Data Distribution Strategies for Mixed Traffic Flows in Software-Defined Networks: A QoE-Driven Approach
Hongming Li, Hongming Li, Hao Li, Hao Li, Yuqing Ji, Ziwei Wang · Applied Sciences · 2025
The rapid proliferation of heterogeneous applications, from latency-critical video delivery to bandwidth-intensive file transfers, poses increasing challenges for modern communication networks. Traditional traffic engineering approaches often fall short in meeting diverse Quality of Experience (QoE) requirements under such conditions. To overcome these limitations, this study proposes a QoE-driven distribution framework for mixed traffic in Software-Defined Networking (SDN) environments. The framework integrates flow categorization, adaptive path selection, and feedback-based optimization to dynamically allocate resources in alignment with application-level QoE metrics. By prioritizing delay-sensitive flows while ensuring efficient handling of high-volume traffic, the approach achieves balanced performance across heterogeneous service demands. In our 15-RSU Mininet tests under service number = 1 and offered demand = 10 ms, JOGAF attains max end-to-end delays of 415.74 ms, close to the 399.64 ms achieved by DOGA, while reducing the number of active hosts from 5 to 3 compared with DOGA. By contrast, HNOGA exhibits delayed growth of up to 7716.16 ms with 2 working hosts, indicating poorer suitability for latency-sensitive flows. These results indicate that JOGAF achieves near-DOGA latency with substantially lower host activation, offering a practical energy-aware alternative for mixed traffic SDN deployments. Beyond generic communication scenarios, the framework also shows strong potential in Intelligent Transportation Systems (ITS), where SDN-enabled vehicular networks require adaptive, user-centric service quality management. This work highlights the necessity of coupling classical traffic engineering concepts with SDN programmability to address the multifaceted challenges of next-generation networking. Moreover, it establishes a foundation for scalable, adaptive data distribution strategies capable of enhancing user experience while maintaining robustness across dynamic traffic environments.