This paper concerns the lifecycle and runtime management of AI components of AI-native networks, in which the AI components are deeply integrated with network functions. The paper presents an overview of the standardised, IT-based, and research approaches to ML pipeline management. To address the identified gaps, we introduce the automated MLOps as a Service concept in which an ML model deployed in the network is continuously monitored for data and concept drifts, as well as network performance degradation, and on that basis, specific ML pipeline operations are triggered to improve the model's operation. The proposed approach allows for selecting local (inplace) or remote training by a network operator or external ML service providers. The benefits of selecting different training options are demonstrated in the AI-based network analytics scenario.

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.

Network Modularization, i.e., the refactoring of the functionalities of 5G Network Functions (NFs), will be an important tool to meet future requirements in the 6G System (6GS). In particular, via network modularization, it is possible to streamline the NF compositions and the inter-Nfinteractions. However, to achieve the highest possible efficiency without loss on inter-operability, we need a clear understanding of the different Key Performance Indicators (KPIs) and modularization techniques which are required to achieve the necessary end-to-end performance. In this work, we investigate the KPIs, the modularization methods prioritized by the research community, and the industrial implications thereof. We also outline the or-chestration requirements for the successful integration of modules to 6GS and the one possible integration of quantum technology to solve the synchronization challenge.

The Cloud Continuum concept proposes integrating Edge, Central Clouds as well as constrained, dynamic and unreliable Far-Edge resources (terminals, IoT) owned by users and different providers and exposing them to services uniformly. The ETSI NFV MANO framework assumes almost static NFV Infrastructure (NFVI) and has no business interfaces nor mechanisms for the dynamic adding or removing data centres. It is also not able to cope with constrained and unreliable resources. The paper presents a modification of the MANO framework to cope with the mentioned issues. In the described concept, all resources are exposed to the orchestrators via the Resource Layer, which also provides business interfaces to different infrastructure providers and improves exposed resources’ reliability by hiding infrastructure dynamicity to a certain extent. The idea also contributes to orchestration scalability by allowing multiple orchestrators atop the same resource pool.

The paper consists of a description of 5G-UASP, a multi-provider platform for drone services. It takes into account the involvement of air traffic management authority in all flight phases. We describe the core components of 5G-UASP and outline the procedures related to flight preparation, execution and termination phases. Instantiation of the concept is presented for 5G network. As it is shown, the usage of network slicing and MEC enables an immersive implementation of the architecture. Two options of instantiation of the reference architecture are included.

Network slicing technology will influence the way in which new networking solutions will be designed and operated. So far, network slicing is often linked with 5G networks, but this approach can be used to deploy any communications network(s) over a common infrastructure. The concept is still a subject of intensive research and standardization. From the point of view of network or service operator, it is necessary to define fundamental quantitative indicators for performance evaluation of the network slicing. Such parameters are often called Key Performance Indicators (KPIs). Network slicing KPIs should deal with network slicing run-time and life-cycle management and orchestration. The paper proposes a set of KPIs for network slicing taking into account the 5G network specifics.

This paper deals with the problem of scalability of network slicing management and orchestration. It includes a description of the DASMO (Distributed Autonomous Slice Management and Orchestration) concept that uses the distributed and autonomous slice management architecture to solve the management scalability problem. In opposite to existing approaches to network slicing management and orchestration that are based on single OSS/BSS combined with ETSI MANO, the DASMO uses the ETSI MANO orchestration combined with multiple in-slice deployed autonomous management platforms. In the paper, the architecture of DASMO is described. Moreover, a discussion on the scalability of DASMO monitoring that is considered as the key problem in slice management is included.

Small-cells are rapidly emerging as the mobile operators' choice to provide additional capacity in current and future mobile networks. However, in order to fully deliver on their promises, small-cells need to address severe interference control and coordination challenges. By centralizing base-band processing in large high-volume computing infrastructures, Cloud-RAN can effectively enable advanced coordination features for dense small-cells deployments. Unfortunately, Cloud-RAN tight bandwidth and latency requirements have made optical fiber the most common solution for the links interconnecting remote radio heads (RRHs) with the base band units (BBUs), i.e. the fronthaul. Recent advances in microwave communications are making wireless fronthauls a viable option especially in dense urban environments where fiber fronthauls could be too rigid for accommodating highly dynamic traffic patterns. In this paper, we provide a novel formulation for the BBU Placement problem where BBU pools are placed at the edges of the network, possibly co-located with macro-cells, and a reconfigurable wireless fronthaul is used in order to provide RRHs with connectivity. To the best of our knowledge this is the first work to tackle the BBU placement problem over a reconfigurable substrate network with mmWave links. We also propose a BBU Placement heuristics, and we evaluate it using a numerical simulator.

Network Function Virtualization (NFV) is emerging as one of the most innovative concepts in the networking landscape. By migrating network functions from dedicated mid-dleboxes to general purpose computing platforms, NFV can effectively reduce the cost to deploy and to operate large networks. However, in order to achieve its full potential, NFV needs to encompass also the radio access network allowing Mobile Virtual Network Operators to deploy custom resource allocation solutions within their virtual radio nodes. Such requirement raises several challenges in terms of performance isolation and resource provisioning. In this work we formalize the Virtual Network Function (VNF) placement problem for radio access networks as an integer linear programming problem and we propose a VNF placement heuristic. Moreover, we also present a proof-of-concept implementation of an NFV management and orchestration framework for Enterprise WLANs. The proposed architecture builds upon a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing nodes leveraging on general computing platforms.

Software-Defined Networking (SDN) has received, in the last years, significant interest from the academic and the industrial communities alike. The decoupled control and data planes found in an SDN allows for logically centralized intelligence in the control plane and generalized network hardware in the data plane. Although the current SDN ecosystem provides a rich support for wired packet-switched networks, the same cannot be said for wireless networks where specific radio data-plane abstractions, controllers, and programming primitives are still yet to be established. In this work, we present a set of programming abstractions modeling the fundamental aspects of a wireless network, namely state management, resource provisioning, network monitoring, and network reconfiguration. The proposed abstractions hide away the implementation details of the underlying wireless technology providing programmers with expressive tools to control the state of the network. We also present a Software-Defined Radio Access Network Controller for Enterprise WLANs and a Python--based Software Development Kit implementing the proposed abstractions. Finally, we experimentally evaluate the usefulness, efficiency and flexibility of the platform over a real 802.11-based WLAN.

In the paper a programmable management framework for SDN networks is presented. The concept is in-line with SDN philosophy - it can be programmed from scratch. The implemented management functions can be case dependent. The concept introduces a new node in the SDN architecture, namely the SDN manager. In compliance with the latest trends in network management the approach allows for embedded management of all network nodes and gradual implementation of management functions providing their code lifecycle management as well as the ability to on-the-fly code update. The described concept is a bottom-up approach, which key element is distributed execution environment (PDEE) that is based on well-established technologies like OSGI and FIPA. The described management idea has strong impact on the evolution of the SDN architecture, because the proposed distributed execution environment is a generic one, therefore it can be used not only for the management, but also for distributing of control or application functions.

Fractal coding and classi#ed transform coding exhibit strong structural similarity, but they use di#erenttypes of redundancy in image data: piecewise self-similarity in one case and local correlation in the other. Comparing performance of the two techniques leads to a quantitative, compression-oriented de#nition of piecewise self-similarity. The amount of piecewise self-similarity is evaluated for sample images. For a moderate number of domain blocks, classi#ed transform coding consistently outperforms fractal coding, and the images are not found to be piecewise self-similar. As the number of domain blocks increases, the performance gap becomes negligible. 1. INTRODUCTION Fractal coding #1# is a recent approach to image compression. Its basic premise is that images exhibit a type of redundancy called piecewise self-similarity. In a piecewise self-similar image, a blockofwaveform data can be related to another one so that the two resemble each other. Compression is achieved if one of th...