Infrastructure-Aware Runtime Protection of Co-Deployed AI Services at the Industrial Edge

Abstract

In modern production lines, a single edge device simultaneously runs multiple AI services like a torque-control model, a vision inspector, a predictive maintenance routine. All these services run on the same CPU and same shared network segment. When the shared network segment saturates, the services do not crash. The services just produce outputs that are inaccurate or stale, and no longer reflect the plant's conditions. Our paper targets this class of silent, partial failure scenarios in Industrial Edge Systems. We treat each co-deployed service as an autonomous agent. We introduce the Runtime Criticality Score (RCS), which each service agent uses to express at any moment, its intrinsic utility and survival urgency under resource constraints. The score is computed from six normalized dimensions covering physical consequence, inference quality, input freshness, recent availability stress, actuator coupling, and available protective actions on the host. Its dimension weights are adjusted online through a lightweight Bayesian update driven by the type of disturbance currently observed, so the same service is ranked differently under a network delay than under thermal throttling. Agents publish their RCS to peers over an open, peer-to-peer discovery channel, and use it to negotiate three things without a central controller: (i) local threshold enforcement on their own host, (ii) migration to a willing peer when the current host can no longer protect them, (iii) reassignment of the coordination role when the incumbent coordinator is itself impaired. Beyond the immediate benefit of catching silent degradation, the framework contributes a concrete instance of decentralized, physically grounded agent coordination. Agents belonging to different owners can advertise capabilities, bid for protection, and reorganize across an open network.