城域网应用层VoIP流量的建模与预测研究
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摘要
网络建模是指建立正式的网络描述与模拟,可实现对未来网络行为的预测。网络预测对网络流量分布、规律的掌握,可以预测网络的发展状况,有助于网络管理和决策。
目前网络行为的建模分析可采用Queuing理论、Petri网、Markov链、Poisson过程等方法,但是这些模型已经不能很好地描述Internet多构性,突发连续性和自相似性等特征。国内外的网络流量预测研究主要集中在网络层和传输层,采用传统时间序列ARIMA模型来描述网络的整体流量趋势,不能很好地描述应用层网络流量的突发特性。新的业务流例如VoIP,不仅改变了当前网络流量的组成和行为特征,而且对Internet网络基础设施提供的QoS提出了新的要求,因此需要对这样一个新的业务流进行行为描述和分析预测,这给我们提出了新的挑战。
针对城域出口链路的应用层VoIP流量突发连续性的问题,本文提出了一种优化的ARIMA季节乘积模型,对城域出口链路的应用层VoIP流量应用该模型进行建模与预测,很好地解决了VOIP网络流量在突降区域波动比较大的情况下,比较精确地描述网络流量。实验证明,该模型比传统的ARIMA模型的误差小,并将该模型应用到城域网应用层VoIP网络流量管理当中,效果显著。本文主要完成了以下几个方面的工作:
(1)针对网络流量在突降区域波动比较大的问题,对传统的ARIMA模型的改进,提出了一种优化的ARIMA季节乘积模型。该模型将突降点与突降左右邻居点提取出来构成突降点和突降左右邻居点时间序列组,并将这些时间序列组进行二次预测,从而达到预测精度的进一步提高。
(2)通过对某城域出口链路上实际采集的应用层VoIP综合统计数据采用优化后的ARIMA季节乘积模型进行建模与预测。实验表明,该模型很好地解决了VoIP网络流量在突降区域波动比较大的情况下,能较精确地描述网络的流量,优化后的ARIMA季节乘积模型比传统的ARIMA模型预测精度提高了5%左右。
(3)应用优化的ARIMA季节乘积模型在城域网应用层VoIP网络流量管理当中。通过借鉴灰色系统的GM (1,1)理论模型分解得到流量趋势成分,将优化后的ARIMA季节乘积模型计算出来的预测值与流量趋势成分模型计算出来的流量阀值相比较,实现VoIP流量的告警应用。
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.
Metro area network plays a key role in the Internet, IP-based voice packet transmission (VoIP) telephone services are currently being deployed nationwide in MANs. traffic flows characteristics of MAN are very important for traffic engineering to improve network performance. The design of appropriate and effective traffic flows models is a desirable task for enterprise, university and ISP networkers. The current network traffic measurement research is mainly concentrated on the flow forecasts and analysis based on network layer or transport layer, and a traditional autoregressive integrated moving average (ARIMA) model which can only describe the overall network traffic trends is used, however, VoIP traffic based on application layer aren’t always consistent with ARIMA model, because the model has not accurately described the complicated structure of today's Internet-unexpected continuity, anomality and the self-similar characteristics. The main contributions of this paper are as follows:
(1) Analyzing the prediction accuracy of the traditional ARIMA model when the network traffics volatile heavily, we find the modeling methodology can be improved efficiency. An improved seasonal ARIMA model is proposed. The volatile points are extracted and rebuilt for prediction so as to improve the prediction accuracy.
(2) By using VoIP comprehensive statistical data collected with NetTurbo on an ISP WAN link, we establish a model of improved multiple seasonal ARIMA and predict the VoIP traffics on output link of this MAN. Experimentation shows that based on an improved seasonal ARIMA model, the traffic can be described more accuretely at the valatile circumstances and the accuracy is almost improved by 5%.
(3) Putting the improved seasonal ARIMA model into management of VoIP network traffics, we can detect the possible time when the VoIP traffics are beyond the threshold, and pre-take measures to ensure the QoS of VoIP.
