Intent-based automation networks: Toward a common reference model for the self-orchestration of industrial intranets

Schulz, Dirk

doi:10.1109/iecon.2016.7792959

Cited by 14 publications

(8 citation statements)

References 3 publications

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“…As a new concept in the field of network automation, the use of IDN enhances the flexibility and stainability of network deployment. The authors in [23] designed reference models in communication network automation systems to support specific communication network service deployments. The intent in [24] is translated into a set of configurations that build the required service chain while meeting the specified QoS and security.…”

Section: Academic Researchmentioning

confidence: 99%

A Survey on Intent-Driven Networks

et al. 2020

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Software defined network and network function visualization enhance the network flexibility and management agility, which increase network fragility and complexity. However, the vast majority of network parameters are manually configured, which makes the configuration failures still inevitable. Future networks should be self-configuring, self-managing, and self-optimizing. Intent-driven network (IDN) is a self-driving network that uses decoupling network control logic and closed-loop orchestration techniques to automate application intents. At present, a unified definition of IDN has not yet been presented, and the research background and current status of IDN are not clear. Considering the emerging applications and research of IDN, in this article, we survey existing technologies, clarify definitions, and summarize features for IDN. Specifically, we discuss the basic architecture and key technologies of IDN. In addition, diversity gains and challenges are analyzed briefly. Finally, some future work is highlighted and wider applications of IDN are provided for further research.

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Section: Academic Researchmentioning

confidence: 99%

A Survey on Intent-Driven Networks

et al. 2020

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“…The micro-operator can be deployed as a closed, open or mixed network [10]- [12], see Table I. A closed network, which can also be described as a vertical specific network [15], is targeted at verticals whose human and/or machine users present a closed users group. According to [10], a closed network can be of Deployment A where the micro-operator is serving one or more tenants with their operations within a -single location or Deployment B where the micro-operator is serving one or more tenants whose operations are at multiple locations, and their communication will be enabled by connecting multiple tenants via an external network e.g.…”

Section: Scenariosmentioning

confidence: 99%

Network Slicing Management Technique for Local 5G Micro-Operator Deployments

Badmus

Matinmikko-Blue

Walia

2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

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Local 5G networks are expected to emerge to serve different vertical sectors' specific requirements. These networks can be deployed by traditional mobile network operators or entrant local operators. With a large number of verticals with different service requirements, while considering the network deployment cost in a single local area, it will not be economically feasible to deploy separate networks for each vertical. Thus, locally deployed 5G networks (aka micro operator networks) that can serve multiple verticals with multiple tenants in a location have gained increasing attention. Network slicing will enable a 5G micro-operator network to efficiently serve the multiple verticals and their tenants with different network requirements. This paper addresses how network slicing management functions can be used to implement, orchestrate and manage network slicing in different deployments of a local 5G micro-operator including the serving of closed, open and mixed customer groups. The paper proposes a descriptive technique by which different network slicing management functionalities defined by 3GPP can be used in coordination to create, orchestrate and manage network slicing for different deployment scenarios of a microoperator. This is based on the network slice instance configuration type that can exist for each scenario. A network slice formation sequence is developed for the closed micro operator network to illustrate the tasks of the management functions. The results indicate that network slicing management plays a key role in designing local 5G networks that can serve different customer groups in the verticals.

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“…The ultimate vision for network management automation is Intent-Based Networking (IBN). In IBN, human controllers only dictate intent policies that define (e.g., in natural language) what the network should do in terms of high-level objectives, without specifying how to achieve them [2], [3]. As illustrated in Figure 1, intents go through a translation stage, where they are rendered (e.g., via natural language processing) into a form that is consumable by the network management entities.…”

Section: Introductionmentioning

confidence: 99%

LossLeaP: Learning to Predict for Intent-Based Networking

Collet

Banchs

Fiore

2022

IEEE INFOCOM 2022 - IEEE Conference on Computer Communications

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Intent-Based Networking mandates that high-level human-understandable intents are automatically interpreted and implemented by network management entities. As a key part in this process, it is required that network orchestrators activate the correct automated decision model to meet the intent objective. In anticipatory networking tasks, this requirement maps to identifying and deploying a tailored prediction model that can produce a forecast aligned with the specific -and typically complex-network management goal expressed by the original intent. Current forecasting models for network demands or network management optimize generic, non-flexible, and manually designed objectives, hence do not fulfil the needs of anticipatory Intent-Based Networking. To close this gap, we propose LossLeaP, a novel forecasting model that can autonomously learn the relationship between the prediction and the target management objective, steering the former to minimize the latter. To this end, LossLeaP adopts an original deep learning architecture that advances current efforts in automated machine learning, towards a spontaneous design of loss functions for regression tasks. Extensive experiments in controlled environments and in practical application case studies prove that LossLeaP outperforms a wide range of benchmarks, including state-of-the-art solutions for network capacity forecasting.

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Intent-based automation networks: Toward a common reference model for the self-orchestration of industrial intranets

Cited by 14 publications

References 3 publications

A Survey on Intent-Driven Networks

A Survey on Intent-Driven Networks

Network Slicing Management Technique for Local 5G Micro-Operator Deployments

LossLeaP: Learning to Predict for Intent-Based Networking

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