Research and practice on technologies for full stack deployment of autonomous networks
Autonomous networks achieve network self management, self optimization, and self repair by building intelligent network infrastructure.Autonomous network was divided into two key stages: AI model building and AI model deployment.However, the industry paid less attention to AI model deployment.The de...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2023-08-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023181/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841533625268436992 |
|---|---|
| author | Fei XUE Bin CHEN Jing LIU Xiaoyang LIANG Lin ZHU Feng WANG Tian LI Liang ZHANG Zhenzhen CHEN Xiao LI |
| author_facet | Fei XUE Bin CHEN Jing LIU Xiaoyang LIANG Lin ZHU Feng WANG Tian LI Liang ZHANG Zhenzhen CHEN Xiao LI |
| author_sort | Fei XUE |
| collection | DOAJ |
| description | Autonomous networks achieve network self management, self optimization, and self repair by building intelligent network infrastructure.Autonomous network was divided into two key stages: AI model building and AI model deployment.However, the industry paid less attention to AI model deployment.The deployment phase of autonomous networks was systematically studied.Firstly, it elaborated on the independent deployment mode and full stack deployment mode of autonomous networks, and pointed out that full stack deployment was the main direction.Secondly, a detailed introduction was given to the full stack architecture with “five layers, dual domains, and four closed-loops”, which achieved full life cycle intelligence through a layered closed-loop design of resources and processes.Then, three core technologies for independent innovation were proposed: AI model training and inference integration to achieve rapid iterative updates of models, AI fabric technology to achieve customized application by rapid construction, and AI model cloud-edge collaborative deployment technology to achieve efficient application.Finally, the effectiveness of these three core technologies was verified through cases such as anomaly detection, smart telecommunication rooms, and equipment inspections.The deployment of autonomous networks was systematically explored, especially in terms of architecture design and core technology innovation, which had important reference value for telecommunication operators’ network digital transformation. |
| format | Article |
| id | doaj-art-41b0c81957f0481b9b90a651e1c79d07 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-08-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-41b0c81957f0481b9b90a651e1c79d072025-01-15T02:58:10ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-08-0139637559560969Research and practice on technologies for full stack deployment of autonomous networksFei XUEBin CHENJing LIUXiaoyang LIANGLin ZHUFeng WANGTian LILiang ZHANGZhenzhen CHENXiao LIAutonomous networks achieve network self management, self optimization, and self repair by building intelligent network infrastructure.Autonomous network was divided into two key stages: AI model building and AI model deployment.However, the industry paid less attention to AI model deployment.The deployment phase of autonomous networks was systematically studied.Firstly, it elaborated on the independent deployment mode and full stack deployment mode of autonomous networks, and pointed out that full stack deployment was the main direction.Secondly, a detailed introduction was given to the full stack architecture with “five layers, dual domains, and four closed-loops”, which achieved full life cycle intelligence through a layered closed-loop design of resources and processes.Then, three core technologies for independent innovation were proposed: AI model training and inference integration to achieve rapid iterative updates of models, AI fabric technology to achieve customized application by rapid construction, and AI model cloud-edge collaborative deployment technology to achieve efficient application.Finally, the effectiveness of these three core technologies was verified through cases such as anomaly detection, smart telecommunication rooms, and equipment inspections.The deployment of autonomous networks was systematically explored, especially in terms of architecture design and core technology innovation, which had important reference value for telecommunication operators’ network digital transformation.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023181/autonomous networkfull stack deploymenttraining and inference integrationcloud edge collaborative deploymentAI fabric |
| spellingShingle | Fei XUE Bin CHEN Jing LIU Xiaoyang LIANG Lin ZHU Feng WANG Tian LI Liang ZHANG Zhenzhen CHEN Xiao LI Research and practice on technologies for full stack deployment of autonomous networks Dianxin kexue autonomous network full stack deployment training and inference integration cloud edge collaborative deployment AI fabric |
| title | Research and practice on technologies for full stack deployment of autonomous networks |
| title_full | Research and practice on technologies for full stack deployment of autonomous networks |
| title_fullStr | Research and practice on technologies for full stack deployment of autonomous networks |
| title_full_unstemmed | Research and practice on technologies for full stack deployment of autonomous networks |
| title_short | Research and practice on technologies for full stack deployment of autonomous networks |
| title_sort | research and practice on technologies for full stack deployment of autonomous networks |
| topic | autonomous network full stack deployment training and inference integration cloud edge collaborative deployment AI fabric |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023181/ |
| work_keys_str_mv | AT feixue researchandpracticeontechnologiesforfullstackdeploymentofautonomousnetworks AT binchen researchandpracticeontechnologiesforfullstackdeploymentofautonomousnetworks AT jingliu researchandpracticeontechnologiesforfullstackdeploymentofautonomousnetworks AT xiaoyangliang researchandpracticeontechnologiesforfullstackdeploymentofautonomousnetworks AT linzhu researchandpracticeontechnologiesforfullstackdeploymentofautonomousnetworks AT fengwang researchandpracticeontechnologiesforfullstackdeploymentofautonomousnetworks AT tianli researchandpracticeontechnologiesforfullstackdeploymentofautonomousnetworks AT liangzhang researchandpracticeontechnologiesforfullstackdeploymentofautonomousnetworks AT zhenzhenchen researchandpracticeontechnologiesforfullstackdeploymentofautonomousnetworks AT xiaoli researchandpracticeontechnologiesforfullstackdeploymentofautonomousnetworks |