Predictive State-Staging: A Decision-Making Framework for Proactive Fault Recovery in SDN

Modern Software-Defined Networks (SDN) are often constrained by the inherent latency of reactive fault recovery and the memory overhead associated with proactive protection schemes. This paper introduces Predictive State-Staging (PSS), a framework that leverages multi-modal data fusion and Long Short-Term Memory (LSTM) networks to predict network failures before they occur. By integrating asynchronous software logs with synchronous gNMI telemetry and traffic fluctuations, PSS assigns a real-time Probability of Failure (PoF) to network paths. Upon exceeding a critical threshold, the framework initiates “shadow flow table” staging—a just-in-time installation of backup forwarding states using OpenFlow group tables and VLAN-based aggregation. Experimental evaluations on a 14-switch, 36-host testbed indicate that PSS achieves sub-5 ms recovery times, significantly outperforming traditional reactive mechanisms while maintaining a 45% reduction in TCAM occupancy compared to static proactive methods; these results are validated at this testbed scale, and this revision adds confidence intervals, significance tests, and an explicit discussion of the scope of these claims (Section VI). This approach aligns with the stringent Ultra-Reliable Low-Latency Communications (URLLC) requirements of 5G and mission-critical IoT infrastructures.