The increasing complexity of cloud-native 6 G networks necessitates intelligent resource management to optimize scalability, energy efficiency, and service reliability. This paper presents an AI-driven self-healing mechanism for dynamic server activation within the a cloud-native system. The proposed framework integrates three key frameworks: the Management and Orchestration Framework (MOF) for policy-based network service orchestration, the Cloud Continuum Framework (CCF) for dynamic resource scaling, and the Artificial Intelligence and Machine Learning Framework (AIMLF) for predictive analytics and anomaly detection. By leveraging AI models, the system continuously monitors workload variations, forecasts resource demand, and dynamically scales computing resources, ensuring optimal energy efficiency and SLA compliance. The proposed self-healing workflow enables proactive server activation and deactivation, addressing load bursts and underutilization scenarios. Numerical evaluations, including real-world traffic data analysis, demonstrate that our approach significantly improves power consumption, load balancing, and resource utilization compared to traditional static resource allocation methods.

The evolution of cloud computing towards a cloud continuum, including cloud, edge, and far-edge resources, is revolutionizing the deployment, management, and orchestration of Network Services (NSs) and applications. Traditional, centralized orchestration approaches are increasingly inadequate for handling the complexity, scale, and dynamic nature of this continuum. In this paper, we present a data-driven approach for AI-powered service orchestration based on the European 6G-CLOUD project. Specifically, we introduce the Decentralized Service Orchestrator (DSO) framework, an AI-powered, decentralized orchestration model that leverages the capabilities of the Artificial Intelligence and Machine Learning Framework (AI/MLF) to enable intelligent, autonomous, and scalable service lifecycle management across heterogeneous environments. Key contributions include the detailed architecture of the DSO, its workflows, and its integration with the Cloud Continuum and with an AI/MLF that manage the AI lifecycle, enabling models provision to the different components. By enabling decentralized AI-driven decision-making, this framework enhances service reliability, scalability, operational efficiency, and innovation acceleration, paving the way for next-generation cloud continuum orchestration.

The energy consumed by buildings is expected to significantly rise in the upcoming years, necessitating intelligent Home Energy Management Systems (HEMS) that create comfortable conditions for their inhabitants, while also offering sustainable and cost-effective solutions. The building environment, however, includes multiple time-varying parameters that cannot be controlled, such as the output of renewable energy sources, the market-dependent electricity prices, the outdoor temperature, as well as the occupants’ energy habits. To overcome these barriers, we propose a hybrid Machine Learning (ML) algorithm for smart HEMS control, leveraging the properties of a decision-making deep deterministic policy gradient model, enhanced by the predictive capabilities of long short-term memory networks. Hence, the proposed algorithm aims to achieve an optimal balance between energy cost and occupant comfort by continuously adjusting the energy provided to the heating, ventilation, and air conditioning system, as well as controlling the energy storage system of the smart home. The proposed hybrid method is validated with simulations using real-world data and compared against baseline approaches, showcasing its effectiveness to achieve an optimal trade-off between the indoor temperature deviation and the average energy cost.

Current and upcoming data-intensive Mission Critical (MC) applications rely on high Quality of Service (QoS) requirements related to connectivity, latency and network reliability. Beyond 5G networks shall accommodate MC services that enable voice, data and video transfer in extreme circumstances, for instance in occurrence of network overloads or infrastructure failures. In this work, we describe the specifications of the architectural framework that enables the roll-out of MC services over 5G networks and beyond, considering recent technological advancements of cloud-native functionalities, network slicing and edge deployments. The network architecture and the deployment process is described in three practical scenarios, including a capacity increase in the service load that necessitates the scaling of the computational resources, the deployment of a dedicated network slice for accommodating the stringent requirement of a MC application and a service migration scenario at the edge to cope with critical failures and QoS degradation. Furthermore, we illustrate the implementation of a Machine Learning (ML) algorithm that is used for overload prediction, validating its ability to predict the capacity increase and notify the components responsible to trigger the appropriate actions, based on a real dataset. To this end, we mathematically define the overload detection problem, as well as generalized prediction tasks in emergency situations and examine the key parameters (proactiveness ability, loockback window, etc.) of the ML model, also comparing its predictions abilities (~93% accuracy in overload detection) against multiple baseline classifiers. Finally, we demonstrate the flexibility of the ML model to achieve reliable predictions in scenarios with diverse requirements.

Maritime and shipping are unambiguously the cornerstones of the global economy and transportation. To improve efficiency, maritime sector activities are focused on the realization of Smart Shipping (SMS), leveraging 6G Communications, Energy Efficiency (EE) and Machine Learning (ML). However, conventional Centralized Machine Learning (CML) cannot be easily applied in the maritime, mainly due to the drawbacks: (i) prohibitive data communication overhead and bandwidth limitations, since CML requires centralization of massive data through transmissions from heterogeneous sources, (ii) excessive energy consumption associated with massive data transfers, (iii) remarkable transmission errors due to harsh propagation conditions, and (iv) data privacy violation, since the data carries sensitive and commercial information. This article proposes a two-fold Federated Learning (FL) scheme (FedShip) to improve the privacy, EE and communication-efficiency of future 6G maritime networks. FedShip uses the Over-the-Air computation (AirComp) principles to exploit the signal superposition property and ensure that local models are accurately and efficiently combined. Using real data regarding the fuel consumption of multiple cargo ships, we compared the FL performance, building multiple timeseries forecasting models, with collaborative ML baselines. AirComp performance was also assessed using simulation data about channel measurements. After optimizing the hyperparameters of the local models, extensive results revealed that: (i) FL shows enhanced fuel prediction accuracy (95.5% relative to the CML), while ensuring data privacy and (ii) AirComp can be adopted to combine the local models with low computation error, offering significant EE and spectrum efficiency improvements, especially when dense 6G scenarios are considered.

Power allocation is strongly related to the coverage and capacity of wireless networks, playing a critical role in the development of 5G networks. This paper proposes a Demand-Driven Power Allocation (DDPA) algorithm aiming to fulfill the requested throughput of individual users and accommodate their needs. DDPA is based on model-free Deep Reinforcement Learning (DRL) approaches and has the ability to proactively adjust the power levels of network transmitters. The performance of the developed algorithm is evaluated for a variety of simulation parameters and variable user demands. According to the presented results, the DDPA scheme exhibits a near-optimal performance for up to 50 users in the network area (i.e. satisfaction percentage exceeds 95%), with each one requesting 1 Mbps. Moreover, performance comparison between DDPA and two typical baseline methods reveals that the former results into enhanced total allocated throughput solutions (i.e. a performance increase by a factor of approximately 9% against baseline methods).