基于偏好和M-Flooding的网格资源发现

为了更有效地解决网格资源发现和定位问题，提出一种利用偏好划分和M-Flooding算法调整的网格资源发现方法。该方法给出衡量资源相似度的新方法及改进的消息扩散方式M-Flooding算法，将网格空间中的节点根据各自的偏好属性划分为不同的偏好组。搜索请求在组内进行传播，从而避免传统盲目搜索所带来的弊端。实验结果表明，该方法能够提高网格资源发现效率，降低资源发现平均路径长度。

Grid Resource Discovery Based on Preference and M-Flooding

In order to solve the problem of grid resource discovery, a grid resource discovery method is presented which is adjusted by preference partition and M-Flooding algorithm. The method gives a new method of measuring resource similarity degree and an improved message diffusion way M-Flooding algorithm. The grid spatial nodes are divided into different groups according to their respective preference attributes. Searching requests are transmitted in groups, which avoids the disadvantages brought by the traditional blind search method. Experimental result shows the method improves the efficiency of grid resource discovery and reduces the average path length of resource discovery.