P2P网络流量控制管理若干关键技术研究
|
摘要
最近几年,P2P技术作为一种新的互联网应用模式迅速风靡全球,作为改变互联网应用模式的一项新技术,P2P业务己悄然占据了互联网业务流量总量的70%以上。P2P流量的无序性在一定程度上打破了网络运营商原有的运营和商业模式,并且巨大的流量也给运营商的流量管理带来了巨大的压力。
提供一个可运营可管理的P2P基础平台,协调好用户、P2P服务提供商、网络提供商各方的利益,使整个P2P产业链健康协调地发展,将是未来的发展方向。为应对P2P流量对网络带宽消耗带来的挑战,运营商一方面通过加大投入,增加带宽来解决问题;另一方面,希望找到合适的技术方法实现P2P流量的有效管理,通过限制或引导P2P流量,避免带宽被P2P流量过多地消耗。因此要实现可靠的数据传输以及合理的网络流量资源分配,就需要根据P2P网络流量的行为特性建立相应的P2P流量识别、预测与控制模型,形成完善的P2P流量控制管理机制。本文在深入分析P2P流量管理技术研究的现状、现有的P2P协议及相关技术的基础上,对P2P流量控制管理研究中的P2P流量检测技术、预测算法和控制机制这些关键技术分别进行了深入研究。为验证本文提出的算法和模型的正确性,最后设计并实现了一个基于移动代理的P2P流量控制管理系统,系统演示结果表明该流量管理系统能对网络中的P2P流量进行有效的检测、控制和管理,从而能保证网络中其他应用业务的带宽使用,为网络运营商提高网络管理水平提供良好的参考应用价值和实际应用意义。本文的主要贡献如下:
(1)针对现有P2P流量识别方法的不足,吸收现有方法的优点,提出了一种基于流量属性特征选择的P2P流量分类方法,通过实验表明基于FCBF选择的SVM算法对P2P流量能进行良好的识别;为了提高P2P流量识别的敏感度和精度还提出了一种基于损失函数的支持向量机P2P流量检测模型,使用模糊隶属度概念对SVM算法的结果进行解释,结合损失函数的概念对SVM算法进行改进,并应用于P2P流量识别,能够对新的未知的P2P协议和加密传输的P2P流进行一定的识别。但是由于基于属性特征选择的P2P流量识别方法速度慢,只能进行离线识别,为此本文提出了一种基于流传输特性的实时P2P流量检测方法,该方法是一种轻量级的实时在线识别方法,能够高效快速的实时在线进行P2P协议的识别。实验结果表明所提出的方法是可行的。
(2)提出了基于小波分析的P2P业务流量预测模型。该模型结合卡尔曼滤波和小波分析技术,使用小波重构实现对一类非平稳随机过程的平稳部分的各个分量进行更新估计,并且使用卡尔曼滤波方法可以在一个周期内利用某个时刻及其以前的信息对其后所有时刻进行预报,即实现了周期内实时的动态多步预报。同时,与小波分析方法相比,该方法具有实时性和递归性,可在时域中对网络流量进行实时的动态估计和分析。并且具有与真实网络流量重合度高,预测误差小的特点,能达到良好的跟踪和预测性能,同传统的非线性预测模型如神经网络等相比,其复杂度较低便于实时预测,可满足网络运营商对P2P流量管理的要求。
(3)为了对P2P流量进行有效的控制,提出了两个流量本地化的策略:即P2P缓存系统和一种基于CDN路由的轻量级节点距离测量方案。前者既为内网用户提供可管理控制的P2P服务,保证用户的P2P应用体验,又能减轻主干网的带宽压力。因此本文对缓存系统部署架构及各个模块的功能和校园网内的实测结果进行了深入研究。后者在节点选择机制上利用网络路由机制,将逻辑距离和实际地理距离结合,有利于实现流量本地化的。仿真实验证明这种节点选择方案对于P2P流量本地化的意义。
(4)为了验证上述部分算法和方法的有效性,本文实现了一个基于移动代理的P2P网络流量管理原型系统。在原型系统的P2P流量检测模块中,结合了流的实时传输特征和传统的DPI方法实现了对P2P流量的实时在线识别;在其P2P流量控制模块中,我们主要结合了传统的流量控制手段。原型系统中的两个模块相互补充,可以单独供网络管理者使用。通过实现的系统,把本文的相关研究成果应用其中,良好的展示了本文的研究极具有使用价值和实际应用意义。
As a new model of Internet applications, Peer-to-Peer (P2P) technology has rapidly become a hot topic in the world with its traffic having occupied more than 70% of the total internet traffic. Meanwhile,the huge P2P random traffic has not only broken the original network operator’s operation and business model but also brings more challenge for ISPs to achieve more effective traffic management.
The future development direction is to provide a P2P foundation platform which is operatable and managable, and can coordinate all benefits of users, P2P serve providers as well as network providers to promote the healthy development of the entire P2P industry chain. In order to deal with the challenge of huge P2P traffic, on the one hand the operator expands the investment and increases the band width. On the other hand, they are looking for appropriate technique to reliaze effective traffic management which can limit and guide the P2P traffic so as to avoid the excessive band comsumption by the P2P traffic. Therefore in order to realize reliable data transmission and reasonable network taffic resource distribution, it is impretive to establish corresponding control management mechanism for P2P taffic detection, forecast and control model according to the behavioral characteristics of P2P network traffic.
Based on the deep research of P2P traffic control management product, current p2p protocols as well as the relevant techqiues, the thesis studies the key mechanisms for P2P control management including P2P taffic detection technology, P2P traffic forecast algorithm and its control mechanism.
The main contribution of this thesis is as follows:
(1) Aiming at the existing deficiencies of P2P traffic identification methods, and absorbing the advantages of existing methods, the thesis proposes a P2P traffic classification based on feature selection and flow properties of traffic. A large number of experiments show that SVM algorithm based on FCBF method does well in P2P traffic identification. To improve the sensitivity and accuracy of SVM algorithm , a P2P traffic detection model based on loss function of SVM is also proposed, which uses the concept of fuzzy theory and loss function of SVM algorithm to explain the results of SVM algorithm. Analysis shows that the improved algorithm is more accurate and can be applied to identify those unknown, new P2P protocol or those which adopt encrypted transmission. However, due to the P2P traffic identification method based on feature selection is slow which can only be used for offline recognition, the thesis presents a lightweight stream characteristics based online identification method which is fast and efficient in real-time P2P traffic identification. Experimental results show that the proposed method is feasible.
(2) A novel P2P traffic forecasting model based on wavelet analysis is proposed. Combined with Kalman filter and wavelet analysis techniques, the proposed model uses wavelet reconstruction to update and estimate the various components of the smooth part of a class of non-stationary random process, meanwhile the Kalman filtering method is used to achieve a dynamic multi-step prediction . The method uses all their previous information to predict the traffic in a cycle. Compared with the wavelet analysis, the proposed method is real-time and recursive and can be used for estimation and analysis of dynamic real-time network traffic. Simulations show that it achieves good tracking and forecasting performance for its high degree of overlap with the real network traffic and its small forecasting errors. Besides, compared with the traditional non-linear prediction models such as neural networks, the proposed methods has the low complexity and is suitable for real time prediction, which can meet the traffic management requirements of P2P network operators.
