The stability of paths in a dynamic network
CoNEXT '05 Proceedings of the 2005 ACM conference on Emerging network experiment and technology
Capacity overprovisioning for networks with resilience requirements
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Similarity based method for manufacturing process performance prediction and diagnosis
Computers in Industry
Passive measurement of one-way and two-way flow lifetimes
ACM SIGCOMM Computer Communication Review
Automated Detection of Load Changes in Large-Scale Networks
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Topology aware internet traffic forecasting using neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Pervasive and Mobile Computing
Characterization of the busy-hour traffic of IP networks based on their intrinsic features
Computer Networks: The International Journal of Computer and Telecommunications Networking
A Short-Term Forecasting Algorithm for Network Traffic Based on Chaos Theory and SVM
Journal of Network and Systems Management
Computer Networks: The International Journal of Computer and Telecommunications Networking
Multivariate fairly normal traffic model for aggregate load in large-scale data networks
WWIC'10 Proceedings of the 8th international conference on Wired/Wireless Internet Communications
Hi-index | 0.00 |
We introduce a methodology to predict when and where link additions/upgrades have to take place in an Internet protocol (IP) backbone network. Using simple network management protocol (SNMP) statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent points of presence (PoPs) and look at its evolution at time scales larger than 1 h. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis (MRA) and linear time series models. Using wavelet MRA, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12-h time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12-h time scale yields accurate estimates for at least 6 months in the future.