A Pareto-based GA for Scheduling HPC Applications on Distributed Cloud Infrastructures

Yacine Kessaci 1 Nouredine Melab 1 El-Ghazali Talbi 1
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with High Performance Computing (HPC). Minimizing energy consumption can significantly reduce the amount of energy bills and then increases the provider's profit. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. In this paper, we present a multi-objective genetic algorithm (MO-GA) that optimizes the energy consumption, CO2 emissions and the generated profit of a geographically distributed cloud computing infrastructure. We also propose a greedy heuristic that aims to maximize the number of scheduled applications in order to compare it with the MO-GA. The two approaches have been experimented using realistic workload traces from Feitelson's PWA Parallel Workload Archive. The results show that MO-GA outperforms the greedy heuristic by a significant margin in terms of energy consumption and CO2 emissions. In addition, MO-GA is also proved to be slightly better in terms of profit while scheduling more applications.
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Communication dans un congrès
HPCS, Jul 2011, Istanbul, Turkey. 2011
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Yacine Kessaci, Nouredine Melab, El-Ghazali Talbi. A Pareto-based GA for Scheduling HPC Applications on Distributed Cloud Infrastructures. HPCS, Jul 2011, Istanbul, Turkey. 2011. 〈inria-00637752〉

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