Modeling of multi-resolution active network measurement time-series
Abstract—Active measurements,on network,paths provide end- to-end network,health status in terms,of metrics,such as band- width, 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 vali- date our,model,selection accuracy,by comparing,how,well our predicted,values using our model,match,the actual measurements.