DynaHyEdge: Fine-Grained Privacy-Aware Online Scheduling for Hybrid Edge Services

Edge clouds must increasingly co-serve privacy-critical streams (e.g., per-user telemetry and industrial control loops) and best-effort utility services (e.g., large language model inference and augmented-reality rendering) on the same constrained nodes to meet strict latency targets and sustain resource utilization. Operating them on disjoint server pools satisfies privacy requirements but leaves capacity restricted because private demand is substantial. Naive colocation improves utilization but cannot offer hard service-level agreements (SLAs) or data-residency guarantees. Hence, we propose DynaHyEdge, a hybrid scheduler that 1) continuously partitions capacity between private and public domains, 2) enforces per-core time isolation with microsecond domain flips, and 3) uses event-driven admission to utilize idle computational resources without preemption. This joint design maximizes on-time completion while provably meeting private-task SLAs, keeping sensitive data local, and reclaiming otherwise idle computational resources. Experiments on real-world and synthetic traces show that DynaHyEdge increases the deadline success rates, decreases the latency, and increases CPU utilization over greedy, fixed-partition, and earliest-deadline-first baselines without compromising privacy.