skip to main content
article

Modeling communication networks with hybrid systems

Published: 01 June 2007 Publication History

Abstract

This paper introduces a general hybrid systems framework to model the flow of traffic in communication networks. The proposed models use averaging to continuously approximate discrete variables such as congestion window and queue size. Because averaging occurs over short time intervals, discrete events such as the occurrence of a drop and the consequent reaction by congestion control can still be captured. This modeling framework, thus, fills a gap between purely packet-level and fluid-based models, faithfully capturing the dynamics of transient phenomena and yet providing significant flexibility in modeling various congestion control mechanisms, different queueing policies, multicast transmission, etc. The modeling framework is validated by comparing simulations of the hybrid models against packet-level simulations. It is shown that the probability density functions produced by the ns-2 network simulator match closely those obtained with hybrid models. Moreover, a complexity analysis supports the observation that in networks with large per-flow bandwidths, simulations using hybrid models require significantly less computational resources than ns-2 simulations. Tools developed to automate the generation and simulation of hybrid systems models are also presented. Their use is showcased in a study, which simulates TCP flows with different roundtrip times over the Abilene backbone.

References

[1]
{1} "The ns Manual (Formerly ns Notes and Documentation)" Oct. 2000 {Online}. Available: http://www.isi.edu/nsnam/ns/ns-documentation.html, The VINT Project.
[2]
{2} V. Misra, W. Gong, and D. Towsley, "Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED," presented at the ACM SIGCOMM, Sep. 2000.
[3]
{3} S. Floyd, M. Handley, J. Padhye, and J. Widmer, "Equation-based congestion control for unicast applications," in Proc. ACM SIGCOMM, Aug. 2000, pp. 43-56.
[4]
{4} A. V. D. Schaft and H. Schumacher, An Introduction to Hybrid Dynamical Systems, ser. Lecture Notes Contr. Inf. Sci. London, U.K.: Springer-Verlag, 2000, 251.
[5]
{5} "Modelica--A unified object-oriented language for physical systems modeling: Tutorial," Modelica Assoc. {Online}. Available: http://www. modelica.org/
[6]
{6} Abilene {Online}. Available: http://abilene.internet2.edu
[7]
{7} QualNet {Online}. Available: http://www.scalable-networks.com
[8]
{8} Scalable Simulation Framework {Online}. Available: http://www. ssfnet.org/
[9]
{9} F. Desbrandes, S. Bertolotti, and L. Dunand, "Opnet 2.4: An environment for communication network modeling and simulation," in Proc. Eur. Simulation Symp., Oct. 1993, pp. 609-614.
[10]
{10} M. Mathis, J. Semke, J. Mahdavi, and T. Ott, "The macroscopic behavior of the TCP congestion avoidance algorithm," ACM Comput. Commun. Rev., vol. 27, no. 3, July 1997.
[11]
{11} S. H. Low, F. Paganini, J. Wang, S. Adlakha, and J. C. Doyle, "Dynamics of TCP/RED and a scalable control," presented at the IEEE INFOCOM, Jun. 2002.
[12]
{12} Y. Guo, W. Gong, and D. Towsley, "Time-stepped hybrid simulation (TSHS) for large scale networks," presented at the IEEE INFOCOM, Mar. 2000.
[13]
{13} K. Kumaran and D. Mitra, "Performance and fluid simulations of a novel shared buffer management system," in Proc. IEEE INFOCOM, Mar. 1998, pp. 1449-1461.
[14]
{14} B. Liu, D. R. Figueiredo, J. K. Yang Guo, and D. Towsley, "A study of networks simulation efficiency: Fluid simulation vs. packet-level simulation," in Proc. IEEE INFOCOM, Apr. 2001, vol. 3, pp. 1244-1253.
[15]
{15} D. Schwetman, "Hybrid simulation models of computer systems," Commun. ACM, vol. 19, pp. 718-723, Sep. 1978.
[16]
{16} R. Pan, B. Prabhakar, K. Psounis, and D. Wischik, "Shrink: A method for scaleable performance prediction and efficient network simulation," presented at the IEEE INFOCOM, Mar. 2003.
[17]
{17} L. Qiu, Y. Zhang, and S. Keshav, "Understanding the performance of many TCP flows," Comput. Netw., vol. 37, pp. 277-306, Nov. 2001.
[18]
{18} S. Floyd, "Connections with multiple congested gateways in packet-switched networks Part 1: One-way traffic," ACM Comput. Commun. Rev., vol. 21, no. 5, pp. 30-47, Oct. 1991.
[19]
{19} S. Shenker, L. Zhang, and D. Clark, "Some observations on the dynamics of a congestion control algorithm," ACM Comput. Comm. Rev., pp. 30-39, Oct. 1990.
[20]
{20} S. Bohacek, J. P. Hespanha, J. Lee, and K. Obraczka, "A hybrid systems modeling framework for fast and accurate simulation of data communication networks," presented at the ACM SIGMETRICS, 2003.
[21]
{21} J. Lee, S. Bohacek, J. P. Hespanha, and K. Obraczka, "Modeling data communication networks using hybrid systems: Extended version," Tech. Rep., Univ. California, Santa Barbara, Apr. 2006.
[22]
{22} B. Sikdar, S. Kalyanaraman, and K. Vastola, "Analytic models for the latency and steady-state throughput of TCP Tahoe, Reno and SACK," in Proc. IEEE GLOBECOM, 2001, pp. 25-29.
[23]
{23} Hybrid systems modeling framework web site {Online}. Available: http://cs.sookmyung.ac.kr/~jslee/hybrid
[24]
{24} K. Fall and S. Floyd, "Simulation-based comparisons of Tahoe Reno and SACK TCP," ACM Comput. Comm. Rev., vol. 27, no. 3, pp. 5-21, Jul. 1996.
[25]
{25} Y. Joo, V. Ribeiro, A. Feldmann, A. Gilbert, and W. Willinger, "The impact of variability on the buffer dynamics in IP networks," presented at the 37th Annu. Allerton Conf. Communications, Control, Computing, Sep. 1999.
[26]
{26} F. Hernández-Campos, J. S. Marron, G. Samorodnitsky, and F. D. Smith, "Variable heavy tail duration in internet traffic," presented at the IEEE/ACM MASCOTS, 2002.
[27]
{27} L. Zhang, S. Shenker, and D. D. Clark, "Observations on the dynamics of a congestion control algorithm: The effects of two-way traffic," presented at the ACM SIGCOMM, Sep. 1991.
[28]
{28} L. Xu, K. Harfoush, and I. Rhee, "Binary increase congestion control for fast long-distance networks," presented at the IEEE INFOCOM, Mar. 2004.
[29]
{29} T. V. Lakshman and U. Madhow, "The performance of TCP/IP for networks with high bandwidth-delay products and random loss," IEEE/ACM Trans. Netw., vol. 5, no. 3, pp. 336-350, Jul. 1997.
[30]
{30} L. Devroye, A Course in Density Estimation. Boston, MA: Birkhauser, 1987.

