%0 Journal Article
%A LI Dong-yang
%A WEI Wei
%A XIE Sheng-xu
%A XING Chang-you
%A ZHANG Guo-min
%T A Heavy Hitter Detection Mechanism in Software Defined Networks
%D 2020
%R 10.13190/j.jbupt.2019-076
%J Journal of Beijing University of Posts and Telecommunications
%P 97-103
%V 43
%N 1
%X SampleFlow, a heavy hitter detection mechanism in software defined networks, is proposed to solve the problems of low detection accuracy and high measurement cost. By combining the technical advantage of sFlow and OpenFlow, SampleFlow firstly detects a set of suspicious heavy hitters by using the coarse-grained sFlow sampling method, and then installs measurement flow entries on specific OpenFlow switches to perform a fine-grained measurement on these suspicious heavy hitters, so as to determine the true heavy hitters. Besides, SampleFlow also uses a sampling position optimization method to decrease the sampling redundancy. Experiment results show that SampleFlow can decrease the measurement cost, and increase the heavy hitter detection accuracy effectively.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2019-076