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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

Abstract

With the popularization and development of network intelligent terminals, additional requirements and challenges have arisen concerning network congestion control and the quality of service based on delay and jitter. Existing network intermediate devices primarily rely on active queue management to control the delay and solve congestion. This paper proposes an active queue management algorithm focusing on time delay demands to solve the large number of packet loss problems in existing active queue management algorithms. Packet loss problems are caused by long delays and unequal network resource allocation. In the TD-AQM algorithm, the network measurement feedback is obtained from the end system and intermediate equipment is returned to the data packet as the basis for queue management. The delay requirement is written in the option field of the IP layer of the data packet, i.e., the longest time of the message in the router. To avoid the flood peak effect, TD-AQM constrains the down lookup range and limits the occupancy accuracy of the lattice. The queue location of packets is determined according to the delay requirements. The experimental results show that TD-AQM can effectively maintain the stability of the queue, ensure that data packets are queued according to the delay requirements, and guarantee an overall low latency compared to other algorithms.


Funded by

国家重点研发计划(2017YFB081703)

国家自然科学基金(61602114)

赛尔网络下一代互联网技术创新项目(NGII20150108,NGII20170406)

江苏省自然科学基金(BK20151416)


References

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  • 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

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

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

    else

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

    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 $P$;

    $P\Leftarrow1$;

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

    $P\Leftarrow0$;

    end if

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

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

    end if

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