Abstract
Traffic sampling technology has been widely deployed in front of many high-speed network applications to alleviate the great pressure on packet capturing.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 a novel algorithm, Least Square Method(LSM), that uses flow statistics formed from sampled packet stream to infer the absolute frequencies of lengths of flows in the unsampled stream. The theoretical analysis shows that the computational complexity of this method is well under control, and the experiment results demonstrate the inferred distributions are as accurate as EM algorithm.
This work is supported in part by the National Grand Fundamental Research 973 Program of China under Grant No.2003CB314804; the National High Technology Research and Development Program of China (2005AA103001); the Key Project of Chinese Ministry of Education under Grant No.105084; the Jiangsu Provincial Key Laboratory of Computer Network Technology No. BM2003201; Jiangsu Planned Projects for Postdoctoral Research Funds.
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Liu, W. (2007). A Novel Algorithm for Estimating Flow Length Distributions–LSM. In: Li, K., Jesshope, C., Jin, H., Gaudiot, JL. (eds) Network and Parallel Computing. NPC 2007. Lecture Notes in Computer Science, vol 4672. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74784-0_45
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DOI: https://doi.org/10.1007/978-3-540-74784-0_45
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