环境自适应的拓扑联合感知虚拟网映射算法

针对虚拟网映射算法环境适应度低、拓扑关联性较差且映射开销较大的问题，该文提出一种环境自适应的拓扑联合感知虚拟网映射算法。首先提出一种加权相对熵排序方法对具有多指标的节点进行量化处理，依环境变化赋予节点指标不同的权值；在虚拟节点排序阶段采用加权相对熵和广度优先搜索算法双重排序，物理节点排序中引入就近度与加权相对熵算法配合使用，实现了对虚拟拓扑和物理拓扑的联合感知；最后利用k-最短路径算法完成虚拟链路映射。仿真结果表明，该算法依据环境变化自适应调整指标权值，提高了虚拟网映射成功率和收益开销比。

Environment Adaptive and Joint Topology Aware Virtual Network Embedding Algorithm

In order to solve the problem of low environmental adaptability, poor topology correlation and large embedding cost in virtual network embedding algorithms, an environment adaptive and joint topology aware virtual network embedding algorithm is proposed. At first, a ranking method of weighted relative entropy is proposed to quantify the nodes with multi-index and the weights are changed according to different environment. The weighted relative entropy and breadth first search algorithm are both used in virtual node ranking phase, the nearest degree is introduced into physical node ranking and all these are used to achieve the joint awareness to the virtual topology and physical topology. Finally, the k-shortest path algorithm is introduced into virtual link embedding. Simulation results show that the proposed algorithm can improve the acceptance radio and the revenue to cost ratio by adjusting the weights according to the environment.