Traffic Prediction and Resource Allocation: A Statistical Delay Bound Approach

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 adaptivity 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 adaptivity 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