目前网络行为的建模分析可采用Queuing理论、Petri网、Markov链、Poisson过程等方法,但是这些模型已经不能很好地描述Internet多构性,突发连续性和自相似性等特征。国内外的网络流量预测研究主要集中在网络层和传输层,采用传统时间序列ARIMA模型来描述网络的整体流量趋势,不能很好地描述应用层网络流量的突发特性。新的业务流例如VoIP,不仅改变了当前网络流量的组成和行为特征,而且对Internet网络基础设施提供的QoS提出了新的要求,因此需要对这样一个新的业务流进行行为描述和分析预测,这给我们提出了新的挑战。
针对城域出口链路的应用层VoIP流量突发连续性的问题,本文提出了一种优化的ARIMA季节乘积模型,对城域出口链路的应用层VoIP流量应用该模型进行建模与预测,很好地解决了VOIP网络流量在突降区域波动比较大的情况下,比较精确地描述网络流量。实验证明,该模型比传统的ARIMA模型的误差小,并将该模型应用到城域网应用层VoIP网络流量管理当中,效果显著。本文主要完成了以下几个方面的工作:
(1)针对网络流量在突降区域波动比较大的问题,对传统的ARIMA模型的改进,提出了一种优化的ARIMA季节乘积模型。该模型将突降点与突降左右邻居点提取出来构成突降点和突降左右邻居点时间序列组,并将这些时间序列组进行二次预测,从而达到预测精度的进一步提高。
(2)通过对某城域出口链路上实际采集的应用层VoIP综合统计数据采用优化后的ARIMA季节乘积模型进行建模与预测。实验表明,该模型很好地解决了VoIP网络流量在突降区域波动比较大的情况下,能较精确地描述网络的流量,优化后的ARIMA季节乘积模型比传统的ARIMA模型预测精度提高了5%左右。
(3)应用优化的ARIMA季节乘积模型在城域网应用层VoIP网络流量管理当中。通过借鉴灰色系统的GM (1,1)理论模型分解得到流量趋势成分,将优化后的ARIMA季节乘积模型计算出来的预测值与流量趋势成分模型计算出来的流量阀值相比较,实现VoIP流量的告警应用。
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.
Metro area network plays a key role in the Internet, IP-based voice packet transmission (VoIP) telephone services are currently being deployed nationwide in MANs. traffic flows characteristics of MAN are very important for traffic engineering to improve network performance. The design of appropriate and effective traffic flows models is a desirable task for enterprise, university and ISP networkers. The current network traffic measurement research is mainly concentrated on the flow forecasts and analysis based on network layer or transport layer, and a traditional autoregressive integrated moving average (ARIMA) model which can only describe the overall network traffic trends is used, however, VoIP traffic based on application layer aren’t always consistent with ARIMA model, because the model has not accurately described the complicated structure of today's Internet-unexpected continuity, anomality and the self-similar characteristics. The main contributions of this paper are as follows:
(1) Analyzing the prediction accuracy of the traditional ARIMA model when the network traffics volatile heavily, we find the modeling methodology can be improved efficiency. An improved seasonal ARIMA model is proposed. The volatile points are extracted and rebuilt for prediction so as to improve the prediction accuracy.
(2) By using VoIP comprehensive statistical data collected with NetTurbo on an ISP WAN link, we establish a model of improved multiple seasonal ARIMA and predict the VoIP traffics on output link of this MAN. Experimentation shows that based on an improved seasonal ARIMA model, the traffic can be described more accuretely at the valatile circumstances and the accuracy is almost improved by 5%.
(3) Putting the improved seasonal ARIMA model into management of VoIP network traffics, we can detect the possible time when the VoIP traffics are beyond the threshold, and pre-take measures to ensure the QoS of VoIP.
引文
[1] Brownlee N,Claffy K.Internet Measurement.IEEE INTERNET COMPUTER,2004,8(5):30-33
[2]张宏莉,方滨兴等.Internet测量与分析综述.软件学报,2003,14(1):110-116
[3] Frost V,Melamed B.Traffic Modeling for Telecommunications Networks. IEEE Communications Magazine,1994,32(3):70-80
[4] Paxson V,Floyd S.Wide-Area Traffic:The Failure of Poisson Modeling.IEEE ACM Transactions on Networking,1995,3(3):226-244
[5] Lan K C,Heidemann J.A measurement study of correlations of Internet Flow characteristics.The International Journal of Computer and Telecommunications Networking,2006,50(1):46-62
[6] Lakhina A,Crovella M,Diot C.Characterization of Network-Wide Traffic Anomalies in Traffic Flows.In:Proc of the 4th ACM SIGCOMM conference on Internet measurement.New York,NY,USA:ACM,2004,201-206
[7] Leland W E,Taqqu M S,Willinger W,et al.On the self-similar nature of Ethernet Traffic(extended version).IEEE/ACM Transactions on Networking (TON),1994,2(1):1-15
[8]李捷,刘瑞新等.一种基于混合模型的实时网络流量预测算法.计算机研究与发展,2006,43(5):806-812
[9] Kolarov A,Atai A.Application of Kalman filter in high speed networks.In:Proc of IEEE GLOBECOM’94.Piscataway,NJ,USA:IEEE,1994,624-628
[10] Basu S, Mukherjee A.Time series models for internet traffic.In:Proc of IEEE INFOCOM’96.San Francisco,CA:ACM,1996,611-620
[11]张树京,齐立心.时间序列分析.北京:清华大学出版社,2003,34-52
[12] Edward G , Box P , Jenkins G . Time Series Analysis : Forecasting and Control.Holden-Day,Incorporated,1990,112-156
[13]邹柏贤,刘强.基于ARMA模型的网络流量预测.计算机研究与发展,2002,39(12):1645-1652
[14] Qiao Y,Skicewicz J,Dinda P.An empirical study of multiscale predictability of network traffic.In:Proc of the 13th IEEE International Symposium on High Performance Distributed Computing.Washington,DC, USA:IEEE Computer Society,2004,66-76
[15]徐科,徐金梧等.基于小波分解的某些非平稳时间序列预测方法.电子学报,2001,29(4):566-568
[16] Sen S,Wang J.Analyzing Peer-To-Peer Traffic across Large Networks.IEEE/ ACM Transactions on Networking(TON),2004,12(2):219-232
[17]刘琼,徐鹏,杨海涛,等.Peer-to-Peer文件共享系统的测量研究.软件学报,2006,17(10):2131-2140
[18] Kim M S,Won Y J,Hong J W.Characteristic analysis of internet traffic from the perspective of flows.computer communication,2005,29(2006):1639-1652
[19]唐雄燕.城域网的技术选择.中国网通集团研究院,华为技术,2007,18
[20] Barakat C,Thiran P,Iannaccone G,et al.A flow-based model for Internet backbone traffic.In:Proc of the 2nd ACM SIGCOMM Workshop on Internet measurment.Marseille,France:IMW,2002,35-47
[21] Papagiannaki K,Taft N,Zhang Z L,et al.Long-Term Forecasting of Internet Backbone Traffic:Observations and Initial Models.In:Proc of IEEE INFOCOM 2003.Piscataway,NJ:IEEE,2003,1110-1124
[22] Fraleigh C,Moon S,Lyles B,et al.Packet-level traffic measurement from the Sprint IP backbone.IEEE Network Magazine,2003,17(6):6-16
[23] Xu K, Zhang Z L, Bhattacharyya S.Profiling Internet Backbone Traffic:Bahavior Models and Applications.ACM SIGCOMM Computer Communication Review,Philadelphia,2005,35(4):169-180
[24] Xie G G,Zhang G X,Yang J H,et al.The Survey on Traffic of Metro Area Network with Measurement On-line.In:Proc of the 20th International Teletraffic Congress.Springer Berlin:Heidelberg,2007,17-21
[25] Xie G G,Yang J H,Issarny V,et al.an accurate and efficient 3-phases measreuemtn method for IP traffic flow on high speed link.In:Proc of the 2007 IEEE Int Conf on Networking.Piscataway,NJ:IEEE,2007,46-51
[26] Karagiannis T,Broido A,Faloutsos M,et al.Transport Layer Identification on P2P Traffic.In:Proc of the 4th ACM SIGCOMM Conference on Internet Measurement 2004.New York,NY,USA:ACM Press,2004,121-134
[27] Moore A W,Zuev D.Internet Traffic Classification Using Bayesian Analysis Techniques.In:Proc of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems.New York,NY,USA:ACM Press,2005,50-60
[28] Moore A W,Papagiannaki K.Toward the Accurate Identification of Network Application.In:Proc of the Passive & Active Measurement Workshop 2005 (PAM2005).Boston:Springer-Verlag GmbH,2005,41-54
[29] Yang J H,Xie G G,Li Z C.A Best-effort Adaptive Sampling Method for Flow-based Traffic Monitoring.Journal of Computing Research and Develop- ment,2006,43(3):402-409
[30] McCreary S,Claffy K.Trends in Wide Area IP Traffic Patterns: A View from Ames Internet Exchange.In:Proc of 13th ITC Specialist Seminar on Measure- ment and Modeling of IP Traffic.San Diego,California,USA,2000,1-11
[31] Fomenkov M, Keys K, Moore D,et al.Longitudinal study of Internet traffic from 1998-2001:a view from 20 high performance sites. In:Proc of Winter International Symposium on Information and Communication Technologies.Can- cun,Mexico:CAIDA,2004,607-613
[32] Dang T D . New Result in Multifractal Trafiic Analysis and Modeling :[Ph.D.Dissertation].Budapest Hungary:Budapest University of Technology and Economics,2002,61-82
[33] Thompson K,Miller G J,Wilder R.Wide-Area Internet Traffic Patterns and Characteristics.Network IEEE,1997,11(6):10-23
[34] Karagiannis T, Molle M, Faloutsos M.Long-Range Dependence:Ten Years of Internet Traffic Modeling.IEEE Internet Computing,2004,08(5):57-64
[35] Jiang M.Analysis of Wireless Data Network Traffic.http://www.ense.sfu. calpeopiefaculty/1jiljalcnl/presentations/michael/michael_thesis/index . htm ,2007-12-03
[36] Frost V S,Melamed B.Traffic modeling for telecommunications networks.IEEE communications magazine,1994,32(3):70-81
[37] Adas A.Trafic models broadband networks.IEEE Communications Magazine, 1997,35(7):82-89
[38] Pang R M, Allman M, Bennett M,et al.A First Look at Modern Enterprise Traffic.In:Proc of the 5th ACM SIGCOMM conference On Internet Measure- ment.Berkeley,CA,USA:USENIX Association,2005,2
[39]刘嘉馄,王公恕.应用随机过程(第二版).北京:科学出版社,2004,23-36
[40]田铮译.时间序列的理论与方法.北京:高等教育出版社,2001,76-85
[41]范金程,梅长林.数据分析.北京:科学出版社,2002,112-141
[42]邹柏贤,姚志强.一种网络流量平稳方法.通信学报,2004,25(8),234-241
[43] Shen D X.Predictive models for proactive network management: Application to a production web server.Watson Research Center:IBM T.J,1999,62(5):34-52
[44] Arnold S F.Mathematical Statistics.Prentice:Hall,1990,256-272
[45] Gilfedder C C,Clegg R G.A decade of Internet research-advances in models and practices.BT Technology Journal archive,2005,23(4):115 -128
[46] Gao C S,Han L X,Cen Z W.A new multifractal traffic model based on the wavelet transforms.In:Proc of The ISCA 14th Int’l Conf.Richardson.Texas,USA,2001,611-623
[47] Duffield N G, Lund C, Thorup M.Learn more,sample less:control of volume and variance in network measurement . IEEE Transactions in Information Theory,2005,51(5):1756-1775
[48]程光,龚俭,丁伟.网络流量宏观行为分析的一种时序分解模型.电子学报,2002,30(11):1633-1637
[49]王立等.业务管理网结构和概念模型的研究与实现.西安交通大学学报.2003,37(8):878-880
[50] Daswani N, Guha S, Jain R.An Experimental Study of the Skype Peer-to-Peer VoIP System.In:Proc of the 5th International Workshop on Peer-to-Peer Internet Telephony Protocol.Santa Barbara,CA,USA,2006,1121-1127
[51] Benjamin B S,Wah W.Analysis and Evaluation of the Skype and Google-Talk VoIP Systems.In:Proc of IEEE International Conference on Multimedia and Expo.Toronto,Ontario,Canada,2006,332-339
[2]张宏莉,方滨兴等.Internet测量与分析综述.软件学报,2003,14(1):110-116
[3] Frost V,Melamed B.Traffic Modeling for Telecommunications Networks. IEEE Communications Magazine,1994,32(3):70-80
[4] Paxson V,Floyd S.Wide-Area Traffic:The Failure of Poisson Modeling.IEEE ACM Transactions on Networking,1995,3(3):226-244
[5] Lan K C,Heidemann J.A measurement study of correlations of Internet Flow characteristics.The International Journal of Computer and Telecommunications Networking,2006,50(1):46-62
[6] Lakhina A,Crovella M,Diot C.Characterization of Network-Wide Traffic Anomalies in Traffic Flows.In:Proc of the 4th ACM SIGCOMM conference on Internet measurement.New York,NY,USA:ACM,2004,201-206
[7] Leland W E,Taqqu M S,Willinger W,et al.On the self-similar nature of Ethernet Traffic(extended version).IEEE/ACM Transactions on Networking (TON),1994,2(1):1-15
[8]李捷,刘瑞新等.一种基于混合模型的实时网络流量预测算法.计算机研究与发展,2006,43(5):806-812
[9] Kolarov A,Atai A.Application of Kalman filter in high speed networks.In:Proc of IEEE GLOBECOM’94.Piscataway,NJ,USA:IEEE,1994,624-628
[10] Basu S, Mukherjee A.Time series models for internet traffic.In:Proc of IEEE INFOCOM’96.San Francisco,CA:ACM,1996,611-620
[11]张树京,齐立心.时间序列分析.北京:清华大学出版社,2003,34-52
[12] Edward G , Box P , Jenkins G . Time Series Analysis : Forecasting and Control.Holden-Day,Incorporated,1990,112-156
[13]邹柏贤,刘强.基于ARMA模型的网络流量预测.计算机研究与发展,2002,39(12):1645-1652
[14] Qiao Y,Skicewicz J,Dinda P.An empirical study of multiscale predictability of network traffic.In:Proc of the 13th IEEE International Symposium on High Performance Distributed Computing.Washington,DC, USA:IEEE Computer Society,2004,66-76
[15]徐科,徐金梧等.基于小波分解的某些非平稳时间序列预测方法.电子学报,2001,29(4):566-568
[16] Sen S,Wang J.Analyzing Peer-To-Peer Traffic across Large Networks.IEEE/ ACM Transactions on Networking(TON),2004,12(2):219-232
[17]刘琼,徐鹏,杨海涛,等.Peer-to-Peer文件共享系统的测量研究.软件学报,2006,17(10):2131-2140
[18] Kim M S,Won Y J,Hong J W.Characteristic analysis of internet traffic from the perspective of flows.computer communication,2005,29(2006):1639-1652
[19]唐雄燕.城域网的技术选择.中国网通集团研究院,华为技术,2007,18
[20] Barakat C,Thiran P,Iannaccone G,et al.A flow-based model for Internet backbone traffic.In:Proc of the 2nd ACM SIGCOMM Workshop on Internet measurment.Marseille,France:IMW,2002,35-47
[21] Papagiannaki K,Taft N,Zhang Z L,et al.Long-Term Forecasting of Internet Backbone Traffic:Observations and Initial Models.In:Proc of IEEE INFOCOM 2003.Piscataway,NJ:IEEE,2003,1110-1124
[22] Fraleigh C,Moon S,Lyles B,et al.Packet-level traffic measurement from the Sprint IP backbone.IEEE Network Magazine,2003,17(6):6-16