(3) To realize P2P traffic localization, two methods are thoroughly studied, namely , a P2P caching system and a lightweight node distance measurement based on the CDN routing. The former scheme can not only provide users within the network the control of P2P services and ensure their P2P applications experience, but also can reduce the backbone bandwidth pressure. Therefore the structure and deployment of P2P caching system as well as its measurement in the campus network environment is thoroughly studied in the thesis.The latter node selection mechanism combined the logical distance with the actual geographical distance which is conducive to flow localization. Finally, some simulation results show its significance in P2P traffic localization.
(4) In order to verify the effectiveness of the proposed algorithms and methods, we implemented a mobile agent-based P2P network traffic management system. In the P2P traffic detection module of the realized system, we combine the stream-based real-time transmission characteristics and the traditional method of DPI for on-line identification of P2P traffic. In its P2P traffic control module, it is mainly a combination of the traditional methods. The realized system includes two modules which complements each other, and can be used separately for network administrators. By applying research results to the realisitic management system of P2P traffic, the thesis exhibits the significance in practical application.
提供一个可运营可管理的P2P基础平台,协调好用户、P2P服务提供商、网络提供商各方的利益,使整个P2P产业链健康协调地发展,将是未来的发展方向。为应对P2P流量对网络带宽消耗带来的挑战,运营商一方面通过加大投入,增加带宽来解决问题;另一方面,希望找到合适的技术方法实现P2P流量的有效管理,通过限制或引导P2P流量,避免带宽被P2P流量过多地消耗。因此要实现可靠的数据传输以及合理的网络流量资源分配,就需要根据P2P网络流量的行为特性建立相应的P2P流量识别、预测与控制模型,形成完善的P2P流量控制管理机制。本文在深入分析P2P流量管理技术研究的现状、现有的P2P协议及相关技术的基础上,对P2P流量控制管理研究中的P2P流量检测技术、预测算法和控制机制这些关键技术分别进行了深入研究。为验证本文提出的算法和模型的正确性,最后设计并实现了一个基于移动代理的P2P流量控制管理系统,系统演示结果表明该流量管理系统能对网络中的P2P流量进行有效的检测、控制和管理,从而能保证网络中其他应用业务的带宽使用,为网络运营商提高网络管理水平提供良好的参考应用价值和实际应用意义。本文的主要贡献如下:
(1)针对现有P2P流量识别方法的不足,吸收现有方法的优点,提出了一种基于流量属性特征选择的P2P流量分类方法,通过实验表明基于FCBF选择的SVM算法对P2P流量能进行良好的识别;为了提高P2P流量识别的敏感度和精度还提出了一种基于损失函数的支持向量机P2P流量检测模型,使用模糊隶属度概念对SVM算法的结果进行解释,结合损失函数的概念对SVM算法进行改进,并应用于P2P流量识别,能够对新的未知的P2P协议和加密传输的P2P流进行一定的识别。但是由于基于属性特征选择的P2P流量识别方法速度慢,只能进行离线识别,为此本文提出了一种基于流传输特性的实时P2P流量检测方法,该方法是一种轻量级的实时在线识别方法,能够高效快速的实时在线进行P2P协议的识别。实验结果表明所提出的方法是可行的。
(2)提出了基于小波分析的P2P业务流量预测模型。该模型结合卡尔曼滤波和小波分析技术,使用小波重构实现对一类非平稳随机过程的平稳部分的各个分量进行更新估计,并且使用卡尔曼滤波方法可以在一个周期内利用某个时刻及其以前的信息对其后所有时刻进行预报,即实现了周期内实时的动态多步预报。同时,与小波分析方法相比,该方法具有实时性和递归性,可在时域中对网络流量进行实时的动态估计和分析。并且具有与真实网络流量重合度高,预测误差小的特点,能达到良好的跟踪和预测性能,同传统的非线性预测模型如神经网络等相比,其复杂度较低便于实时预测,可满足网络运营商对P2P流量管理的要求。
(3)为了对P2P流量进行有效的控制,提出了两个流量本地化的策略:即P2P缓存系统和一种基于CDN路由的轻量级节点距离测量方案。前者既为内网用户提供可管理控制的P2P服务,保证用户的P2P应用体验,又能减轻主干网的带宽压力。因此本文对缓存系统部署架构及各个模块的功能和校园网内的实测结果进行了深入研究。后者在节点选择机制上利用网络路由机制,将逻辑距离和实际地理距离结合,有利于实现流量本地化的。仿真实验证明这种节点选择方案对于P2P流量本地化的意义。
(4)为了验证上述部分算法和方法的有效性,本文实现了一个基于移动代理的P2P网络流量管理原型系统。在原型系统的P2P流量检测模块中,结合了流的实时传输特征和传统的DPI方法实现了对P2P流量的实时在线识别;在其P2P流量控制模块中,我们主要结合了传统的流量控制手段。原型系统中的两个模块相互补充,可以单独供网络管理者使用。通过实现的系统,把本文的相关研究成果应用其中,良好的展示了本文的研究极具有使用价值和实际应用意义。
As a new model of Internet applications, Peer-to-Peer (P2P) technology has rapidly become a hot topic in the world with its traffic having occupied more than 70% of the total internet traffic. Meanwhile,the huge P2P random traffic has not only broken the original network operator’s operation and business model but also brings more challenge for ISPs to achieve more effective traffic management.
The future development direction is to provide a P2P foundation platform which is operatable and managable, and can coordinate all benefits of users, P2P serve providers as well as network providers to promote the healthy development of the entire P2P industry chain. In order to deal with the challenge of huge P2P traffic, on the one hand the operator expands the investment and increases the band width. On the other hand, they are looking for appropriate technique to reliaze effective traffic management which can limit and guide the P2P traffic so as to avoid the excessive band comsumption by the P2P traffic. Therefore in order to realize reliable data transmission and reasonable network taffic resource distribution, it is impretive to establish corresponding control management mechanism for P2P taffic detection, forecast and control model according to the behavioral characteristics of P2P network traffic.
Based on the deep research of P2P traffic control management product, current p2p protocols as well as the relevant techqiues, the thesis studies the key mechanisms for P2P control management including P2P taffic detection technology, P2P traffic forecast algorithm and its control mechanism.
The main contribution of this thesis is as follows:
(1) Aiming at the existing deficiencies of P2P traffic identification methods, and absorbing the advantages of existing methods, the thesis proposes a P2P traffic classification based on feature selection and flow properties of traffic. A large number of experiments show that SVM algorithm based on FCBF method does well in P2P traffic identification. To improve the sensitivity and accuracy of SVM algorithm , a P2P traffic detection model based on loss function of SVM is also proposed, which uses the concept of fuzzy theory and loss function of SVM algorithm to explain the results of SVM algorithm. Analysis shows that the improved algorithm is more accurate and can be applied to identify those unknown, new P2P protocol or those which adopt encrypted transmission. However, due to the P2P traffic identification method based on feature selection is slow which can only be used for offline recognition, the thesis presents a lightweight stream characteristics based online identification method which is fast and efficient in real-time P2P traffic identification. Experimental results show that the proposed method is feasible.
(2) A novel P2P traffic forecasting model based on wavelet analysis is proposed. Combined with Kalman filter and wavelet analysis techniques, the proposed model uses wavelet reconstruction to update and estimate the various components of the smooth part of a class of non-stationary random process, meanwhile the Kalman filtering method is used to achieve a dynamic multi-step prediction . The method uses all their previous information to predict the traffic in a cycle. Compared with the wavelet analysis, the proposed method is real-time and recursive and can be used for estimation and analysis of dynamic real-time network traffic. Simulations show that it achieves good tracking and forecasting performance for its high degree of overlap with the real network traffic and its small forecasting errors. Besides, compared with the traditional non-linear prediction models such as neural networks, the proposed methods has the low complexity and is suitable for real time prediction, which can meet the traffic management requirements of P2P network operators.
