Modeling of multi-resolution active network measurement time-series

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Modeling of multi-resolution active network measurement time-series
Abstract—Active measurements on network paths provide endto-end network health status in terms of metrics such as bandwidth, delay, jitter and loss. Hence, they are increasingly being used for various network control and management functions on the Internet. For purposes of network health anomaly detection and forecasting involved in these functions, it is important to accurately model the time-series process of active measurements. In this paper, we describe our time-series analysis of two typical active measurement data sets collected over several months: (i) routine, and (ii) event-laden. Our analysis suggests that active network measurements follow the moving average process. Specifically, they possess ARIMA(0,1,q) model characteristics with low q values, across multi-resolution timescales. We validate our model selection accuracy by comparing how well our predicted values using our model match the actual measurements.
Prasad Calyam, Ananth Devulapalli
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where LCN
Authors Prasad Calyam, Ananth Devulapalli
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