Authors - Sara Sadiq Jawad, Dheyaa Jasim Kadhim Abstract - Software-defined networks (SDNs) suffer from dynamic congestion due to the nature of the data traffic they transmit. This includes both aggregated mobile network activity (such as video streaming, social media access, browsing, messaging, and mobile app usage) and real backbone internet traffic traces (which contain diverse packet flows from broadband services, cloud computing systems, downloads, server connections, and large-scale network transactions). This congestion reduces service quality and leads to poor resource allocation. Therefore, traffic prediction is considered a smart and efficient transition for SDNs. This paper proposes a deep learning-based predictive system for forecasting incoming traffic using two types of data (periodic Milano and bursty MAWI dataset). It also examines the impact of periodic and bursty traffic types on the prediction models used by the proposed system; and on the integration mechanisms used to translate predictions into actionable data. The results show that periodic Milano traffic requires temporal learning, while bursty MAWI traffic requires clipping, alignment, log scale, and robust prediction. Using MAWI traffic also required alignment between the training and evaluation phases through the application of cross-domain adaptation, unlike the Milano traffic which showed a direct response. The proposed system also demonstrated improved throughput, reduced congestion, and more stable decision-making.
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