(3) To realize P2P traffic localization, two methods are thoroughly studied, namely , a P2P caching system and a lightweight node distance measurement based on the CDN routing. The former scheme can not only provide users within the network the control of P2P services and ensure their P2P applications experience, but also can reduce the backbone bandwidth pressure. Therefore the structure and deployment of P2P caching system as well as its measurement in the campus network environment is thoroughly studied in the thesis.The latter node selection mechanism combined the logical distance with the actual geographical distance which is conducive to flow localization. Finally, some simulation results show its significance in P2P traffic localization.
(4) In order to verify the effectiveness of the proposed algorithms and methods, we implemented a mobile agent-based P2P network traffic management system. In the P2P traffic detection module of the realized system, we combine the stream-based real-time transmission characteristics and the traditional method of DPI for on-line identification of P2P traffic. In its P2P traffic control module, it is mainly a combination of the traditional methods. The realized system includes two modules which complements each other, and can be used separately for network administrators. By applying research results to the realisitic management system of P2P traffic, the thesis exhibits the significance in practical application.
引文
[1] Matei R, Iamnitchi A, Foster P. Mapping the Gnutella network[J]. Internet Computing, IEEE, 2002, 6(1): 50-57.
[2] Granary. http://hpc.cs.tsinghua.edu.cn/granary/.
[3] Li J. Peer-to-Peer multimedia applications[C]. Proceedings of the 14th annual ACM international conference on Multimedia table of contents, Santa Barbara, CA, USA: ACM, 2006: 3– 6.
[4] Madhukar, A, Williamson, C. A Longitudinal Study of P2P Traffic Classification[C] Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 2006 (MASCOTS 2006). 14th IEEE International Symposium on Digital Object Identifier , Monterey, California, USA: 179 - 188
[5] P. Haffner, S. Sen, O. Spatscheck, and D. Wang,“ACAS:Automated Construction of Application Signatures,”ACM SIGCOMM Workshop on Mining Network Data (MineNet 2005), PA, USA, August, 2005:107-202
[6] Cisco Systems Inc. NetFlow services solutions guide. http://www.cisco.com/,2007.
[7] Cache Logic. http://www.cachelogic.com, 2007.
[8] Allot Communications Ltd. http://www.allot.com, 2007.
[9] Verso Technologies. http://www.verso.com/, 2007.
[10] P2P终结者. http://www.netsoft2005.com/chs/, 2010.
[11] QQSG. http://www.qqtechnology.com/, 2010.
[12]华为Eudemon系列防火墙白皮书. http://www.huawei.com/, 2010.
[13] Panabit. http://www.panabit.com/, 2010.
[14]郭振滨,裘正定.P2P资源流行度测量与建模[J].通信学报,2009,30(3):66-73
[15] K. Gummadi, R. Dunn, S. Saroiu, S. Gribble, H. Levy, and J.Zahorjan, Measurement, Modeling, and Analysis of a Peer-to-Peer File Sharing Workload, Proceedings of the 10th ACM Symposium on Operating Systems Principles (SOSP-10) NY, USA, October 2003: 314-310
[16] Karagiannis T, Broido A, Faloutsos M. Transport layer identification of P2P traffic[C].Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, ACM Press, 2004, 121-134.
[17] Pouwelse J, Garbacki P, Epema D, et al. The bittorrent p2p file-sharing system: Measurements and analysis[J]. Peer-to-Peer Systems, 2005: 205-216.
[18] Pouwelse J A, Garbacki P, Epema D, et al. A measurement study of the bittorrent peer-to-peer file-sharing system[R]. Delft University of Technology Parallel and Distributed Systems Report Series, Technical Report PDS-2004-007, 2004.
[19] Liang J, Kumar R, Ross K W. The FastTrack overlay: A measurement study[J]. Computer Networks, 2006, 50(6): 842-858.
[20] Karagiannis T, Broido A, Brownlee N, et al. Is p2p dying or just hiding? [C]. Global Telecommunications Conference (GLOBECOM'04), IEEE, 2004, 3: 1532-1538.
[21] Soldani C. Peer-to-peer behaviour detection by TCP flows analysis[J]. Engineer dissertation, University of Liege, France, 2004:25-30.
[22] Moore A,Papagian-naki K.Toward the accurate identifieation of network applications[C]. In Proc. Passive and Active Measurem Workshop(PAM2005).Boston,MA,USA:ACMPress,March,APril 2005:41-54.
[23] Sen S, Spatscheck O, Wang D. Accurate, scalable in-network identification of p2p traffic using application signatures[C]. Proceedings of the 13th International Conference on World Wide Web, ACM, 2004: 512-521.
[24] Liu B, Li Z T, Tu H. A methodology for P2P traffic measurement using application signature work-in-progress[C]. Proceedings of the 2nd international conference on Scalable information systems, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2007: 20-21.
[25] Duan L J, Yu Y F, Hanb L, et al. A P2P Application Signatures Discovery Algorithm[J]. Complex Systems Concurrent Engineering, 2007: 441-448.
[26] John W, Tafvelin S. Analysis of internet backbone traffic and header anomalies observed[C]. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, Kyoto, Japan, 2007:111-116.
[27] Bleul H, Rathgeb E P. A simple, efficient and flexible approach to measure multi-protocol Peer-to-Peer traffic[J]. Lecture Notes in Computer Science, 2005, 3421: 606-616.
[28] Karamcheti V, Geiger D, Kedem Z, et al. Detecting malicious network traffic using inverse distributions of packet contents[C]. Proceedings of the 2005 ACM SIGCOMM Workshop on Mining Network Data, Philadelphia, USA 2005: 165-170.
[29] S. Saroiu, K. Gummadi, and S. Gribble,“A Measurement Study of Peer-to-Peer File Sharing Systems,”Proceedings of the Multimedia Computing and Networking (MMCN 2002), California, January 2002:18-25
[30] Karagiannis T, Broido A, Faloutsos M. Transport layer identification of P2P traffic[C]. Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, ACM, 2004:121-134.
[31] Mchugh J, Mcleod R, Nagaonkar V. Passive network forensics: behavioural classification of network hosts based on connection patterns[J]. ACM SIGOPS Operating Systems Review, 2008, 42(3): 99-111.
[32] Erman J, Mahanti A, Arlitt M, et al. Identifying and discriminating between web and peer-to-peer traffic in the network core[C]. Proceedings of the 16th International Conference on World Wide Web, ACM, 2007:883-892.
[33] Qi Y, Xu B, He F, et al. Towards high-performance flow-level packet processing on multi-core network processors[C]. Proceedings of the 3rd ACM/IEEE Symposium on Architecture for Networking and Communications Systems, ACM, 2007: 17-26.
[34] Erman J, Arlitt M, Mahanti A. Traffic classification using clustering algorithms[C]. Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, ACM, 2006:281-286.
[35] Spognardi A, Lucarelli A, Di Pietro R. A methodology for P2P file-sharing traffic detection[C]. Second International Workshop on Hot Topics in Peer-to-Peer Systems, IEEE, 2005: 52-61.
[36] Plissonneau L, Costeux J L, Brown P. Analysis of peer-to-peer traffic on ADSL[J]. Passive and Active Network Measurement, 2005: 69-82.
