Time sensitive network applications, for example in Intra-Vehicular Networks, aim to give predictable end-to-end latency guarantees. As a consequence, processing resources of involved host systems remain partially unused, because they are reserved for rare worst cases. This circumstance provides the opportunity to reduce dimensioning overheads by managing the load on the nodes flexibly within the network. In our proposed approach, a SmartNIC involving an FPGA-based load balancer achieves dynamic routing of flows whilst preserving end-to-end latency guarantees. A flow-oriented online network measurement component continuously supervises network traffic with regards to compliance to flow specifications and constraints such as bounded one-way delay, absence of packet loss, and jitter. We use the supervisor to enhance forwarding decisions on the data plane. Initial evaluation yields a saving potential of around 30 %. We showcase quick dynamic reconfiguration of the FPGA when triggered by real-time measurement of the one-way delay using realistic automotive network traffic.

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Self-driving and multimedia systems have common implications: increased demand on network bandwidth and computation nodes. To cope with the current and future challenges, intra-vehicular networks (IVNs) change their layout. They are built around powerful central nodes connected to the rest of the vehicle via Ethernet. The usage of Ethernet presents a challenge, as it by design lacks support for deterministic behavior, which is crucial for real-time systems. Therefore, the IEEE Time-Sensitive Networking (TSN) task group offers standards introducing low-latency and deterministic communication into Ethernet based networks allowing coexistence of best-effort and real-time traffic. To understand the coexistence challenges, these new networked systems need to be thoroughly evaluated with IVN requirements in mind. To assess various topologies, configurations, and data traffic types in IVN setups, we introduce Environment for Generic In-vehicular Networking Experiments—EnGINE. It allows, among many others, repeatable, reproducible, and replicable TSN experiments with high precision and flexibility. EnGINE is based on commercial off-the-shelf hardware and uses the flexible Ansible framework for experiment orchestration. This allows us to configure various topologies emulating realistic behavior of IVNs or other time sensitive systems used, e.g., in industrial automation. Obtaining such realism is challenging using simulations. Based on available related work, we further address the challenges found in those networks, especially IVNs. We derive TSN domain framework requirements, provide details on design decisions for the EnGINE, and present results to show its capabilities. The results present relevant network metrics based on collected data. A key focus is on the experiment campaigns realism achieved by real IVNs’ data footage and the OS optimizations to offer real-time behavior. We believe that EnGINE provides the ideal environment for TSN experiments from different domains.