Development of an Intelligent Service Delivery System to Increase Efficiency of Software Defined Networks
Joldasbayev S. Sapakova S. Zhaksylyk A. Kulambayev B. Armankyzy R. Bolysbek A.
2023Science and Information Organization
International Journal of Advanced Computer Science and Applications
2023#14Issue 12644 - 656 pp.
The burgeoning complexity in network management has garnered considerable attention, specifically focusing on Software-Defined Networking (SDN), a transformative technology that addresses limitations inherent in traditional network infrastructures. Despite its advantages, SDN is often susceptible to bottlenecks and excessive load issues, underscoring the necessity for more robust load balancing solutions. Previous research in this realm has predominantly concentrated on employing static or dynamic methodologies, encapsulating only a handful of parameters for traffic management, thereby limiting their effectiveness. This study introduces an innovative, intelligence-led approach to service delivery systems in SDN, specifically by orchestrating packet forwarding-encompassing both TCP and UDP traffic-through a multi-faceted analysis utilizing twelve distinct parameters elaborated in subsequent sections. This research leverages advanced machine learning algorithms, notably K-Means and DBSCAN clustering, to discern patterns and optimize traffic distribution, ensuring a more nuanced, responsive load balancing mechanism. A salient feature of this methodology involves determining the ideal number of operational clusters to enhance efficiency systematically. The proposed system underwent rigorous testing with an escalating scale of network packets, encompassing counts of 5,000 to an extensive 10,000,000, to validate performance under varying load conditions. Comparative analysis between K-Means and DBSCAN’s results reveals critical insights into their operational efficacy, corroborated by juxtaposition with extant scholarly perspectives. This investigation’s findings significantly contribute to the discourse on adaptive network solutions, demonstrating that an intelligent, parameter-rich approach can substantively mitigate load-related challenges, thereby revolutionizing service delivery paradigms within Software-Defined Networks.
classification , Load balancing , machine learning , server , software
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International Information Technology University, Almaty, Kazakhstan
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Department of Computer Engineering, International University of Information Technology, Almaty, Kazakhstan
Satbayev University, Almaty, Kazakhstan
Turan University, Almaty, Kazakhstan
Bachelor Student at International Information Technology University, Almaty, Kazakhstan
International Information Technology University
Al-Farabi Kazakh National University
Department of Computer Engineering
Satbayev University
Turan University
Bachelor Student at International Information Technology University
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