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)
Using adaptive linear prediction to support real-time VBR video under RCBR network service model
IEEE/ACM Transactions on Networking (TON)
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Self-Similar Network Traffic and Performance Evaluation
Self-Similar Network Traffic and Performance Evaluation
Proceedings of the 2006 international conference on Wireless communications and mobile computing
An architectural framework for support of quality of service in packet networks
IEEE Communications Magazine
Predictive dynamic bandwidth allocation for efficient transport of real-time VBR video over ATM
IEEE Journal on Selected Areas in Communications
Forecasting-based sampling decision for accurate and scalable anomaly detection
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Adaptive admission control algorithm in a QoS-aware Web system
Information Sciences: an International Journal
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The paper presents a predictive approach to network resource allocation techniques. The rationale of this work is to use measurements to estimate future traffic behavior by prediction, and to use such an estimation to define the amount of future network resources that will be required by the considered traffic. In this framework, the paper presents the analysis and performance evaluation of classical and chaotic techniques for network traffic prediction. The performance parameters considered in the analysis are: the accuracy of predictors in capturing the actual behavior of traffic; the computational complexity for a realistic integration of such predictors into experimental testbeds; and the responsiveness with respect to traffic pattern variations. The analysis results show that the classical normalized linear mean square predictor achieves a satisfactory trade-off among the above mentioned metrics as it presents a medium level of complexity while achieving high performance in terms of prediction accuracy and responsiveness to network traffic changes. Then, using the normalized linear mean square predictor, we derive a bandwidth allocation strategy, named statistical delay bound (SDB), which guarantees a probabilistic bound on the delay experienced by packets traversing a network node. The paper presents the performance analysis of SDB showing that, in spite of the simplicity of the adopted predictive algorithm, the proposed measurement based technique allows to fulfill the project requirements and candidates for actual experimentation into prototypal routers which supports QoS mechanisms.