KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

A New Class-Based Traffic Queue Management Algorithm in the Internet

  • Zhu, Ye (Department of Electrical and Computer Engineering, Cleveland State University)
  • Published : 2009.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Facing limited network resources such as bandwidth and processing capability, the Internet will have congestion from time to time. In this paper, we propose a scheme to maximize the total utility offered by the network to the end user during congested times. We believe the only way to achieve our goal is to make the scheme application-aware, that is, to take advantage of the characteristics of the application. To make our scheme scalable, it is designed to be class-based. Traffic from applications with similar characteristics is classified into the same class. We adopted the RED queue management mechanism to adaptively control the traffic belonging to the same class. To achieve the optimal utility, the traffic belonging to different classes should be controlled differently. By adjusting link bandwidth assignments of different classes, the scheme can achieve the goal and adapt to the changes of dynamical incoming traffic. We use the control theoretical approach to analyze our scheme. In this paper, we focus on optimizing the control on two types of traffic flows: TCP and Simple UDP (SUDP, modeling audio or video applications based on UDP). We derive the differential equations to model the dynamics of SUDP traffic flows and drive stability conditions for the system with both SUDP and TCP traffic flows. In our study, we also find analytical results on the TCP traffic stable point are not accurate, so we derived new formulas on the TCP traffic stable point. We verified the proposed scheme with extensive NS2 simulations.

Keywords

References

  1. B. Braden, D. Clark, J. Crowcroft, B. Davie, S. Deering, D. Estrin, S. Floyd, V. Jacobson, G. Minshall, C. Partridge, L. Peterson, K. Ramakrishnan, S. Shenker, J. Wroclawski, and L. Zhang, “Recommendations on queue management and congestion avoidance in the internet,” United States, 1998.
  2. S. Floyd and V. Jacobson, “Random early detection gateways for congestion avoidance,” IEEE/ACM Trans. Netw., vol. 1, no. 4, pp. 397-413, 1993. https://doi.org/10.1109/90.251892
  3. M. May, T. Bonald, and J.-C. Bolot, “Analytic evaluation of RED performance,” in Proc. of INFOCOM 2000, Tel-Aviv, Israel, Mar. 2000.
  4. W. Feng, D. Kandlur, D. Saha, and K. G. Shin, “BLUE: A new class of active queue management algorithms,” Tech. Rep. CSE-TR-387-99, 15, 1999.
  5. T. J. Ott, T. V. Lakshman, and L. H. Wong, “Sred: Stabilized red,” in Proc. of INFOCOM, pp. 1346-1355, 1999.
  6. L. Zhu, G. Cheng, and N. Ansari, “Local stability of random exponential marking,” IEE Proceedings-Communications, vol. 150, no. 5, pp. 367-370, 2003. https://doi.org/10.1049/ip-com:20030601
  7. L. Tan, G. Peng, and S. Chan, “Adaptive rem: random exponential marking with improved robustness,” Electronics Letters, vol. 43, no. 2, pp. 133-135, 2007. https://doi.org/10.1049/el:20072911
  8. R. Pan, B. Prabhakar, L. Breslau, and S. Shenker, “Approximate fair allocation of link bandwidth,” IEEE Micro, vol. 23, no. 1, pp. 36-43, 2003.
  9. A. Tang, J. Wang, and S. Low, “Understanding choke: throughput and spatial characteristics,” IEEE/ACM Transactions on Networking, vol. 12, no. 4, pp. 694-707, Aug. 2004. https://doi.org/10.1109/TNET.2004.833162
  10. S. Floyd, “Red: Discussions of setting parameters,” http://www.aciri.org/floyd/REDparameters.txt.
  11. M. May, J. Bolot, C. Diot, and B. Lyles, “Reasons not to deploy RED,” in Proc. of 7th. International Workshop on Quality of Service (IWQoS’99), London, pp. 260-262, June 1999.
  12. M. Christiansen, K. Jeffay, D. Ott, and F. D. Smith, “Tuning red for web traffic,” SIGCOMM Comput. Commun. Rev., vol. 30, no. 4, pp. 139-150, 2000. https://doi.org/10.1145/347057.347418
  13. D. D. Clark and W. Fang, “Explicit allocation of best-effort packet delivery service,” IEEE/ACM Trans. Netw., vol. 6, no. 4, pp. 362-373, 1998. https://doi.org/10.1109/90.720870
  14. CISCO Systems, Inc., “Distributed weighted random early detection,” http://www.cisco.com/ univercd/cc/td/doc/product/software/ios111/cc111/wred.pdf.
  15. R. Jain, K. K. Ramakrishnan, and D.-M. Chiu, “Congestion avoidance in computer networks with a connectionless network layer,” pp. 140-156, 1988.
  16. V. Jacobson, “Congestion avoidance and control,” SIGCOMM Comput. Commun. Rev., vol. 25, no. 1, pp. 157-187, 1995. https://doi.org/10.1145/205447.205462
  17. D.-M. Chiu and R. Jain, “Analysis of the increase and decrease algorithms for congestion avoidance in computer networks,” Comput. Netw. ISDN Syst., vol. 17, no. 1, pp. 1-14, 1989. https://doi.org/10.1016/0169-7552(89)90019-6
  18. V. Misra, W.-B. Gong, and D. Towsley, “Fluid-based analysis of a network of aqm routers supporting tcp flows with an application to red,” in SIGCOMM ’00: Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication. New York, NY, USA: ACM, pp. 151-160, 2000.
  19. Information Sciences Institute, “The network simulator,” http://www.isi.edu/nsnam/ns/index.html.
  20. C. V. Hollot, V. Misra, D. F. Towsley, and W. Gong, “A control theoretic analysis of red,” in INFOCOM, pp. 1510-1519, 2001.
  21. F. Ren, C. Lin, and B. Wei, “A nonlinear control theoretic analysis to tcp-red system,” Computer Networks, vol. 49, no. 4, pp. 580-592, 2005. [Online]. Available: http://www.sciencedirect.com/ science/article/B6VRG-4FS21DP-1/2/fc2a036a6d2a58b5e33e71be84e66f1b https://doi.org/10.1016/j.comnet.2005.01.016
  22. Z.-z. Li and Y.-p. Chen, “A control theoretic analysis of mixed tcp and udp traffic under red based on nonlinear dynamic model,” in ICITA ’05: Proceedings of the Third International Conference on Information Technology and Applications (ICITA’05) Volume 2. Washington, DC, USA: IEEE Computer Society, pp. 747-750, 2005.
  23. A. Demers, S. Keshav, and S. Shenker, “Analysis and simulation of a fair-queuing algorithm,” in SIGCOMM, pp. 1-12, 1989.
  24. H. Saran, S. Keshav, and C. R. Kamanet, “A scheduling discipline and admission control policy for xunet2,” in Proc. of the Fourth International Workshop on Network and Operating System Support for Digital Audio and Video, pp. 89-101, 1993.
  25. S. J. Golestani, “A framing strategy for congestion management,” IEEE Journal on Selected Areas in Communications, vol. 9, no. 7, pp. 1064-1077, Sep. 1991. https://doi.org/10.1109/49.103553
  26. C. Kalmanek, H. Kanakia, and S. Keshav, “Rate controlled servers for very high speed networks,” in Proc. Globecom, pp. 12-20, 1990.
  27. I. R. Philp, “Scheduling real-time messages in packet-switched networks,” Ph.D. dissertation, Champaign, IL, USA, adviser-Jane W. Liu, 1996.
  28. A. K. Parekh and R. G. Gallager, “A generalized processor sharing approach to flow control inintegrated services networks: the single-node case,” IEEE/ACM Transactions on Networking, vol. 1, no. 3, pp. 344-357, June 1993. https://doi.org/10.1109/90.234856
  29. M. Shreedhar and G. Varghese, “Efficient fair queueing using deficit round robin,” SIGCOMM Comput. Commun. Rev., vol. 25, no. 4, pp. 231-242, 1995. https://doi.org/10.1145/217391.217453
  30. Y. Zhu, “Optimizing RED Control to Maximize Utility,” Technical Report, CSUECE-TR-08-01, Cleveland State University, http://academic.csuohio.edu/zhu_y/pub/trCSU0801.pdf
  31. L. Bernaille, L. Teixeira, R. Akodkenou, I. Soule, and K. Salamatian, “Traffic classification on the fly,” SIGCOMM Comput. Commun. Rev. 36, 2, Apr. 2006.
  32. J. Erman, M. Arlitt, and, A. Mahanti, “Traffic classification using clustering algorithms,” In Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, Pisa, Italy, September 11-15, 2006.