A Robust Adaptive Congestion Control Strategy for Large Scale Networks with Differentiated Services Traffic
In this paper, a robust decentralized congestion control strategy is developed for a large scale network with Differentiated Services (Diff-Serv) traffic. The network is modeled by a nonlinear fluid flow model corresponding to two classes of traffic, namely the premium traffic and the ordinary traffic. The proposed congestion controller does take into account the associated physical network resource limitations and is shown to be robust to the unknown and time-varying delays. Our proposed decentralized congestion control strategy is developed on the basis of Diff-Serv architecture by utilizing a robust adaptive technique. A Linear Matrix Inequality (LMI) condition is obtained to guarantee the ultimate boundedness of the closed-loop system. Numerical simulation implementations are presented by utilizing the QualNet and Matlab software tools to illustrate the effectiveness and capabilities of our proposed decentralized congestion control strategy.
<p>[1] http://www.ietf.org/html.charters/OLD/diffserv-charter.html, accessed in,
Feb 2009.
[2] S. Floyd and V. Jacobson, Random early detection gateways for congestion
avoidance, IEEE/ACM Transaction on Networking, vol. 1, no. 4, pp.
397-413, August 1993.
[3] B. K. Lee, L. K. John, and E. John, Architectural enhancements for
network congestion control applications, IEEE Transactions on Very
Large Scale Systems, vol. 14, no. 6, pp. 609615, June 2006.
[4] M. Baines, B. Nandy, P. Pieda, N. Seddigh, and M. Devetsikiotis,
Using TCP models to understand bandwidth assurance in a differentiated
services network, Nortel Technical Report, Tech. Rep, July 2000.
[5] V. Jacobson, Congestion avoidance and control, in Proc. Symp. Communication
Architectures and Protocols, Stanford, CA, pp. 314–329, 1988.
[6] A. Segall, The modeling of adaptive routing in data communication
networks, IEEE Transactions on Communications, vol. 25, pp. 85–95,
1997.
[7] A. Kolarov and G. Ramamurthy, A control theoretic approach to the
design of closed loop rate based flow control for high speed ATM
networks, IEEE/ACM Transactions on Networking, vol. 1, pp. 293–301,
1997.
[8] C. Chrysostomou, A. Pitsillides, L. Rossides, and A. Sekercioglu, Fuzzy
logic controlled RED: congestion control in TCP/IP differentiated services
networks, Journal Soft Computing, vol. 8, no. 2, pp. 79–92, December
2003.
[9] A. Pitsillides and J. Lambert, Adaptive congestion control in ATM
based networks: Quality of service with high utilization, J. Computer
Communications, vol. 20, pp. 129–139, 1997.
[10] H. Wu, K. Long, S. Cheng, and J. Ma, Direct congestion control
scheme (DCCS) for differentiated services IP networks, Proc. of the IEEE
Global Telecommunications Conference GLOBECOM’01, pp. 2290–
2294, November 2001.
[11] L. Su, R. Zheng, and J. C. Hou, An active queue management scheme for
Internet congestion control and its application to differentiated services,
Proc. of the 9th International Conf. on Computer Communications and
Networks, pp. 62-68, October 2000.
[12] N. Zhang, M. Yang, Y. Jing, and S. Zhang, Congestion control for Diff-
Serv network using second-order sliding mode control, IEEE Transactions
on Industrial Electronics, vol. 56, no. 9, pp. 3330–3336, September 2009.
[13] A. Pitsillides, P. Ioannou, M. Lestas, and L. Rossides, Adaptive nonlinear
congestion controller for a differentiated-services framework, IEEE
Transactions on Networking, vol. 13, 94–107, 2005.
[14] V. Utkin and L. Hoon , Chattering problem in sliding mode control
systems Proc. of the International Workshop on Variable Structure
Systems, VSS’06 , pp. 346–350, June 2006.
[15] R. R. Chen and K. Khorasani, An adaptive congestion control technique
for networked control systems, Proc. of the IEEE Conf. on Industrial
Electronics and Applications, pp. 2791–2796, May 2007.
