Run-Time Recovery and Failure Analysis of Time-Triggered Traffic in Time Sensitive Networks

Reliability is one of the major concerns of Time Sensitive Networking (TSN). Current systems mostly rely on static redundancy to protect functionality from permanent component failures. This greatly increases the cost of Time-Triggered (TT) flows. Instead, Software Defined Networking (SDN) enables dynamic redundancy. Disrupted traffic can be rerouted by a centralized controller to reduce the cost while maintaining reliability. This paper presents an approach to compute alternative paths at run-time and analyze their impact on reliability. We define a novel three-mode recovery scheme, which includes full functionality, reduced functionality, and emergency halt modes. Run-time recovery for TT flows is explored using Integer Linear Programming (ILP) and a heuristic algorithm. Then, a Markov chain-based design-time reliability analysis is developed to evaluate the Mean Time to Reduced Functionality Mode (MTTRF) and Mean Time to Failure (MTTF) of run-time recoverable systems. Our experiments show that run-time recovery provides better protection against multi-point failures than static redundancy. Compared with the state of the art, our proposed ILP has better routing efficiency. The proposed heuristic algorithm can perform routing and scheduling in polynomial time, but it tends to route multicast flows to longer paths than ILP. Furthermore, when applied to realistic recovery scenarios, our proposed ILP improves the MTTF by up to $2\times $ and the average execution time by up to $20\times $ than the raw ILP of the state of the art. Although less efficient with multicast flows, the heuristic algorithm achieves similar reliability as the ILP, and its worst-case recovery time is below $100ms$ on an embedded ARM processor.