NET22024-05-15T13:48:13+00:00

NET2 – Automated and Intent-based network and service management in 6G

Tuesday, 4 June 2024, 16:00-17:30, room Gorilla Room 4

Session Chair: Faqir Zarrar Yousaf (NEC Laboratories Europe GmbH, DE)

Towards Intent-Based Network Management for the 6G System Adopting Multimodal Generative AI
Dimitrios Brodimas, Kostis Trantzas, Besiana Ioanna Agko, Georgios Christos Tziavas, Christos Tranoris, Spyros Denazis and Alexios Birbas (University of Patras, Greece)
The emerging concept of delivering Network-as-a-Service (NaaS) foresees the deployment and reconfiguration of the next-generation networks, such as 6G, in a dynamic and elastic manner, tailored to the respective stakeholder’s intention. Taking this into account, the efficient management and orchestration of both telecommunication and computational resources across the network domains, i.e. access, transport and core presents a considerable challenge, even for network experts. To tackle this complexity, this paper explores the implementation of an intent-based management framework. The framework receives a high-level description of the desired network capabilities along with supplementary files, e.g. deployment descriptors, and translates them into configuration files consumable by the network itself. In order to achieve this, the paper establishes a translation pipeline that leverages the employment of emerging multimodal generative artificial intelligence (GenAI) models, specifically Large Language Models (LLMs), and open industry-ready standard templates. The adoption of those two emerging technologies offers high dynamicity on the interpretation process of the user’s intent, while ensuring that its outcome is compatible with every orchestrator or next-generation Operating Support System (Next-gen OSS) that adheres to those standards.

Towards Industrial/Multimedia TSN Network Slice Management: A Bottom-Up Approach
Gilson Miranda, Jr (University of Antwerp & Imec-IDLab, Belgium); Nina Slamnik-Krijestorac (University of Antwerp-IMEC, Belgium); Johann M. Marquez-Barja (University of Antwerpen & imec, Belgium); Daniel Fernandes Macedo (Universidade Federal de Minas Gerais, Brazil)
Network slicing enables multiple virtual networks to share physical resources, allowing network operators to deliver highly customizable and efficient networking solutions that meet the diverse requirements of modern applications. The automated management of network slices has been studied in the last years to make such solutions more flexible, ready to support new applications, and capable of optimizing network resource utilization. Many works in the literature give a top-down approach, focusing on the high-level decision processes, and relying on abstracted infrastructure managers and simulation tools to apply/execute such decisions. In this work, we leverage components that we previously developed for network monitoring, flexible traffic shaping, and Software-Defined Time-Sensitive Networking control, to create a bottom-up approach toward automated slice management. We describe the intricate coordination of elements required for an automated control loop and present the results achieved with a proof-of-concept executed in a real testbed of wired and Wi-Fi nodes. The results show the capability of the system to correctly identify the bottleneck of a flow and apply corrective actions to reestablish its intended performance level.

Closed-Loop Automation in 6G for Minimum Downtime Task Continuity in Surveillance Cobots
Rafael Pires and Pawani Porambage (VTT Technical Research Centre of Finland, Finland); Henry Blue (VTT Technical Research Center of Finland, Finland); Jere Malinen (VTT Technical Research Centre of Finland, Finland); Michael De Angelis, Pietro Giardina and Giada Landi (Nextworks, Italy); Kimmo Ahola (VTT Technical Research Center of Finland, Finland); Matti Laukkanen (VTT Technical Research Centre of Finland Ltd, Finland)
This research delves deeply into the integration of collaborative robots (cobots) in the context of advancing 6G technology, aiming to enhance situational awareness. The research underscores the critical importance of Closed-Loop (CL) cobot control within the evolving 6G network architecture, enabling increased automation and improved adaptability. The study provides a comprehensive background on Intent-Based Networks (IBN) and CL mechanisms, introduces an application scenario, and outlines a framework for implementing CL systems in cobot cluster networks. An experimental setup is detailed, and a CL-enabled application migration use-case is demonstrated. This work contributes valuable insights to the ongoing discussion on the evolution of next-generation networks.

Multi-Stakeholder Intent-Based Service Management Automation for 6G Networks
Pol Alemany and Raul Muñoz (Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain); Jose Ordonez-Lucena (Telefonica I+D, Spain); Ricard Vilalta (Centre Tecnològic de Telecomunicacions de Catalunya-CERCA (CTTC-CERCA), Spain); Diego Lopez (Telefonica I+D, Spain); Mathieu Boussard and Huy Quang Tran (Nokia Bell Labs, France); Pawani Porambage and Rafael Pires (VTT Technical Research Centre of Finland, Finland); Henry Blue (VTT Technical Research Center of Finland, Finland); Jere Malinen (VTT Technical Research Centre of Finland, Finland); Mikko A. Uusitalo, Mohammed S. Elbamby and Mohamed K. Abdel-Aziz (Nokia Bell Labs, Finland); Patrik Rugeland (Ericsson Research, Sweden); Josue Cisneros (Ericsson, Sweden); Flavio Brito (Ericsson AB, Sweden); Elham Dehghan Biyar (Istanbul Technical University & Ericsson, Turkey); Mehmet Karaca (Ericsson Research, Turkey); Anastasios Zafeiropoulos (Institute of Communication and Computer Systems/National Technical University of Athens, Greece); Ioannis Tzanettis (National Technical University of Athens, Greece); Pietro Giardina and Giada Landi (Nextworks, Italy); Xosé Ramón Sousa-Vázquez (Optare Solutions SL, Spain); Sylvaine Kerboeuf (Nokia Bell Labs, France)
This article presents a detailed intent-based framework with the objective to assist on the management of services and network resources in the current convoluted telco scenario. First, it introduces the context and complexity of telco systems to justify why intents may be required within the multi-stakeholder scenario with multiple and different actors (e.g., operators, providers, etc.). By removing the technicalities from the tenants’ level and leaving them to the management system itself the approach aims to improve the interactions among all the actors and make the whole E2E control infrastructure more efficient. This paper illustrates the outcomes from the Hexa-X-II project on the management of intents with the main aspects and components of the designed framework and how they interact to reach a model in which the use of Close-Loops is key to manage each intent request individually while being coordinated with the others.

Time-Based Coordination in Intent-Driven Management for Vehicular Service Orchestration
Raúl Cuervo Bello (IMEC & University of Antwerpen, Belgium); Nina Slamnik-Krijestorac (University of Antwerp-IMEC, Belgium); Johann M. Marquez-Barja (University of Antwerpen & imec, Belgium)
Intent-driven network management has become an important part of autonomous systems in B5G towards 6G networks, by enabling flexibility in the interaction among applications, operators and users. Intents play an important role in the communication of road users like autonomous vehicles and pedestrians to edge computing services. As sensor technologies for modern vehicles are cheaper, smaller, diverse and computing capable, more demand for applications and services on the road is increasing. A flexible intent interpretation and coordination are needed to deal with the dynamic environment and constantly changing goals. This paper presents a proof-of-concept for zero-touch network and service management (ZSM) for vehicular communication services, using an intent management entity (IME) to translate user objectives into actionable directives. The paper describes a realistic testbed setup at the Smart Highway in Antwerp, Belgium, where a deep reinforcement learning (DRL) algorithm is used to optimize the selection of roadside units (RSUs) for service orchestration. The paper also discusses the challenges and opportunities of enhancing the IME with time-based intent coordination, using artificial intelligence and machine learning (AI/ML) techniques to estimate the waiting time and priority of intents in a queue1. The paper aims to demonstrate the benefits of ZSM and intent-driven management for vehicular edge computing and autonomous network management frameworks.

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