Document Type : Original Article
Author
Department of Computer Engineering, Faculty of Basic Sciences and Engineering, Gonbad Kavous University, Gonbad Kavous, Iran
Abstract
Congestion remains a critical challenge in Software Defined Networks (SDNs), particularly in data center and Internet of Things (IoT) environments where high traffic dynamics and low latency requirements coexist. While SDN enables centralized traffic control, many existing congestion management approaches are either reactive—responding only after congestion occurs—or rely on computationally intensive learning-based prediction models that limit practical deployment. This paper proposes a lightweight and proactive congestion prediction and mitigation framework that tightly integrates real-time network monitoring, early congestion anticipation, and adaptive traffic rerouting. The approach predicts congestion using aggregated link utilization and delay metrics over sliding windows and proactively installs alternative OpenFlow forwarding rules based on optimized path selection using Dijkstra’s algorithm. Unlike complex predictive models, the proposed method requires no training data and incurs minimal controller overhead, making it suitable for real-world SDN deployments. The framework is implemented using the RYU controller and evaluated in a Mininet-based data center topology. Experimental results demonstrate significant improvements in throughput, packet loss, retransmission rate, and network efficiency compared to shortest-path routing and reactive update schemes. These results highlight the effectiveness of combining lightweight prediction with proactive control, offering a practical and scalable solution for congestion management in modern SDN environments.
Keywords
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