遗传算法在高负载XMPP中的路径优化应用

基于XMPP协议的网络是由大量部署在网络中的客户端,服务器和网关三种实体节点共同组成。高负载条件下,节点规模的不确定性、节点运算性能的可变性、最长路由跳数的不确定性,导致在现有的算法和有限时间下,无法预知源节点发出的数据包是否能够到达目标节点。本文利用遗传算法对高负载下XMPP网络的路径进行优化推导求解,得出本算法的应用可行性,从而为提高路径运行效率提供参考。

The network based on the XMPP protocol is composed of huge number of client, server and gateway entity nodes, which are widely deployed. Under high load eonditions, because of variability of the node counts, the node computing performance and the hops, we can＇t predict whether or not the data packets from source node can be sent to end node in finite time, if we depend on the off-the-shelf path algorithms. In this paper, we just study the optimization path problems based on genetic algorithm, and supply the result for high-load XMPP network routing. The conclusion for practice proves the optimization path can imprnve the routing efficiency.