Packet-Scale Congestion Control Paradigm

Lovewell, Rebecca; Yin, Qianwen; Zhang, Tianrong; Kaur, Jasleen; Smith, F. Donelson

doi:10.1109/tnet.2016.2591018

Cited by 5 publications

(2 citation statements)

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“…Taking the widely used random early detection (RED) algorithm for example, exponentially weighted moving average (EWMA) of queue length is adopted as the congestion signal, where the forgetting factor plays an important role. 6 This algorithm, however, faces the same challenge that has been raised as the second disadvantage, ie, how to determine the optimal forgetting factor. First and foremost, if the inflow rate itself is quite high and the queue length is small, the average queue-length-based AQM algorithms will drop a much smaller fraction of packets than it should and, thus, makes the algorithm inept at detecting incipient congestion.…”

Section: Introductionmentioning

confidence: 99%

“…12 In order to filter out transient fluctuations and smoothen the congestion signal, an EWMA of sending rate is then chosen in the work of Lovewell. 6 This algorithm, however, faces the same challenge that has been raised as the second disadvantage, ie, how to determine the optimal forgetting factor. If the forgetting factor is set large, the algorithm will fail to smoothen abnormal data caused by bursty traffic or network noise, whereas on the contrary, it will result in overreliance on historical data.…”

Section: Introductionmentioning

confidence: 99%

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An average queue‐length‐difference–based congestion detection algorithm in TCP/AQM network

Zhu

Luo

Yang

et al. 2018

Adaptive Control & Signal

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Congestion detection in transmission control protocol/active queue management networks remains a challenging problem in which the choosing of congestion signal is one of the most important factors. Exponentially weighted moving average of queue length, the most widely used congestion signal, is facing difficulties in detecting incipient congestion and quantifying the optimal forgetting factor. Aiming at these 2 disadvantages, we propose an average queue-length-difference-based congestion detection algorithm where exponentially weighted moving average of queue-length difference is chosen as the congestion signal with the theoretical optimal forgetting factor deduced. First, by defining the queue-length difference as the state, the corresponding state equation is derived from the fluid model. Second, we prove that the inflow traffic in state equation is a discrete-time martingale, which can be transformed to a Wiener process according to the martingale representation theorem. Noticing that the observation of state will be coupled with noise because of the unstable transmission, the state estimation is then derived with the application of recursive least squares filter. The filter gain of state estimation, which is a function of the noise-signal ratio, corresponds to the optimal forgetting factor in average queue-length-difference-based congestion detection algorithm. Simulation results in NS-3 and MATLAB illustrate the effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%