Adaptive random sampling for traffic volume measurement

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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