Перегляд за Автор "Kulakov, Yurii"

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    Intelligent traffic management method in software-defined networks based on behavioral classification and adaptive priority service
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Oboznyi, Dmytro; Kulakov, Yurii
    The growing complexity of modern enterprise network environments demands sophisticated traffic management solutions that can provide quality of service (QoS) guarantees for encrypted and heterogeneous flows. Existing traffic management approaches face significant challenges when dealing with encrypted protocols and diverse application requirements, resulting in performance degradation for critical services and inefficient resource utilization. This paper addresses the problem of intelligent traffic management in software-defined networks through behavioral classification and adaptive priority service mechanisms. The study examines the development and implementation of an integrated traffic management method that combines behavioral deep packet inspection, class-based queuing, and weighted random early detection algorithms. The research investigates how behavioral flow characteristics remain observable in encrypted traffic environments and how these patterns can be leveraged for effective QoS provisioning. The proposed method utilizes packet timing patterns, connection behaviors, and flow statistics to classify traffic without relying on payload inspection or predefined port assignments. Experimental validation through discrete-event simulation demonstrates significant performance improvements compared to traditional first-in-first-out mechanisms. The behavioral classification component achieves over 95% classification accuracy. The experimental results demonstrate up to 97.5% improvement in latency performance and 0% packet loss for high-priority traffic. Integrating behavioral traffic recognition with adaptive queue management within a programmable network framework provides an effective and innovative approach to maintaining stable service quality in encrypted, multi-service environments. The proposed method is compatible with existing software-defined network controllers and can be deployed without modification of application protocols or infrastructure components.
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    The method of SDN clustering for controller load balancing
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2020) Dolynnyi, Oleksandr; Nikolskiy, Serhiy; Kulakov, Yurii
    The paper proposes the method of SDN clustering using connections density and controller load distribution that solves the problem of controller load balancing. Clustering efficiency criteria have been considered, including fault tolerance, controller-to-switch and intercontroller latency and network limitations. Review of the key clustering methods has been performed, and the base algorithm for modification has been chosen. Density-based controller placement algorithm is modified to solve the problem of multicontroller placement. Metric of the node boundary index is introduced to advance the efficiency of proposed algorithm. A software implementation of the developed algorithm has been created, and its performance has been tested. The algorithm’s modelling results have been compared with those of the other algorithms using the metrics of distribution of service traffic propagation delay and computational speed in relation to network topology size.