این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
International Journal of Engineering، جلد ۳۹، شماره ۳، صفحات ۷۳۹-۷۵۵
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Deep Learning- based Smart Traffic Prediction for Enhanced Quality of Service in Software-defined Networking
چکیده انگلیسی مقاله Software-defined networking (SDN) separates the control and data planes that utilizes a centralized controller for efficient management and control. By collecting traffic flow attributes through the southbound interface, SDN enables flexible packet forwarding. However, high traffic volumes in large-scale dynamic networks, such as data centers, can cause congestion, packet loss, and delays. Real-time traffic forecasting is essential to mitigate these issues but it requires capturing complex spatiotemporal relationships which traditional models struggle to achieve. This paper addresses these challenges by extending an SDN dataset using traffic traces from a real-world network and introducing a novel hybrid traffic prediction model that combines long short-term memory (LSTM) and gradient boost regression (GBR) for accurate traffic prediction in SDN environments. This study creates a dataset with complex models for classification and regression. The proposed model predicts network throughput based on four critical routing metrics: hop count, latency, packet loss, and queue length. The predictions are utilized within a smart selection protocol, namely the adaptive selection protocol (ASP), to dynamically adjust routing decisions to significantly enhance the overall network performance. Simulation results demonstrate that the proposed LSTM-GBR model achieves an accuracy score of 0.996 for predicting throughput values while reducing congestion and latency and improving throughput by 39% and 200% when integrating the prediction process with the ASP technique compared to the traditional prediction methods. Implemented using Mininet with Python, the proposed approach demonstrates significant improvement in network efficiency and scalability, offering robust solutions for real-time traffic management in SDN environments.
کلیدواژه‌های انگلیسی مقاله Traffic prediction model,real traffic,Software-Defined Network,Deep Learning,Data Center,Throughput
نویسندگان مقاله A. Mahdi |
University of Samarra, Samarra, Iraq

A. Al Saadi |
University of Technology, Baghdad, Iraq

نشانی اینترنتی https://www.ije.ir/article_220446_4904077b2bb2dd5c4c7536e01198eaad.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات