面向软件定义网络的服务功能链优化部署算法研究

针对网络功能虚拟化(NFV)环境下，现有服务功能链部署方法无法在优化映射代价的同时保证服务路径时延的问题，该文提出一种基于IQGA-Viterbi学习算法的服务功能链优化部署方法。在隐马尔可夫模型参数训练过程中，针对传统Baum-Welch算法训练网络参数容易陷入局部最优的缺陷，改进量子遗传算法对模型参数进行训练优化，在每一迭代周期内通过等比例复制适应度最佳种群的方式，保持可行解多样性和扩大空间搜索范围，进一步提高模型参数的精确度。在隐马尔科夫链求解过程中，针对隐含序列无法直接观测这一难点，利用Viterbi算法能精确求解隐含序列的优势，解决有向图网络中服务路径的优化选择问题。仿真实验结果表明，与其它部署算法相比，所提IQGA-Viterbi学习算法能有效降低网络时延和映射代价的同时，提高了网络服务的请求接受率。

Research on Placement Algorithm of Service Function Chaining Oriented to Software Defined Networking

For Network Function Virtualization (NFV) environment, the existing placement methods can not guarantee the mapping cost while optimizing the network delay, a service function chaining optimal placement algorithm is proposed based on the IQGA-Viterbi learning algorithm. In the training process of Hidden Markov Model (HMM) parameters, the traditional Baum-Welch algorithm is easy to fall into the local optimum, so the quantum genetic algorithm is proposed, which can better optimize the model parameters. In each iteration, the improved algorithm maintains the diversity of feasible solutions and expands the scope of the spatial search by replicating the best fitness population with equal proportion, thus improving the accuracy of the model parameters. In the process of solving Hidden Markov chain, to overcome the problem that can not be directly observed for hidden sequences, Viterbi algorithm can solve the implicit sequences exactly and solve the problem of optimal service paths in the directed graph. Experimental results show that the network delay and mapping costs are lower compared with the existing algorithms. In addition, the acceptance ratio of requests is raised.