Dependability-Aware Routing and Scheduling for Time-Sensitive Networking

TL;DR

This paper presents a method to synthesize dependable TSN configurations that ensure real-time, safety, and security requirements using constraint programming and metaheuristics, addressing security and redundancy in safety-critical networks.

Contribution

It introduces a combined approach using constraint programming and simulated annealing for optimal and scalable configuration synthesis in dependability-aware TSN systems.

Findings

Constraint programming finds optimal solutions for small cases.

Metaheuristic provides good solutions for larger cases.

Both methods effectively ensure safety and security requirements.

Abstract

Time-Sensitive Networking (TSN) extends IEEE 802.1 Ethernet for safety-critical and real-time applications in several areas, e.g., automotive, aerospace or industrial automation. However, many of these systems also have stringent security requirements, and security attacks may impair safety. Given a TSN-based distributed architecture, a set of applications with tasks and messages, as well as a set of security and redundancy requirements, we are interested to synthesize a system configuration such that the real-time, safety and security requirements are upheld. We use the Timed Efficient Stream Loss-Tolerant Authentication (TESLA) low-resource multicast authentication protocol to guarantee the security requirements, and redundant disjunct message routes to tolerate link failures. We consider that tasks are dispatched using a static cyclic schedule table and that the messages use the time-sensitive traffic class in TSN, which relies on schedule tables (called Gate Control Lists, GCLs) in the network switches. A configuration consists of the schedule tables for tasks as well as the disjoint routes and GCLs for messages. We propose a Constraint Programming-based formulation which can be used to find an optimal solution with respect to our cost function. Additionally, we propose a Simulated Annealing based metaheuristic, which can find good solution for large test cases. We evaluate both approaches on several test cases.