Title :

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

Author(s) :

Kessaci, Yacine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Nouredine, Melab [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]

Journal title :

Cluster Computing

Pages :

451–468

Publisher :

Springer Verlag

Publication date :

2012-05-02

ISSN :

1386-7857

English keyword(s) :

scheduling
cloud computing
green computing
resource allocation
multi-objective opti- mization
genetic algorithm

HAL domain(s) :

Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL