Wireless Personal Communications

, Volume 104, Issue 1, pp 37–55 | Cite as

A New Modified Dropping Function for Congested AQM Networks

  • Sanjeev Patel
  • Karmeshu


Active queue management schemes are used to reduce the number of dropped packets at the routers. Random early detection uses dropping probability which is calculated based on the average queue size. Further it is modified according to the value of the count indicating the number of unmarked packets that have arrived after a marked packet. The impact of random variable i.e. number of packets arrived after a marked packet over the dropping pattern is investigated. The proposed model achieves smooth dropping pattern which results in improvement of quality of service parameters. A new model for dropping probability is proposed with different dropping function. The effect of new dropping probability results in the increase of the throughput and reduction of the expected end-to-end delay. An important finding is that the choice of modified dropping function significantly affects the performance measures of the networks.


Active queue management Random early detection Modified dropping probability End-to-end delay Throughput Loss-rate Traffic load Buffer size 



The author would like to thank Prof. Karmeshu for his useful suggestions and guidance. The help provided by him is highly acknowledged. It will not be possible to complete this paper without his support and time to time discussion with him. We are thankfull to the distinguished referee for their comments and suggestions.


  1. 1.
    Floyd, S., & Jacobson, V. (1993). Random early detection gateways for congestion avoidance. IEEE/ACM Transactions on Networking, 1(4), 397–413.CrossRefGoogle Scholar
  2. 2.
    Floyd, S., Gummadi, R., & Shenker, S. (2001). Adaptive RED: An algorithm for increasing the robustness of RED’s active queue management, Berkeley, CA. Accessed 23 Feb 2017.
  3. 3.
    Hollot, C. V., Misra, V., Towsley, D., & Gong, W. (2001). On designing improved controllers for AQM routers supporting TCP flows. In Proceedings of IEEE INFOCOM (pp. 1726–1734).Google Scholar
  4. 4.
    Hollot, C. V., Misra, V., Towsley, D., & Gong, W. (2002). Analysis and design of controllers for AQM routers supporting TCP flows. IEEE Transactions on Automatic Control, 47(6), 945–959.MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Feng, G., Agarwal, A. K., Jayaraman, A., & Siew, C. K. (2004). Modified RED gateways under bursty traffic. IEEE Communications Letters, 8(5), 323–325.CrossRefGoogle Scholar
  6. 6.
    Feng, C. W., Huang, L. F., Xu, C., & Chang, Y. C. (2017). Congestion control scheme performance analysis based on nonlinear RED. IEEE Systems Journal, 11(4), 2247–2254.CrossRefGoogle Scholar
  7. 7.
    Adams, R. (2013). Active queue management: A survey. IEEE Communations Surveys and Tutorials, 15(3), 1425–1476.CrossRefGoogle Scholar
  8. 8.
    Bhatnagar, S., Patel, S., & Karmeshu (2018). A stochastic approximation approach to active queue management. Telecommunication Systems, 68(1), 89–104.CrossRefGoogle Scholar
  9. 9.
    Otero, A. S., & Atiquzzaman, M. (2011). Adaptive localized active route maintenance mechanism to improve performance of VoIP. Journal of Communications, 6(1), 68–78.CrossRefGoogle Scholar
  10. 10.
    Narman, H., Hossain, M. S., & Atiquzzaman, M. (2015). Management and analysis of multi class traffic in single and multi-band systems. Wireless Personal Communications, 83(1), 231–258.CrossRefGoogle Scholar
  11. 11.
    Misra, S., Tiwari, V., & Obaidat, M. S. (2009). LACAS: Learning automata-based congestion avoidance scheme for healthcare wireless sensor networks. IEEE Journal on Selected Areas in Communications, 27(4), 466–479.CrossRefGoogle Scholar
  12. 12.
    Misra, S., Oommen, B. J., Yanamandra, S., & Obaidat, M. S. (2010). Random early detection for congestion avoidance in wired networks: The discretized pursuit learning-automata-like solution. IEEE Transactions on Systems, Man and Cybernetics, Part B, 40(1), 66–76.CrossRefGoogle Scholar
  13. 13.
    Domzal, J., Wojcik, R., Cholda, P., Stankiewicz, R., & Jajszczyk, A. (2016). Efficient congestion control mechanism for flow-aware networks. International Journal of Communication Systems, 29(4), 787–800.CrossRefGoogle Scholar
  14. 14.
    Wang, J., Rong, L., & Liu, Y. (2008). A robust proportional controller for AQM based on optimized second-order system model. Computer Communications, 31(10), 2468–2477.CrossRefGoogle Scholar
  15. 15.
    Karmeshu, Patel, S., & Bhatnagar, S. (2017). Adaptive mean queue size and its rate of change: Queue management with random dropping. Telecommunication Systems, 65(2), 281–295.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJaypee Institute of Information TechnologyNoidaIndia
  2. 2.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

Personalised recommendations