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

Jakub Szyguła,Dariusz Marek,Katarzyna Filus,Adam Domański,Szymon Mendla,Joanna Domańska

doi:10.3390/s21154979

Abstract

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 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.

Highlights

Cisco predicts that by 2022, the Internet traffic will increase to 77 exabytes per month due to the rapid development of mobile technologies
To increase the readability of the paper, we present only two extreme cases - the results obtained for a non-self-similar traffic (H = 0.5) and for a traffic with high degree of Long-Range Dependence (LRD)
The results show that when the number of last n elements of queue occupancy history taken as a CNN input is too small (CNN History < 100), independent of the degree of self-similarity of the traffic, the number of dropped packets, and the average queue length, approximates the results obtained using the sets of controllers PI α 2 and PI α 3

Summary

Introduction

Cisco predicts that by 2022, the Internet traffic will increase to 77 exabytes per month due to the rapid development of mobile technologies. The mobile data transfer will increase sevenfold compared to 2017, with an average annual growth of 46% [1]. The rapid increase in the number of Internet users as well as the transmission of multimedia content of increasing quality force the continuous development of data transmission mechanisms. Wide area networks have their origins in the 1970s and were created for the American army. The most important aspect of the network based on a distributed architecture was to deliver reliable transmission of data and low connection costs. The design assumptions proposed at the beginning turned out to be insufficient over the years

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Conclusion