Machine learning techniques for transmission parameters classification in multi-agent managed network
Authors:
- Dariusz Żelasko,
- Paweł Pławiak,
- Joanna Kołodziej
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 (NuSVC), 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).
- Record ID
- CUT8f063b3cde1a4f0da5ce30293eac1aba
- Publication categories
- ; ;
- Author
- Pages
- 699-707
- Other elements of collation
- rys.; schem.; tab.; Bibliografia (na s.) - 707; Bibliografia (liczba pozycji) - 30; Oznaczenie streszczenia - Abstr.
- Substantive notes
- Punktacja MNiSW/MEiN (rozdział) - 20
- Book
- Lefevre Laurent, Laurent Lefevre Varela Carlos A., Carlos A. Varela Pallis George George Pallis [et al.] (eds.): CCGRID 2020 : 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, 11-14 May 2020 Melbourne, Australia, 2020, Los Alamitos, California [etc.], Institute of Electrical and Electronics Engineers, IEEE, ISBN 978-1-7281-6095-5
- Keywords in English
- multi-agent system, QoS , Pay&Require, resource allocation, economical models, machine learning
- DOI
- DOI:10.1109/CCGrid49817.2020.00-20 Opening in a new tab
- URL
- https://ieeexplore.ieee.org/document/9139643 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 140
- Publication indicators
- Citation count
- 3
- Additional fields
- Indeksowana w: Web of Science, Scopus, CORE
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT8f063b3cde1a4f0da5ce30293eac1aba/
- URN
urn:pkr-prod:CUT8f063b3cde1a4f0da5ce30293eac1aba
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.