An AQM Controller Based on Feed-Forward Neural Networks for Stable Internet

Bisoy, Sukant Kishoro; Pattnaik, Prasant Kumar

doi:10.1007/s13369-017-2767-9

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…With the improvement of artificial neural network and fuzzy logic control theory, some researchers tried to apply intelligent control theory to design an AQM algorithm based on a proportion integral differential (PID) controller. The self-learning ability of a neuron can adjust the three parameters of PID online, so that these algorithms [31,32] can obtain stable performance under the fluctuating network environment.…”

Section: Active Queue Managementmentioning

confidence: 99%

The Yellow Active Queue Management Algorithm in ICN Routers Based on the Monitoring of Bandwidth Competition

2021

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Deploying the active queue management (AQM) algorithm on a router is an effective way to avoid packet loss caused by congestion. In an information-centric network (ICN), routers not only play a role of packets forwarding but are also content service providers. Congestion in ICN routers can be further summarized as the competition between the external forwarding traffic and the internal cache response traffic for limited bandwidth resources. This indicates that the traditional AQM needs to be redesigned to adapt to ICN. In this paper, we first demonstrated mathematically that allocating more bandwidth for the upstream forwarding flow could improve the quality of service (QoS) of the whole network. Secondly, we propose a novel AQM algorithm, YELLOW, which predicts the bandwidth competition event and adjusts the input rate of request and the marking probability adaptively. Afterwards, we model YELLOW through the totally asymmetric simple exclusion process (TASEP) and deduce the approximate solution of the existence condition for each stationary phase. Finally, we evaluated the performance of YELLOW by NS-3 simulator, and verified the accuracy of modeling results by Monte Carlo. The simulation results showed that the queue of YELLOW could converge to the expected value, and the significant gains of the router with low packet loss rate, robustness and high throughput.

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Section: Active Queue Managementmentioning

confidence: 99%

The Yellow Active Queue Management Algorithm in ICN Routers Based on the Monitoring of Bandwidth Competition

2021

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“…Bisoy and Pattnaik [ 39 ] used feed-forward neural network to create an AQM mechanism, namely FFNN-AQM. The network consisted of two input neurons, three neurons in a single hidden layer and the single output neuron.…”

Section: Related Workmentioning

confidence: 99%

Supervised Learning of Neural Networks for Active Queue Management in the Internet

Szyguła

Domański

Domańska

et al. 2021

Sensors

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The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router’s queue before the buffer is full. The aim of the work is to use machine learning to create a model that copies the behavior of the AQM PIα mechanism. We create training samples taking into account the self-similarity of network traffic. The model uses fractional Gaussian noise as a source. The quantitative analysis is based on simulation. During the tests, we analyzed the length of the queue, the number of rejected packets and waiting times in the queues. The proposed mechanism shows the usefulness of the Active Queue Management mechanism based on Neural Networks.

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“…The feed-forward neural network AQM "FFNN-AQM" [44] was designed to deal with heterogynous traffics flow and to predict the future queue length value. The FFNN-AQM weights update based on time-varying to achieve stabilize queueing length.…”

Section: Aqm With Neural Network Algorithmmentioning

confidence: 99%

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

Mahawish

Hassan

2021

IJEECS

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The congestion on the internet is the main issue that affects the performance of transition data over the network. An algorithm for congestion control is required to keep any network efficient and reliable for transfer traffic data of the users. Many Algorithms had been suggested over the years to improve the control of congestion that occurs in the network such as drop tail packets. Recently there are many algorithms have been developed to overcome the drawback of the drop tail procedure. One of the important algorithms developed is active queue management (AQM) that provides efficient congestion control by reducing drop packets, this technique considered as a base for many other congestion control algorithms schema. It works at the network core (router) for controlling the drop and marking of packets in the router's buffer before the congestion inception. In this study, a comprehensive survey is done on the AQM Algorithm schemas that proposed and modification these algorithms to achieve the best performance, the classification of AQM algorithms based on queue length, queue delay, or both. The advantages and limitations of each algorithm have been discussed. Also, debate the intelligent techniques procedure with AQM algorithm to achieve optimization in performance of algorithm operation. Finally, the comparison has been discussed among algorithms to find the weakness and powerful of each one based on different metrics.

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An AQM Controller Based on Feed-Forward Neural Networks for Stable Internet

Cited by 14 publications

References 30 publications

The Yellow Active Queue Management Algorithm in ICN Routers Based on the Monitoring of Bandwidth Competition

The Yellow Active Queue Management Algorithm in ICN Routers Based on the Monitoring of Bandwidth Competition

Supervised Learning of Neural Networks for Active Queue Management in the Internet

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

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