6G networks are expected to deliver ultra-low latency, high reliability, and real-time intelligence for emerging services such as interactive Augmented Reality (AR), autonomous robotics, and digital twins. Achieving these requirements in practice demands tight coordination between networking, computing, and control domains, spanning RAN, transport, edge, and cloud. However, current 5G deployments lack pervasive telemetry, fine-grained observability, and automated control mechanisms capable of reacting at the time scales required by latency-sensitive applications. This paper presents a full integrated demonstration of DESIRE6G, a cloud-native 6G-ready architecture that leverages programmable data planes with P4 for flexible routing and telemetry using an implementation of novel data plane protocols, achieves distributed optimization of service deployment and runtime monitoring and reconfiguration via secure multi-agent systems (MAS), combined with intent-based orchestration layer for end-to-end service assurance. The system is validated on the federated ARNO testbed using a real distributed AR application involving a remotely-controlled drone as a User-Equipment that is equipped with a camera streaming a live video through the DESIRE6G network to a Kubernetes edge cluster that executes serverless inference functions for object detection and recognition, the video is then augmented with object information and shown on a Quest 3 AR headset. The MAS monitors the end-to-end latency in real time through P4 Telemetry and responds to changes in network conditions by reconfiguring the affected segments, while the Kubernetes monitoring provides real-time visibility and scalability across different segments. Overall, three hierarchical service assurance loops are demonstrated: (i) In-Network Control (INC) executing microsecond-scale congestion recovery in the P4 data plane, (ii) Infrastructure Management Layer (IML) performing millisecond-scale function migration and scaling, and (iii) MAS-driven cross-domain optimization operating at sub-second time scales to resolve RAN latency anomalies. Evaluation results show stable end-to-end latency in the 15–25 ms range in steady-state conditions, with fast recovery during induced congestion ≤ 1 . 5 ms data plane reroute via P4 INC.

The EU-funded EMPYREAN project (empyrean-horizon.eu) aims to establish a hyper-distributed computing paradigm, leveraging collaborative, heterogeneous IoT devices and federated resources. EMPYREAN focuses on developing technologies for efficient AI workload processing, secure distributed edge storage and cloud-native application development. It will offer open and standardised APIs and use open-source platforms. EMPYREAN's capabilities will be demonstrated through three use cases: advanced manufacturing, smart agriculture, and warehouse automation.

The need for real-time analytics and faster decision-making mechanisms has led to the adoption of hardware accelerators such as GPUs and FPGAs within the edge cloud computing continuum. However, their programmability and lack of orchestration mechanisms for seamless deployment make them difficult to use efficiently. We address these challenges by presenting SERRANO, a project for transparent application deployment in a secure, accelerated, and cognitive cloud continuum. In this work, we introduce the SERRANO platform and its software, orchestration, and deployment services, focusing on its methods for automated GPU/FPGA acceleration and efficient, isolated, and secure deployments. By evaluating these services against representative use cases, we highlight SERRANO 's ability to simplify the development and deployment process without sacrificing performance.

We present an end-to-end solution to facilitate the seamless execution of hardware-accelerated compute-intensive tasks on heterogeneous hardware platforms spanning the Cloud-Edge continuum. Our approach includes a programming interface, orchestration, application management components, the vAccel framework, and a library of hardware-accelerated kernels. These components enable a Function-as-a-Service (FaaS) based operational flow that supports numerous diverse use cases while minimizing the time required for the developer to integrate their code and for the vendor to provide hardware acceleration capabilities to end users. Experimental results showcase the merits of our approach.

6G's superpower must be simplicity, which should not be viewed as a constraint, but rather as the organic outcome of using the most advanced technologies at our disposal. Programmability in the data plane, cloud-native features like automatic scaling and failover, transparent acceleration of both network functions and applications and AI-driven optimizations are already present. We only need to integrate these different innovations into a consistent architecture and offer a simple yet powerful solution for the very different applications that would use future mobile networks. The application space is getting more and more heterogeneous, e.g., legacy Internet-based services still using the good old TCP protocol, future media services relying on multipath transport - always utilizing the best available connection, or control applications of robots or drones requiring extreme low and stable latency. The different applications will require very different Key Performance Indicators (KPIs) from the network. In this paper, we present a novel architecture called DESIRE6G (D6G) architecture that aims to fulfill these requirements by integrating the key technological innovations mentioned above. Besides supporting the diverse KPIs of future applications, the novel architecture should also simplify the mobile network itself by promoting modularity and service-based network function selection which can replace traditional control plane centric solutions, e.g., for handover.

Big Data applications receive an ever-increasing amount of attention, thus becoming a dominant class of applications that are deployed over virtualized environments. Cloud environments entail a large amount of complexity relative to I/O performance. The use of Big Data increases the complexity of I/O management as well as its characterization and prediction: As I/O operations become growingly dominant in such applications, the intricacies of virtualization, different storage back ends and deployment setups significantly hinder our ability to analyze and correctly predict I/O performance. To that end, this work proposes an end-to-end modeling technique to predict performance of I/O--intensive Big Data applications running over cloud infrastructures. We develop a model tuned over application and infrastructure dimensions: Primitive I/O operations, data access patterns, storage back ends and deployment parameters. The trained model can be used to predict both I/O but also general task performance. Our evaluation results show that for jobs which are dominated by I/O operations, such as I/O-bound MapReduce jobs, our model is capable of predicting execution time with an accuracy close to 90% that decreases as application processing becomes more complex.

Providing scalable clustered storage in a cost-effective way depends on the availability of an efficient network block device (nbd) layer. We study the performance of gmblock, an nbd server over Myrinet utilizing a direct disk-to-NIC data path which bypasses the CPU and main memory bus. To overcome the architectural limitation of a low number of outstanding requests, we focus on overlapping read and network I/O for a single request, in order to improve throughput. To this end, we introduce the concept of synchronized send operations and present an implementation on Myrinet/GM, based on custom modifications to the NIC firmware and associated userspace library. Compared to a network block sharing system over standard GM and the base version of gmblock, our enhanced implementation supporting synchronized sends delivers 81% and 44% higher throughput for streaming block I/O, respectively.