article

A factor analytic approach to inferring congestion sharing based on flow level measurements

Authors:

Gustavo de Veciana,

Brian L. EvansAuthors Info & Claims

IEEE/ACM Transactions on Networking (TON), Volume 15, Issue 1

Pages 67 - 79

https://doi.org/10.1109/TNET.2006.890103

Published: 01 February 2007 Publication History

Abstract

Internet traffic primarily consists of packets from elastic flows, i.e., Web transfers, file transfers, and e-mail, whose transmissions are mediated via the Transmission Control Protocol (TCP). In this paper, we develop a methodology to process TCP flow measurements in order to analyze throughput correlations among TCP flow classes that can be used to infer congestion sharing in the Internet. The primary contributions of this paper are: 1) development of a technique for processing flow records suitable for inferring congested resource sharing; 2) evaluation of the use of factor analysis on processed flow records to explore which TCP flow classes might share congested resources; and 3) validation of our inference methodology using bootstrap methods and nonintrusive, flow level measurements collected at a single network site. Our proposal for using flow level measurements to infer congestion sharing differs significantly from previous research that has employed packet level measurements for making inferences. Possible applications of our method include network monitoring and root cause analysis of poor performance.

References

[1]

{1} S. Shenker, "Fundamental design issues for the future Internet," IEEE J. Sel. Areas Commun., vol. 13, no. 7, pp. 1176-1188, Sep. 1995.

[2]

{2} IP Monitoring Project. Sprint Corp. {Online}. Available: http://ipmon. sprintlabs.com

[3]

{3} N. Brownlee, C. Mills, and G. Ruth, "Traffic flow measurement: Architecture," IETF RFC 2722, Oct. 1999.

[4]

{4} NetFlow. Cisco Systems, Inc. {Online}. Available: http://www. cisco.com

[5]

{5} sFlow. sFlow.org {Online}. Available: http://www.sflow.org

[6]

{6} Argus. QoSient, llc {Online}. Available: http://www.qosient.com/argus

[7]

{7} C. Estan and G. Varghese, "New directions in traffic measurement and accounting," in Proc. ACM SIGCOMM Workshop on Internet Measurement , Nov. 2001, pp. 75-80.

[8]

{8} N. Duffield, C. Lund, and M. Thorup, "Properties and prediction of flow statistics from sampled packet streams," in Proc. ACM SIGCOMM Workshop on Internet Measurement, Nov. 2002, pp. 159-171.

[9]

{9} S. Savage, N. Cardwell, and T. Anderson, "The case for informed transport protocols," in Proc. IEEE Workshop on Hot Topics in Operating Systems, Mar. 1999, pp. 58-63.

[10]

{10} K. Harfoush, A. Bestavros, and J. Byers, "Robust identification of shared losses using end-to-end unicast probes," in Proc. IEEE Int. Conf. Network Protocols, Nov. 2000, pp. 22-36.

[11]

{11} D. Rubenstein, J. Kurose, and D. Towsley, "Detecting shared congestion of flows via end-to-end measurement," IEEE/ACM Trans. Netw., vol. 10, no. 3, pp. 381-395, Jun. 2002.

[12]

{12} M. S. Kim, T. Kim, Y. Shin, S. S. Lam, and E. J. Powers, "A wavelet-based approach to detect shared congestion," in Proc. ACM Conf. Applications, Technologies, Architectures, and Protocols for Computer Communications, Aug. 2004, pp. 293-306.

[13]

{13} M. Rabbat, R. Nowak, and M. Coates, "Network tomography and the identification of shared infrastructure," in Proc. IEEE Asilomar Conf. Signals, Systems and Computers, Nov. 2002, pp. 34-38.

[14]

{14} D. Katabi, I. Bazzi, and X. Yang, "A passive approach for detecting shared bottlenecks," in Proc. IEEE Int. Conf. Computer Communications and Networks, 2001, pp. 174-181.

[15]

{15} D. Arifler, G. de Veciana, and B. L. Evans, "Network tomography based on flow level measurements," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, May 2004, vol. 2, pp. 437-440.

[16]

{16} D. Arifler, G. de Veciana, and B. L. Evans, "Inferring path sharing based on flow level TCP measurements," in Proc. IEEE Conf. Communications, Jun. 2004, vol. 4, pp. 2054-2059.

[17]

{17} D. Arifler and B. L. Evans, "Factor analysis of network flow throughput measurements for inferring congestion sharing," in Proc. 13th Eur. Signal Processing Conf., Antalya, Turkey, Sep. 2005.

[18]

{18} L. Massoulié and J. W. Roberts, "Bandwidth sharing and admission control for elastic traffic," Telecommun. Syst., vol. 15, pp. 185-201, Jun. 2000.

[19]

{19} A. A. Kherani and A. Kumar, "Stochastic models for throughput analysis of randomly arriving elastic flows in the Internet," in Proc. IEEE INFOCOM, 2002, vol. 2, pp. 1014-1023.

[20]

{20} Y. Zhang, L. Breslau, V. Paxson, and S. Shenker, "On the characteristics and origins of Internet flow rates," in Proc. ACM Conf. Applications, Technologies, Architectures, and Protocols for Computer Communications , 2002, vol. 32, pp. 309-322.

