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In this paper with the aid of genetic algorithm and fuzzy theory, we presenta hybrid job scheduling approach, which considers the load balancing of thesystem and reduces total execution time and execution cost. We try to modifythe standard Genetic algorithm and to reduce the iteration of creatingpopulation with the aid of fuzzy theory. The main goal of this research is toassign the jobs to the resources with considering the VM MIPS and length ofjobs. The new algorithm assigns the jobs to the resources with considering thejob length and resources capacities. We evaluate the performance of ourapproach with some famous cloud scheduling models. The results of theexperiments show the efficiency of the proposed approach in term of executiontime, execution cost and average Degree of Imbalance (DI).

Hybrid Genetic Algorithm for Cloud Computing Applications