Machine learning techniques for transmission parameters classification in multi-agent managed network

TytułMachine learning techniques for transmission parameters classification in multi-agent managed network
Publication TypeConference Paper
Rok publikacji2020
AutorzyŻelasko D, Pławiak P, Kołodziej J
Conference Name20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)
Date Published05/2020
PublisherIEEE
Conference LocationMelbourne, Australia
Słowa kluczoweComputer networks, Jitter, Machine learning, Monitoring, Packet loss, Quality of service
Abstract

Looking at the rapid development of computer networks, it can be said that the transmission quality assurance is very important issue. In the past there were attempts to implement Quality of Service (QoS) techniques when using various network technologies. However QoS parameters are not always assured. This paper presents a novel concept of transmission quality determination based on Machine Learning (ML) methods. Transmission quality is determined by four parameters - delay, jitter, bandwidth and packet loss ratio. The concept of transmission quality assured network proposed by Pay&Require was presented as a novel multi-agent approach for QoS based computer networks. In this concept the essential part is transmission quality rating which is done based on transmission parameters by ML techniques. Data set was obtained based on the experience of the users test group. For our research we designed a machine learning system for transmission quality assessment. We obtained promising results using four classifiers: Nu-Support Vector Classifier (Nu-SVC), C-Support Vector Classifier (C-SVC), Random Forest Classifier, and K-Nearest Neighbors (kNN) algorithm. Classification results for different methods are presented together with confusion matrices. The best result, 87% sensitivity (overall accuracy), for the test set of data, was achieved by Nu-SVC and Random Forest (13/100 incorrect classifications).

URLhttps://ieeexplore.ieee.org/document/9139643
DOI10.1109/CCGrid49817.2020.00-20

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