OVERLOAD AND TRAFFIC MANAGEMENT OF MESSAGE SOURCES WITH DIFFERENT PRIORITY OF SERVICE
Valerii Kozlovskyi
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (Ukraine)
https://orcid.org/0000-0003-0234-415X
Valerii Kozlovskyi
National Aviation University, Faculty of Cyber Security and Software Engineering, Department of Information Protection System, Kyiv, Ukraine (Ukraine)
https://orcid.org/0000-0002-8301-5501
Andrii Toroshanko
National Aviation University, Faculty of Cyber Security and Software Engineering, Department of Information Protection System, Kyiv, Ukraine (Ukraine)
https://orcid.org/0000-0002-0816-657X
Oleksandr Toroshanko
toroshanko@gmail.comTaras Shevchenko National University of Kyiv (Ukraine)
http://orcid.org/0000-0002-2354-0187
Natalia Yakumchuk
Lutsk National Technical University (Ukraine)
https://orcid.org/0000-0002-8173-449X
Abstract
The scheme of dynamic management of traffic and activity of message sources with different priority of service is considered. The scheme is built on the basis of the neuroprognostic analysis model and the gradient descent method. For prediction and early detection of overload, the apparatus of the general theory of sensitivity with indirect feedback and control of activity of message sources is used. The control algorithm is started at the bottleneck of the network node. It uses a recursive prediction approach where the neural network output is referred to as many steps as defined by a given prediction horizon. Traffic with a higher priority is served without delay using the entire available bandwidth. Low-priority traffic will use the remaining bandwidth not used by higher-priority traffic. An algorithm for estimating the maximum available bandwidth of a communication node for traffic with a low service priority has been developed. This approach makes it possible to improve the efficiency of channel use without affecting the quality of service for high-priority traffic.
Keywords:
telecommunication network, overload prediction, sensitivity function, neural network, gradient descent method, service priorityReferences
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Authors
Valerii KozlovskyiNational Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Ukraine
https://orcid.org/0000-0003-0234-415X
Authors
Valerii KozlovskyiNational Aviation University, Faculty of Cyber Security and Software Engineering, Department of Information Protection System, Kyiv, Ukraine Ukraine
https://orcid.org/0000-0002-8301-5501
Authors
Andrii ToroshankoNational Aviation University, Faculty of Cyber Security and Software Engineering, Department of Information Protection System, Kyiv, Ukraine Ukraine
https://orcid.org/0000-0002-0816-657X
Authors
Oleksandr Toroshankotoroshanko@gmail.com
Taras Shevchenko National University of Kyiv Ukraine
http://orcid.org/0000-0002-2354-0187
Authors
Natalia YakumchukLutsk National Technical University Ukraine
https://orcid.org/0000-0002-8173-449X
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