Marco Liess, M.Sc.

With the advent of research on the next generation of
mobile communications 6G, we are engaged in exploring
architecture extensions for Smart Network Interface Cards
(SmartNICs). To enable adaptive, energy-efficient and
low-latency network interfaces, we are prototyping a
custom packet processing pipeline on FPGA-based NICs,
partially based on the open-nic project
(https://github.com/Xilinx/open-nic).

Modern server architectures face constant challenges in
performance and energy efficiency. SmartNICs offer a
promising solution by offloading packet preprocessing and collecting real-time traffic analytics. These capabilities allow servers to dynamically adapt to changing network conditions and processing demands. However, operating at speeds of 100 Gbps generates massive data volumes that require sophisticated monitoring and debugging capabilities.

This thesis focuses on designing and implementing advanced hardware extensions for debugging and tracing SmartNIC packet processing pipelines using Hardware Description Language (HDL). The developed system will provide critical visibility into high-speed packet processing operations and monitoring logic.

• Developing trace collection mechanisms compatible with 100 Gbps line rates
• Engineering efficient solutions for capturing, moving, and storing large volumes of trace data
• Implementing strategies to avoid performance degradation during trace collection
• Applying suitable postprocessing and generating visualizations of key information

Prerequisites

With the advent of research on the next generation of mobile communications 6G, LIS is engaged in exploring architectures and architecture extensions for networking hardware, as well as improving the interaction between SmartNIC hardware, operating system, and application software. As 6G aims to support mission-critical applications, the demand for deterministic, real-time processing within network infrastructure has become paramount. The recent mainline integration of the real-time scheduler in Linux presents a unique opportunity to explore how operating system scheduling decisions directly impact networking performance in time-sensitive environments.

The incoming traffic load and with it the computing requirements on network processing nodes such as edge servers can span multiple magnitudes in a matter of milliseconds and less. This makes the task of load balancing and efficient scheduler decisions increasingly difficult, especially considering additional requirements like priority-awareness.

This thesis investigates the critical intersection of Linux scheduling policies and real-time networking performance. The research goals of this thesis include:

• Evaluating the performance implications of different Linux scheduling policies on networking performance
• Analyzing how scheduling decisions affect deterministic behavior in time-sensitive networking applications
• Assessing efficient load balancing mechanisms and the availability of priority-awareness for specific flows
• Identifying and potentially developing SmartNIC extensions to enhance Linux scheduling decisions

Prerequisites

With the advent of research on the next generation of
mobile communications 6G, we are engaged in exploring
architecture extensions for Smart Network Interface Cards
(SmartNICs). To enable adaptive, energy-efficient and
low-latency network interfaces, we are prototyping a
custom packet processing pipeline on FPGA-based NICs,
partially based on the open-nic project
(https://github.com/Xilinx/open-nic).

To test the performance of a SmartNIC-assisted server
under peak loads and achieve precise measurements of
key performance indicators (KPIs) such as throughput and latency, high performance packet trace replay and measurements are required. As software mechanisms for replay of 100Gbps traffic is difficult, an FPGA-based Network Tester for 100 Gbps links shall be implemented and tested. For this, the Alveo U55C FPGA-based SmartNICs shall be used. A previous implementation for 10Gbps links (FlueNT10G) can be used for reference.

The goal of this work is to implement the required logic modules in HDL (Verilog), integrate these modules into the OpenNIC Shell platform and test the design on the Alveo U55C FPGAs. Additionally, a software-interface to control the network tester and feed the packet traces should be adapted from the FlueNT10G. The design should also be evaluated regarding the performance of the packet replay as well as the precision in throughput and latency measurement.

Prerequisites

With the advent of research on the next generation of
mobile communications 6G, we are engaged in exploring
architecture extensions for Smart Network Interface Cards
(SmartNICs). To enable adaptive, energy-efficient and
low-latency network interfaces, we are prototyping a
custom packet processing pipeline on FPGA-based NICs,
partially based on the open-nic project
(https://github.com/Xilinx/open-nic).

To improve the performance and energy efficiency of a
modern server, SmartNICs can be used to preprocess
incoming packets and gather characteristics on traffic and processing requirements. This information can be used to change the processing behavior of the server and react to the dynamic network and processing requirements. Hereby, a decisive task is the performant and accurate tracking of key metrics to characterize the current state of the server, both regarding the incoming traffic and the computational aspects in the processing resources of the server.

The goal of this work is to implement monitoring logic for key metrics, such as packet arrival rate, pipeline utilization, queue fill levels, etc. as hardware extensions in the SmartNIC using HDL. A key research question of this work targets finding out how accurate tracking of the CPU utilization is possible in the SmartNIC with minimal software-side intrusiveness. A useful tool for future proof and software-defined networking processing in the SmartNIC is the P4 framework. Therefore, a possible integration of the developed monitoring output into the P4 framework (as "externs") should be further evaluated.  This should be accompanied by a P4 runtime in software to control the hardware pipeline in an asynchronous manner over longer timespans.

Prerequisites