摘要
Based on the massive data collected with a passive network monitoring equipment placed in China＇s backbone, we present a deep insight into the network backbone traffic and evaluate various ways for inproving traffic classifying efficiency in this pa- per. In particular, the study has scrutinized the net- work traffic in terms of protocol types and signatures, flow length, and port distffoution, from which mean- ingful and interesting insights on the current Intemet of China from the perspective of both the packet and flow levels are derived. We show that the classifica- tion efficiency can be greatly irrproved by using the information of preferred ports of the network applica- tions. Quantitatively, we find two traffic duration thresholds, with which 40% of TCP flows and 70% of UDP flows can be excluded from classification pro- cessing while the in^act on classification accuracy is trivial, i.e., the classification accuracy can still reach a high level by saving 85% of the resources.
Based on the massive data collected with a passive network monitoring equipment placed in China's backbone,we present a deep insight into the network backbone traffic and evaluate various ways for improving traffic classifying efficiency in this paper.In particular,the study has scrutinized the network traffic in terms of protocol types and signatures,flow length,and port distribution,from which meaningful and interesting insights on the current Internet of China from the perspective of both the packet and flow levels are derived.We show that the classification efficiency can be greatly improved by using the information of preferred ports of the network applications.Quantitatively,we find two traffic duration thresholds,with which 40% of TCP flows and 70% of UDP flows can be excluded from classification processing while the impact on classification accuracy is trivial,i.e.,the classification accuracy can still reach a high level by saving 85% of the resources.
基金
This paper was partially supported by the National Natural Science Foundation of China under Crant No. 61072061
111 Project of China under Crant No. B08004
the Fundamental Research Funds for the Central Universities under Grant No. 2009RC0122. References
