The landscape of modern computing systems is shifting towards architectures built by combining available services under the “everything as a service” paradigm. These architectures are deployed on distributed cloud-edge infrastructures, aiming to provide innovative services to a wide range of users. However, it is crucial for these systems to address environmental sustainability concerns. This poses challenges in operating such systems in open, dynamic, and uncertain environments while minimizing their energy consumption. To tackle these challenges, we propose a decentralized service assembly approach that ensures the assembly is energetically self-sustainable by relying on locally harvested and stored energy. In our contribution, we introduce a general service selection template that enables the derivation of different selection policies. These policies guide the construction and maintenance of the service assembly. To evaluate their effectiveness in meeting the sustainability requirements, we conduct a comprehensive set of simulation experiments, providing valuable insights.

The transition to AI-native 6G networks embeds artificial intelligence (AI) across all network functions, enabling automation, efficiency, and advanced services. The AI-native approach is more than just a technical enhancement; it is a fundamental shift that drives the evolution toward autonomous networks, unlocking new advancements in network capabilities and user experience. A key enabler is AI as a Service (AIaaS), which allows Communication Service Providers (CSPs) to expose AI capabilities via standardized APIs. Built on Machine Learning Operations (MLOps), AIaaS streamlines AI model lifecycle management, reducing complexity for developers and optimizing AI-driven workflows.This paper investigates the integration of AIaaS into network exposure frameworks such as the 3rd Generation Partnership Project (3GPP) Network Exposure Function (NEF), Service Enabler Architecture Layer (SEAL), and the open-source CAMARA interface emphasizing the interacting and collaborating robots (cobots) use case in the context of industrial automation. We discuss how AIaaS enables CSPs to offer AI-powered services with guaranteed Quality of Service (QoS), driving innovation across industries and shaping the future of AI-native 6G networks.

The ever-growing complexity in networks and the various future use cases with diverse, and often stringent performance requirements call for a higher level of automation. A tool to achieve this higher level of automation is intent-based management, where the human operator only communicates via high-level intents with the network. Intents are declarative in nature, they specify the desired state but don’t specify how to achieve it. The system will assure that the intents are fulfilled, by monitoring the behavior of the network, and adjusting its configurations if needed. In future networks that will serve multiple tenants and use cases at the same time, many intents will co-exist. As the resources are limited, conflicts may arise between intents. Therefore, a solution is needed for conflict detection and resolution. In this paper, we present a system that is capable of handling multiple intents and detecting and resolving conflicts at run-time. To show the feasibility, we implement our approach in an end-to-end prototype.