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Applying reinforcement learning to scheduling strategies in an actual grid environment
by Bernardo Fortunato Costa, Marta Mattoso, Ines Dutra
International Journal of High Performance Systems Architecture (IJHPSA), Vol. 2, No. 2, 2009

 

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.

 

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