Publication | Closed Access
Fault-Resilient Topology Planning and Traffic Configuration for IEEE 802.1Qbv TSN Networks
43
Citations
11
References
2018
Year
Unknown Venue
Time-sensitive NetworkingEngineeringNetwork PlanningNetwork AnalysisIeee 802.1QbvTraffic ConfigurationDelay-tolerant NetworkingScalable RoutingSystems EngineeringFault-resilient Topology PlanningTopology ControlComputer EngineeringComputer ScienceSeamless RedundancyNetwork Routing AlgorithmFault-tolerant NetworkSurvivable NetworkEdge ComputingTopology Synthesis ProblemRobust Routing
Time-Sensitive Networking (TSN) is a set of IEEE standards that are being developed to enable a reliable and real-time communication based on Ethernet technology. It supports Time-Triggered (TT) traffic to allow a low latency as well as deterministic timing behavior. TSN adapts the concept of seamless redundancy to ensure interruption-free fault-resilience. In this paper, our goal is to synthesize a network topology that supports seamless redundant transmission for TT messages. Therefore, we propose a greedy heuristic algorithm for joint topology, routing, and schedule synthesis. The proposed algorithm is capable to generate fault-resilient topology that guarantee feasible routing and scheduling for TT traffic. In particular, the topology is constructed iteratively such that all messages are routed through disjoint paths with a feasible schedule and the network cost is minimized. To achieve this goal, we formulate the topology synthesis problem as iterative path selection problem. Starting from a weighted undirected graph which represents an initial fully-connected network, the cost implied of using each link is mapped as arcs weights in the graph. Then, we adapt Yen's algorithm to iteratively find the minimum-cost paths for the considered messages. The scalability and the efficiency of the proposed approach are demonstrated using 380 synthetic test cases. The results show that the proposed approach is capable of finding fault-resilient topology with up to 50% less cost compared to the typical approach. Moreover, the approach scalability is validated e.g., it handles 24 ECUs with 600 messages problems within an average time of 8 sec.
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