Proactive eviction of flow entry for SDN based on hidden Markov model

作     者：Gan HUANG Hee Yong YOUN Gan HUANG;Hee Yong YOUN

作者机构：College of SoftwareSungkyunkwan UniversitySuwon440746Korea 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2020年第14卷第4期

页      面：107-116页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was partly supported by the Institute for Information&communications Technology Promotion(IITP)grant funded by the Korea government(MSIT)(2016-0-00133,Research on Edge computing via collective intelligence of hyper connection IoT nodes) Korea,under the National Program for Excellence in SW supervised by the IITP(Institute for Information&communications Technology Promotion)(2015-0-00914) Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(2016R1A6A3A11931385,Research of key technologies based on software defined wireless sensor network for realtime public safety service,2017R1A2B2009095,Research on SDN-based WSN Supporting Real-time Stream Data Processing and Multiconnectivity) the second Brain Korea 21 PLUS project 

主　　题：SDN OpenFlow flow entry eviction HMM matching probability 

摘      要：With the fast development of software defined network(SDN),numerous researches have been conducted for maximizing the performance of ***,flow tables are utilized in OpenFlows witch for *** to the space limitation of flow table and switch capacity,various issues exist in dealing with the *** existing schemes typically employ reactive approach such that the selection of evicted entries occurs when timeout or table miss *** this paper a proactive approach is proposed based on the prediction of the probability of matching of the *** eviction occurs proactively when the utilization of flow table exceeds a threshold,and the flow entry of the lowest matching probability is *** matching probability is estimated using hidden Markov model(HMM).Computersimulation reveals that it significantly enhances the prediction accuracy and decreases the number of table misses compared to the standard Hard timeout scheme and Flow master scheme.