AI-driven Network Function Virtualization (NFV) service chaining is emerging as a key enabler for automation in edge-to-cloud infrastructures. However, existing standards and orchestration frameworks lack mechanisms for fairness, coordination, and dynamic resource sharing across distributed AI agents. This paper proposes a hierarchical architecture that integrates adaptive agent placement and fairness-aware scheduling for AI-based NFV coordination. At its core, the Multi-Agent Dynamic Bandwidth Environment (MADBE) framework leverages deep reinforcement learning to enable agents to collaboratively allocate shared bandwidth while meeting latency and throughput constraints. Experimental results demonstrate that MADBE significantly improves convergence speed, reduces conflict rates, and maintains bandwidth utilization near the 90% threshold, outperforming state-of-the-art baselines. Moreover, MADBE sustains near-zero violation rates for service-level constraints, even under dynamic and heterogeneous workloads. These results highlight the potential of fairness-aware, multi-agent coordination in next-generation 5G/6G networks and industrial automation environments.

In industrial multi-access edge computing (MEC), novel collaborative systems involving multiple automated guided vehicles (AGVs) require the execution of both critical tasks and computational tasks, such as AI-based collision avoidance. However, previous research focused mainly on scheduling process-related tasks and overlooked computational tasks. Moreover, scheduling tasks between AGVs in collaborative systems poses an integer problem, which is challenging to solve in polynomial time, highlighting the need for computationally efficient algorithms. This paper proposes a deep reinforcement learning (DRL)-based approach to multi-task scheduling (DRL-MTS) in multi-AGV systems. It involves dynamically applying a catalog of DRL models, each tailored to different numbers of AGVs. Evaluation results demonstrate that the proposed inter-AGV DRL-MTS strategy closely approaches the optimal solution, reaching up to 96% task completion compared to a 98% of the optimal solution, while significantly reducing decision times. Moreover, the training time for these models has been reduced threefold using datasets from existing optimization solvers, and transfer learning has further cut training times by up to 51%.

Resource distribution among diverse administrative domains, network operators, and geographical locations across the edge-to-cloud continuum requires suitable communication and management and orchestration (MANO) mechanisms among orchestration domains. In multi-access edge computing (MEC) environments, efficient application lifecycle management and system federation are essential for scalability, optimal resource utilization, and ensuring service continuity and reliability. Existing orchestration solutions, typically designed for centralized cloud architectures, often fall short in accommodating application delay requirements and in managing the dynamic and distributed nature of MEC resources effectively. This paper introduces a cloud-native, platform-agnostic MEC Orchestrator (MEO) with enhancements over the ETSI MEC architecture, albeit aligned with GSMA and ETSI MEC federation standards, that supports cross-platform MANO and resource controllability. Federation is supported through a new MEO-to-MEO interface that enables application migration across MEC systems. Experimental results demonstrate a 95% instantiation success rate for instantiation, overperforming the baseline Kubernetes scheduler, and 93.3% for migration requests within federated MEC systems in high request volume scenarios.

The advancement towards B5G/6G relies on the synthesis of connect-compute platforms and their use in highly heterogeneous clusters featuring hardware accelerators. While these accelerators offer improved computational efficiency, sill, they make development, deployment, and orchestration of services more complex, with limited flexibility, and necessitate domain-specific knowledge. In AI@EDGE we are targeting seamless integration of such diverse platforms for executing AI-related tasks. This paper focuses on acceleration aspects and presents a MEC system that facilitates AI servicing over a cluster of FPGA, GPU, and CPU nodes. To this end, we develop our custom tools for generating multi-variant AI models, informative function descriptors, flexible MEC orchestrators, and runtime resource managers. The results show successful interoperability, with generic Python models getting deployed/migrated across distinct platforms for performance gains in the area of 10x.

Virtualization in cellular networks is one of the key areas of research where technologies, infrastructure and challenges are rapidly changing as 5G system architecture demands a paradigm shift. This paper aims to study the viability and the performance of cloud-native infrastructures for hosting network functions. The selected frameworks implement both the 4G and the 5G stacks and their network functions. This work considers a variety of scenarios for enabling the deployment of a distributed and open-source cellular network: a baremetal setup, an all-docker-based setup and the proposed Kubernetes setup. Moreover, an analysis of the impact that the Radio Access Network (RAN) and the Core Network (CN) have on computational resource utilization is presented as the network conditions vary. The design proposed in this work has been validated and analyzed using the proposed prototype and testbed. This paper proposes a design to increase resource usage flexibility and performance and reduction of deployment time. The analysis of the gathered data reveals that the deployments of containerized cellular networks display better performance in terms of flexibility, low startup times, and ease of deployment while consuming the same resources as the non-containerized.

Edge to Cloud Continuum is a concept that integrates cloud computing and cellular networks that has been gaining popularity due to its potential to provide a seamless user experience and address the challenges of managing complex multi-domain networks involving massive IoT devices. Enabling intelligence in the Edge to Cloud Continuum can further enhance its capabilities, offering benefits such as reduced latency, improved scalability, enhanced resource utilization, and increased context awareness. This paper provides insights into the opportunities and challenges of enabling intelligence in Edge to Cloud Continuum, highlighting the potential of this technology. This study presents a comprehensive review of the existing literature on enabling intelligence in Edge to Cloud Continuum, to reach the research questions that will construct the PhD. Various tools and technologies that can be used to integrate intelligence into the Edge to Cloud Continuum system were explored and analyzed. In addition, this study provides a detailed work plan for the upcoming months of the project.

Domain-specific languages (DSLs) are small languages tailored to a certain application area, like logistics, web application testing or smart city planning. Traditionally, the use of DSLs has been limited to a static setting in desktop or web editors. However, in this paper, we claim that DSLs can be central components of mobile collaborative applications. In our vision, graphical DSLs can be extended to make use of mobility and context, and integrate heterogeneous information gathered from open APIs. We call this new generation languages "active DSLs".We foresee a range of scenarios where active DSLs can be useful. On the one hand, they can be used more flexibly in remote locations by enabling local collaboration of several mobile devices using their short-range communication capabilities. On the other hand, they can be extended with contextual features like geolocation, allowing the integration of maps and geo-services within the DSL, or the DSL rendering customization in response to contextual information. Active DSLs can also retrieve information from open APIs, in which case, models defined with the DSL become aggregators of heterogeneous data.In this paper, we explain our vision for active DSLs and the first steps towards its realization in the DSL-comet tool. The tool permits creating and using mobile graphical DSLs on iOS devices, and their seamless use in desktop environments.