An algorithm for estimation of flow length distributions using heavy-tailed feature

Authors:
Weijiang Liu;Jian Gong;Wei Ding;Guang Cheng
Affiliations:
Department of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China;Department of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China;Department of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China;Department of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Year:
2006

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Abstract

Routers have the ability to output statistics about packets and flows of packets that traverse them. Since however the generation of detailed traffic statistics does not scale well with link speed, increasingly passive traffic measurement employs sampling at the packet level. Packet sampling has become an attractive and scalable means to measure flow data on high-speed links. However, knowing the number and length of the original flows is necessary for some applications. This paper provides an algorithm that uses flow statistics formed from sampled packet stream to infer the absolute frequencies of lengths of flows in the unsampled stream. We achieve this through statistical inference and by exploiting heavy-tailed feather. We also investigate the impact on our results of different packet sampling rate. The experiment results show the inferred distributions are accurate in most cases.