[37] Kim M S, Kang H J, Hong J W. Towards peer-to-peer traffic analysis using flows[J].Self-Managing Distributed Systems, 2004: 55-67.
[38] Collins M, Reiter M. Finding peer-to-peer file-sharing using coarse network behaviors[J]. Lecture Notes in Computer Science, 2006, 4189: 1-17.
[39] Auld T, Moore A W, Gull S F, et al. Bayesian neural networks for Internet traffic classification. IEEE Transactions on Neural Networks, 2007, 8(1): 223-239
[40] Li W, Canini M, Moore A W, et al. Efficient application identification and the temporal and spatial stability of classification schema[J]. Computer Networks, 2009, 53(6): 790-809.
[41] Weka. http://www.cs.waikato.ac.nz/ml/weka/.
[42] Moore A, Zuev D, Crogan M. Discriminators for use in flow-based classification[R]. Technical Report RR-05-13, Dept. of Computer Science, Queen Mary, University of London, 2005
[43]张学工.关于统计学习理论与支持向量机[J].自动化学报, 2000, 26(1): 32-42.
[44] Boser B E, Guyon I M, Vapnik V N. A training algorithm for optimal margin classifiers[C]. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, ACM Press, 1992:144-152.
[45] Cortes C, Vapnik V. Support-vector networks[J]. Machine learning, 1995, 20(3): 273-297.
[46] Sch Lkopf B, Burges C, Vapnik V. Extracting support data for a given task[C]. Proceedings of the First International Conference on Knowledge Discovery & Data Mining, AAAI Press, 1995:262-267.
[47] Vladimir V N. The nature of statistical learning theory[M]. NY: Springer-Verlag, 1995.
[48] Vapnik V N. Statistical learning theory[M]. New York: Wiley, 1998.
[49]许建华,张学工.统计学习理论[M].北京:电子工业出版社, 2004.
[50]肖建华.智能模式识别方法[M].广州:华南理工大学出版社, 2006.
[51]吴敏,王汝传.一种基于损失函数的SVM算法在P2P流量检测中的应用[J].计算机科学, 2009, 36(12): 76-80.
[52] Nguyen T T T, Armitage G. A survey of techniques for internet traffic classification using machine learning[J]. Communications Surveys & Tutorials, IEEE, 2008, 10(4): 56-76.
[53] Kim H, Claffy K C, Fomenkov M, et al. Internet traffic classification demystified: myths,caveats, and the best practices[C]. in ACM SIGCOMM CoNEXT, New York, NY, Dec 2008:223-229
[54] Perényi M, Molnár S. Enhanced Skype traffic identification[C]. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2007:1-9.
[55] Memon G, Rejaie R, Guo Y, et al. Large-scale monitoring of DHT traffic[C]. USENIX Association, 2009: 11:351-358.
[56] Analyzer. http://analyzer.polito.it/.
[57] Zuev D, Moore A W. Traffic classification using a statistical approach[J]. Passive and Active Network Measurement, 2005: 321-324.
[58] Risso F, Baldini A, Bonomi F. Extending the NetPDL language to support traffic classification[C]. IEEE Global Telecommunications Conference (GLOBECOM'07), IEEE, 2007: 22-27.
[59] Park K, Willinger W. Self-similar network traffic and performance evaluation[M]. Purdue University, 2000.
[60] Abry P, Veitch D. Wavelet analysis of long-range-dependent traffic[J]. IEEE Transactions on Information Theory, 1998, 44(1): 2-15.
[61] Barral J, Mandelbrot B. Multiplicative products of cylindrical pulses[J]. Probability Theory Related Fields, 2002, 3(124): 409-430.
[62]魏武雄.时间序列分析——单变量多变量方法[M].北京:人民大学出版社, 2005.
[63] Gao J, Yee T. Adaptive estimation in partially linear autoregressive models[J]. Canadian Journal of Statistics, 2000, 28(3): 571-586.
[64] Sadek N, Khotanzad A. Multi-scale high-speed network traffic prediction using k-factor gegenbauer ARMA model[C]. IEEE International Conference on Communications, IEEE, 4: 2148-2152.
[65] Box G. E. P.,Jenkins G. M.,Reinsel G. C.时间序列分析预测与控制[M].北京:中国统计出版社, 1997.
[66] Leland W E, Taqqu M S, Willinger W, et al. On the self-similar nature of Ethernet traffic (extended version)[J]. IEEE/ACM Transactions on Networking, 1994, 2(1): 1-15.
[67] Yu G, Zhang C. Switching ARIMA model based forecasting for traffic flow[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'04), Montreal, Quebec, Canada, 2004, 2: 429-432.
[68] Sang A, Li S. A predictability analysis of network traffic[C]. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies(INFOCOM '00), Tel Aviv, Israel., 2000, 1: 342-351.
[69]舒炎泰,王雷,张连芳,等.基于FARIMA模型的Internet网络业务预报[J].计算机学报, 2001, 24(1): 46-54.
[70] Basu S, Mukherjee A, Klivansky S. Time series models for internet traffic[C]. Fifteenth Annual Joint Conference of the IEEE Computer Societies(INFOCOM '96.) San Francisco, CA, USA. 1996, 2: 611-620.
[71]王振龙.时间序列分析[M].北京:中国统计出版社, 2002.
[72] Park K, Willinger W. Self-similar network traffic and performance evaluation[M]. USA: Wiley Online Library, 2000.
[73] Riedi R H, Crouse M S, Ribeiro V J, et al. A multifractal wavelet model with application to network traffic[J]. IEEE Transactions on Information Theory, 1999, 45(3): 992-1018.
[74] Abry P, Veitch D, Flandrin P. Long‐range Dependence: Revisiting Aggregation with Wavelets[J]. Journal of Time Series Analysis, 1998, 19(3): 253-266.
[75]叶世伟,史忠植.神经网络原理[M].北京:机械工业出版社, 2004.
[76] Yu E S, Chen C Y R. Traffic prediction using neural networks[C]. IEEE Global Telecommunications Conference (GLOBECOM'93), IEEE, 1993: 991-995.
[77] Abry P, Veitch D. Wavelet analysis of long-range-dependent traffic[J]. IEEE Transactions on Information Theory, 1998, 44(1): 2-15.
[78] Abry P, Flandrin P, Taqqu M S, et al. Self-similarity and long-range dependence through the wavelet lens[J]. Long-range dependence: theory and applications, 2002: 527-556.
[79] Feldmann A, Gilbert A C, Willinger W. Data networks as cascades: Investigating the multifractal nature of Internet WAN traffic[C]. ACM SIGCOMM Computer Communication Review, ACM, Vancouver 1998, 28: 42-55.
[80] Feldmann A, Gilbert A C, Huang P, et al. Dynamics of IP traffic: A study of the role of variability and the impact of control[C]. ACM SIGCOMM Computer Communication Review, ACM, Cambridge, Massachusetts. 1999, 29: 301-313.
[81] Riedi R H, Crouse M S, Ribeiro V J, et al. A multifractal wavelet model with application to network traffic[J]. IEEE Transactions on Information Theory, 1999, 45(3): 992-1018.
[82]李捷,刘瑞新,刘先省,等.一种基于混合模型的实时网络流量预测算法[J].计算机研究与发展, 2006, 43(5): 806-812.
[83]白翔宇,叶新铭,蒋海.基于小波变换与自回归模型的网络流量预测[J].计算机科学, 2007, 34(7): 47-54.
