Future 6G networks are expected to heavily utilize machine learning capabilities in a wide variety of applications with features and benefits for both, the end user and the provider. While the options for utilizing these technologies are almost endless, from the perspective of network architecture and standardized service, the deployment decisions on where to execute the AI-tasks are critical, especially when considering the dynamic and heterogeneous nature of processing and connectivity capability of 6G networks. On the other hand, conceptual and standardization work is still in its infancy, as to how to categorizes ML applications in 6G landscapes; some of them are part of network management functions, some target the inference itself, while many others emphasize model training. It is likely that future mobile services may all be in the AI domain, or combined with AI. This work makes a case for the serverless computing paradigm to be used to this end. We first provide an overview of different machine learning applications that are expected to be relevant in 6G networks. We then create a set of general requirements for software engineering solutions executing these workloads from them and propose and implement a high-level edge-focused architecture to execute such tasks. We then map the ML-serverless paradigm to the case study of 6G architecture and test the resulting performance experimentally for a machine learning application against a setup created in a more traditional, cloud-based manner. Our results show that, while there is a trade-off in predictability of the response times and the accuracy, the achieved median accuracy in a 6G setup remains the same, while the median response time decreases by around 25% compared to the cloud setup.

翻译：未来的6G网络预计将在各类应用中广泛利用机器学习能力，为终端用户和供应商带来特性与效益。尽管利用这些技术的可能性近乎无限，但从网络架构和标准化服务的角度来看，决定在何处执行AI任务至关重要，尤其是在考虑6G网络处理与连接能力的动态性和异构性时。另一方面，关于如何在6G场景中对机器学习应用进行分类的概念性和标准化工作仍处于起步阶段：部分应用属于网络管理功能，部分专注于推理过程，而许多其他应用则侧重模型训练。未来的移动服务很可能全部属于AI领域，或与AI相结合。本研究论证了采用无服务器计算范式实现这一目标的可行性。我们首先概述了预计在6G网络中具有相关性的各类机器学习应用，随后从这些应用中提取出一套执行此类工作负载的软件工程解决方案的通用需求，并提出并实现了一种以边缘为核心的高层架构来执行此类任务。接着，我们将ML-无服务器范式映射到6G架构的案例研究中，并针对一个机器学习应用，通过实验测试了该范式与传统云基础架构的性能对比。实验结果表明，虽然在响应时间可预测性与准确性之间存在权衡，但6G架构实现的中位准确率与传统云架构持平，而中位响应时间较云架构降低了约25%。