An Improved Adaptive Sampling Method for Traffic Measurement

The emergency of high speed links brings great challenges on online traffic monitoring and measurement. Due to the capacity restriction of traffic sampling system, an accurate and efficient sampling method is highly demanded. Fixed probability sampling is the simplest technique for detecting bigger traffic flows while discarding the smaller ones which consist almost more than 80% of the count of whole traffic flows. The smaller traffic flows are vital for the analysis of network traffic. Data streaming algorithm can collect data from high speed links online and efficiently. SGS (sketch guided sampling) is based on this algorithm and can evaluate accurately the distribution of flow sizes. But its accuracy declines rapidly when the sampling speed exceeds the capacity of the monitoring system. In this paper, an adaptive sampling method for real time network traffic measurement on high speed links based on the SGS method is proposed, called SRGS (sketch and resources guided sampling). The SRGS method takes the system capacity as an important parameter to adjust the sampling probability. Experiment results show that the SRGS method can adjust the package sampling probability according to the current flow sizes and the capacity in time. And it is more accurate than the SGS method.