Network Intelligence for Virtualized RAN Orchestration: The DAEMON Approach

Gramaglia, Marco; Camelo, Miguel; Fuentes, Lidia; Ballesteros, Joaquin; Baldoni, Gabriele; Cominardi, Luca; García‐Saavedra, Andrés; Fiore, Marco

doi:10.1109/eucnc/6gsummit54941.2022.9815816

Cited by 8 publications

(10 citation statements)

References 12 publications

(27 reference statements)

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“…Additionally, closed-loop control facilitates the optimization of the RAN deployment itself [51,110,111]. Researchers have proposed zero-touch orchestration frameworks, fault-tolerant techniques, and efficient matching schemes between different RAN components, all contributing to better resource utilization and overall network efficiency [51,[112][113][114].…”

Section: Open Ran Principle #3: Ai-based Closed-loop Controlmentioning

confidence: 99%

Empowering the 6G Cellular Architecture With Open RAN

Polese,

Dohler,

Dressler

et al. 2024

IEEE J. Select. Areas Commun.

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Innovation and standardization in 5G have brought advancements to every facet of the cellular architecture. This ranges from the introduction of new frequency bands and signaling technologies for the radio access network (RAN), to a core network underpinned by micro-services and network function virtualization (NFV). However, like any emerging technology, the pace of real-world deployments does not instantly match the pace of innovation. To address this discrepancy, one of the key aspects under continuous development is the RAN with the aim of making it more open, adaptive, functional, and easy to manage.In this paper, we highlight the transformative potential of embracing novel cellular architectures by transitioning from conventional systems to the progressive principles of Open RAN. This promises to make 6G networks more agile, cost-effective, energy-efficient, and resilient. It opens up a plethora of novel use cases, ranging from ubiquitous support for autonomous devices to cost-effective expansions in regions previously underserved. The principles of Open RAN encompass: (i) a disaggregated architecture with modular and standardized interfaces; (ii) cloudification, programmability and orchestration; and (iii) AIenabled data-centric closed-loop control and automation. We first discuss the transformative role Open RAN principles have played in the 5G era. Then, we adopt a system-level approach and describe how these Open RAN principles will support 6G RAN and architecture innovation. We qualitatively discuss potential performance gains that Open RAN principles yield for specific 6G use cases. For each principle, we outline the steps that research, development and standardization communities ought to take to make Open RAN principles central to next-generation cellular network designs.

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Section: Open Ran Principle #3: Ai-based Closed-loop Controlmentioning

confidence: 99%

Empowering the 6G Cellular Architecture With Open RAN

Polese,

Dohler,

Dressler

et al. 2024

IEEE J. Select. Areas Commun.

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“…Moreover, its plugin interface enables extensibility by allowing new connectors to be quickly developed. Such capabilities have already proven their suitability as transport for ML workflows in the context of DAEMON, where it has been used to implement an N-MAPE-K framework [15].…”

Section: Zenohmentioning

confidence: 99%

A Dataflow-Oriented Approach for Machine-Learning-Powered Internet of Things Applications

Baldoni,

Teixeira,

Guimarães

et al. 2023

Electronics

Self Cite

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The rise of the Internet of Things (IoT) has led to an exponential increase in data generated by connected devices. Machine Learning (ML) has emerged as a powerful tool to analyze these data and enable intelligent IoT applications. However, developing and managing ML applications in the decentralized Cloud-to-Things continuum is extremely complex. This paper proposes Zenoh-Flow, a dataflow programming framework that supports the implementation of End-to-End (E2E) ML pipelines in a fully decentralized manner and abstracted from communication aspects. Thus, it simplifies the development and upgrade process of the next-generation ML-powered applications in the IoT domain. The proposed framework was demonstrated using a real-world use case, and the results showcased a significant improvement in overall performance and network usage compared to the original implementation. Additionally, other of its inherent benefits are a significant step towards developing efficient and scalable ML applications in the decentralized IoT ecosystem.

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“…Beyond 5G (B5G) and 6G networks set forth a vision for endto-end NI coordination aimed at ensuring a conflict-free and synergic operation of the many NI algorithms running across schedulers, controllers, and orchestrators in the network [3], [5]. As a first step in the rigorous design of a complete framework for the joint operation of NI instances, we have identified a set of gaps in the current frameworks for mobile network management that the main Standard-Defining Organizations (SDOs) propose.…”

Section: Gaps and Requirements For Network Intelligence (Ni) Orchestr...mentioning

confidence: 99%

“…In our previous paper [3], we showed how the N-MAPE-K framework could be leveraged to model, in a unified way, two algorithms for Radio Access Network virtualization (vRAN)…”

Section: B Nif Representation Frameworkmentioning

confidence: 99%

“…While indeed data is a fundamental aspect that has to be taken into account, there are other ones that shall also be addressed, such as the openness of the analytics across network domains [2] or the definition of an ontology for the intelligence algorithms [3]. In this paper we present a novel Network Intelligence Plane (NIP), developed within the context of the EU-funded DAEMON project [4], and to be included in the traditional control and user plane network functions already envisioned by, e.g., 3GPP systems.…”

Section: Introductionmentioning

confidence: 99%

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DAEMON: A Network Intelligence Plane for 6G Networks

Camelo

Gramaglia

Soto

et al. 2022

2022 IEEE Globecom Workshops (GC Wkshps)

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While there is a clear trend towards network automation through the usage of Artificial Intelligence (AI) and Machine Learning (ML) solutions, the major reference network architectures are still not natively including all the mechanisms needed to handle Network Intelligence (NI). This paper introduces a novel architecture proposed within the EUfunded DAEMON project, which includes a Network Intelligence Plane (NIP) that natively integrates NI into the network operation, management, and orchestration procedures. We do so by analyzing the gaps in current reference architectures and designing a Network Intelligence Orchestration (NIO) that handles the most important NI-related mechanisms such as lifecycle management, coordination, and data management.

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Network Intelligence for Virtualized RAN Orchestration: The DAEMON Approach

Cited by 8 publications

References 12 publications

Empowering the 6G Cellular Architecture With Open RAN

Empowering the 6G Cellular Architecture With Open RAN

A Dataflow-Oriented Approach for Machine-Learning-Powered Internet of Things Applications

DAEMON: A Network Intelligence Plane for 6G Networks

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