基于马尔可夫决策过程的网格资源发现

Grid Resource Discovery Based on Markov Decision Processes

传统的网格资源发现方法没有考虑节点和资源本身性能的优劣性。针对这一问题,提出了基于马尔可夫决策过程(MDP)的网格资源发现方法,利用其对固定目标的最优搜索理论建立MDP模型实现报酬最优的资源发现,并对该模型仿真分析,验证其性能。

The performance quality of grid nodes and resources is not considered in traditional methods of grid resources discovery. Aim at this problem, this paper proposes a method of grid resources discovery based on Markov Decision Processes （MDP）. Using it＇s superior search theory of fixed target to establish a MDP model to search a resource with most reward, and do a simulation analysis for this model to validate it＇s performance.