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城域网应用层流量预测模型 被引量:8

Traffic Prediction Models of Traffics at Application Layer in Metro Area Network
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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)~~
关键词 城域网 应用层流量 时间序列 ARIMA季节乘积混合模型 流量预测 metro area network application layer traffic time series ARIMA seasonal multiplemodel flow forecasting
作者简介 袁小坊,1971年生,1993年本科毕业于湖南大学半导体物理与器件专业,1999年获取湖南大学控制理论与控制工程工学硕士学位.现任教于湖南大学计算机与通信学院.主要研究方向为网络监测与建模. 陈楠楠,1984年生,硕士研究生,主要研究方向为网络监测与流量分析. 王东,1964年生,博士,教授,主要研究方向为网络测试与性能评估、无线网络和移动计算等. 谢高岗,1974年生,1996年本科毕业于湖南大学物理系,1999,2002年先后获取湖南大学计算机科学专业工学硕士与博土学位.2005至2006年,获FFCSA博士后基金资助,在法国国家计算机与自动化研究所(INRIA)进行博士后研究.现为中国科学院计算技术研究所下一代互联网研究中一tb副主任,研究员,博士生导师.中国计算机学会高级会员,中国计算机学会容错计算专业委员会委员,IEEE Member.已发表论文70余篇.主要研究方向为网络监测、分析、模型化与优化控制、分布式移动计算. 张大方,1959年生,博士,教授,博士生导师,主要研究方向为网络测量与分析、软件测试、网络安全等.
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参考文献20

  • 1Nevil Brownlee, Claffy Kc. Internet Measurement [C]//Proc of IEEE Internet Computer. Piseataway, NJ: IEEE, 2004: 30-33
  • 2Xie Gaogang, Zhang Guangxing, Yang Jianhua, et al. The survey on traffic of metro area network with measurement on-line [C]//Proe of the 20th Int Teletraffie Congress. Berlin: Springer, 2007:666-677
  • 3张宏莉,方滨兴,胡铭曾,姜誉,詹春艳,张树峰.Internet测量与分析综述[J].软件学报,2003,14(1):110-116. 被引量:110
  • 4Chadi Barakat, Patrick Thiran, Gianluca Iannaccone, et al. A flow-based model for Internet backbone traffic [C]//Proc of IMW'02. New York: ACM, 2002: 35-47
  • 5Basu S, Mukherjee A. Time series models for Internet traffic [C] //Proc of IEEE INFOCOM'96. Piscataway, NJ: IEEE, 1996:611-620
  • 6Konstantina Papagiannaki, Nina Taft, Zhang Zhili, et al. Long-term forecasting of Internet backbone traffic: Observations and initial models pC] //Proc of IEEE Infocom 2003. Piscataway, NJ: IEEE, 2003:1110-1124
  • 7邹柏贤,刘强.基于ARMA模型的网络流量预测[J].计算机研究与发展,2002,39(12):1645-1652. 被引量:107
  • 8Vern Paxson, Sally Floyd. Wide area traffic: The failure of Poisson modeling [J]. IEEE ACM Trans on Networking, 1995, 3(3): 226-244
  • 9Lan K C, Heidemann J. A measurement study of correlations of Internet flow characteristics [J]. Computer Networks; The International Journal of Computer and Telecommunications Networking, 2006, 50 (1) : 46-62
  • 10Leland W, Taqqu M, Willinger W, et al. On the self-similar nature of Ethernet Traffic [J]. IEEE/ACM Trans on Networking, 1994, 2(1): 1-15

二级参考文献12

  • 1罗发龙,李衍达.神经网络信号处理研究评述[J].电子瞭望,1993(9):5-10. 被引量:13
  • 2王叔子.时间序列分析的工程应用[M].武汉:华中理工大学出版社,1992..
  • 3[1]Paxson V. End-to-End routing behavior in the Internet. IEEE/ACM Transactions on Networking, 1997,5(5):601~615.
  • 4[2]Kalidindi S, Zekauskas MJ. Surveyor: an infrastructure for Internet performance measurements. In: Proceedings of the INET'99. San Jose, 1999. http://www.isoc.org/inet99/proceedings/4h/4h_2.htm.
  • 5[3]Claffy K, Monk TE, McRobb D. Internet tomography. Nature, 1999, January 7. http://www.nature.com/nature/webmatters/tomog/ tomog.html.
  • 6[4]Burch H, Cheswick B. Mapping the Internet. IEEE Computer, 1999,32(4):97~98.
  • 7[5]Wolski R, Spring N, Hayes J. The network weather service: a distributed resource performance forecasting service for metacomputing. Journal of Future Generation Computing Systems, 1999,15(5):757~768.
  • 8[6]Chang H, Jamin S, Willinger W. Inferring AS-level Internet topology from router-level path traces. In: Proceedings of the SPIE ITCom 2001. 2001. http://citeseer.nj.nec.com/chang01inferring.html.
  • 9[7]Govindan R, Tangmunarunkit H. Heuristics for Internet map discovery. In: Proceedings of the IEEE INFOCOM 2000, Vol 3. 2000. 1371~1380. http://citeseer.nj.nec.com/govindan00heuristics.html.
  • 10[8]Munzner T. Interactive visualization of large graphs and networks [Ph.D. Thesis]. Stanford University, 2000.

共引文献316

同被引文献75

  • 1马力,张高明,苟娟迎.一种基于小波变换的校园网流量预测方法研究[J].计算机科学,2012,39(S2):69-73. 被引量:4
  • 2徐志江,李式巨,官军.IEEE 802.11网络中增强的退避算法[J].电子与信息学报,2004,26(10):1527-1533. 被引量:8
  • 3崔莉,鞠海玲,苗勇,李天璞,刘巍,赵泽.无线传感器网络研究进展[J].计算机研究与发展,2005,42(1):163-174. 被引量:730
  • 4王升辉,裘正定.结合多重分形的网络流量非线性预测[J].通信学报,2007,28(2):45-50. 被引量:40
  • 5Hellerstein J L, Zhang F, Shahabuddin P. An approach to predictive detection for service management[ C] //IM' 99. New York: IEEE Publishing, 1999: 309-322.
  • 6Feather F, Maxion R. Fault detection in an ethernet network using anomaly signature matching [ C ] //ACM SIGCOMM. San Francisco: ACM Press, 1993:279- 288.
  • 7Zhang Y, Ge Z, Greenberg A, et al, Network anomography [ C ] //ACM SIGCOMM Internet Measurement Conference. Berkeley: ACM Press, 2005 : 1-14.
  • 8Box G E P, Jenkins G M. Reinsel G C. Time series analysis : forecasting and control[ M ]. Beijing : Posts and Telecom Press, 2005.
  • 9Hood C S, Ji C Y. Proactive network-fault detection [J]. IEEE Transactions on Reliability, 1997, 46 (3) : 333-341.
  • 10Kind A, Stoecklin M P, Dimitropoulos X. Histogrambased traffic anomaly detection [ J]. IEEE Transactions on Network Service Management, 2009, 6(2) : 110-121.

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