Abstract
Modern computer systems practically cannot function without a computer network. New concepts of data transmission are emerging, e.g., programmable networks. However, the development of computer networks entails the need for development in one more aspect, i.e., the quality of the data transmission through the network. The data transmission quality can be described using parameters, i.e., delay, bandwidth, packet loss ratio and jitter. On the basis of the obtained values, specialists are able to state how measured parameters impact on the overall quality of the provided service. Unfortunately, for a non-expert user, understanding of these parameters can be too complex. Hence, the problem of translation of the parameters describing the transmission quality appears understandable to the user. This article presents the concept of using Machine Learning (ML) to solve the above-mentioned problem, i.e., a dynamic classification of the measured parameters describing the transmission quality in a certain scale. Thanks to this approach, describing the quality will become less complex and more understandable for the user. To date, some studies have been conducted. Therefore, it was decided to use different approaches, i.e., fusion of a neural network (NN) and a genetic algorithm (GA). GA’s were choosen for the selection of weights replacing the classic gradient descent algorithm. For learning purposes, 100 samples were obtained, each of which was described by four features and the label, which describes the quality. In the reasearch carried out so far, single classifiers and ensemble learning have been used. The current result compared to the previous ones is better. A relatively high quality of the classification was obtained when we have used 10-fold stratified cross-validation, i.e., SEN = 95% (overall accuracy). The incorrect classification was 5/100, which is a better result compared to previous studies.
Highlights
Nowadays, we can observe a constant increase in the use of computer networks and a dynamic development of techniques used in this type of networks
Thanks to Pay and Require, it is possible to provide data transmission services at the level expected by the customer and this quality is guaranteed
This paper proposes an new method of classifying the quality of the data transmission in the Pay and Require (P&R) network
Summary
We can observe a constant increase in the use of computer networks and a dynamic development of techniques used in this type of networks. Parameters describing the transmission quality are defined as Quality of Service (QoS) [1,2] Service, which in this case means the transmission of data over the network. Transmission quality can be described by several parameters such as: transmission delay, bandwidth, packet loss ratio, and jitter. It is the values of these parameters that have an impact on the QoS as to which the client has specific expectations. QoS is obtained by classifying the traffic and prioritizing it [3] Another solution that was proposed for the purpose of ensuring the quality of the data transmission service is Pay and Require (P&R).
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