The computing continuum aims to break the isolation of edge and cloud computing, creating a smooth and heterogeneous infrastructure surface for deploying applications. However, the continuum is practically fragmented, with infrastructure managed in isolation by various parties in a vertical dimension, i.e., edge, fog, and cloud layers, and horizontally, i.e., computing, network, and storage domains. This scenario negatively impacts the fulfillment of the application objectives. We propose inCoord, an intent-aware solution that enables the creation of a unified computing continuum by coordinating its instances to fulfill application objectives. Each instance represents a cluster of (compute, storage, or network) nodes with their own manager component. Traditionally, systems take low-level actions on these instances. In contrast, inCoord learns their emerging behaviors and adapts their managers’ objectives to fulfill the application’s intents. Here, through a Reinforcement-Learning-based Proof of Concept, we show the potential of this system to understand emerging behavior and manage multi-domain, independently managed instances.

The paper demonstrates the VITAL-5G platform capabilities to improve the adoption and effectiveness of 5G and beyond solutions within the Transport & Logistic domain by bridging the knowledge gap between industry stakeholders, network experts, and service developers. Therefore, in this paper, we present four distinct capabilities the VITAL-5G platform offers i) Facilitating deployment of vertical service in the 5G network, ii) Real-time monitoring of network and service performance and iii) Advanced failure diagnostics. IV) Utilization of 5G slices. To enhance the adoption and effectiveness of 5G and beyond solutions within the T&L domain, we showcase how the VITAL-5G platform hides the operational complexity from the experimenters, allowing them to express their network, application, and hardware requirements in a human-readable format, while in turn deploying complex services on the 5G SA infrastructure.

The entertainment and gaming industries continue to evolve, so they are becoming increasingly reliant on advanced network capabilities to deliver immersive, real-time experiences. In-network computing (INC) is a transformative paradigm in the design of the network architecture in 6G networks. It facilitates the offloading of computational tasks from devices to edge nodes and central servers, enabling faster data processing in network nodes and reducing latency and response times for high-demand applications, enhancing efficiency. This way, it will be possible to provide the final users with advanced applications like Augmented Reality (AR) and Virtual Reality (VR). In this paper we introduce a 6G Network Infrastructure testbed designed for application execution and testing, supporting INC, and we present an Adaptive 360° Video Streaming service using INC facilities. The testbed has been designed to represent a vertical application for SLICES-RI and SUNRISE-6G projects. Some numerical results will assesses the performance of this dynamic video streaming system.

This paper presents the design and implementation of an Artificial Intelligence Control Plane (AICP) for deterministic networks, emphasizing the Proof-of-Concept (PoC) demonstration. The AICP framework integrates AI, digital twin technology, and real-time telemetry to manage complex network environments, ensuring reliable and low-latency communication. The PoC showcases the practical viability of the AICP by dynamically adapting to varying network demands and maintaining stringent to the Key Performance Indicator (KPI)s of the service. Extensive testing and real-world simulations highlight the framework's potential to enhance the efficiency and resilience of industrial communication networks.

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.

BGP reconvergence events involving a large number of prefixes may result in the loss of large amounts of traffic. Based on the observation that a very small number of prefixes carries the vast majority of traffic, we propose Power Prefixes Prioritization (PPP) to ensure the routes of these popular BGP prefixes converge first. By doing so, we significantly reduce the amount of traffic lost during reconvergence events. To achieve this, PPP obtains an ordered list of popular prefixes through traffic inspection, and configures the resulting prefix rank in the BGP routers to prioritize the processing and advertisement of BGP routes. We model the benefits of PPP over traditional BGP processing in terms of traffic loss for both generic and a Zipf traffic distribution, and we consider the impact of sampling in the process of obtaining the prefix rank. Applying the mechanism to real traffic traces obtained from WIDE, we show that PPP reduces the amount of traffic lost by an order of magnitude, even when we configure it to use conservative sampling rates. We prototype our proposal in Quagga to show the feasibility of its implementation, and we observe similar traffic loss reduction. PPP can be deployed incrementally, as it is implemented purely as a change in the router-internal BGP processing behavior.

A 5G network provides several service types, tailored to specific needs such as high bandwidth or low latency. On top of these communication services, verticals are enabled to deploy their own vertical services. These vertical service instances compete for the resources of the underlying common infrastructure. We present a resource arbitration approach that allows to handle such resource conflicts on a high level and to provide guidance to lower-level orchestration components.