Applying reinforcement learning to scheduling strategies in an actual grid environment

• Tipo: Artigo em Revista Científica Internacional
• Data: 2009

Autores

Costa, BF (Autor) Outra; Mattoso, M (Autor) Outra; Dutra, I (Autor) FCUP

Revista

Título: International Journal of High Performance Systems Architecture

Vol. 2 Nº 2

Páginas: 116-128

ISSN: 1751-6528

Editora: Inderscience Publishers

Indexação

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-007-SNT

DOI: 10.1504/ijhpsa.2009.032029

Abstract (EN): Grid environments are dynamic and heterogeneous by nature, therefore requiring adaptive scheduling strategies. Reinforcement learning is an interesting and simple adaptive approach that may work well in actual grid environments. In this work, we employ reinforcement learning to classify available resources in a grid environment, giving support to two scheduling algorithms, AG and MQD. We study the makespan optimisation and load balancing. An algorithm known as RR is used for normalising purposes. Copyright © 2009 Inderscience Enterprises Ltd.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Notas: Special issue on Parallel and Distributed Systems, Applications and Architectures. - DOI: 10.1504/IJHPSA.2009.032029. - Keywords: grid computing; scheduling strategies; load balancing algorithms; heterogeneous environments; reinforcement learning; resources classification; makespan optimisation.

Nº de páginas: 13