A New Modified Dropping Function for Congested AQM Networks

Patel, Sanjeev; Karmeshu,

doi:10.1007/s11277-018-6007-8

Cited by 25 publications

(23 citation statements)

References 14 publications

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“…In the first algorithm of this type, [ 17 ], a simple linear dropping function was proposed. Then some other shapes of the dropping function were investigated, among them a broken line with increasingly steeper slope [ 18 ], en exponential function [ 19 ], polynomials of second and third degree, [ 20 , 21 ], a mixture of linear and cubic functions, [ 22 ], and a product of a linear function with its logarithm, [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

AQM based on the queue length: A real-network study

Barczyk

Chydziñski

2022

PLoS ONE

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Active Queue Management (AQM) is recommended by Internet Engineering Task Force to mitigate the bufferbloat phenomenon in the Internet. In this paper, we show the results of comprehensive measurements carried out in our university network, in which a device with an AQM algorithm, designed and programmed for this purpose, was running. The implemented AQM algorithm was based on the dropping function, i.e. arriving packets were dropped randomly, with the probability being a function of the queue length. Several different dropping function forms, proposed in the networking literature, were used, in addition to the classic FIFO queue (no AQM). The experiment lasted over a month, during which the state of the network was measured and recorded several thousand times. This made the results independent of the natural fluctuations of the users’ behavior and the network load. Conclusions on the general performance improvement offered by the implemented AQM, as well as the differences in the performance between particular forms of the dropping function, were reached. Some of these conclusions differ from those drawn previously from simulations. This underlines the need for carrying measurements of new AQMs in real, operating networks, with complex, natural traffic.

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Section: Introductionmentioning

confidence: 99%

AQM based on the queue length: A real-network study

Barczyk

Chydziñski

2022

PLoS ONE

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Section: Aqm Methods For Video Streamingmentioning

confidence: 99%

Improving Perceived Quality of Live Adaptative Video Streaming

Santos

Silva

Pedroso

2021

Entropy

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Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.

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“…For high congested links, RED and ARED schemes induce a higher delay, increase the number of discarded packets and are not efficient to keep a good throughput. In order to solve those problems, Patel and Karmeshu [31] suggested a new method to evaluate the discard probability: if the average queue size is between min th and max th , packets are discarded with probability given by p 2 = 1 − {p 1 [−log( p 1 )]/(count + 1)}, with p 1 = p b . The results show that the AQM scheme prevents the queue length from exceeding max th , increasing the throughput.…”

Section: Aqm Methodsmentioning

confidence: 99%