This paper presents an in-network machine learning (ML) approach for classifying Augmented Reality (AR) and Cloud Gaming (CG) traffic using programmable hardware. Random Forest (RF) models are deployed in a P411P4: Programming Protocol-independent Packet Processors data plane capable of processing Real-time Transport Protocol (RTP) traffic features like Frame Size (FS) and Inter-Frame Interval (IFI) for efficient classification. The classifier marks AR and CG traffic with Explicit Congestion Notification (ECN) codepoints to integrate with the Low Latency, Low Loss, Scalable Throughput (L4S) features of the programmable switch. The RF model prioritizes AR/CG traffic using Differentiated Services Code-Point (DSCP) assignments and modular ECN marking. The classification performance is evaluated using accuracy, precision, recall, and F1-score, while time overhead is assessed based on nodal processing time incurred during deployment by replaying AR/CG traffic. The P4 implementations for P4Pi22https://eng.ox.ac.uk/computing/projects/programmable-hardware/p4pi.(V1Model) and Tofino Native Architecture (TNA) are all publicly available.

The increasing demand for real-time industrial applications demands deterministic communication across networked devices. Time-sensitive networking (TSN) offers reliable performance to meet these requirements, compatible with the current Real-Time Ethernet (RTE) solutions applied in the industry. While TSN ensures determinism inside Ethernet networks, its integration with 5G/6G wireless technologies can overcome flexibility limitations. This paper explores key challenges in integrating TSN with mobile networks, focusing on device synchronization and flow mapping. We introduce a tool aligned with the 3GPP specifications that covers time synchronization across TSN domains via 5G and implements flow mapping based on application-specific quality of service (QoS) requirements. We evaluate these features under various scenarios, including a use case for an industrial production line use case. Our solution is developed as an open-source framework for the OMNeT++ simulator, supporting reproducibility and continuous updates and paving the way for customizable network solutions.

Applying QoE analysis to optimize usage of cellular spectrum is of high interest to mobile network operators. A key challenge is to be able to perform QoE measurement across very different types of apps, from DASH VoD to interactive applications such as Video Conferencing and Cloud Gaming. This paper presents AMVOTS, a QoE measurement system developed by AT&T, which is flexible enough to support a large range of application types and network conditions. We also discuss using AMVOTS as part of a closed loop to prototype QoE-aware radio resource allocation.

Despite having received less attention compared to the control and application plane aspects of software-defined networking (SDN), the data plane is a critical piece of the puzzle. P4 takes SDN datapaths to the next level by unlocking deep programmability through a target-independent high-level programming language that can be compiled to run on a variety of targets (e.g., ASIC, FPGA, and GPU). This paper presents the design and evaluation of our sweet spot approach on SDN datapaths, offering three contending characteristics, namely, performance, portability, and scalability in multiple realistic scenarios. The focus is on our Multi-Architecture Compiler System for Abstract Data Planes proposal, which blends the high-level protocol-independent programmability of P4 with low-level but cross-platform (HW & SW) Application Programming Interfaces brought by OpenDataPlane, this way supporting many different vendors and architectures. Besides the performance evaluation for varying packet sizes and memory lookup tables, we investigate the impact of increasing pipeline complexity ranging from elemental L2 switching to more complex data center and border network gateways. We investigate the scalability for increasing the number of cores and evaluate a novel method for run-time core reallocation. Furthermore, we run experiments on different target platforms (e.g., ×86, ARM, 10G/100G), inducing different ways of packet mangling through specific drivers (e.g., DPDK and Netmap), and compare the results to state-of-the-art datapath alternatives.

Deep programmability of dataplane pipelines is one of the tenets of the evolving Software Defined Networking (SDN) paradigm. Despite recent efforts on high performance programmable devices, achieving fully programmability (protocol independent) of heterogeneous dataplane implementations still pose numerous challenges. The P4 language is emerging as a strong candidate top-down approach to describe a protocol independent datapath pipeline, agnostic to network platforms. Meanwhile, the OpenDataPlane (ODP) project follows an open-source, bottom-up approach seeking multi-architecture APIs to write platform independent dataplane applications. In this paper, we present Multi-Architecture Compiler System for Abstract Dataplanes (MACSAD) as an approach to converge P4 and ODP through a common compilation process delivering portability of dataplane applications without compromising target performance improvements. We validate our prototype implementation through experimental evaluation of L2 and L3 dataplane applications on different target platforms (×86, ×86+DPDK, ARM-SoC).

Software Defined Networking (SDN) strives for deep programmable hardware and software dataplanes without giving up on performance. Domain Specific Languages (DSL) such as P4 seek to provide top-down high-level capabilities to define the datapath pipeline agnostic to the network platform and independent from any network protocols. At the crossroads, bottom-up industry efforts at the OpenDataPlane (ODP) initiative are pursuing open-source multiarchitecture APIs for dataplane programmability across various networking platforms. Towards P4 code reuse for various targets (portability), we propose MACSAD as a compiler system that brings together the higher-level P4 language and the abstract, target-independent ODP APIs. The demo showcases two P4 applications compiled into heterogeneous datapath platforms supporting ODP.