Static and dynamic job scheduling with communication aware policy in cluster computing

Parallel jobs submitted to processors should be efficiently scheduled to achieve high performance. Early scheduling strategies for parallel jobs make use of either space-sharing approach or time-sharing approach. The scheduling strategy proposed in this work, makes use of both the policies for parallel jobs while scheduling under clusters. Static and dynamic scheduling algorithms were developed for communication intensive jobs. The algorithms are used to handle different types of jobs such as serial, parallel and mixed jobs. For performance evaluation, the workload from Grid5000 platform is considered. The main objective is to achieve performance and power improvement. The dynamic scheduling algorithm with communication aware policy gives better performance when compared to static scheduling algorithm that is tested under the given workload.

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► We model scheduling algorithms with communication aware policy.
► Static algorithms for scheduling serial, parallel and mixed jobs are developed.
► Dynamic algorithm for scheduling serial, parallel or mixed jobs is developed.
► The workload from Grid5000 is considered for performance evaluation.
► Dynamic algorithm performs well for better resource utilization and performance.