改进PSO-LSSVM算法的SDN网络流量预测模型

SDN技术解决了IP网络布设困难、更新繁琐等突出问题, 近年来发展迅速. 本文针对SDN网络流量预测问题, 提出首先采用混沌理论对时间序列样本群进行相空间重构, 随后引入最小二乘支持向量机(LSSVM)构建SDN网络流量预测模型, 并结合改进的粒子群算法(PSO)对其关键参数进行优化. 实验结果证明, 该模型有效提高了SDN网络流量预测精度与误差控制水平, 具有良好的实际应用价值.

SDN-Based Traffic Prediction Model Based on Improved PSO-LSSVM Algorithm

The Software Defined Networking (SDN) technology, which has been booming in recent years, solves the prominent problems of IP networks such as layout difficulty and complex updates. In response to SDN-based traffic prediction, the chaos theory is used to reconstruct the phase space of the time series sample group. Then, the Least Squares Support Vector Machine (LSSVM) is introduced to build the SDN-based traffic prediction model, and the key parameters are optimized by the improved Particle Swarm Optimization (PSO) algorithm. The experimental results show that the model effectively improves the accuracy and error control level of SDN-based traffic prediction and is valuable in practical application.