Efficient Exploration on Worst-Case Delay Performance of Networked Industrial Control Systems via Network Calculus and Deep Learning

As computer networking technologies such as Ethernet gain momentum in modern industrial control systems (ICSs), deterministic delay performance, which refers to the worst-case latencies provisioned by the network infrastructure, has become a critical property required by real-time control, automation, and operations. Although analytical methods that identify exact worst-case bounds on flow-specific communication delays have been proposed, there remain two hurdles discouraging further explorations of network-wide deterministic delay performance: (i) methods based on mixed integer linear programming (MILP) have high computational complexities; and (ii) existing methods are flow-specific. This paper proposes a deep-learning-assisted approach to understanding the deterministic delay performance of networked industrial control systems. Transforming flow-specific worst-case delay bounding into network-wide deterministic delay analysis, our approach facilitates the incorporation of application-specific hard-real-time performance constraints. By incorporating graph neural networks (GNNs), our approach significantly reduces the runtime costs and provides sufficiently tight approximations to the optimal MILP solutions. Through a combination of numerical analyses and simulations, we demon-strate that our approach enables the timely exploration of various worst-case scenarios, thereby paving the way for agile service provisioning in time-critical and/or delay-sensitive ICSs as well as model-based design of self-healing networked ICSs.