Applying reinforcement learning to scheduling strategies in an actual grid environment. (5th March 2010)

• Record Type: Journal Article
• Authors: Costa, Bernardo Fortunato; Mattoso, Marta; Dutra, Ines
• Is Part Of: International journal of high performance systems architecture. Volume 2:Number 2(2009)
• Page Start: 116
• Page End: 128
• Publication Date: 2010-03-05
• Subjects: grid computing -- scheduling strategies -- load balancing algorithms -- heterogeneous environments -- reinforcement learning -- resources classification -- makespan optimisation; Computer architecture -- Periodicals; Computer systems -- Periodicals; High performance computing -- Periodicals; 004.205
• Languages: English
• ISSNs: 1751-6528

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.