Self-Tuning the Parameter of Adaptive Non-linear Sampling Method for Flow Statistics

—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.

• Type: Conference
• Year: 2009
• Where: CSE
• Authors: Chengchen Hu, Bin Liu
• Keywords: Adaptive Non-Linear Sampling, Computer Science And Engineering, CSE 2009, Parameter Self-tuning Algorithm, Parameter Setting Problem