Long-Term Forecasting of Internet Backbone Traffic

Long-Term Forecasting of Internet Backbone Traffic: Observations and Initial Models

We introduce a methodology to predict when and where link additions/upgrades have to take place in an IP backbone network. Using SNMP statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent PoPs and look at its evolution at time scales larger than one hour. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and v...

Long range dependence in Internet backbone traffic

♣ This research was sponsored by WorldCom, Inc. Abstract—We report the first statistical analysis of Internet backbone traffic, based on traces with levels of aggregation 10 times larger and timestamp accuracy 1000 times better than in previous studies. We analyze the first three moments, marginal distributions and correlation structures of packet size, packet inter-arrival time, byte count and...

Characterization and Classification of Internet Backbone Traffic

We contribute to an improved understanding of Internet traffic characteristics by measuring and analyzing modern Internet backbone data. We start the thesis with an overview of several important considerations for passive Internet traffic collection on large-scale network links. The lessons learned from a successful measurement project on academic Internet backbone links can serve as guidelines...

Understanding the Long-Term Self-Similarity of Internet Traffic

Long-term power-law fluctuation in Internet traffic

Scale-free properties of observed Internet packet flow are discussed. The data is obtained by a multi router traffic grapher (MRTG) system for 9 months. Internet packet flow is analyzed using the detrended fluctuation analysis. By extracting the average daily trend, the data shows clear power-law fluctuations. The exponents of the fluctuation for the incoming and outgoing flow are almost unity....