SliceOps: Explainable MLOps for streamlined automation-native 6G networks
Sixth-generation (6G) network slicing is the backbone of future communications systems. It inaugurates the era of extreme ultra-reliable and low-latency communication (xURLLC) and pervades the digitalization of the various vertical immersive use cases. Since 6G inherently underpins artificial intell...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/405954 |
| Acceso en línea: | https://hdl.handle.net/2117/405954 https://dx.doi.org/10.1109/MWC.007.2300144 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Resource allocation Mobile communication systems Artificial intelligence Data models 6G mobile communication Training Network slicing Monitoring Load modeling Aprenentatge automàtic Assignació de recursos Comunicacions mòbils, Sistemes de Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils |
| Sumario: | Sixth-generation (6G) network slicing is the backbone of future communications systems. It inaugurates the era of extreme ultra-reliable and low-latency communication (xURLLC) and pervades the digitalization of the various vertical immersive use cases. Since 6G inherently underpins artificial intelligence (AI), we propose a systematic and standalone slice termed SliceOps that is natively embedded in the 6G architecture, which gathers and manages the whole AI lifecycle through monitoring, re-training, and deploying the machine learning (ML) models as a service for the 6G slices. By leveraging machine learning operations (MLOps) in conjunction with eXplainable AI (XAI), SliceOps strives to cope with the opaqueness of black-box AI using explanation-guided reinforcement learning (XRL) to fulfill transparency, trustworthi- ness, and interpretability in the network slicing ecosystem. This article starts by elaborating on the architectural and algorithmic aspects of SliceOps. Then, the deployed cloud-native SliceOps working is exemplified via a latency-aware resource allocation problem. The deep RL (DRL)-based SliceOps agents within slices provide AI services aiming to allocate optimal radio resources and impede service quality degradation. Simulation results demon- strate the effectiveness of SliceOps-driven slicing. The article dis- cusses afterward the SliceOps challenges and limitations. Finally, the key open research directions corresponding to the proposed approach are identified. |
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