Self-Tuning the Parameter of Adaptive Non-linear Sampling Method for Flow Statistics
Chengchen Hu, Bin Liu · 2009
Flow statistics is a basic task of passive measurement and has been widely used to characterize the state of the network.Adaptive Non-Linear Sampling (ANLS)is one of the most accurate and memory-efficient flow statistics method proposed recently. This paper studies the parameter setting problem for ANLS. A parameter self-tuning algorithm is proposed in this paper, which enlarges the parameter to a equilibrium tuning point and renormalizes the counter when counter overflows. It is demonstrated that the estimation error of ANLS with parameter self-tuning algorithm is improved by about 89 times for real trace,70 times for Pareto traffic scenario and 370 times for exponential traffic, while giving the same memory size.