SCIENTIA SINICA Informationis, Volume 49 , Issue 10 : 1321-1332(2019) https://doi.org/10.1360/N112019-00010

Active queue management algorithm for time delay demand

More info
  • ReceivedJan 16, 2019
  • AcceptedJun 18, 2019
  • PublishedOct 17, 2019








[1] Grazia C A, Patriciello N, Klapez M, et al. Mitigating congestion and Bufferbloat on satellite networks through a rate-based AQM. In: Proceedings of the IEEE International Conference on Communications, Paris, 2017. 1--6. Google Scholar

[2] Casoni M, Grazia C A, Klapez M. How to avoid TCP congestion without dropping packets: An effective AQM called PINK. Comput Commun, 2017, 10349-60 CrossRef Google Scholar

[3] Sanjeev Patel. Performance analysis on RED for stabilized queue. In: Proceedings of the IEEE Seventh International Conference on Contemporary Computing, Noida, 2014. 306--311. Google Scholar

[4] Sanjeev Patel. Performance analysis and modeling of congestion control algorithms based on active queue management. In: Proceedings of the IEEE International Conference on Signal Processing and Communication, Vancouver, 2013. 449--454. Google Scholar

[5] Tarun Jain, Annappa B, Mohit P, et al. Performance evaluation of CoDel for active queue management in wired-cum-wireless networks. In: Proceedings of the IEEE Fourth International Conference on Advanced Computing Communication Technologies, Rohtak, 2014. 381--385. Google Scholar

[6] Tahiliani M P, Shet K C, Basavaraju T G. CARED: Cautious Adaptive RED gateways for TCP/IP networks. J Network Comput Appl, 2012, 35857-864 CrossRef Google Scholar

[7] Liu W Y, Liu B, Zou X L. Congestion control algorithm based on dynamic threshold. Application Research of Computers, 2013, 30:3459-3461. Google Scholar

[8] Alsaaidah A, Zalisham M, Fadzli M, et al. Gentle-BLUE: A New Method for Active Queue Management. In: Proceedings of the International Conference on Advanced Computer Science Applications and Technologies, Amman, 2015. 67--72. Google Scholar

[9] Nichols K, Jacobson V. Controlling queue delay. Commun ACM, 2012, 5542 CrossRef Google Scholar

[10] Tian Z H, Yu X Z, Zhang H L, et al. A real-time network intrusion forensics method based on evidence reasoning network. Chin J Comput, 2014, (05). Google Scholar

[11] Zhu J, Luo T, Yang L. An average queue-length-difference-based congestion detection algorithm in TCP/AQM network. Int J Adapt Control Signal Process, 2018, 32742-752 CrossRef Google Scholar

[12] Bisoy S K, Pattnaik P K, Pati B. Design and analysis of a stable AQM controller for network congestion control. IJCNDS, 2018, 20143 CrossRef Google Scholar

[13] Iskandar M N. Active Queue Management (AQM) Performance Analysis Based On Controlled Delay (CoDel) Against Bufferbloat On Real-Time Application: Procedia Computer Science. 2017, 2:119. Google Scholar

[14] Sheikhan M, Shahnazi R, Hemmati E. Adaptive active queue management controller for TCP communication networks using PSO-RBF models. Neural Comput Applic, 2013, 22933-945 CrossRef Google Scholar

[15] Okokpujie K, Chukwu E C, Noma-Osaghae E. Novel Active Queue Management Scheme for Routers in Wireless Networks. IRECAP, 2018, 852 CrossRef Google Scholar

[16] Hamidian H, Beheshti M T H. A robust fractional-order PID controller design based on active queue management for TCP network. Int J Syst Sci, 2018, 49211-216 CrossRef Google Scholar

[17] Tahiliani M P, Shet K C. Analysis of cautious adaptive RED (CARED). In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics, Mysore, 2013. 1029--1034. Google Scholar

[18] Mühlenthaler M, Wanka R. Fairness in academic course timetabling. Ann Oper Res, 2016, 239171-188 CrossRef Google Scholar

  • Figure 1

    Frame of TD-AQM

  • Figure 2

    Queue structure

  • Figure 3

    Example of flood peak effect

  • Figure 4

    Queue management policy of TD-AQM

  • Figure 5

    Experimental topology

  • Figure 6

    (Color online) Performance comparison.(a) Throughput rate; (b) fairness

  • Figure 7

    (Color online) Queue stability comparison.(a) TD-AQM; (b) other algorithms

  • Table 1   Comparison of time demand satisfaction
    Number of flows Standard normal distribution Uniform distribution No time delay demand
    20 0.81 0.82 0.91
    40 0.84 0.81 0.85
    60 0.90 0.78 0.82
    100 0.86 0.77 0.81

    Algorithm 1 Constrain down lookup range

    Require:Time delay demand TimeDemand, Average delay of single packet $t$;

    Output:Extreme searchable downward position last_ position;

    ${last\underline{ }position}\Leftarrow1$;

    if ${\rm~TimeDemand}~=~0$ then

    ${pre\underline{ }position}\Leftarrow1$;

    ${last\underline{ }position}\Leftarrow1$;


    ${pre\underline{ }position}=\frac{{\rm~TimeDemand}}{t}$;

    end if

    if ${pre\underline{ }position}\leq100$ then

    ${last\underline{ }position}\Leftarrow1$;

    end if

    if ${pre\underline{ }position}>100$ then

    ${last\underline{ }position}\Leftarrow50+50\times\frac{1}{1+({pre\underline{ }position}-100)}$;

    end if


    Algorithm 2 Limit virtual occupancy accuracy

    Require:Estimate enqueue position pre_ position, extreme searchable downward position last_ position, lattice $n$, instantaneous queue length $q_i$, average queue length $q_{\rm~avg}$;

    Input:Lattice $n$ hit rate $P$;


    if $n>{pre\underline{ }position}~\&\&~n<{last\underline{ }position}$ then


    end if

    if ${pre\underline{ }position}>100$ then

    $P=\frac{1}{{pre\underline{ }position}-n}\times\log_{q_i}q_{\rm~avg}$;

    end if


Contact and support