CLARA: Closed Loop-based Zero-touch Network Management Framework

Sousa, Nathan F. Saraiva de; Rothenberg, Christian Esteve

doi:10.1109/nfv-sdn53031.2021.9665048

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…The eventual goal is then to reduce the difference between the desired and the actual state. The authors in [29] proposed a zero-touch network management framework based on CLs, which is a policy-driven network management system capable of automating network management in a hierarchical multi-domain environment architecture based on ZSM through a collaborative distributed CL as well as an artificial intelligence approach. This approach reduces the complexity of network and service management and enables service assurance through the implementation of adaptive features.…”

Section: B Cls For Network Managementmentioning

confidence: 99%

Intent-Driven Closed-Loop Control and Management Framework for 6G Open RAN

Zhang,

Yang,

Dong

et al. 2024

IEEE Internet Things J.

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Future mobile networks should provide on-demand services for various industries and applications with the stringent guarantees of quality of experience (QoE), which highly challenge the flexibility of network management. However, the diverse requirements of QoE and the management of heterogeneous networks create significant pressure towards communication service providers (CSPs). In the 6th generation mobile networks, the CSPs should guarantee resilient performance for the communication service consumers with less human involvement. In this work, we turn to intent-driven network and on-demand slice management, and to decrease the complexity and cost in full life cycle slice management, we first present an intentdriven closed-loop (CL) control and management framework that automates the deployment of network slices and manages resources intelligently based on the extended CL architecture. And then, we explore and exploit the deep reinforcement learning algorithm to address the problem of resource allocation, which is formulated as a Markov decision process. Finally, we demonstrate the feasibility of the proposed framework by deploying the open radio access network (RAN) infrastructure in OpenAirInterface platform and realizing the CL control and management with near real-time RAN intelligent controller. The emulation results demonstrate the effectiveness of slicing performance, measured in terms of delay and rate.

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Section: B Cls For Network Managementmentioning

confidence: 99%

Intent-Driven Closed-Loop Control and Management Framework for 6G Open RAN

Zhang,

Yang,

Dong

et al. 2024

IEEE Internet Things J.

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“…The integration of AI/ML into the architectures of these SDOs is an area of active research [20], [21], [22]. Prior works have explored the potential of embedding intelligent algorithms into network management processes to achieve the goal of zero-touch management [23], [21], [6], [24], [25], [26]. The aim is to provide innovative network services and management to meet the requirements of future applications [20].…”

Section: Related Work and Monb5g Vision A Related Workmentioning

confidence: 99%

A Novel Approach for Scalable and Sustainable 6G Networks

Blanco,

Zeydan,

Barrachina-Muñoz

et al. 2024

IEEE Open J. Commun. Soc.

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Hierarchical, distributed, scalable and Artificial Intelligence (AI)-based management of a massive number of network slices in different domains with the goal of zero-touch management is a major challenge for 6G networks. In this paper, we first propose a new vision for distributed network management and orchestration based on existing standardization architectures. This vision aims to embed AI/Machine Learning (ML) into the AI/ML architectures of Standardization Development Organizations (SDOs) such as the 3rd Generation Partnership Project (3GPP), the European Telecommunications Standards Institute (ETSI) and the International Telecommunication Union (ITU). Our second contribution is a numerical comparison of the benefits of the proposed distributed management and orchestration approach in terms of energy savings through Federated Learning (FL). The experimental topology includes a sophisticated infrastructure with VR streaming clients and servers, a monitoring system (MS), core network elements, aggregation server for federated learning (FL) and analytics engines (AEs). The deployment uses Kubernetes (K8s) and a top orchestrator that works together with an AI/ML model tailored to the envisioned use case. Experimental studies emulating the demanding Virtual Reality (VR) video streaming have demonstrated the effectiveness of the MonB5G framework in optimizing resource management, reducing overhead and improving energy efficiency. In particular, when convergence is achieved, the monitoring overhead is reduced by more than 11 times compared to the centralised SLA-constrained algorithm, along with data-driven management systems. This led to a more than 10-fold improvement in energy efficiency. At the end of the paper, we also discuss experimental results, VR video streaming specific challenges, scalability considerations and lessons learned throughout the implementation.

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“…According to the ETSI ZSM standard group, "An autonomous network (AN) is a network that self-operates according to the business goals with no human intervention beyond the initial supply of input (e.g., intent, goals, policies, certain configuration data) by the human operator" [155,156]. The ultimate goal in network automation is to develop networks that can operate independently, guided by high-level policies set by the provider or user.…”

Section: Autonomous Network: Self-x Network Managementmentioning

confidence: 99%

Towards a Software-Defined Industrial IoT-Edge Network for Next-Generation Offshore Wind Farms: State of the Art, Resilience, and Self-X Network and Service Management

Mwangi,

Sahay,

Fumagalli

et al. 2024

Energies

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Offshore wind farms are growing in complexity and size, expanding deeper into maritime environments to capture stronger and steadier wind energy. Like other domains in the energy sector, the wind energy domain is continuing to digitalize its systems by embracing Industry 4.0 technologies such as the Industrial Internet of Things (IIoT), virtualization, and edge computing to monitor and manage its critical infrastructure remotely. Adopting these technologies creates dynamic, scalable, and cost-effective data-acquisition systems. At the heart of these data-acquisition systems is a communication network that facilitates data transfer between communicating nodes. Given the challenges of configuring, managing, and troubleshooting large-scale communication networks, this review paper explores the adoption of the state-of-the-art software-defined networking (SDN) and network function virtualization (NFV) technologies in the design of next-generation offshore wind farm IIoT–Edge communication networks. While SDN and NFV technologies present a promising solution to address the challenges of these large-scale communication networks, this paper discusses the SDN/NFV-related performance, security, reliability, and scalability concerns, highlighting current mitigation strategies. Building on these mitigation strategies, the concept of resilience (that is, the ability to recover from component failures, attacks, and service interruptions) is given special attention. The paper highlights the self-X (self-configuring, self-healing, and self-optimizing) approaches that build resilience in the software-defined IIoT–Edge communication network architectures. These resilience approaches enable the network to autonomously adjust its configuration, self-repair during stochastic failures, and optimize performance in response to changing conditions. The paper concludes that resilient software-defined IIoT–Edge communication networks will play a big role in guaranteeing seamless next-generation offshore wind farm operations by facilitating critical, latency-sensitive data transfers.

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CLARA: Closed Loop-based Zero-touch Network Management Framework

Cited by 10 publications

References 19 publications

Intent-Driven Closed-Loop Control and Management Framework for 6G Open RAN

Intent-Driven Closed-Loop Control and Management Framework for 6G Open RAN

A Novel Approach for Scalable and Sustainable 6G Networks

Towards a Software-Defined Industrial IoT-Edge Network for Next-Generation Offshore Wind Farms: State of the Art, Resilience, and Self-X Network and Service Management

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