[84]雷霆,余镇危.一种网络流量预测的小波神经网络模型[J].计算机应用, 2006, 26(3): 526-528.
[85]曹建华.基于灰色神经网络模型的网络流量预测算法研究[D].江南大学, 2008.
[86]李丽.基于支持向量机的网络流量预测研究[D].南京理工大学, 2007.
[87] Qiao Y, Skicewicz J, Dinda P A. An empirical study of the multiscale predictability of network traffic[C]. 13th IEEE Int’l Symposium on High Performance Distributed Computing, Honolulu, Hawaii, USA. 2004: 66-76.
[88] Sen S, Wang J. Analyzing peer-to-peer traffic across large networks[J]. IEEE/ACM Transactions on Networking, 2004, 12(2): 219-232.
[89] Azzouna N B, Guillemin F. Impact of peer-to-peer applications on wide area network traffic: an experimental approach[C]. Global Telecommunications Conference, Dallas, Texas,UK, 2004, 3: 1544-1548.
[90] Hamada T, Chujo K, Chujo T, et al. Peer-to-peer traffic in metro networks: analysis, modeling, and policies[C]. IEEE International Conference on NOMS, Seoul,Korea, 2004, 1: 425-438.
[91] Karagiannis T, Broido A, Brownlee N, et al. File-sharing in the Internet: A characterization of P2P traffic in the backbone[J]. University of California, Riverside, USA, Tech. Rep, 2003.
[92]洪飞,吴志美.基于小波的多尺度网络流量预测模型.计算机学报,2006,29(l):166一170.
[93]刘琼,徐鹏,杨海涛. Peer-to-Peer文件共享系统的测量研究[J].软件学报, 2006, 17(10): 2131-2140.
[94] Joo S D, Lee C W, Chung Y. Analysis and modeling of traffic from residential high speed Internet subscribers[J]. Information Networking. Networking Technologies for Broadband and Mobile Networks, 2004: 410-419.
[95] Liu G, Hu M, Fang B, et al. Explaining BitTorrent traffic self-similarity[J]. Parallel and Distributed Computing: Applications and Technologies, 2005: 231-240.
[96] S. Sen, O. Spatscheck, and D. Wang,“Accurate, Scalable In-Network Identification of P2P Traffic using Application Signatures,”Proceedings of the 13th International World Wide Web Conference, NY, USA, May 2004: 512-521.
[97]Park K, Willinger W. Self-similar Network Traffic and Performance Evaluation. New York, NY, USA: John Wiley & Sons Inc, 2000: 39-88
[98]李建平,唐远炎.小波分析方法的应用[M].重庆大学出版社, 1999.
[99]徐佩霞,孙功宪.小波分析与应用实例[M].中国科学技术大学出版社, 2001.
[100]韩志杰,王汝传.一种新的P2P网络流量预测模型[J].计算机科学, 2008, 35(9): 39-41.
[101] Kolarov A, Atai A, Hui J. Application of Kalman filter in high-speed networks[C]. Global Telecommunications Conference, 1994.(GLOBECOM'94) San Francisco, USA., 1994: 624-628.
[102] Kalman R E. A new approach to linear filtering and prediction problems[J]. Journal of basic Engineering, 1960, 82(1): 35-45.
[103] Kalman R E, Bucy R S. New results in linear filtering and prediction theory[J]. Transactions of the ASME. Series D, Journal of Basic Engineering, 1961, 83: 95-107.
[104] Julier S J, Uhlmann J K. A new extension of the Kalman filter to nonlinear systems[C]. In The Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing,Simulation and Controls, Multi Sensor Fusion, Tracking and Resource Management II, SPIE, 1997, 3: 26.
[105] Harvey A C. Forecasting, structural time series models and the Kalman filter[M]. Cambridge University Press, Cambridge, 1990.
[106] Qiao Y, Skicewicz J, Dinda P A. An empirical study of the multiscale predictability of network traffic[C]. 13th IEEE Int’l Symposium on High Performance Distributed Computing (HPDC'04), Honolulu, Hawaii,USA, 2004: 66-76.
[107] Zheng T, Girgis A A, Makram E B. A hybrid wavelet-Kalman filter method for load forecasting[J]. Electric power systems research, 2000, 54(1): 11-17.
[108]许佳.基于IPv6的P2P流量识别与控制的研究[D].西安:西安科技大学, 2008. 109]林宇,程时端,邬海涛,等. IP网端到端性能测量技术研究的进展[J].电子学报, 2003, 31(8): 1227-1233.
[110] Christof B, Gianluca I,Thomas Z,Christophe D,et al. Comparison of Tail Drop and Active Queue Management Performance for bulk-data and Web-linke Internet Traffic[C]. In Proceedings of IEEE ISCC 2001. Hammameth,Tunesia. 2001:122-129
[111] Maymounkov P, Mazieres D. Kademlia: A peer-to-peer information system based on the xor metric[J]. Peer-to-Peer Systems, 2002: 53-65.
[112] Wierzbicki A, Leibowitz N, Ripeanu M, et al. Cache replacement policies revisited: The case of P2P traffic[C]. Cluster Computing and the Grid, 2004(CCGrid 2004) ,Chicago, Illinois, USA, 2004: 182-189.
[113] Dán G. Cache-to-cache: Could ISPs cooperate to decrease peer-to-peer content distribution costs?[J]. IEEE Transactions on Parallel and Distributed Systems, 2011.
[114] Ye M, Wu J, Xu K. Caching the P2P traffic in ISP network[C].Communications, 2008. ICC '08. IEEE International Conference on COMMUNCATIONS ..Beijing,China.May 2008 :5876 - 5880
[115] Sniffer. http://www.sniffer.net.cn.
[116] Su A J, Choffnes D, Bustamante F E, et al. Relative network positioning via CDN redirections[C]. 28th IEEE International Conference on Distributed Computing Systems (ICDCS 2008), Beijing, China. June 2008: 377-386.
[117] White Paper1The Ins and Outs of Content Delivery Networks[R] . StanfordUniversity ,2001.
[118] Akamai Management Group .http:// www.akamai.com.
[119] Tata S, Patel J M. Estimating the Selectivity of tf-idf based Cosine Similarity Predicates[J]. ACM SIGMOD Record, 2007, 36(4): 75-80.
[120]王汝传,徐小龙,黄海平.智能Agent及其在信息网络中的应用[M].北京:北京邮电大学出版社, 2006.
[121]张云勇,刘锦德.移动Agent技术[M].北京:淸华大学出版社, 2003.
[122] Puliafito A, Tomarchio O. Using mobile agents to implement flexible network management strategies[J]. Computer Communications, 2000, 23(8): 708-719.
[123] Goldszmidt G, Yemini Y, Yemini S. Network management by delegation: the MAD approach[C]. Proceedings of the 1991 conference of the Centre for Advanced Studies on Collaborative research, 1991: 347-361.
[124] Pinheiro R, Poylisher A, Caldwell H. Mobile agents for aggregation of network management data[C]. Agent Systems and Applications and Third International Symposium on Mobile Agents. Palm Springs, CA, USA,1999:130-140.
[125] Gavalas D, Greenwood D, Ghanbari M, et al. Advanced network monitoring applications based on mobile/intelligent agent technology[J]. Computer Communications, 2000, 23(8): 720-730.
[126] Baldi M, Gai S, Picco G. Exploiting code mobility in decentralized and flexible network management[C]. Proceedings of the First International Workshop on Mobile Agents (MA '97), Berlin, Germany , April, 7-8, 1997,, 1997: 13-26.
