Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case
Today’s organizations have been embracing digital transformation to boost the quality of living within IoT-based smart-sustainable environments (e.g., healthcare, factories, vehicles, etc.). At the same time, augmenting the network infrastructure surface with billions of new devices accom...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10336815/ |
| _version_ | 1827585391944794112 |
|---|---|
| author | Yosra Njah Aris Leivadeas John Violos Matthias Falkner |
| author_facet | Yosra Njah Aris Leivadeas John Violos Matthias Falkner |
| author_sort | Yosra Njah |
| collection | DOAJ |
| description | Today’s organizations have been embracing digital transformation to boost the quality of living within IoT-based smart-sustainable environments (e.g., healthcare, factories, vehicles, etc.). At the same time, augmenting the network infrastructure surface with billions of new devices accommodating myriad applications creates the need for network automation through different technologies, such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Big Data Analytics (BDA). However, to devise an end-to-end self-driving and autonomous network, the manual configuration of network parameters and devices should be limited or even vanished. The recently emerged Intent-based Networking (IBN) paradigm introduces an additional building block enabling the network to adapt its settings automatically according to high-level user demands (intents) while hiding low-level details of the underlying infrastructure (e.g., configurations in millions of network devices). This paper initiates a deeper discussion regarding service automation over a Hospital 4.0 environment, from translating user requests to service profiling (unstructured intent refinement), deployment, and assurance. First, we discuss the design challenges of joining an intent-based framework as a convenient plane to an SDN-based platform. Following, we focus on an intelligent intent refinement system based on the Named Entity Recognition (NER) approach, an application of Natural Language Processing (NLP). This IBN-NER system deploys an extensible network policy model and the pre-trained Google’s BERT (Bidirectional Encoder Representations from Transformers) algorithm, fine-tuned with a Healthcare 4.0 dataset. The proposed intent refinement framework is evaluated via extensive simulations with an incremental number of heterogeneous intents. Our simulation results show promising performance with only one epoch for all dataset sizes and all policy model entities tested. For example, with 5000 intents, our system provides the highest accuracy with 86%; meanwhile, the well-known benchmarks in the NER problem, namely BiLSTM-CRF, BiLSTM, and LSTM, with ten epochs, provide 57%, 31%, and 26%, respectively. |
| first_indexed | 2024-03-08T23:44:54Z |
| format | Article |
| id | doaj.art-9b0d4131d5bd4130a9b68b34eee86808 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T23:44:54Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9b0d4131d5bd4130a9b68b34eee868082023-12-14T00:02:01ZengIEEEIEEE Access2169-35362023-01-011113656513657610.1109/ACCESS.2023.333818910336815Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use CaseYosra Njah0https://orcid.org/0000-0002-5790-1008Aris Leivadeas1https://orcid.org/0000-0002-2996-6824John Violos2Matthias Falkner3https://orcid.org/0000-0002-4920-873XDepartment of Software and IT Engineering, École de Technologie Supérieure, Montreal, CanadaDepartment of Software and IT Engineering, École de Technologie Supérieure, Montreal, CanadaDepartment of Software and IT Engineering, École de Technologie Supérieure, Montreal, CanadaCisco’s SP Sales CTO Group, Cisco Systems Inc., Ottawa, CanadaToday’s organizations have been embracing digital transformation to boost the quality of living within IoT-based smart-sustainable environments (e.g., healthcare, factories, vehicles, etc.). At the same time, augmenting the network infrastructure surface with billions of new devices accommodating myriad applications creates the need for network automation through different technologies, such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Big Data Analytics (BDA). However, to devise an end-to-end self-driving and autonomous network, the manual configuration of network parameters and devices should be limited or even vanished. The recently emerged Intent-based Networking (IBN) paradigm introduces an additional building block enabling the network to adapt its settings automatically according to high-level user demands (intents) while hiding low-level details of the underlying infrastructure (e.g., configurations in millions of network devices). This paper initiates a deeper discussion regarding service automation over a Hospital 4.0 environment, from translating user requests to service profiling (unstructured intent refinement), deployment, and assurance. First, we discuss the design challenges of joining an intent-based framework as a convenient plane to an SDN-based platform. Following, we focus on an intelligent intent refinement system based on the Named Entity Recognition (NER) approach, an application of Natural Language Processing (NLP). This IBN-NER system deploys an extensible network policy model and the pre-trained Google’s BERT (Bidirectional Encoder Representations from Transformers) algorithm, fine-tuned with a Healthcare 4.0 dataset. The proposed intent refinement framework is evaluated via extensive simulations with an incremental number of heterogeneous intents. Our simulation results show promising performance with only one epoch for all dataset sizes and all policy model entities tested. For example, with 5000 intents, our system provides the highest accuracy with 86%; meanwhile, the well-known benchmarks in the NER problem, namely BiLSTM-CRF, BiLSTM, and LSTM, with ten epochs, provide 57%, 31%, and 26%, respectively.https://ieeexplore.ieee.org/document/10336815/Healthcare 4.0intent-based networkingnetwork automationintent refinementservice policy mappingnamed entity recognition |
| spellingShingle | Yosra Njah Aris Leivadeas John Violos Matthias Falkner Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case IEEE Access Healthcare 4.0 intent-based networking network automation intent refinement service policy mapping named entity recognition |
| title | Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case |
| title_full | Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case |
| title_fullStr | Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case |
| title_full_unstemmed | Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case |
| title_short | Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case |
| title_sort | toward intent based network automation for smart environments a healthcare 4 0 use case |
| topic | Healthcare 4.0 intent-based networking network automation intent refinement service policy mapping named entity recognition |
| url | https://ieeexplore.ieee.org/document/10336815/ |
| work_keys_str_mv | AT yosranjah towardintentbasednetworkautomationforsmartenvironmentsahealthcare40usecase AT arisleivadeas towardintentbasednetworkautomationforsmartenvironmentsahealthcare40usecase AT johnviolos towardintentbasednetworkautomationforsmartenvironmentsahealthcare40usecase AT matthiasfalkner towardintentbasednetworkautomationforsmartenvironmentsahealthcare40usecase |