[23] Xu K, Zhang Z L, Bhattacharyya S.Profiling Internet Backbone Traffic:Bahavior Models and Applications.ACM SIGCOMM Computer Communication Review,Philadelphia,2005,35(4):169-180
[24] Xie G G,Zhang G X,Yang J H,et al.The Survey on Traffic of Metro Area Network with Measurement On-line.In:Proc of the 20th International Teletraffic Congress.Springer Berlin:Heidelberg,2007,17-21
[25] Xie G G,Yang J H,Issarny V,et al.an accurate and efficient 3-phases measreuemtn method for IP traffic flow on high speed link.In:Proc of the 2007 IEEE Int Conf on Networking.Piscataway,NJ:IEEE,2007,46-51
[26] Karagiannis T,Broido A,Faloutsos M,et al.Transport Layer Identification on P2P Traffic.In:Proc of the 4th ACM SIGCOMM Conference on Internet Measurement 2004.New York,NY,USA:ACM Press,2004,121-134
[27] Moore A W,Zuev D.Internet Traffic Classification Using Bayesian Analysis Techniques.In:Proc of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems.New York,NY,USA:ACM Press,2005,50-60
[28] Moore A W,Papagiannaki K.Toward the Accurate Identification of Network Application.In:Proc of the Passive & Active Measurement Workshop 2005 (PAM2005).Boston:Springer-Verlag GmbH,2005,41-54
[29] Yang J H,Xie G G,Li Z C.A Best-effort Adaptive Sampling Method for Flow-based Traffic Monitoring.Journal of Computing Research and Develop- ment,2006,43(3):402-409
[30] McCreary S,Claffy K.Trends in Wide Area IP Traffic Patterns: A View from Ames Internet Exchange.In:Proc of 13th ITC Specialist Seminar on Measure- ment and Modeling of IP Traffic.San Diego,California,USA,2000,1-11
[31] Fomenkov M, Keys K, Moore D,et al.Longitudinal study of Internet traffic from 1998-2001:a view from 20 high performance sites. In:Proc of Winter International Symposium on Information and Communication Technologies.Can- cun,Mexico:CAIDA,2004,607-613
[32] Dang T D . New Result in Multifractal Trafiic Analysis and Modeling :[Ph.D.Dissertation].Budapest Hungary:Budapest University of Technology and Economics,2002,61-82
[33] Thompson K,Miller G J,Wilder R.Wide-Area Internet Traffic Patterns and Characteristics.Network IEEE,1997,11(6):10-23
[34] Karagiannis T, Molle M, Faloutsos M.Long-Range Dependence:Ten Years of Internet Traffic Modeling.IEEE Internet Computing,2004,08(5):57-64
[35] Jiang M.Analysis of Wireless Data Network Traffic.http://www.ense.sfu. calpeopiefaculty/1jiljalcnl/presentations/michael/michael_thesis/index . htm ,2007-12-03
[36] Frost V S,Melamed B.Traffic modeling for telecommunications networks.IEEE communications magazine,1994,32(3):70-81
[37] Adas A.Trafic models broadband networks.IEEE Communications Magazine, 1997,35(7):82-89
[38] Pang R M, Allman M, Bennett M,et al.A First Look at Modern Enterprise Traffic.In:Proc of the 5th ACM SIGCOMM conference On Internet Measure- ment.Berkeley,CA,USA:USENIX Association,2005,2
[39]刘嘉馄,王公恕.应用随机过程(第二版).北京:科学出版社,2004,23-36
[40]田铮译.时间序列的理论与方法.北京:高等教育出版社,2001,76-85
[41]范金程,梅长林.数据分析.北京:科学出版社,2002,112-141
[42]邹柏贤,姚志强.一种网络流量平稳方法.通信学报,2004,25(8),234-241
[43] Shen D X.Predictive models for proactive network management: Application to a production web server.Watson Research Center:IBM T.J,1999,62(5):34-52
[44] Arnold S F.Mathematical Statistics.Prentice:Hall,1990,256-272
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