A Pareto-based Metaheuristic for Scheduling HPC Applications on a Geographically Distributed Cloud Federation

Abstract : Reducing energy consumption is an increasingly important issue in cloud computing, more specif- ically when dealing with High Performance Comput- ing (HPC). Minimizing energy consumption can signif- icantly reduce the amount of energy bills and then in- crease 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, 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.
Type de document :
Article dans une revue
Journal of Cluster Computing, Springer, 2012
Liste complète des métadonnées

Littérature citée [24 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00749048
Contributeur : Yacine Kessaci <>
Soumis le : mardi 6 novembre 2012 - 15:22:12
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13
Document(s) archivé(s) le : jeudi 7 février 2013 - 03:46:22

Fichier

Kessaci_ClusterJournal.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00749048, version 1

Citation

Yacine Kessaci, Melab Nouredine, El-Ghazali Talbi. A Pareto-based Metaheuristic for Scheduling HPC Applications on a Geographically Distributed Cloud Federation. Journal of Cluster Computing, Springer, 2012. 〈hal-00749048〉

Partager

Métriques

Consultations de la notice

465

Téléchargements de fichiers

527