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.

In this work, authors present a Hardware based strategy for IEEE802.1CB Network Reliability embedded in Automotive Gateways (GW). It is a new approach for HW efficient and cost-effective integration of Frame Replication and Elimination for Reliability (FRER) algorithm within automotive Network-on-Chip / System-on-Chip. In essence, it is a HW architecture that permits to manage the complex integration of IEEE802.1CB within In-Vehicle Networks. The FRER algorithm is split into smaller functionalities which are allocated across the different processing stages of the GW, maximizing device and network performance. The proposed architecture has a strong focus on Functional Safety, introducing important enhancements to overcome IEEE802.1CB limitations identified in the state of the art: data content verification, meaningful network diagnosability and performance verification. It moves a step forward towards fail operational systems and the compliance with ISO 26262, contributing to future autonomous driving networking solutions.

Driver assistance, self-driving, and multimedia systems have two common implications: increasing demand on network bandwidth and the need for more powerful computation nodes. As a result, intra-vehicular networks (IVNs) change their layout. They are built around central nodes connected to the rest of the vehicle via Ethernet. The usage of Ethernet presents a challenge, as it lacks support for deterministic behavior by design. The solution is found within the IEEE Time-Sensitive Networking (TSN) standards, introducing real-time, low-latency, and deterministic communication into the Ethernet ecosystem. These new networked systems need to be thoroughly evaluated with IVN requirements in mind. To assess numerous configurations of IVN setups, in this work, we introduce a novel Environment for Generic In-vehicular Networking Experiments — EnGINE. It allows, among many others, repeatable, reproducible, and replicable TSN experiments with high precision and flexibility, which is not possible to run using proprietary solutions. EnGINE is based exclusively on commercial off-the-shelf components and is orchestrated by a flexible Ansible framework. This approach allows us to configure various topologies emulating realistic IVNs behavior, which is challenging using simulations. Based on available related work, we further address the challenges found in the IVNs. We derive additional requirements for experiments in the TSN domain and present our approach to fulfill them in an experimental setting. We believe that EnGINE provides the ideal environment for TSN network experiments.

Ethernet is increasingly used in areas where time-critical and safety-relevant data are transported over the network along with best-effort flows, for example in intra vehicle networks or industrial networks. The resulting complex network architectures, time-sensitive networking configurations and system interactions are hard to foresee during the design phase. Therefore, it is hard to rule out any violations of flow specifications or timing and reliability requirements, especially in the presence of unpredictable failures.

In this work we introduce the concept of Elastic Queueing Engine (EQE) for Time Sensitive Networking (TSN) which is a new networking-optimized queueing strategy designed to maximize Quality of Service (QoS) and usability of resources in time sensitive networks. The proposed dynamic run-time adaptation of queues to network status exploits usability of resources by introducing three new degrees of elasticity in Hardware (HW), i.e., the system is able to dynamically adapt to events in incoming traffic by optimizing the usage of available resources. The three new degrees of elasticity introduced by EQE are: dynamic queues size, dynamic Internal Priority Value (IPV) management and dynamic Gate Control List (GCL). Apart from inventing the queueing closed-loop control algorithms, EQE synthesizes these three functionalities in HW as co-processors attached to the system CPU to guarantee a real-time response to ingress frames, aimed at contributing towards fail-safe and fail-operational In-Vehicle Network (IVN) solutions. Our results show that as long as there is available memory in the system, EQE for TSN can completely avoid frame drops in scenarios where static queues systems would be compromised, while other network KPIs (maximum latency, cost) remain similar to implementations without the proposed new features.