An empirical study of the robustness of energy-aware schedulers for high performance computing systems under uncertainty

Abstract

This article presents an empirical evaluation of energy-aware schedulers under uncertainties in both the execution time of tasks and the energy consumption of the computing infrastructure. We address an important problem with direct application in current clusters and distributed computing systems, by analyzing how the list scheduling techniques proposed in a previous work behave when considering errors in the execution time estimation of tasks and realistic deviations in the power consumption. The experimental evaluation is performed over realistic workloads and scenarios, and validated by in-situ measurements using a power distribution unit. Results demonstrate that errors in realworld scenarios have a significant impact on the accuracy of the scheduling algorithms. Different online and offline scheduling approaches were evaluated, and online approach showed improvements of up to 32% in computing performance and up to 18% in energy consumption over the offline approach using the same scheduling algorithm.