Title :
Self-Tuning the Parameter of Adaptive Non-linear Sampling Method for Flow Statistics
Author :
Hu, Chengchen ; Liu, Bin
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Abstract :
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.
Keywords :
Pareto analysis; distributed processing; nonlinear systems; sampling methods; self-adjusting systems; storage management; Pareto traffic scenario; adaptive nonlinear sampling; counter overflows; equilibrium tuning point; estimation error; exponential traffic; memory size; memory-efficient flow statistics; parameter self-tuning algorithm; Counting circuits; Estimation error; Fluid flow measurement; Laboratories; Random access memory; Sampling methods; Statistics; Telecommunication traffic; Tuning; Volume measurement; Network measurement; counting; flow statistics; unbiased estimation;
Conference_Titel :
Computational Science and Engineering, 2009. CSE '09. International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4244-5334-4
Electronic_ISBN :
978-0-7695-3823-5
DOI :
10.1109/CSE.2009.19
