Multi-cluster Grid Resource Dispatch Model Based on Reinforcement Learning
基于强化学习的多机群网格资源调度模型

For resolving the problem to effectively match between Data Parallel Computing (DPC) and computational resources in Multi-cluster Grid that composed of many computer dusters, a grid resource dispatch model based on reinforcement learning is discussed. A series of formal definitions, such as the Multi-cluster Grid (MCG), the Logical Computer Cluster(LCC), the DPC and the Agents, are given. Using the mechanism of cooperation and competition of Multi-Agent, the knowledge-base re- vising techniques based on reinforcement learning, the effective match methods are studied. The resource dispatch model is de- scribed. The analysis and experiment results show that this model effectively resolves the problems of optimized use of re- sources in Multi-cluster Grid. It can be fit for the data paralld computing in Grid.