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

Ivanoe De Falco 1 Eryk Laskowski 2 Richard Olejnik 3 Umberto Scafuri 1 Ernesto Tarantino 1 Marek Tudruj 2, 4
1 ICAR - Institute of High Performance Computing and Networking
2 Institute of Computer Science [Warszawa]
3 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
4 PJIIT - Polish-Japanese Institute of Information Technology
Abstract : 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.
Keywords : multicore architecture Load balancing Extremal Optimization
Type de document :
Article dans une revue
IEEE Computer Society, 2014, 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), pp.228--231. 〈10.1109/PDP.2014.56〉
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https://hal.inria.fr/hal-01243220
Contributeur : Richard Olejnik <>
Soumis le : lundi 14 décembre 2015 - 16:39:45
Dernière modification le : mardi 18 décembre 2018 - 16:49:58

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Ivanoe De Falco, Eryk Laskowski, Richard Olejnik, Umberto Scafuri, Ernesto Tarantino, et al.. Extremal Optimization with Guided State Changes in Load Balancing of Distributed Programs . IEEE Computer Society, 2014, 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), pp.228--231. 〈10.1109/PDP.2014.56〉. 〈hal-01243220〉

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