摘要
Internet流量是具有复杂非线性组合特征的季节性时间序列.目前国内外的网络流量预测研究主要集中在网络层和传输层,仅采用单一的ARMA(n,n-1)模型来描述网络的整体流量趋势,但该模型无法描述应用层流量的季节特性.因此提出基于应用层的流量预测分析模型,对国内某城域网出口链路上的应用层流量序列采用ARIMA季节乘积混合模型(p,d,q)(P,D,Q)s建模并预测.实验结果表明,在同一个城域网中不同的应用层流量表现出不同的行为特征,经ARIMA季节乘积混合模型(p,d,q)(P,D,Q)s预测的应用层流量趋势与实际曲线基本相似,平均绝对百分比误差在10%左右.
Complexity and diversity of Internet traffic are constantly growing. Networking researchers become aware of the need to constantly monitor and reevaluate their assumptions in order to ensure that the conceptual models correctly represent reality. Internet traffic today is a complex nonlinear combination of the seasonal time series. The current network traffic measurement research is mainly concentrated on the flow forecasts and analysis based on network layer or transport layer. However, a single ARMA （n, n-1） model is used, which can only describe the overall network traffic trends, while different traffics based on the application layer aren＇t always consistent with ARMA （n, n-1） model. Presented in this paper are traffic prediction models based on application layer, which use ARIMA seasonal multiple model （p, d, q）（P, D, Q）s for modeling and forecasting the seasonal time series from China＇s exports of a metro area network link. Experimental results show that different application layer traffics perform different traffic behavior characteristics, and with the establishment of different application-layer flow prediction models, forecasting trends are very similar with the actual flow curves, and mean absolute percentage errors are around 10%. The authors firstly presents ARIMA seasonal multiple model as traffic prediction models based on application layer.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2009年第3期434-442,共9页
Journal of Computer Research and Development
基金
国家“九七三”重点基础研究发展计划基金项目(2007CB310702)
国家自然科学基金网络与信息安全重大专项基金项目(90604015)
中国科学院重大科研装备研制项目(YZ200824)~~
