Title :
Using probability network to infer link-level loss rate
fDate :
29 June-1 July 2002
Abstract :
It is important to obtain link-level performance data, such as loss rate and delay on each segment, which can help us to understand dynamic features of network traffics and identify bottlenecks. This knowledge also assists us to build and construct networks for better performance. Using end-to-end measurement to find link level performance has advantages in eliminating the burden from routers or switches. Instead of using classic statistical methods, such as maximum likelihood estimation, we use the probability network (a direct acyclic graph) to achieve the same goal. In addition, given a loss model, such as Bernoulli, the probability method can dynamically adjust the parameters to trace the traffic change. Simulations based on ns2 were conducted and the data received from the simulations were inferred by a corresponding probability network; the result is very encouraging.
Keywords :
graph theory; inference mechanisms; probability; telecommunication network management; telecommunication network planning; telecommunication traffic; Bernoulli loss model; direct acyclic graph; end-to-end measurement; link-level loss rate; maximum likelihood estimation; network control; network design; network management; network traffic; probability network; statistical methods; Australia; Computer science; Data analysis; Maximum likelihood estimation; Probability; Probes; Statistics; Switches; Telecommunication traffic; Traffic control;
Conference_Titel :
Communications, Circuits and Systems and West Sino Expositions, IEEE 2002 International Conference on
Print_ISBN :
0-7803-7547-5
DOI :
10.1109/ICCCAS.2002.1180727