[127]张松,阎保平.基于移动Agent的网络管理框架及实现[J].计算机研究与发展, 1999, 36(8): 1007-1010.
[128] IBM Japan Research Group.Aglets Workbench, http://aglets.trl.ibm.co.jp.
[129]吴刚,王怀民,吴泉源.一个移动智能体位置管理与可靠通信的算法[J].软件学报, 2002, 13(2): 269-273.
[130] Shen Z H, Wang H. Network Data Packet Capture and Protocol Analysis on Jpcap-Based[C]. Proceedings of the 2009 International Conference on Information Management,Innovation Management and Industrial Engineering, USA: IEEE COMPUTER SOC. 2009, 3: 329-332.
[2] Granary. http://hpc.cs.tsinghua.edu.cn/granary/.
[3] Li J. Peer-to-Peer multimedia applications[C]. Proceedings of the 14th annual ACM international conference on Multimedia table of contents, Santa Barbara, CA, USA: ACM, 2006: 3– 6.
[4] Madhukar, A, Williamson, C. A Longitudinal Study of P2P Traffic Classification[C] Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 2006 (MASCOTS 2006). 14th IEEE International Symposium on Digital Object Identifier , Monterey, California, USA: 179 - 188
[5] P. Haffner, S. Sen, O. Spatscheck, and D. Wang,“ACAS:Automated Construction of Application Signatures,”ACM SIGCOMM Workshop on Mining Network Data (MineNet 2005), PA, USA, August, 2005:107-202
[6] Cisco Systems Inc. NetFlow services solutions guide. http://www.cisco.com/,2007.
[7] Cache Logic. http://www.cachelogic.com, 2007.
[8] Allot Communications Ltd. http://www.allot.com, 2007.
[9] Verso Technologies. http://www.verso.com/, 2007.
[10] P2P终结者. http://www.netsoft2005.com/chs/, 2010.
[11] QQSG. http://www.qqtechnology.com/, 2010.
[12]华为Eudemon系列防火墙白皮书. http://www.huawei.com/, 2010.
[13] Panabit. http://www.panabit.com/, 2010.
[14]郭振滨,裘正定.P2P资源流行度测量与建模[J].通信学报,2009,30(3):66-73
[15] K. Gummadi, R. Dunn, S. Saroiu, S. Gribble, H. Levy, and J.Zahorjan, Measurement, Modeling, and Analysis of a Peer-to-Peer File Sharing Workload, Proceedings of the 10th ACM Symposium on Operating Systems Principles (SOSP-10) NY, USA, October 2003: 314-310
[16] Karagiannis T, Broido A, Faloutsos M. Transport layer identification of P2P traffic[C].Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, ACM Press, 2004, 121-134.
[17] Pouwelse J, Garbacki P, Epema D, et al. The bittorrent p2p file-sharing system: Measurements and analysis[J]. Peer-to-Peer Systems, 2005: 205-216.
[18] Pouwelse J A, Garbacki P, Epema D, et al. A measurement study of the bittorrent peer-to-peer file-sharing system[R]. Delft University of Technology Parallel and Distributed Systems Report Series, Technical Report PDS-2004-007, 2004.
[19] Liang J, Kumar R, Ross K W. The FastTrack overlay: A measurement study[J]. Computer Networks, 2006, 50(6): 842-858.
[20] Karagiannis T, Broido A, Brownlee N, et al. Is p2p dying or just hiding? [C]. Global Telecommunications Conference (GLOBECOM'04), IEEE, 2004, 3: 1532-1538.
[21] Soldani C. Peer-to-peer behaviour detection by TCP flows analysis[J]. Engineer dissertation, University of Liege, France, 2004:25-30.
[22] Moore A,Papagian-naki K.Toward the accurate identifieation of network applications[C]. In Proc. Passive and Active Measurem Workshop(PAM2005).Boston,MA,USA:ACMPress,March,APril 2005:41-54.
[23] Sen S, Spatscheck O, Wang D. Accurate, scalable in-network identification of p2p traffic using application signatures[C]. Proceedings of the 13th International Conference on World Wide Web, ACM, 2004: 512-521.
[24] Liu B, Li Z T, Tu H. A methodology for P2P traffic measurement using application signature work-in-progress[C]. Proceedings of the 2nd international conference on Scalable information systems, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2007: 20-21.
[25] Duan L J, Yu Y F, Hanb L, et al. A P2P Application Signatures Discovery Algorithm[J]. Complex Systems Concurrent Engineering, 2007: 441-448.
[26] John W, Tafvelin S. Analysis of internet backbone traffic and header anomalies observed[C]. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, Kyoto, Japan, 2007:111-116.
[27] Bleul H, Rathgeb E P. A simple, efficient and flexible approach to measure multi-protocol Peer-to-Peer traffic[J]. Lecture Notes in Computer Science, 2005, 3421: 606-616.
[28] Karamcheti V, Geiger D, Kedem Z, et al. Detecting malicious network traffic using inverse distributions of packet contents[C]. Proceedings of the 2005 ACM SIGCOMM Workshop on Mining Network Data, Philadelphia, USA 2005: 165-170.
[29] S. Saroiu, K. Gummadi, and S. Gribble,“A Measurement Study of Peer-to-Peer File Sharing Systems,”Proceedings of the Multimedia Computing and Networking (MMCN 2002), California, January 2002:18-25
[30] Karagiannis T, Broido A, Faloutsos M. Transport layer identification of P2P traffic[C]. Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, ACM, 2004:121-134.
[31] Mchugh J, Mcleod R, Nagaonkar V. Passive network forensics: behavioural classification of network hosts based on connection patterns[J]. ACM SIGOPS Operating Systems Review, 2008, 42(3): 99-111.
[32] Erman J, Mahanti A, Arlitt M, et al. Identifying and discriminating between web and peer-to-peer traffic in the network core[C]. Proceedings of the 16th International Conference on World Wide Web, ACM, 2007:883-892.
[33] Qi Y, Xu B, He F, et al. Towards high-performance flow-level packet processing on multi-core network processors[C]. Proceedings of the 3rd ACM/IEEE Symposium on Architecture for Networking and Communications Systems, ACM, 2007: 17-26.
[34] Erman J, Arlitt M, Mahanti A. Traffic classification using clustering algorithms[C]. Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, ACM, 2006:281-286.
[35] Spognardi A, Lucarelli A, Di Pietro R. A methodology for P2P file-sharing traffic detection[C]. Second International Workshop on Hot Topics in Peer-to-Peer Systems, IEEE, 2005: 52-61.
[36] Plissonneau L, Costeux J L, Brown P. Analysis of peer-to-peer traffic on ADSL[J]. Passive and Active Network Measurement, 2005: 69-82.
[37] Kim M S, Kang H J, Hong J W. Towards peer-to-peer traffic analysis using flows[J].Self-Managing Distributed Systems, 2004: 55-67.
[38] Collins M, Reiter M. Finding peer-to-peer file-sharing using coarse network behaviors[J]. Lecture Notes in Computer Science, 2006, 4189: 1-17.
[39] Auld T, Moore A W, Gull S F, et al. Bayesian neural networks for Internet traffic classification. IEEE Transactions on Neural Networks, 2007, 8(1): 223-239
[40] Li W, Canini M, Moore A W, et al. Efficient application identification and the temporal and spatial stability of classification schema[J]. Computer Networks, 2009, 53(6): 790-809.
