On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Active virtual network management protocol
PADS '99 Proceedings of the thirteenth workshop on Parallel and distributed simulation
A Case for Exploiting Self-Similarity of Network Traffic in TCP
ICNP '02 Proceedings of the 10th IEEE International Conference on Network Protocols
A new algorithm for measurement-based admission control in integrated services packet networks
PfHSN '96 Proceedings of the TC6 WG6.1/6.4 Fifth International Workshop on Protocols for High-Speed Networks V
Forecasting network performance to support dynamic scheduling using the network weather service
HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
An Evaluation of Linear Models for Host Load Prediction
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
An Empirical Study of the Multiscale Predictability of Network Traffic
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
An In-Depth, Analytical Study of Sampling Techniques for Self-Similar Internet Traffic
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
On the predictability of large transfer TCP throughput
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Modeling Internet backbone traffic at the flow level
IEEE Transactions on Signal Processing
Wavelet analysis of long-range-dependent traffic
IEEE Transactions on Information Theory
TCP Vegas: end to end congestion avoidance on a global Internet
IEEE Journal on Selected Areas in Communications
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Traffic prediction constitutes a hot research topic of network metrology. Thus, tuning the prediction model parameters is very crucial to achieve accurate prediction. This work focuses on the design, the empirical evaluation and the analysis of the behavior of linear models for predicting the throughput of a single link. In this work, the AutoRegressive Integrated Moving Average (ARIMA) model and the linear minimum mean square error (LMMSE) are used for predicting. Via experimentation on real network traffic, we study the effect of some parameters on the prediction performance in terms of error such as the number of last observations of the throughput (i.e. lag) needed as inputs for the model, the data granularity, variance and packet size distribution. We also investigate multi-step prediction that is the number of steps that could be predicted in the future. Besides, we performed a set of predictions based on packets size. Unexpectedly, we find that using more than two lags as inputs for the prediction model increases the prediction error. We find that using the last observation as the predicted value provides the same 1-step prediction performance as ARIMA or LMMSE model. The ARIMA model provides an acceptable multi-step prediction performance. Experimental results show also that there is a granularity value at which the multistep prediction is more accurate. We also find that the prediction of classified packets based on their size is possible. Especially, throughput of 1,500-byte packets is the less predictable.