[21]

{21} D. Chiu and R. Jain, "Analysis of the increase and decrease algorithms for congestion avoidance in computer networks," Computer Networks and ISDN Systems, vol. 17, no. 1, pp. 1-14, 1989.

[22]

{22} S. B. Fred, T. Bonald, A. Proutiere, G. Régnié, and J. W. Roberts, "Statistical bandwidth sharing: A study of congestion at flow level," in Proc. ACM Conf. Appl., Tech., Arch., and Protocols for Computer Communications , Aug. 2001, pp. 111-122.

[23]

{23} P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting , 2nd ed. New York: Springer-Verlag, 2002.

[24]

{24} E. Altman, F. Baccelli, and J. Bolot, "Discrete-time analysis of adaptive rate control mechanisms," in High Speed Networks and Their Performance . Amsterdam: North-Holland, 1994, pp. 121-140.

[25]

{25} E. Altman, T. Basar, and R. Srikant, "Robust rate control for ABR sources," in Proc. IEEE INFOCOM, 1998, vol. 1, pp. 166-173.

[26]

{26} D. Arifler, "Network tomography based on flow level measurements," Ph.D. dissertation, The University of Texas, Austin, 2004.

[27]

{27} M. E. Crovella and A. Bestavros, "Self-similarity in World Wide Web traffic: Evidence and possible causes," IEEE/ACM Trans. Netw., vol. 5, no. 6, pp. 835-846, Dec. 1997.

[28]

{28} A. B. Downey, "The structural causes of file size distributions," in Proc. IEEE Symp. Modeling, Analysis and Simulation of Computer and Telecommunication Systems, Aug. 2001, pp. 361-370.

[29]

{29} M. Mitzenmacher, "A brief history of generative models for power law and lognormal distributions," in Proc. Allerton Conf. Communications, Control, and Computing, Oct. 2001, pp. 182-191.

[30]

{30} A. C. Rencher, Multivariate Statistical Inference and Applications. New York: Wiley, 1998.

[31]

{31} H. F. Kaiser, "The application of electronic computers to factor analysis," Educ. Psychol. Meas., vol. 20, pp. 141-151, 1960.

[32]

{32} S. J. Devlin, R. Gnanadesikan, and J. R. Kettenring, "Robust estimation of dispersion matrices and principal components," J. Amer. Statist. Assoc., vol. 76, no. 374, pp. 354-362, Jun. 1981.

[33]

{33} S. J. Devlin, R. Gnanadesikan, and J. R. Kettenring, "Robust estimation and outlier detection with correlation coefficients," Biometrika, vol. 62, no. 3, pp. 531-545, 1975.

[34]

{34} D. Efron and R. J. Tibshirani, An Introduction to the Bootstrap . London, U.K.: Chapman & Hall, 1993.

[35]

{35} OPNET Modeler 9.0. OPNET Technologies, Inc. {Online}. Available: http://www.opnet.com

[36]

{36} V. Paxson, "End-to-end routing behavior in the Internet," IEEE/ACM Trans. Netw., vol. 5, no. 5, pp. 601-615, Oct. 1997.

[37]

{37} N. Brownlee and K. Claffy, "Understanding Internet traffic streams: Dragonflies and tortoises," IEEE Commun. Mag., vol. 40, no. 10, pp. 110-117, Oct. 2002.

[38]

{38} Z. V. Lambert, A. R. Wildt, and R. M. Durand, "Approximating confidence intervals for factor loadings," Multivariate Behavioral Res., vol. 26, no. 3, pp. 421-434, 1991.

Cited By

Dixon COlshefski DJain VDeCusatis CFelter WCarter JBanikazemi MMann VTracey JRecio R(2014)Software defined networking to support the software defined environmentIBM Journal of Research and Development10.1147/JRD.2014.230036558:2-3(3-3)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1147/JRD.2014.2300365
Nguyen HThiran PDovrolis CRoughan M(2007)Network loss inference with second order statistics of end-to-end flowsProceedings of the 7th ACM SIGCOMM conference on Internet measurement10.1145/1298306.1298339(227-240)Online publication date: 24-Oct-2007
https://dl.acm.org/doi/10.1145/1298306.1298339

Index Terms

A factor analytic approach to inferring congestion sharing based on flow level measurements
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
    2. Metrics
2. Networks
  1. Network properties
    1. Network structure
  2. Network services
    1. Network management

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cover image IEEE/ACM Transactions on Networking

IEEE/ACM Transactions on Networking Volume 15, Issue 1

February 2007

245 pages

ISSN:1063-6692

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IEEE Press

Publication History

Published: 01 February 2007

Published in TON Volume 15, Issue 1

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Cited By

Dixon COlshefski DJain VDeCusatis CFelter WCarter JBanikazemi MMann VTracey JRecio R(2014)Software defined networking to support the software defined environmentIBM Journal of Research and Development10.1147/JRD.2014.230036558:2-3(3-3)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1147/JRD.2014.2300365
Nguyen HThiran PDovrolis CRoughan M(2007)Network loss inference with second order statistics of end-to-end flowsProceedings of the 7th ACM SIGCOMM conference on Internet measurement10.1145/1298306.1298339(227-240)Online publication date: 24-Oct-2007
https://dl.acm.org/doi/10.1145/1298306.1298339

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