maximum benefit, satisfying the varying QoS of user's jobs, is the ultimate goal of a cloud provider. As this scheduling problem belongs to the family of combinatorial problems, it cannot be formulated as a linear programming problem and any simple rule or algorithm cannot achieve the optimal solution in finite time. In this paper, a genetic algorithm approach to cost based multi QoS job scheduling has been proposed. A model for cloud computing environment has been also proposed and some popular genetic cross over operators, like PMX, OX, CX and mutation operators, swap and insertion mutation are used to produce a better schedule. The algorithm guarantees the best solution in finite time.