Next-generation automotive networks will be characterized by a high number of interconnected sensors, actuators and applications on electronic control units communicating with each other over a high-speed Ethernet backbone network. As these applications have various criticalities, high volumes of fluctuating traffic with different priorities will have to be processed in a reliable and efficient manner. To cope with these challenges, we present Priority-aware Inter-Server Receive Side Scaling (prioRSS), a new SmartNIC-based hardware accelerator designed for automotive compute nodes. prioRSS builds upon Receive Side Scaling and introduces priority-awareness into an intra- and inter-node load balancer. It uses a priority-partitioned indirection table within which flows of the same priority are bundled. Low-latency reconfigurations issued by a Network Health Monitoring software allow for adapting the table content to changing network conditions. Simulative evaluations and comparisons to a priority-unaware version of our design show that prioRSS enables per-priority resource assignments without degrading end-to-end packet latencies while using the same table memory space. Paired with a priority-aware scheduler, end-to-end latencies of high priority flows can be notably reduced compared to average packet latencies, at the expense of lowest priority traffic. The best results are acquired when partitioning the table proportionally to the associated traffic share.

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.

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.

Monitoring and optimizing network performance is essential for data center operators and service providers. To better understand network performance, the throughput limiting factor of TCP connections can be analyzed. Previous research introduces approaches for the offline analysis of root causes of TCP throughput based on previously captured traffic. With increasing computational power and packet processing capabilities on commodity hardware, previously existing analysis limitations get overcome nowadays.This paper presents a scalable tool to analyze the throughput limitation of TCP flows in real-time, i.e., while the tool observes traffic on a network interface. We describe the adaption of existing approaches for TCP throughput limitation analysis and required passive capacity estimation to satisfy online analysis requirements.We evaluate the effectiveness and accuracy of our implementation with a generated data set for different TCP congestion control algorithms and varying network parameters. Furthermore, we survey the performance of our implementation with thousands of concurrent flows and discuss trade-offs and limitations of such sophisticated analysis in real-time.We provide our code as free and open-source.