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

Chengchen Hu, Bin Liu
2009 2009 International Conference on Computational Science and Engineering  
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
more » ... hat 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.
doi:10.1109/cse.2009.19 dblp:conf/cse/HuL09 fatcat:qi5lsdach5fa5b55e4qzif25vi