粗粒度网络流量的灰色模型预测

doi:10.13190/jbupt.201001.7.sunhl

北京邮电大学学报 ›› 2010, Vol. 33 ›› Issue (1): 7-11.doi: 10.13190/jbupt.201001.7.sunhl

粗粒度网络流量的灰色模型预测

孙韩林¹；金跃辉¹；崔毅东^1,2；程时端¹

（1.北京邮电大学网络与交换技术国家重点实验室，北京 100876； 2.北京邮电大学信息与通信工程学院，北京 100876）

收稿日期:2009-05-18 修回日期:2009-11-30 出版日期:2010-02-28 发布日期:2010-02-28
通讯作者: 孙韩林

Large-Time Scale Network Traffic Short-Term Prediction by Grey Model

SUN Han-lin¹；JIN Yue-hui¹；CUI Yi-dong^1,2；CHENG Shi-duan¹

(1.State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876， China； 2.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876， China)

Received:2009-05-18 Revised:2009-11-30 Online:2010-02-28 Published:2010-02-28
Contact: SUN Han-lin

摘要/Abstract

摘要：

在实际网络流量上研究了新陈代谢灰色模型(MGM)预测流量. 预测结果表明，灰色模型建模长度远小于流量序列主周期长度时，预测精度较高. 灰色模型预测流量宜采用小量数据建模，此时残差修正对提高预测精度影响很小,预测不需采用残差灰色模型(RGM). 对比了灰色模型与自回归综合滑动平均模型（ARIMA）和Elman神经网络（ENN）模型的预测结果，灰色模型远优于ARIMA，与ENN相当. 灰色模型的优点是能自适应网络流量的变化.

关键词: 网络流量预测, 灰色理论, 灰色模型

Abstract:

The metabolic grey model (MGM) is investigated for network traffic prediction. It is shown that, when the MGM modeling-length is shorter than the traffic primary-period length, the accuracy is satisfactory. Small modeling-length MGM is preferred. Residual grey model (RGM) is not needed for its contribution to accuracy improvement is limited. The prediction of MGM, auto regressive integrated moving average（ARIMA ）and Elman neural network(ENN) are compared. The MGM accuracy is better than the ARIMAs, and equals to the ENNs. Furthermore, MGM is adaptive to traffic changes.

Key words: network traffic prediction, grey theory, grey model

孙韩林，金跃辉，崔毅东，程时端. 粗粒度网络流量的灰色模型预测[J]. 北京邮电大学学报, 2010, 33(1): 7-11.

SUN Han-lin¹；JIN Yue-hui¹；CUI Yi-dong^1,2；CHENG Shi-duan¹. Large-Time Scale Network Traffic Short-Term Prediction by Grey Model[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2010, 33(1): 7-11.

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粗粒度网络流量的灰色模型预测

Large-Time Scale Network Traffic Short-Term Prediction by Grey Model

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