Adaptive Packet Sampling for Flow Volume Measurement

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 and analyze an adaptive, stratified random packet sampling technique for flow-leveltraffic measurement. In particular, we address the theoretical and practical issues involved. Through theoretical studies and experiments, 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.