Title :

Extremal Optimization with Guided State Changes in Load Balancing of Distributed Programs

Author(s) :

Falco, Ivanoe De [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]

Journal title :

IEEE Computer Society
22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)

Pages :

228--231

Publication date :

2014-02-12

English keyword(s) :

Load balancing
Extremal Optimization
multicore architecture

HAL domain(s) :

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

English abstract : [en]

The paper concerns methods for using Extremal Optimization (EO) for processor load balancing during execution of distributed programs. A load balancing algorithm for clusters of multicore processors is presented and ...
Show more >The paper concerns methods for using Extremal Optimization (EO) for processor load balancing during execution of distributed programs. 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 processors to receive the migrated tasks. To decrease the complexity of selection for migration, we propose a guided EO algorithm which assumes a two step stochastic selection during the solution improvement based on two separate fitness functions. The functions are based on specific program 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 an EO - based algorithm with random placement of migrated tasks and a classic genetic algorithm.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