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 increase 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 make HPC applications consuming less energy. In this paper, we present a multi-objective genetic algorithm (MO-GA) that optimizes the energy consumption, CO 2 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 CO 2 emissions. In addition, MO-GA is also proved to be slightly better in terms of profit while scheduling more applications.
In REF ) the authors present a multi-objective genetic algorithm, named MO-GA, to optimize energy consumption, carbon dioxide emissions and generated profit of a geographically distributed Cloud computing infrastructure.
A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation
Cluster Computing
Cluster Computing
Computer Science