Authors: Bernardo Fortunato Costa, Marta Mattoso, Ines Dutra
Addresses: COPPE, Federal University of Rio de Janeiro, Cidade Universitaria, Centro de Tecnologia, Bloco H, Sala 319, Caixa Postal: 68511 CEP: 21941-972, Rio de Janeiro, RJ, Brazil. ' COPPE, Federal University of Rio de Janeiro, Cidade Universitaria, Centro de Tecnologia, Bloco H, Sala 319, Caixa Postal: 68511 CEP: 21941-972, Rio de Janeiro, RJ, Brazil ' Department of Computer Science, Faculty of Sciences, Rua do Campo Alegre, 1021, 4169-007, Porto, Portugal
Abstract: 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.
Keywords: grid computing; scheduling strategies; load balancing algorithms; heterogeneous environments; reinforcement learning; resources classification; makespan optimisation.
International Journal of High Performance Systems Architecture, 2009 Vol.2 No.2, pp.116 - 128
Received: 09 Jun 2009
Accepted: 26 Nov 2009
Published online: 04 Mar 2010 *