[16] K. Bouyoucef and K. Khorasani, A robust distributed congestion control
strategy for differentiated-services network, IEEE Transactions on
Industrial Electronics, vol. 56, no. 3, pp. 608–617, March 2009.
[17] K. Bouyoucef and K. Khorasani, A sliding mode-based congestion
control for time delayed differentiated-services networks, Proc. of the
IEEE Mediterranean Conference on Control & Automation, T31-022,
June 2007.
[18] R. R. Chen and K. Khorasani, A Markovian jump congestion control
strategy for mobile ad-hoc networks with differentiated services traffic,
Proc. of the 29th Chinese Control Conference, Beijing, China, July 2010.
[19] S. Floyd, Metrics for the evaluation of congestion control mechanism,
RFC 5166, Informational, Networking Group, March 2008.
[20] N. Dukkipati and N. McKeown, Why flow-completion time is the right
metric for congestion control, ACM SIGCOMM, January 2006.
[21] C. Agnew, Dynamic modeling and control of congestion-prone systems,
Operational Research, vol. 23, no. 2, pp. 361–367, 1976.
[22] J. Filipiak, Modeling and Control of Dynamic Flows in Communication
Networks, New York: Springer-Verlag, 1988.
[23] D. Tipper, Y. Qian, and X. Hou, Modeling the time-varying behavior
of mobile Ad Hoc networks, Proc. of the ACM International Workshop
on Modeling Analysis and Simulation of Wireless and Mobile Systems
(MSWiM 2004), pp. 12–19, 2004.
[24] P. Ioannou and J. Sun, Robust Adaptive Control, Englewood Clliffs, NJ:
Prentice-Hall, 1996.
[25] http://www.scalable-networks.com/ accessed in, Feb 2009.</p>
<p>[1] http://www.ietf.org/html.charters/OLD/diffserv-charter.html, accessed in,
Feb 2009.
[2] S. Floyd and V. Jacobson, Random early detection gateways for congestion
avoidance, IEEE/ACM Transaction on Networking, vol. 1, no. 4, pp.
397-413, August 1993.
[3] B. K. Lee, L. K. John, and E. John, Architectural enhancements for
network congestion control applications, IEEE Transactions on Very
Large Scale Systems, vol. 14, no. 6, pp. 609615, June 2006.
[4] M. Baines, B. Nandy, P. Pieda, N. Seddigh, and M. Devetsikiotis,
Using TCP models to understand bandwidth assurance in a differentiated
services network, Nortel Technical Report, Tech. Rep, July 2000.
[5] V. Jacobson, Congestion avoidance and control, in Proc. Symp. Communication
Architectures and Protocols, Stanford, CA, pp. 314–329, 1988.
[6] A. Segall, The modeling of adaptive routing in data communication
networks, IEEE Transactions on Communications, vol. 25, pp. 85–95,
1997.
[7] A. Kolarov and G. Ramamurthy, A control theoretic approach to the
design of closed loop rate based flow control for high speed ATM
networks, IEEE/ACM Transactions on Networking, vol. 1, pp. 293–301,
1997.
[8] C. Chrysostomou, A. Pitsillides, L. Rossides, and A. Sekercioglu, Fuzzy
logic controlled RED: congestion control in TCP/IP differentiated services
networks, Journal Soft Computing, vol. 8, no. 2, pp. 79–92, December
2003.
[9] A. Pitsillides and J. Lambert, Adaptive congestion control in ATM
based networks: Quality of service with high utilization, J. Computer
Communications, vol. 20, pp. 129–139, 1997.
[10] H. Wu, K. Long, S. Cheng, and J. Ma, Direct congestion control
scheme (DCCS) for differentiated services IP networks, Proc. of the IEEE
Global Telecommunications Conference GLOBECOM’01, pp. 2290–
2294, November 2001.
[11] L. Su, R. Zheng, and J. C. Hou, An active queue management scheme for
Internet congestion control and its application to differentiated services,
Proc. of the 9th International Conf. on Computer Communications and
Networks, pp. 62-68, October 2000.
[12] N. Zhang, M. Yang, Y. Jing, and S. Zhang, Congestion control for Diff-
Serv network using second-order sliding mode control, IEEE Transactions
on Industrial Electronics, vol. 56, no. 9, pp. 3330–3336, September 2009.
[13] A. Pitsillides, P. Ioannou, M. Lestas, and L. Rossides, Adaptive nonlinear
congestion controller for a differentiated-services framework, IEEE
Transactions on Networking, vol. 13, 94–107, 2005.
[14] V. Utkin and L. Hoon , Chattering problem in sliding mode control
systems Proc. of the International Workshop on Variable Structure
Systems, VSS’06 , pp. 346–350, June 2006.
[15] R. R. Chen and K. Khorasani, An adaptive congestion control technique
for networked control systems, Proc. of the IEEE Conf. on Industrial
Electronics and Applications, pp. 2791–2796, May 2007.
[16] K. Bouyoucef and K. Khorasani, A robust distributed congestion control
strategy for differentiated-services network, IEEE Transactions on
Industrial Electronics, vol. 56, no. 3, pp. 608–617, March 2009.
[17] K. Bouyoucef and K. Khorasani, A sliding mode-based congestion
control for time delayed differentiated-services networks, Proc. of the
IEEE Mediterranean Conference on Control & Automation, T31-022,
June 2007.
[18] R. R. Chen and K. Khorasani, A Markovian jump congestion control
strategy for mobile ad-hoc networks with differentiated services traffic,
Proc. of the 29th Chinese Control Conference, Beijing, China, July 2010.
[19] S. Floyd, Metrics for the evaluation of congestion control mechanism,
RFC 5166, Informational, Networking Group, March 2008.
[20] N. Dukkipati and N. McKeown, Why flow-completion time is the right
metric for congestion control, ACM SIGCOMM, January 2006.
[21] C. Agnew, Dynamic modeling and control of congestion-prone systems,
Operational Research, vol. 23, no. 2, pp. 361–367, 1976.
[22] J. Filipiak, Modeling and Control of Dynamic Flows in Communication
Networks, New York: Springer-Verlag, 1988.
[23] D. Tipper, Y. Qian, and X. Hou, Modeling the time-varying behavior
of mobile Ad Hoc networks, Proc. of the ACM International Workshop
on Modeling Analysis and Simulation of Wireless and Mobile Systems
(MSWiM 2004), pp. 12–19, 2004.
[24] P. Ioannou and J. Sun, Robust Adaptive Control, Englewood Clliffs, NJ:
Prentice-Hall, 1996.
[25] http://www.scalable-networks.com/ accessed in, Feb 2009.</p>
@article{"International Journal of Information, Control and Computer Sciences:65054", author = "R. R. Chen and K. Khorasani", title = "A Robust Adaptive Congestion Control Strategy for Large Scale Networks with Differentiated Services Traffic", abstract = "In this paper, a robust decentralized congestion control strategy is developed for a large scale network with Differentiated Services (Diff-Serv) traffic. The network is modeled by a nonlinear fluid flow model corresponding to two classes of traffic, namely the premium traffic and the ordinary traffic. The proposed congestion controller does take into account the associated physical network resource limitations and is shown to be robust to the unknown and time-varying delays. Our proposed decentralized congestion control strategy is developed on the basis of Diff-Serv architecture by utilizing a robust adaptive technique. A Linear Matrix Inequality (LMI) condition is obtained to guarantee the ultimate boundedness of the closed-loop system. Numerical simulation implementations are presented by utilizing the QualNet and Matlab software tools to illustrate the effectiveness and capabilities of our proposed decentralized congestion control strategy.
", keywords = "Congestion control, Large scale networks, Decentralized control, Differentiated services traffic, Time-delay systems.", volume = "7", number = "9", pages = "1196-7", }
