Machine Learning in Network Slicing—A Survey
5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10103689/ |
| _version_ | 1797839229364994048 |
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| author | Hnin Pann Phyu Diala Naboulsi Razvan Stanica |
| author_facet | Hnin Pann Phyu Diala Naboulsi Razvan Stanica |
| author_sort | Hnin Pann Phyu |
| collection | DOAJ |
| description | 5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances in machine learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on ML in network slicing, identifying major categories and sub-categories in the literature. Lessons learned are also presented and open research challenges are discussed, together with potential solutions. |
| first_indexed | 2024-04-09T15:54:42Z |
| format | Article |
| id | doaj.art-f5e0266c5c9b4db394da98b2286200cd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T15:54:42Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f5e0266c5c9b4db394da98b2286200cd2023-04-25T23:00:20ZengIEEEIEEE Access2169-35362023-01-0111391233915310.1109/ACCESS.2023.326798510103689Machine Learning in Network Slicing—A SurveyHnin Pann Phyu0https://orcid.org/0000-0002-9400-5085Diala Naboulsi1https://orcid.org/0000-0002-5313-9378Razvan Stanica2https://orcid.org/0000-0002-4479-3976Département de Génie Logiciel et des Technologies de l’information, École de Technologie Supérieure, Université du Québec, Montreal, CanadaDépartement de Génie Logiciel et des Technologies de l’information, École de Technologie Supérieure, Université du Québec, Montreal, CanadaInria, CITI, INSA Lyon, University of Lyon, Villeurbanne, France5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances in machine learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on ML in network slicing, identifying major categories and sub-categories in the literature. Lessons learned are also presented and open research challenges are discussed, together with potential solutions.https://ieeexplore.ieee.org/document/10103689/Network slicing5G networkmachine learning |
| spellingShingle | Hnin Pann Phyu Diala Naboulsi Razvan Stanica Machine Learning in Network Slicing—A Survey IEEE Access Network slicing 5G network machine learning |
| title | Machine Learning in Network Slicing—A Survey |
| title_full | Machine Learning in Network Slicing—A Survey |
| title_fullStr | Machine Learning in Network Slicing—A Survey |
| title_full_unstemmed | Machine Learning in Network Slicing—A Survey |
| title_short | Machine Learning in Network Slicing—A Survey |
| title_sort | machine learning in network slicing x2014 a survey |
| topic | Network slicing 5G network machine learning |
| url | https://ieeexplore.ieee.org/document/10103689/ |
| work_keys_str_mv | AT hninpannphyu machinelearninginnetworkslicingx2014asurvey AT dialanaboulsi machinelearninginnetworkslicingx2014asurvey AT razvanstanica machinelearninginnetworkslicingx2014asurvey |