An adaptive strategy for high-speed network flow compression

Abstract

Collecting network traffic is becoming a more challenging task in passive network measurement due to the rapid growth of link speed. Flow-based network traffic capture and storage is an efficient way for high-speed network measurement. Based on the statistical investigation of the correlations between flow size and the maximum packet interinterval time of consecutive packets within a flow, we obtain the empirical conditional distribution functions for some popular TCP protocol-based application flows, and then propose a probability-guaranteed adaptive timeout algorithm (PGAT) for flow termination decision. The assessment criteria for the flow termination decision algorithm is systematically developed. Comparisons on flow generation ratio, flow intact ratio, and mean flow extra retaining time metrics indicate that the PGAT algorithm can obtain more attractive performance than other schemes.