10.07.2026; Кропивницький, Україна: X Міжнародна наукова конференція «Міжгалузеві диспути: динаміка та розвиток сучасних наукових досліджень»
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AN INVESTIGATION INTO SOFTWARE-DEFINED MULTI-SERVICE NETWORK PERFORMANCE OPTIMIZATION UTILIZING MACHINE LEARNING PARADIGMS

  • Ulvi Rafizade
Ulvi Rafizade
Odlar Yurdu University; Azerbaijan Technological University
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Опубліковано 10.07.2026

Як цитувати

Rafizade, U. (2026). AN INVESTIGATION INTO SOFTWARE-DEFINED MULTI-SERVICE NETWORK PERFORMANCE OPTIMIZATION UTILIZING MACHINE LEARNING PARADIGMS. Матеріали конференцій МЦНД, (10.07.2026; Кропивницький, Україна), 141–149. вилучено із https://archives.mcnd.org.ua/index.php/conference-proceeding/article/view/1744

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Анотація

This study evaluates the performance characteristics of multiservice Software-Defined Networks (SDNs) through the application of machine learning (ML) paradigms. Leveraging this empirical analysis, we propose a novel framework for calculating performance metrics in multiservice SDN environments utilizing ML techniques. The developed mathematical methodology incorporates the probabilistic and temporal dynamics inherent to multiservice networks, integrating a multi-step forecasting mechanism to predict SDN throughput. Utilizing these computational models, we derive analytical expressions to quantify Quality of Service (QoS) parameters and the Quality of Experience (QoE) of distinct traffic flows. Finally, numerical evaluations of the multi-step time series forecasting are presented, with predictive accuracy rigorously validated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics.
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Посилання

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