计算机科学
编配
调度(生产过程)
云计算
人工智能
软件
机器学习
分布式计算
操作系统
运营管理
艺术
视觉艺术
经济
音乐剧
作者
Nikolaos Apostolakis,Marco Gramaglia,Livia Elena Chatzieleftheriou,Tejas Subramanya,Albert Banchs,Henning Sanneck
标识
DOI:10.1109/jsac.2023.3336155
摘要
Next-generation mobile networks will rely on their autonomous operation. Virtual Network Functions empowered by Artificial Intelligence (AI) and Machine Learning (ML) can adapt to varying environments that encompass both network conditions and the cloud platform executing them. In this view, it becomes paramount to understand why AI/ML algorithms made a decision, to be able to reason upon those decisions and, eventually, take further decisions related to e.g ., network orchestration. In this paper, we present ATHENA, an ML-based radio resource scheduler for virtualized Radio Access Network (RAN) system. Our real-software implementation shows that the proposed ML-based approach can outperform the baseline solution. We discuss how additional re-orchestration actions can be taken by analyzing our scheduling decisions and learning from the past.
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