基于自适应算法的动态网格服务选择方法

针对网格服务的动态性、时序性和随机性，给出了一种基于Q-learning的动态网格服务选择方法，用于求解具有不完全信息的网格环境中的服务组合。对满足马尔可夫决策过程的服务组合提出了一种支持不完备信息描述的网格服务描述模型，实现了对服务组合整个生命周期的描述。提出了一种改进的Q-learning 算法，动态、自适应地对服务选择中不同选择进行预估，并给出不同情况下的最优选择决策。仿真实验表明了该方法较传统的贪心选择算法具有优越性与实用性。

Approach of Dynamic Grid Service Selection Based on Self-adapting Algorithm

In order to improve the efficiency of grid service selection, a new approach based on Q-learning is proposed. A new model based on Markov decision processes is proposed and the correlative novel algorithm is implemented with the adaptive ability of improved Q-learning for dynamic grid service selection. The experiment results show that the method is more effective than the traditional ones. Thus, it provides a good solution for grid service selection.