Adaptive Resource Discovery in Grid Computing Based on Reinforcement Learning

Abstract

Grid is a distributed computing environment. There are lots of resources in grid environment that are heterogeneous and geographically distributed. By receiving a resource request the resource discovery mechanism should return an appropriate resource if there exist one. Resource discovery is a challenging problem because of the heterogeneity and distribution of resources. In this paper, we propose and evaluate an adaptive resource discovery algorithm using reinforcement learning for grid computing that can be used for multi resource requests. The algorithm achieves the most suitable node that can satisfy the requested resource by using the past experience of agents. We compare our model with random walk resource discovery through simulation and the results show that the proposed algorithm provides higher success rate, less message passing and shorter response time. Also the algorithm leads to load balancing in whole grid.