Digital Twins (DTs) are emerging as key enablers for Connected and Autonomous Vehicles (CAVs), offering virtual representations that support various applications ranging from offline, large-scale traffic analysis to real-time driver assistance. These use cases pose significantly diverse Quality of Service (QoS) requirements on DTs, including ultra-low latency for real-time synchronization with the physical counterparts. Deploying DTs at the network edge offers a promising solution, considering the increasingly advanced compute and network resources potentially available in a city-wide infrastructure. However, edge deployments introduce additional complexity: DT developers must deal with heterogeneous resources, optimize their usage for different QoS levels, and handle vehicle mobility. That process requires a high level of specialization and makes development time-consuming and error-prone. In this paper, we first introduce a DT communication model based on three key interfaces: to physical devices, to peer DTs, and to centralized applications. We then analyze the distinct QoS requirements of these interfaces and propose the adoption of a data distribution platform that maps them directly to edge network capabilities, hiding complexity and easing the DT development process. Early evaluations on a real testbed demonstrate the platform's potential to meet CAV DTs' QoS demands efficiently.

Time synchronization is crucial in the architecture of modern communication networks, supporting numerous high-stakes applications like financial transactions, autonomous vehicle control, and data center operations. While traditional time synchronization protocols, specifically the Network Time Protocol (NTP) and Precision Time Protocol (PTP), are reliable for various applications, they fall short in scenarios requiring ultra-high precision and resilience. To address these limitations, this paper provides a comprehensive comparative analysis of two emerging quantum technologies, namely Time-Correlated Entangled Photons (TCEP) and Optical Lattice Clocks (OLC). Using Monte Carlo simulations, we examined the synchronization in terms of the accuracy of these technologies under various noise conditions, revealing that while TCEP works perfectly in low-noise environments, its efficacy diminishes significantly with increasing noise levels. On the contrary, OLCs demonstrate consistent performance across various noise levels, making them more versatile for diverse application scenarios. This study is foundational for integrating quantum technologies in time synchronization for communication networks and sheds light on their merits and challenges. Our findings open new avenues for research in scalability, environmental resilience, and the development of hybrid quantum-classical timekeeping systems. Integrating quantum-enhanced time synchronization into a communication network will be the key step for achieving full-fledged Quantum Internet and quantum-enhanced communication networks.

The emerging paradigm of resource disaggregation enables the deployment of cloud-like services across a pool of physical and virtualized resources, interconnected using a network fabric. This design embodies several benefits in terms of resource efficiency and cost-effectiveness, service elasticity and adaptability, etc. Application domains benefiting from such a trend include cyber-physical systems (CPS), tactile internet, 5G networks and beyond, or mixed reality applications, all generally embodying heterogeneous Quality of Service (QoS) requirements. In this context, a key enabling factor to fully support those mixed-criticality scenarios will be the network and the system-level support for time-sensitive communication. Although a lot of work has been conducted on devising efficient orchestration and CPU scheduling strategies, the networking aspects of performance-critical components remain largely unstudied. Bridging this gap, we propose KuberneTSN, an original solution built on the Kubernetes platform, providing support for time-sensitive traffic to unmodified application binaries. We define an architecture for an accelerated and deterministic overlay network, which includes kernel-bypassing networking features as well as a novel userspace packet scheduler compliant with the Time-Sensitive Networking (TSN) standard. The solution is implemented as tsn-cni, a Kubernetes network plugin that can coexist alongside popular alternatives. To assess the validity of the approach, we conduct an experimental analysis on a real distributed testbed, demonstrating that KuberneTSN enables applications to easily meet deterministic deadlines, provides the same guarantees of bare-metal deployments, and outperforms overlay networks built using the Flannel plugin.

Edge cloud computing is a promising programming and deployment paradigm to empower delay-sensitive applications. By executing close to the network edge, distributed applications can have quicker reactions to event occurrence and consequently prompter dynamic adaptations. In addition, recent improvements in connectivity support allow developers to benefit from heterogeneous and alternative communication technologies (e.g., RDMA, DPDK, XDP, etc.) to meet the requirements of network-intensive edge applications. However, exploiting these technologies makes applications statically tailored to a specific network interface; this significantly limits the potential of edge cloud computing, where application components should be able to migrate seamlessly at runtime. INSANE aims at solving that issue by exposing a technology-agnostic middleware API that lets developers simply specify their QoS communication requirements; the dynamic selection of the most appropriate technology on the currently hosting edge node is delegated to INSANE. The paper also presents how it is possible to develop two different INSANE-based applications (a decentralized messaging system and an image streaming framework) with a few lines of code. Finally, an extensive performance evaluation shows that our middleware adds very limited ns-scale overhead to the raw acceleration technologies.

The recent trend of moving cloud computing capabilities to the edge of the network is reshaping the way applications and their middleware supports are designed, deployed, and operated. This new model envisions a continuum of virtual resources between the traditional cloud and the network edge, which is potentially more suitable to meet the heterogeneous Quality of Service (QoS) requirements of the supported application domains. Yet, mission-critical applications such as those in manufacturing, automation, or automotive, still rely on communication standards like the Time-Sensitive Networking (TSN) protocol and 5G to ensure a deterministic network behavior: in this context, virtualization might introduce unacceptable network perturbations. In this paper, we demonstrate that latency-sensitive applications can execute in virtual machines without disruptions to their network operations. We propose a novel approach to support the TSN protocol in virtual machines through a precise clock synchronization method and we implement it in integration with state-of-the-art and highly-efficient network virtualization techniques. Our experimental results show that it is possible to achieve deterministic and ultra-low latency end-to-end communication in the cloud continuum, for example providing a guaranteed sub-millisecond latency between remote virtual machines.

Next-generation AI applications benefit from executing close to the network edge to better exploit co-locality to datasources and controlled actuators, and to meet stringent latency requirements. In the edge-enabled cloud continuum, time and safety-critical traffic coexists with best-effort flows, resulting in heterogeneous requirements that current networking middleware and frameworks struggle to support. This paper proposes INSANE, INtegrated Selective Acceleration at the Network Edge, the first edge-oriented middleware that integrates different network acceleration techniques (XDP, DPDK, RDMA, and TSN) within the same data distribution service. INSANE offers a uniform and simple interface, useful to support common data distribution patterns, that allow developers to exploit at runtime the most suitable network technology available in the dynamically determined deployment environment.

The extension of cloud computing concepts to edge devices will lead to the coexistence of a wide range of applications with heterogeneous quality of service (QoS) requirements. Hence the need to move towards a more fluid model based on a continuum of virtual resources. In this paper, we propose a network virtualization model to support applications with ultra-low latency communication requirements and finally compare our results with those of a physical network.

Industrial networked computing environments are expected to serve a wide range of applications with heterogeneous Quality-of-Service (QoS) requirements. This capability demands for novel, QoS-aware network management and configuration techniques resilient in the face of network changes. State-of-the-art approaches only focus on aspects related to the management of network devices. In this work, we move a step further, proposing an end-to-end QoS management approach in Time-Sensitive Networking (TSN) compliant networks, capable of handling reconfiguration events e.g., link-drop. Shedding some light on our proposal, we first discuss its functional building blocks, successively validating the approach on a real TSN testbed.