自相似网络通信量的滑动平均预测

研究表明具有重尾特性的自相似性网络通信量表现出长程相关的突发性。对这种不同于传统电话网通信量的长程相关的网络通信量进行描述及预测十分重要。本文在基于对称alpha-平稳分布过程的自相似通信量模型基础上，提出了两种新的对具有重尾特性自相似网络通信量的滑动平均预测方法。一种是协变正交意义下的线性无偏预测；另一种是双曲线渐近意义下具有对称平稳新息的滑动平均预测，能使预测偏差最小化。对Bellcore实验室与Lawrence实验室的原始数据进行预测实验，预测结果表明两种预测方法准确有效。

Moving Average Forecasting for Network Traffic with Self-Similar

Network traffic with heavy-tallness shows long-range burstiness which is totally different from conventional traffic in telephone network. Characterization and forecast for the long-range dependence traffic is very important for network performance analysis and network design. Two moving average predictors based on alpha-stable self-similar traffic model are presented. First predictor is a linear unbiased estimator based on covariation-orthogonality. Another predictor is asymptotically moving average forecast with symmetrical stable innovation and it can minimize the dispersion. Forecasting experiments for actual traffic trace from Bellcore Laboratory and Lawrence Berkeley Laboratory show that two predictors are accurate and reliable.