Prediction for long range dependent traffic

Abstract

In recent years, empirical studies on network traffic both in various network configurations including local area networks (LAN) and wide area networks (WAN) convincingly show that the actual traffic exhibit self-similarity and long range dependence (LRD), which are very different from that predicted by traditional telecommunication traffic models, such as Poisson process. Self-similarity in nature brings about the long range dependent burstiness of network traffic. The experimental evidences reveal that the heavy tailness is the key cause for the self-similarity of the network traffic. We present two distinctive predictors based on alpha-stable innovation for the LRD traffic. The two predictors can minimize the dispersion according to the minimum dispersion criteria with infinite variance. The final predicted values are obtained by combining the previous two individual predicted values. The predicted results for the actual traces show that the two individual predictors are precise and effective, the last compound predictors can enhance the final predicted accuracy.