A Real Time Network Traffic Prediction Algorithm Based on Hybrid Model

Distributed applications use predictions of network traffic to sustain their performance by adapting their behaviors. It has been recognized that the network traffic consists of a majority of linear part and a small quantity of non-linear part which can not be neglected. However, existent network traffic prediction algorithms only utilize either linear or non-linear methods to solve the problem and can not provide enough accuracy and realtime due to the isolated adoption. A hybrid network traffic prediction algorithm, is provided, in which Kalman filter (KF) and wavelet are combined. Thus the linear part can be processed by KF and the non-linear part can be done by wavelet. Simulation results show that the proposed algorithm can guarantee higher accuracy and better realtime than those algorithms based singly on linear or non-linear method.