Cited By

View all
  • (2023)Basis transform in linear switched system models from input–output dataInternational Journal of Adaptive Control and Signal Processing10.1002/acs.367737:12(3151-3168)Online publication date: 3-Dec-2023
  • (2017)A layered and aggregated queuing network simulator for detection of abnormalitiesProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242265(1-12)Online publication date: 3-Dec-2017
  • (2015)Simulation alternatives for the verification of networked cyber-physical systemsMicroprocessors & Microsystems10.1016/j.micpro.2015.09.00139:8(843-853)Online publication date: 1-Nov-2015
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image IEEE/ACM Transactions on Networking
IEEE/ACM Transactions on Networking  Volume 15, Issue 3
June 2007
249 pages

Publisher

IEEE Press

Publication History

Published: 01 June 2007
Published in TON Volume 15, Issue 3

Author Tags

  1. TCP
  2. UDP
  3. congestion control
  4. data communication networks
  5. hybrid systems
  6. simulation

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 22 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Basis transform in linear switched system models from input–output dataInternational Journal of Adaptive Control and Signal Processing10.1002/acs.367737:12(3151-3168)Online publication date: 3-Dec-2023
  • (2017)A layered and aggregated queuing network simulator for detection of abnormalitiesProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242265(1-12)Online publication date: 3-Dec-2017
  • (2015)Simulation alternatives for the verification of networked cyber-physical systemsMicroprocessors & Microsystems10.1016/j.micpro.2015.09.00139:8(843-853)Online publication date: 1-Nov-2015
  • (2013)Model predictive control over delay-based differentiated services control networksProceedings of the Conference on Design, Automation and Test in Europe10.5555/2485288.2485558(1117-1122)Online publication date: 18-Mar-2013
  • (2013)Spatio-temporal hybrid automata for safe cyber-physical systemsProceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems10.1145/2502524.2502535(71-80)Online publication date: 8-Apr-2013
  • (2010)How to model a TCP/IP network using only 20 parametersProceedings of the Winter Simulation Conference10.5555/2433508.2433608(849-860)Online publication date: 5-Dec-2010

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media