数据流特征感知的交换机流表智能更新方法

针对软件定义网络（SDN）中交换机流表匹配率低的问题，提出了数据流特征感知的交换机流表智能更新方法。首先，论述流表项的生存超时时间timeout对数据包匹配的影响，并且分析比较基于先进先出（FIFO）、近期最少使用（LRU）等一般方法存在的不足；其次，根据流表项的生存时间和数据流的特征密切相关的思想，利用基于隐马尔可夫模型（HMM）的深度流检测（DFI）技术对数据流进行分类；最后，根据流表资源和控制器计算资源状况，实现对不同类型数据流流表项的智能更新。采用校园数据中心网络行为数据的模拟实验表明，与流表更新的一般方法相比，智能方法能使流表匹配率提高5%以上，对SDN交换机的管理有实际意义。

Intelligent update method for flow table in switch through analyzing data flow characteristics

To address the low matching rate of flow table, an intelligent update method for flow table in Software Defined Network (SDN) switch was proposed. First, the impact of timeout value on the packet matching was described, besides, the shortcomings of First In First Out (FIFO), Least Recently Used (LRU) and other common methods were analyzed and compared. Secondly, based on the reality of survival time of the flow entry related closely to the characteristics of data flow, the Hidden Markov Model (HMM)-based Deep Flow Inspection (DFI) technology was used to classify the data flow. Finally, according to the condition of the flow table resources and controller's computing resources, the intelligent update of the flow entry of different type of data flow was realized. The simulation experiments conducted on data center behavior data of real campus indicate that the proposed method can improve more than 5% of the matching rate compared with the common methods, and it has a practical significance to the management of the SDN switch.