A genetic: algorithm approach to cost-based multi-QoS job scheduling in cloud computing environment
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
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De-centralized job scheduling is implemented by job migrations between neighboring grid nodes. To optimize node selection of a new-submitted job, the job may be migrated many times. Here, the hill climbing method is used to determine the migration route. Experiments simulate de-centralized job scheduling, including node adjacencies, local scheduling of grid nodes, and grid workload. Compared with k-distributed and auction methods, hill climbing-based scheduling usually can enhance processor utilization, and can reduce bounded slowdown.