Autonomic Resource Contention-aware Scheduling

This month the Journal of Software: Practice and Experience has published online our paper entitled “Autonomic resource contention-aware scheduling”. It can be accessed here.

The complexity of computing systems introduces a few issues and challenges such as poor performance and high energy consumption. In this paper, we first define and model resource contention metric for high performance computing workloads as a performance metric in scheduling algorithms and systems at the highest level of resource management stack to address the main issues in computing systems. Second, we propose a novel autonomic resource contention-aware scheduling approach architected on various layers of the resource management stack. We establish the relationship between distributed resource management layers in order to optimize resource contention metric. The simulation results confirm the novelty of our approach.

This work is the result of a collaboration with Prof. Lucio Grandinetti‘s research group from University of Calabria, Italy.

J.L. Vázquez-Poletti

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