Deliverable 2.3: Final DAEMON Network Intelligence framework and toolsets
Creators: name: Municio, Esteban
name: Ginés García-Avilés
name: Bazco-Nogueras, Antonio
name: Fiore, Marco
name: Collet, Alan
name: Alcalá, Sergi
name: Camelo, Miguel
name: Soto, Paula
name: De Vleeschauwer, Danny
name: García-Saavedra, Andres
name: Salvat, Josep Xavier
name: Lutu, Andra
name: Gramaglia, Marco
name: Branchs, Albert
name: Prodomo, Vittorio
name: García, Alberto
name: Fuentes, Lidia
name: Ballesteros, Joaquín
name: Amor, Mercedes
name: Pinto, Monica
name: Kosmatos, Evangelos
name: Páez, Ivan
Publisher: name: Zenodo
Year: 2023
Subjects: DAEMON
H2020
BEYOND
5G
ADAPTATIVE
SELF-LEARNING
MOBILE
NETWORKS

description: This is the third and last public deliverable of WP2 of the DAEMON project. It builds upon the material of<br> the previous deliverable of WP2, i.e., D2.2 [1], and on activities and results achieved during the second<br> iteration of the project in WP3 D3.2 [2], WP4 D4.2 [3], and WP5 D5.2 [4]. As a result, the document<br> describes the following content.<br> First, it provides the final update on the functional and non-functional requirements of the eight NIassisted<br> functionalities (Reconfigurable Intelligent surfaces control - RISC, Multi-timescale Edge resource<br> management – MTERM, In-backhaul support for service management – IBSSI, Compute-aware radio<br> scheduling – CAWRS, Energy-aware VNF control and orchestration – EAWVNF, Self-learning MANO –<br> SLMANO, Capacity forecasting – CFORE, and Automated anomaly response – AARES) tackled by<br> DAEMON at the end of the WP2. Although no new updates were added to the functionalities, we assess<br> the risks to achieve the requirements and its current completion status. For the requirements that were<br> not finalized at the time of this deliverable, we also specify what is required to successfully finalize it and<br> in which deliverable (e.g., WP3 D3.3, WP4 D4.3, or WP5 D5.3) the results will be provided.<br> Second, it presents the final updates of the Network Intelligence Plane (NIP), a collection of modules and<br> interfaces responsible for managing NI within the network. In this deliverable, the NIP has evolved, and it<br> is presented as a unified framework that brings together (i) the operational hierarchy of NI components<br> and their orchestration and (ii) the N-MAPE-K representation of the NI components. By doing so, we make<br> another step forward toward the vision of a complete NIP initially presented in D2.2 [1].<br> Third, in addition to the unified DAEMON framework, we also identify and present in detail the specific<br> needs that NI algorithms pose on the NIP. Moreover, we analyze their specificity in terms of challenges<br> towards the procedures for NI management at the Network Intelligence Orchestrator (NIO) level. We<br> also devise and describe the functionalities that the NIO shall provide to support such requirements and<br> how they fit the whole architecture together. The architectural design is complemented by presenting<br> and discussing the interfaces required to allow communication between NIP components and with<br> external entities such as the RAN controller and the 5G Core systems. These interfaces are also enablers<br> for designing the set of procedures that address the needs and challenges introduced in this document.<br> Fourth, this document provides the final, comprehensive overview of the literature review carried out by<br> the project, focused on the integration of machine learning and NI in mobile network management. The<br> survey highlights key trends in current research and showcases the distinctive contributions made by the<br> DAEMON project. The insights that originated from this analysis also support our final updates to the<br> project guidelines, including new ones, for practical NI design. As in D2.2 [1], these guidelines focus on<br> two main directions: i) NI design tailored to the needs of B5G network management, orchestration, and<br> control, and ii) NI design that considers the use of more traditional, more straightforward, or interpretable<br> models to avoid overburdening the system with data-heavy models and promotes the utilization of<br> models that are easier to understand and interpret.<br> We closed this document with additional closing remarks and two appendices containing<br> complementary information related to the functional requirements and the literature review.