Abstract
Traffic measurement and monitoring are an important component of network management and traffic engineering. With high-speed Internet backbone links, efficient and effective packet sampling techniques for traffic measurement and monitoring are not only desirable, but also increasingly becoming a necessity. Since the utility of sampling depends on the accuracy and economy of measurement, it is important to control sampling error. In this paper, we propose an adaptive packet sampling technique for flow-level traffic measurement with stratification approach. We employ and advance sampling theory in order to ensure the accurate estimation of large flows. With real network traces, we demonstrate that the proposed sampling technique provides unbiased estimation of flow size with controllable error bound, in terms of both packet and byte counts for elephant flows, while avoiding excessive oversampling.
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Baek-Young Choi received her Ph.D. in 2003 in Computer Science and Engineering from the University of Minnesota, Twin Cities. She received her B.S. degree in 1993 from Pusan National University, Korea and M.S. degree in 1995 from Pohang University of Science and Technology, Korea. After her Ph.D. she was a post-doctoral researcher at Sprint Advanced Technology Labs, and a 3M McKnight Distinguished Visiting Assistant Professor at the University of Minnesota, Duluth. From 2005, Dr. Choi is an assistant professor at the University of Missouri, Kansas City.
Zhi-Li Zhang received a B.S. degree in computer science from Nanjing University, China, in 1986 and his M.S. and Ph.D. degrees in computer science from the University of Massachusetts in 1992 and 1997. In 1997 he joined the Computer Science and Engineering faculty at the University of Minnesota, where he is currently a professor. From 1987 to 1990, he conducted research in Computer Science Department at rhus University, Denmark, under a fellowship from the Chinese National Committee for Education. He has held visiting positions at Sprint Advanced Technology Labs; IBM T.J. Watson Research Center; Fujitsu Labs of America, Microsoft Research China, and INRIA, Sophia-Antipolis, France.
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Choi, BY., Zhang, ZL. Adaptive random sampling for traffic volume measurement. Telecommun Syst 34, 71–80 (2007). https://doi.org/10.1007/s11235-006-9023-z
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DOI: https://doi.org/10.1007/s11235-006-9023-z