Home // International Journal On Advances in Networks and Services, volume 11, numbers 3 and 4, 2018 // View article

Adjustment of the QoS Parameters on Routers with Neural Network Implementation

Authors:
Irina Topalova
Pavlinka Radoyska

Keywords: traffic congestion, Quality of Service, early detection, queue management, neural network

Abstract:
Applying Quality of Service mechanisms to modern communications is essential for the efficiency and for the traffic reliability. The various Quality of Service methods are based on queues management depending on the individual traffic parameters. Choosing Quality of Service parameters on the edge network devices defines the management queue and packet discard/queued parameters on the intermediate devices. The proposed research explores the possibility of automatically adapting to the already selected class based Quality of Service policy of new users added to the backbone of the network. In addition, a method for queue adjustment has been suggested and tested, taking into account the current queue of the added user. A neural network is trained to automatically adapt new end users to the quality of service policy, already set by other end users and accepted by intermediate routers. The obtained results show that the automated adaptation of the Quality of Service parameters to the already set ones is possible for the intermediate routers. A software application, implementing the method in a network segment, is presented. The positive consequences of applying the proposed method are discussed.

Pages: 143 to 151

Copyright: Copyright (c) to authors, 2018. Used with permission.

Publication date: December 30, 2018

Published in: journal

ISSN: 1942-2644