Congestion avoidance and control
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
Proceedings of the 7th annual international conference on Mobile computing and networking
New directions in traffic measurement and accounting
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Generalized autoregressive moving average modeling of the Bellcore data
LCN '00 Proceedings of the 25th Annual IEEE Conference on Local Computer Networks
Predictive dynamic bandwidth allocation for efficient transport of real-time VBR video over ATM
IEEE Journal on Selected Areas in Communications
TCP Vegas: end to end congestion avoidance on a global Internet
IEEE Journal on Selected Areas in Communications
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Traffic prediction model is critically important for network performance evaluation and services quality. Traditional traffic prediction models cannot reflect the characteristics of self-similar traffic. Current long-range prediction models, however, are too complex to be used as online traffic predictors. This paper presents two new traffic predictors which are MMSEP and NMSEP. They are based on minimum mean square error. Time series and control theory are used to build the mathematic models. By modifying the way of calculating the predicted error, MMESP and NMSEP can reflect the burst of self-similar traffic in multiple timescales. When compared with FARIMA model which is one of the best fractional predictor, numerical results of experiments show that MMSEP and NMSEP can achieve accuracy with less than 5% of errors while keeping simplify in computation and low memory used.