Resource Allocation in the Grid with Learning Agents |
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Authors: | Email author" target="_blank">Aram?GalstyanEmail author Karl?Czajkowski Kristina?Lerman |
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Affiliation: | (1) Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA 90292-6695, USA |
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Abstract: | One of the main challenges in Grid computing is efficient allocation of resources (CPU – hours, network bandwidth, etc.) to
the tasks submitted by users. Due to the lack of centralized control and the dynamic/stochastic nature of resource availability,
any successful allocation mechanism should be highly distributed and robust to the changes in the Grid environment. Moreover,
it is desirable to have an allocation mechanism that does not rely on the availability of coherent global information. In
this paper we examine a simple algorithm for distributed resource allocation in a simplified Grid-like environment that meets
the above requirements. Our system consists of a large number of heterogenous reinforcement learning agents that share common
resources for their computational needs. There is no explicit communication or interaction between the agents: the only information
that agents receive is the expected response time of a job it submitted to a particular resource, which serves as a reinforcement
signal for the agent. The results of our experiments suggest that even simple reinforcement learning can indeed be used to
achieve load balanced resource allocation in large scale heterogenous system. |
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Keywords: | Grid multi-agent system reinforcement learning resource allocation |
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