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Adaptive active queue management controller for TCP communication networks using PSO-RBF models

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Abstract

Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. Active queue management (AQM) is a promising technique to congestion control for reducing packet loss and improving network utilization in transmission control protocol (TCP)/Internet protocol (IP) networks. AQM policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts, and the adoption of a suitable control policy. Radial bias function (RBF)-based AQM controller is proposed in this paper. RBF as a nonlinear controller is suitable as an AQM scheme to control congestion in TCP communication networks since it has nonlinear behavior. Particle swarm optimization (PSO) algorithm is also employed to derive RBF output weights such that the integrated-absolute error is minimized. Furthermore, in order to improve the robustness of RBF controller, an error-integral term is added to RBF equation. The output weights and the coefficient of the integral error term in the latter controller are also optimized by PSO algorithm. It should be noted that in both proposed controllers the parameters of radial basis functions are selected to symmetrically partition the input space. The results of the comparison with adaptive random early detection (ARED), random exponential marking (REM), and proportional-integral (PI) controllers are presented. Integral-RBF has better performance not only in comparison with RBF but also with ARED, REM and PI controllers in the case of link utilization while packet loss rate is small.

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References

  1. Huang CJ, Cheng CL, Chuang YT, Jang JS (2006) Admission control schemes for proportional differentiated services enabled internet servers using machine learning techniques. Expert Syst Appl 31:458–471

    Article  Google Scholar 

  2. Kim KJ, Jeong IJ, Park JC, Park YJ, Kim CG, Kim TH (2007) The impact of network service performance on customer satisfaction and loyalty: high-speed internet service case in Korea. Expert Syst Appl 32:822–831

    Article  Google Scholar 

  3. Jacobson V (1988) Congestion avoidance and control. In: Proceedings of the ACM SIGCOMM, pp 314–329

  4. Floyd S, Jacobson V (1993) Random early detection gateways for congestion avoidance. IEEE/ACM Trans Network 1:397–413

    Article  Google Scholar 

  5. Kunniyur S, Srikant R (2001) Analysis and design of an adaptive virtual queue (AVQ) algorithm for active queue management. In: Proceedings of the ACM SIGCOMM, pp 123–134

  6. Athuraliya S, Low SH, Li VH, Yin Q (2001) REM: active queue management. IEEE Network 15:48–53

    Article  Google Scholar 

  7. Aweya J, Ouellette M, Montuno DY, Felske K (2008) Design of rate-based controllers for active queue management in TCP/IP networks. Comput Communicat 31:3344–3359

    Article  Google Scholar 

  8. Cho HC, Fadali SM, Lee H (2008) Adaptive neural queue management for TCP networks. Comput Electr Eng 34:447–469

    Article  MATH  Google Scholar 

  9. Zhang W, Tan L, Peng G (2009) Dynamic queue level control of TCP/RED systems in AQM routers. Comput Electr Eng 35:59–70

    Article  MATH  Google Scholar 

  10. Yu L, Ma M, Hu W, Shi Z, Shu Y (2011) Design of parameter tunable robust controller for active queue management based on H ∞ control theory. J Network Comput Appl 34:750–764

    Article  Google Scholar 

  11. Feng W, Kandlur DD, Saha D, Shin KG (1999) A self-configuring RED gateway. In: Proceedings of the IEEE INFOCOM, pp 1320–1328

  12. Lin D, Morris R (1997) Dynamics of random early detection. In: Proceedings of the ACM SIGCOM, pp 127–137

  13. Qtt TJ, Lakshman TV, Wong L (1999) SRED: stabilized RED. In: Proceedings of the IEEE INFOCOM, pp 1346–1355

  14. Anjum F, Tassiulas L (1999) Fair bandwidth sharing among adaptive and nonadaptive flows in the internet. In: Proceedings of the IEEE INFOCOM, pp 1412–1420

  15. Nabeshima M (2002) Improving the performance of active buffer management with per-flow information. IEEE Commun Lett 6:306–308

    Article  Google Scholar 

  16. Liu S, Başar T, Srikant R (2005) Exponential-RED: a stabilizing AQM scheme for low- and high-speed TCP protocol. IEEE/ACM Trans Networking 13:1068–1081

    Article  Google Scholar 

  17. Aweya J, Ouellette M, Montuno DY, Chapman A (2001) A control theoretic approach to active queue management. Comput Networks 36:203–235

    Article  Google Scholar 

  18. Xiong N, Vasilakos AV, Yang LT, Wang C-X, Kannan R, Chang C-C, Pan Y (2010) A novel self-tuning feedback controller for active queue management supporting TCP flows. Inf Sci 180:2249–2263

    Article  MathSciNet  Google Scholar 

  19. Xiong N, Yang LT, Yang Y, Defago X, He Y (2008) A novel numerical algorithm based on self-tuning controller to support TCP flows. Mathe Comput Simul 79:1178–1188

    Article  MathSciNet  MATH  Google Scholar 

  20. Zhang C, Yin J, Cai Z, Chen W (2010) RRED: robust RED algorithm to counter low-rate denial-of-service attacks. IEEE Commun Lett 14:489–491

    Article  Google Scholar 

  21. Feng W, Shin KG, Kandlur DD, Saha D (2002) The BLUE active queue management algorithms. IEEE/ACM Trans Networking 10:513–528

    Article  Google Scholar 

  22. Long C, Zhao B, Guan X, Yang J (2005) The YELLOW queue management algorithm. Comput Networks 47:525–550

    Article  Google Scholar 

  23. Feng W-C, Kapadia A, Thulasidasan S (2002) GREEN: proactive queue management over a best-effort network. In: Proceedings of the IEEE GLOBECOM, pp 1774–1778

  24. Haykin S (1999) Radial-basis function networks. In: Neural networks-a comprehensive foundation, Chap. 5, 2nd edn. Prentice Hall, pp 256–318

  25. Yao D (2002) High-resolution EEG mapping: a radial-basis function based approach to the scalp Laplacian estimate. Clin Neurophysiol 113:956–967

    Article  Google Scholar 

  26. Eberhart RC, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization. In: Proceedings of the international conference on evolutionary programming, pp 611–616

  27. Hollot CV, Misra V, Towsley D, Gong WB (2001) On designing improved controllers for AQM routers supporting TCP flows. In: Proceedings of the IEEE INFOCOM, pp 1726–1734

  28. Li Y, Ko KT, Chen G (2005) A Smith predictor-based PI-controller for active queue management. IEICE Trans Commun 88:4293–4300

    Article  Google Scholar 

  29. Chang X, Muppala JK (2006) A stable queue-based adaptive controller for improving AQM performance. Comput Networks 50:2204–2224

    Article  MATH  Google Scholar 

  30. Hollot C, Misra V, Towsley D, Gong WB (2002) Analysis and design of controllers for AQM routers supporting TCP flows. IEEE Trans Automat Contr 47:945–959

    Article  MathSciNet  Google Scholar 

  31. Kim KB, Low SH (2003) Analysis and design of AQM based on state-space models for stabilizing TCP. In: Proceedings of the American control conference, pp 260–265

  32. Ren F, Lin C, Wei B (2005) A robust active queue management algorithm in large delay network. Comput Communicat 28:485–493

    Article  Google Scholar 

  33. Wang X, Wang Y, Zhou H, Huai X (2006) PSO-PID: a novel controller for AQM routers. In: Proceedings of the IEEE/IFIP WOCN, pp 1–5

  34. Chen C-K, Kuo H-H, Yan J-J, Liao T-L (2009) GA-based PID active queue management control design for a class of TCP communication networks. Expert Syst Appl 36:1903–1913

    Article  Google Scholar 

  35. Wang J-S, Gao Z-W, Shu Y-T (2007) RBF-PID based adaptive active queue management algorithm for TCP network. In: Proceedings of the IEEE international conference on control and automation, pp 171–176

  36. Farokhian Firuzi M, Haeri M (2005) Active queue management in TCP networks based on self tuning control approach. In: Proceedings of the IEEE conference on control applications, pp 904–909

  37. Di Fatta G, Re GL, Urso A (2002) A fuzzy approach for the network congestion problem. Lecture Notes Comput Sci 2329:286–295

    Article  Google Scholar 

  38. Fengyuan RYR, Xiuming S (2002) Design of a fuzzy controller for active queue management. Comput Communicat 25:874–883

    Article  Google Scholar 

  39. Rahmani R, Kanter T, Åhlund C (2010) a self configuring fuzzy active queue management controller in heterogeneous networks. In: Proceedings of the international conference on telecommunications, pp 634–641

  40. Lima MM de AE, de Fonseca NLS, Geromel JC (2004) An optimal active queue management controller. In: Proceedings of the IEEE international conference on communications, pp 2261–2266

  41. Bigdeli N, Haeri M (2009) Predictive functional control for active queue management in congested TCP/IP networks. ISA Trans 48:107–121

    Article  Google Scholar 

  42. Chen C-K, Hung Y-C, Liao T-L, Yan J-J (2007) Design of robust active queue management controllers for a class of TCP communication networks. Inf Sci 177:4059–4071

    Article  MATH  Google Scholar 

  43. Chen C-K, Liao T-L, Yan J-J (2009) Active queue management controller design for TCP communication networks: variable structure control approach. Chaos Solitons Fractals 40:227–285

    MathSciNet  Google Scholar 

  44. Fengyuan R, Chuang L, Xunhe Y, Xiuming S, Fubao W (2002) A robust active queue management algorithm based on sliding mode variable structure control. In: Proceedings of the IEEE INFOCOM, pp 13–20

  45. Mahdi Alavi SM, Hayes MJ (2009) Robust active queue management design: a loop-shaping approach. Comput Commun 32:324–331

    Article  Google Scholar 

  46. Bigdeli N, Haeri M (2009) CDM-based design and performance evaluation of a robust AQM method for dynamic TCP/AQM networks. Comput Commun 32:213–229

    Article  Google Scholar 

  47. Park EC, Lim H, Park KJ, Choi CH (2004) Analysis and design of the virtual rate control algorithm for stabilizing queues in TCP networks. Comput Networks 44:17–41

    Article  Google Scholar 

  48. Quet PF, Ozbay H (2004) On the design of AQM supporting TCP flows using robust control theory. IEEE Trans Autom Control 49:1031–1036

    Article  MathSciNet  Google Scholar 

  49. Manfredi S, di Bernardo M, Garofalo F (2009) Design, validation and experimental testing of a robust AQM control. Control Eng Pract 17:394–407

    Article  Google Scholar 

  50. Rahnami K, Arabshahi P, Gray A (2005) Neural network based model reference controller for active queue management of TCP flows. In: Proceedings of the IEEE international conference on aerospace, pp 1696–1704

  51. Lochin E, Talavera B (2011) Managing Internet routers congested links with a Kohonen-RED queue. Eng Appl Artificial Intell 24:77–86

    Article  Google Scholar 

  52. Misra V, Gong WB, Towsley DF (2000) Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED. In: Proceedings of the ACM SIGCOMM, pp 151–160

  53. Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355

    MathSciNet  MATH  Google Scholar 

  54. Poggio T, Girosi F (1990) Networks for approximation and learning. Proc IEEE 78:1481–1497

    Article  Google Scholar 

  55. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neutral networks, pp 1942–1948

  56. Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. In: Proceedings of the international conference on evolutionary programming, pp 591–601

  57. Suganthan PN (1999) Particle swarm optimiser with neighborhood operator. In: Proceedings of the IEEE congress on evolutionary computation, pp 1958–1962

  58. Yoshida H, Fukuyama Y, Takayama S, Nakanishi Y (1999) A particle swarm optimization for reactive power and voltage control in electric power systems considering voltage security assessment. In: Proceedings of the IEEE international conference on systems, man, and cybernetics, vol. 6, pp 497–502

  59. Naka S, Genji T, Yura T, Fukuyama Y (2001) Practical distribution state estimation using hybrid particle swarm optimization. In: Proceedings of the IEEE power engineering society winter meeting, vol. 2, pp 815–820

  60. Ratnaweera A, Halgamuge S, Watson H (2003) Particle swarm optimization with self-adaptive acceleration coefficients. In: Proceedings of the first international conference on fuzzy systems and knowledge discovery, pp 264–268

  61. van den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci 176:937–971

    Article  MATH  Google Scholar 

  62. Engelbrecht AP (2007) Computational intelligence-an introduction, chap. 16, 2nd edn. Wiley, London, pp 289–357

    Book  Google Scholar 

  63. Floyd S, Gummadi R, Shenker S (2001) Adaptive RED: a algorithm for increasing the robustness of RED’s active queue management. (http://www.icir.org/floyd/papers/adaptiveRed.pdf)

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Acknowledgments

This work is supported by Islamic Azad University South Tehran Branch under a research project entitled as “Design and Simulation of Optimal Neural Controllers for Active Queue Management in TCP Communication Networks.”

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Authors and Affiliations

  1. Department of Communication Engineering, Faculty of Engineering, Islamic Azad University, South Tehran Branch, P.O. Box: 11365-4435, Tehran, Iran

    Mansour Sheikhan

  2. Modeling and Optimization Research Center in Science and Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran

    Reza Shahnazi

  3. Department of Electronic Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran

    Ehsan Hemmati

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  1. Mansour Sheikhan
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  2. Reza Shahnazi
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  3. Ehsan Hemmati
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Correspondence to Mansour Sheikhan.

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Sheikhan, M., Shahnazi, R. & Hemmati, E. Adaptive active queue management controller for TCP communication networks using PSO-RBF models. Neural Comput & Applic 22, 933–945 (2013). https://doi.org/10.1007/s00521-011-0786-0

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