[41] Weka. http://www.cs.waikato.ac.nz/ml/weka/.
[42] Moore A, Zuev D, Crogan M. Discriminators for use in flow-based classification[R]. Technical Report RR-05-13, Dept. of Computer Science, Queen Mary, University of London, 2005
[43]张学工.关于统计学习理论与支持向量机[J].自动化学报, 2000, 26(1): 32-42.
[44] Boser B E, Guyon I M, Vapnik V N. A training algorithm for optimal margin classifiers[C]. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, ACM Press, 1992:144-152.
[45] Cortes C, Vapnik V. Support-vector networks[J]. Machine learning, 1995, 20(3): 273-297.
[46] Sch Lkopf B, Burges C, Vapnik V. Extracting support data for a given task[C]. Proceedings of the First International Conference on Knowledge Discovery & Data Mining, AAAI Press, 1995:262-267.
[47] Vladimir V N. The nature of statistical learning theory[M]. NY: Springer-Verlag, 1995.
[48] Vapnik V N. Statistical learning theory[M]. New York: Wiley, 1998.
[49]许建华,张学工.统计学习理论[M].北京:电子工业出版社, 2004.
[50]肖建华.智能模式识别方法[M].广州:华南理工大学出版社, 2006.
[51]吴敏,王汝传.一种基于损失函数的SVM算法在P2P流量检测中的应用[J].计算机科学, 2009, 36(12): 76-80.
[52] Nguyen T T T, Armitage G. A survey of techniques for internet traffic classification using machine learning[J]. Communications Surveys & Tutorials, IEEE, 2008, 10(4): 56-76.
[53] Kim H, Claffy K C, Fomenkov M, et al. Internet traffic classification demystified: myths,caveats, and the best practices[C]. in ACM SIGCOMM CoNEXT, New York, NY, Dec 2008:223-229
[54] Perényi M, Molnár S. Enhanced Skype traffic identification[C]. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2007:1-9.
[55] Memon G, Rejaie R, Guo Y, et al. Large-scale monitoring of DHT traffic[C]. USENIX Association, 2009: 11:351-358.
[56] Analyzer. http://analyzer.polito.it/.
[57] Zuev D, Moore A W. Traffic classification using a statistical approach[J]. Passive and Active Network Measurement, 2005: 321-324.
[58] Risso F, Baldini A, Bonomi F. Extending the NetPDL language to support traffic classification[C]. IEEE Global Telecommunications Conference (GLOBECOM'07), IEEE, 2007: 22-27.
[59] Park K, Willinger W. Self-similar network traffic and performance evaluation[M]. Purdue University, 2000.
[60] Abry P, Veitch D. Wavelet analysis of long-range-dependent traffic[J]. IEEE Transactions on Information Theory, 1998, 44(1): 2-15.
[61] Barral J, Mandelbrot B. Multiplicative products of cylindrical pulses[J]. Probability Theory Related Fields, 2002, 3(124): 409-430.
[62]魏武雄.时间序列分析——单变量多变量方法[M].北京:人民大学出版社, 2005.
[63] Gao J, Yee T. Adaptive estimation in partially linear autoregressive models[J]. Canadian Journal of Statistics, 2000, 28(3): 571-586.
[64] Sadek N, Khotanzad A. Multi-scale high-speed network traffic prediction using k-factor gegenbauer ARMA model[C]. IEEE International Conference on Communications, IEEE, 4: 2148-2152.
[65] Box G. E. P.,Jenkins G. M.,Reinsel G. C.时间序列分析预测与控制[M].北京:中国统计出版社, 1997.
[66] Leland W E, Taqqu M S, Willinger W, et al. On the self-similar nature of Ethernet traffic (extended version)[J]. IEEE/ACM Transactions on Networking, 1994, 2(1): 1-15.
[67] Yu G, Zhang C. Switching ARIMA model based forecasting for traffic flow[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'04), Montreal, Quebec, Canada, 2004, 2: 429-432.
[68] Sang A, Li S. A predictability analysis of network traffic[C]. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies(INFOCOM '00), Tel Aviv, Israel., 2000, 1: 342-351.
[69]舒炎泰,王雷,张连芳,等.基于FARIMA模型的Internet网络业务预报[J].计算机学报, 2001, 24(1): 46-54.
[70] Basu S, Mukherjee A, Klivansky S. Time series models for internet traffic[C]. Fifteenth Annual Joint Conference of the IEEE Computer Societies(INFOCOM '96.) San Francisco, CA, USA. 1996, 2: 611-620.
[71]王振龙.时间序列分析[M].北京:中国统计出版社, 2002.
[72] Park K, Willinger W. Self-similar network traffic and performance evaluation[M]. USA: Wiley Online Library, 2000.
[73] Riedi R H, Crouse M S, Ribeiro V J, et al. A multifractal wavelet model with application to network traffic[J]. IEEE Transactions on Information Theory, 1999, 45(3): 992-1018.
[74] Abry P, Veitch D, Flandrin P. Long‐range Dependence: Revisiting Aggregation with Wavelets[J]. Journal of Time Series Analysis, 1998, 19(3): 253-266.
[75]叶世伟,史忠植.神经网络原理[M].北京:机械工业出版社, 2004.
[76] Yu E S, Chen C Y R. Traffic prediction using neural networks[C]. IEEE Global Telecommunications Conference (GLOBECOM'93), IEEE, 1993: 991-995.
[77] Abry P, Veitch D. Wavelet analysis of long-range-dependent traffic[J]. IEEE Transactions on Information Theory, 1998, 44(1): 2-15.
[78] Abry P, Flandrin P, Taqqu M S, et al. Self-similarity and long-range dependence through the wavelet lens[J]. Long-range dependence: theory and applications, 2002: 527-556.
[79] Feldmann A, Gilbert A C, Willinger W. Data networks as cascades: Investigating the multifractal nature of Internet WAN traffic[C]. ACM SIGCOMM Computer Communication Review, ACM, Vancouver 1998, 28: 42-55.
[80] Feldmann A, Gilbert A C, Huang P, et al. Dynamics of IP traffic: A study of the role of variability and the impact of control[C]. ACM SIGCOMM Computer Communication Review, ACM, Cambridge, Massachusetts. 1999, 29: 301-313.
[81] Riedi R H, Crouse M S, Ribeiro V J, et al. A multifractal wavelet model with application to network traffic[J]. IEEE Transactions on Information Theory, 1999, 45(3): 992-1018.
[82]李捷,刘瑞新,刘先省,等.一种基于混合模型的实时网络流量预测算法[J].计算机研究与发展, 2006, 43(5): 806-812.
[83]白翔宇,叶新铭,蒋海.基于小波变换与自回归模型的网络流量预测[J].计算机科学, 2007, 34(7): 47-54.
[84]雷霆,余镇危.一种网络流量预测的小波神经网络模型[J].计算机应用, 2006, 26(3): 526-528.
[85]曹建华.基于灰色神经网络模型的网络流量预测算法研究[D].江南大学, 2008.
[86]李丽.基于支持向量机的网络流量预测研究[D].南京理工大学, 2007.
[87] Qiao Y, Skicewicz J, Dinda P A. An empirical study of the multiscale predictability of network traffic[C]. 13th IEEE Int’l Symposium on High Performance Distributed Computing, Honolulu, Hawaii, USA. 2004: 66-76.
[88] Sen S, Wang J. Analyzing peer-to-peer traffic across large networks[J]. IEEE/ACM Transactions on Networking, 2004, 12(2): 219-232.
