Titre :

Extremal Optimization applied to load balancing in execution of distributed programs

Auteur(s) :

de Falco, Ivanoe [Auteur]
Institute of High Performance Computing and Networking [ICAR]
Laskowski, Eryk [Auteur]
Institute of Computer Science [Warszawa]
Olejnik, Richard [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Scafuri, Umberto [Auteur]
Institute of High Performance Computing and Networking [ICAR]
Tarantino, Ernesto [Auteur]
Institute of High Performance Computing and Networking [ICAR]
Tudruj, Marek [Auteur]
Polish-Japanese Institute of Information Technology [PJIIT]
Institute of Computer Science [Warszawa]

Titre de la revue :

Applied Soft Computing

Pagination :

501-513

Éditeur :

Elsevier

Date de publication :

2015-05

ISSN :

1568-4946

Mot(s)-clé(s) en anglais :

Distributed programs
Extremal optimization
Load balancing

Discipline(s) HAL :

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

Résumé en anglais : [en]

The paper describes methods for using Extremal Optimization (EO) for processor load balancing during execution of distributed applications. A load balancing algorithm for clusters of multicore processors is presented and ...
Lire la suite >The paper describes methods for using Extremal Optimization (EO) for processor load balancing during execution of distributed applications. A load balancing algorithm for clusters of multicore processors is presented and discussed. In this algorithm the EO approach is used to periodically detect the best tasks as candidates for migration and for a guided selection of the best computing nodes to receive the migrating tasks. To decrease the complexity of selection for migration, the embedded EO algorithm assumes a two-step stochastic selection during the solution improvement based on two separate fitness functions. The functions are based on specific models which estimate relations between the programs and the executive hardware. The proposed load balancing algorithm is assessed by experiments with simulated load balancing of distributed program graphs. The algorithm is compared against a greedy fully deterministic approach, a genetic algorithm and an EO-based algorithm with random placement of migrated tasks.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL