The Community for Technology Leaders
RSS Icon
Jan. 28, 2013 to Jan. 30, 2013
ISBN: 978-1-4673-5740-1
pp: 595-599
JunSeong Kim , School of Electrical and Electronics Engineering, Chung-Ang University, 221 Heukseok-dong, Dongjak-gu, Seoul, 156-756 Korea
Techniques of network status estimation and traffic prediction are required for network control and user applications in the contexts where a variety of traffic data sources are available. Due to the difficulty of estimating applications' network demands and the difficulty of predicting network load, however, the management of network resources has often been ignored. This paper presents a heuristic of network status classification that has been observed in various scale operational networks. The basic idea of the approach is that network traffic repeat cycles of congested states and that a variation of network latency is strongly correlated with the past history of the latency. We directly monitor network load by continually measuring end-to-end network latencies in real operational networks and classify network traffic status with respect to the stability and the burstiness of the latencies. The experimental results showed that the proposed method is capable of evaluating network traffic status and reflecting the related fluctuations.
time series, end-to-end network latency, network stability, network burstiness, network traffic status
JunSeong Kim, "A classification of network traffic status for various scale networks", ICOIN, 2013, 2013 International Conference on Information Networking (ICOIN), 2013 International Conference on Information Networking (ICOIN) 2013, pp. 595-599, doi:10.1109/ICOIN.2013.6496693
38 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool