Priority-Aware SDN Orchestration for Surgical IoMT: A Joint Optimization of Hit Ratio and Latency With Dynamic Resource Reallocation

Surgical Internet of Medical Things (IoMT) applications impose stringent Quality of Service (QoS) demands, requiring ultra-low latency and high reliability for critical data streams like control signals or high-definition video. Conventional resource orchestration methods often fall short, failing to guarantee performance for high-priority surgical tasks, particularly under dynamic network conditions, including varied wireless channel quality, and potential congestion. This paper introduces the Priority-Aware SDN Orchestration (PASO) framework, leveraging Software-Defined Networking (SDN) for centralized network visibility and control to address these critical challenges. PASO implements a novel joint optimization strategy focused primarily on maximizing the completion rate of critical surgical tasks that adhere strictly to their maximum delay constraints. A key distinguishing feature is its dynamic resource reallocation mechanism, which employs the preemption of lower-priority, non-medical traffic to proactively secure network resources and guarantee QoS for vital surgical flows, even during periods of high network load. A computationally efficient, priority-aware greedy algorithm operationalizes this strategy on the SDN controller. Comparative simulations against Random, Particle Swarm Optimization (PSO), Simulated Annealing (SA), and a Deep Reinforcement Learning (DRL) agent as baselines demonstrate PASO’s substantial effectiveness. It significantly reduces end-to-end delay (e.g., up to 70% less than PSO under high load) and drastically improves the completion rate for critical surgical tasks (over 80% higher than PSO under high load), while also promoting balanced network utilization. PASO offers a robust solution for dependable, low-latency surgical IoMT operations through its targeted optimization and preemptive resource management capabilities.