A Hybrid Prediction for Non-Gaussian Self-Similar Traffic

摘要：

There is growing evidence shows that non- Gaussian,namely heavy tailness is the key cause of burstiness in self-similar traffic.We present three predictors including autoregressive (AR),moving average (MA)and fractional autoregressive integrated moving average (FARIMA)based on the symmetrical non-Gaussian self-similar traffic model.The three predictors can minimize the dispersion according to the minimum dispersion criteria with infinite variance.The final predicted values are attained by combining the previous three individual predicted values.Our predicted results for the actual trace collected from Bellcore Lab and Lawrence Berkeley Lab show that the three individual predictors are precise and reliable, the compound predictors can enhance the final predicted accuracy.

关键词： self-similar non-Gaussian traffic prediction

作者: Yong Wen Guangxi Zhu

作者单位: School of Computer Science & Technology Huazhong University of Science & Technology Wuhan, Hubei, P. Department of Electronic & Information Engineering Huazhong University of Science & Technology Wuhan

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）