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A Kalman filtering algorithm for self-similar traffic prediction. (Chinese. English summary) Zbl 1212.68005

Summary: A noise on-line estimation Kalman filtering (NOEKF) algorithm is presented to deal with the inaccurate self-similar traffic prediction in network congestion control. The proposed algorithm is independent of the feedback information from traffic sources, and predicts the traffic through observing both the current and previous traffics in a node. Both the state equation and the observation equation are established, and, then, an optimal recursive formula for the estimation of the state vector is given. By taking the unknown noise statistics of both the state equation and the observation equation into account, an on-line estimation method with forgetting factor is used to estimate the noise statistics. Comparisons with existing algorithms show that the NOEKF algorithm has the advantages of high accuracy and minor prediction error. Simulation results show that the NOEKF algorithm predicts self-similar traffic more accurately than the classical Kalman filtering and time series prediction algorithm do, and that the prediction error is reduced by more than 60%.

MSC:

68M10 Network design and communication in computer systems
93E11 Filtering in stochastic control theory
62M20 Inference from stochastic processes and prediction
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