Real-time estimation of the parameters of long-range dependence
IEEE/ACM Transactions on Networking (TON)
Fractal Traffic Models for Internet Simulation
ISCC '00 Proceedings of the Fifth IEEE Symposium on Computers and Communications (ISCC 2000)
Proceedings of the 35th conference on Winter simulation: driving innovation
Aggregation of heavy-tailed on-off flows is multifractal
ICCS '02 Proceedings of the The 8th International Conference on Communication Systems - Volume 01
A wavelet-based joint estimator of the parameters of long-range dependence
IEEE Transactions on Information Theory
A multifractal wavelet model with application to network traffic
IEEE Transactions on Information Theory
A practical approach for providing QoS in the Internet backbone
IEEE Communications Magazine
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In this paper, we address the problem of real-time estimation of multifractal features for network traffic. The algorithm accuracy is the major concern in the proposed algorithm. From a statistical point of view, the higher the number of samples used in the estimation, the more accurate the results. However, network traffic in long intervals of time may have a heterogeneous scaling behavior, which would make the estimation results meaningless. We then propose an adaptive strategy that adjusts the length of the estimation interval based on local traffic features, i.e., it is enlarged as long as the traffic shows a homogeneous behavior. The development of this strategy relies on analyzing the variability of multifractality over time in real traffic traces. Simulation results show that the proposed algorithm is characterized by a higher accuracy with respect to a fixed approach.