Accurate and Efficient Traffic Monitoring Using Adaptive Non-Linear Sampling Method

Abstract

Sampling technology has been widely deployed in measurement systems to control memory consumption and processing overhead. However, most of the existing sampling methods suffer from large estimation errors in analyzing small-size flows. To address the problem, we propose a novel adaptive non-linear sampling (ANLS) method for passive measurement. Instead of statically configuring the sampling rate, ANLS dynamically adjusts the sampling rate for a flow depending on the number of packets having been counted. We provide the generic principles guiding the selection of sampling function for sampling rate adjustment. Moreover, we derive the unbiased flow size estimation, the bound of the relative error, and the bound of required counter size for ANLS. The performance of ANLS is thoroughly studied through theoretic analysis and experiments under synthetic/real network data traces, with comparison to several related sampling methods. The results demonstrate that the proposed ANLS can significantly improve the estimation accuracy, particularly for small-size flows, while maintain a memory and processing overhead comparable to existing methods.