[89] Azzouna N B, Guillemin F. Impact of peer-to-peer applications on wide area network traffic: an experimental approach[C]. Global Telecommunications Conference, Dallas, Texas,UK, 2004, 3: 1544-1548.
[90] Hamada T, Chujo K, Chujo T, et al. Peer-to-peer traffic in metro networks: analysis, modeling, and policies[C]. IEEE International Conference on NOMS, Seoul,Korea, 2004, 1: 425-438.
[91] Karagiannis T, Broido A, Brownlee N, et al. File-sharing in the Internet: A characterization of P2P traffic in the backbone[J]. University of California, Riverside, USA, Tech. Rep, 2003.
[92]洪飞,吴志美.基于小波的多尺度网络流量预测模型.计算机学报,2006,29(l):166一170.
[93]刘琼,徐鹏,杨海涛. Peer-to-Peer文件共享系统的测量研究[J].软件学报, 2006, 17(10): 2131-2140.
[94] Joo S D, Lee C W, Chung Y. Analysis and modeling of traffic from residential high speed Internet subscribers[J]. Information Networking. Networking Technologies for Broadband and Mobile Networks, 2004: 410-419.
[95] Liu G, Hu M, Fang B, et al. Explaining BitTorrent traffic self-similarity[J]. Parallel and Distributed Computing: Applications and Technologies, 2005: 231-240.
[96] S. Sen, O. Spatscheck, and D. Wang,“Accurate, Scalable In-Network Identification of P2P Traffic using Application Signatures,”Proceedings of the 13th International World Wide Web Conference, NY, USA, May 2004: 512-521.
[97]Park K, Willinger W. Self-similar Network Traffic and Performance Evaluation. New York, NY, USA: John Wiley & Sons Inc, 2000: 39-88
[98]李建平,唐远炎.小波分析方法的应用[M].重庆大学出版社, 1999.
[99]徐佩霞,孙功宪.小波分析与应用实例[M].中国科学技术大学出版社, 2001.
[100]韩志杰,王汝传.一种新的P2P网络流量预测模型[J].计算机科学, 2008, 35(9): 39-41.
[101] Kolarov A, Atai A, Hui J. Application of Kalman filter in high-speed networks[C]. Global Telecommunications Conference, 1994.(GLOBECOM'94) San Francisco, USA., 1994: 624-628.
[102] Kalman R E. A new approach to linear filtering and prediction problems[J]. Journal of basic Engineering, 1960, 82(1): 35-45.
[103] Kalman R E, Bucy R S. New results in linear filtering and prediction theory[J]. Transactions of the ASME. Series D, Journal of Basic Engineering, 1961, 83: 95-107.
[104] Julier S J, Uhlmann J K. A new extension of the Kalman filter to nonlinear systems[C]. In The Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing,Simulation and Controls, Multi Sensor Fusion, Tracking and Resource Management II, SPIE, 1997, 3: 26.
[105] Harvey A C. Forecasting, structural time series models and the Kalman filter[M]. Cambridge University Press, Cambridge, 1990.
[106] Qiao Y, Skicewicz J, Dinda P A. An empirical study of the multiscale predictability of network traffic[C]. 13th IEEE Int’l Symposium on High Performance Distributed Computing (HPDC'04), Honolulu, Hawaii,USA, 2004: 66-76.
[107] Zheng T, Girgis A A, Makram E B. A hybrid wavelet-Kalman filter method for load forecasting[J]. Electric power systems research, 2000, 54(1): 11-17.
[108]许佳.基于IPv6的P2P流量识别与控制的研究[D].西安:西安科技大学, 2008. 109]林宇,程时端,邬海涛,等. IP网端到端性能测量技术研究的进展[J].电子学报, 2003, 31(8): 1227-1233.
[110] Christof B, Gianluca I,Thomas Z,Christophe D,et al. Comparison of Tail Drop and Active Queue Management Performance for bulk-data and Web-linke Internet Traffic[C]. In Proceedings of IEEE ISCC 2001. Hammameth,Tunesia. 2001:122-129
[111] Maymounkov P, Mazieres D. Kademlia: A peer-to-peer information system based on the xor metric[J]. Peer-to-Peer Systems, 2002: 53-65.
[112] Wierzbicki A, Leibowitz N, Ripeanu M, et al. Cache replacement policies revisited: The case of P2P traffic[C]. Cluster Computing and the Grid, 2004(CCGrid 2004) ,Chicago, Illinois, USA, 2004: 182-189.
[113] Dán G. Cache-to-cache: Could ISPs cooperate to decrease peer-to-peer content distribution costs?[J]. IEEE Transactions on Parallel and Distributed Systems, 2011.
[114] Ye M, Wu J, Xu K. Caching the P2P traffic in ISP network[C].Communications, 2008. ICC '08. IEEE International Conference on COMMUNCATIONS ..Beijing,China.May 2008 :5876 - 5880
[115] Sniffer. http://www.sniffer.net.cn.
[116] Su A J, Choffnes D, Bustamante F E, et al. Relative network positioning via CDN redirections[C]. 28th IEEE International Conference on Distributed Computing Systems (ICDCS 2008), Beijing, China. June 2008: 377-386.
[117] White Paper1The Ins and Outs of Content Delivery Networks[R] . StanfordUniversity ,2001.
[118] Akamai Management Group .http:// www.akamai.com.
[119] Tata S, Patel J M. Estimating the Selectivity of tf-idf based Cosine Similarity Predicates[J]. ACM SIGMOD Record, 2007, 36(4): 75-80.
[120]王汝传,徐小龙,黄海平.智能Agent及其在信息网络中的应用[M].北京:北京邮电大学出版社, 2006.
[121]张云勇,刘锦德.移动Agent技术[M].北京:淸华大学出版社, 2003.
[122] Puliafito A, Tomarchio O. Using mobile agents to implement flexible network management strategies[J]. Computer Communications, 2000, 23(8): 708-719.
[123] Goldszmidt G, Yemini Y, Yemini S. Network management by delegation: the MAD approach[C]. Proceedings of the 1991 conference of the Centre for Advanced Studies on Collaborative research, 1991: 347-361.
[124] Pinheiro R, Poylisher A, Caldwell H. Mobile agents for aggregation of network management data[C]. Agent Systems and Applications and Third International Symposium on Mobile Agents. Palm Springs, CA, USA,1999:130-140.
[125] Gavalas D, Greenwood D, Ghanbari M, et al. Advanced network monitoring applications based on mobile/intelligent agent technology[J]. Computer Communications, 2000, 23(8): 720-730.
[126] Baldi M, Gai S, Picco G. Exploiting code mobility in decentralized and flexible network management[C]. Proceedings of the First International Workshop on Mobile Agents (MA '97), Berlin, Germany , April, 7-8, 1997,, 1997: 13-26.
[127]张松,阎保平.基于移动Agent的网络管理框架及实现[J].计算机研究与发展, 1999, 36(8): 1007-1010.
[128] IBM Japan Research Group.Aglets Workbench, http://aglets.trl.ibm.co.jp.
[129]吴刚,王怀民,吴泉源.一个移动智能体位置管理与可靠通信的算法[J].软件学报, 2002, 13(2): 269-273.
[130] Shen Z H, Wang H. Network Data Packet Capture and Protocol Analysis on Jpcap-Based[C]. Proceedings of the 2009 International Conference on Information Management,Innovation Management and Industrial Engineering, USA: IEEE COMPUTER SOC. 2009, 3: 329-332.