Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) are two technologies expected to play a vital role in 5G and beyond networks. However, adequate mechanisms are required to meet the dynamically changing network service demands to utilize the network resources optimally and also to satisfy the demanding QoS requirements. Particularly in multi-domain scenarios, the additional challenge of isolation and data privacy among domains needs to be tackled. To this end, centralized and distributed Artificial Intelligence (AI)-driven resource orchestration techniques (e.g., virtual network function (VNF) autoscaling) are foreseen as the main enabler. In this work, we propose deep learning models, both centralized and federated approaches, that can perform horizontal and vertical autoscaling in multi-domain networks. The problem of autoscaling is modelled as a time series forecasting problem that predicts the future number of VNF instances based on the expected traffic demand. We evaluate the performance of various deep learning models trained over a commercial network operator dataset and investigate the pros and cons of federated learning over centralized learning approaches. Furthermore, we introduce the AI-driven Kubernetes orchestration prototype that we implemented by leveraging our MEC platform and assess the performance of the proposed deep learning models in a practical setup.

中文翻译：

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在多域5G网络中进行预测性VNF自动扩展的集中和联合学习

网络功能虚拟化（NFV）和多路访问边缘计算（MEC）是两项有望在5G及以后的网络中发挥至关重要作用的技术。然而，需要足够的机制来满足动态变化的网络服务需求，以最佳地利用网络资源并满足苛刻的QoS需求。特别是在多域方案中，需要解决域间隔离和数据隐私的其他挑战。为此，预见到集中和分布式人工智能（AI）驱动的资源编排技术（例如，虚拟网络功能（VNF）自动缩放）将成为主要的推动力。在这项工作中，我们提出了集中式和联合式方法的深度学习模型，它们可以在多域网络中执行水平和垂直自动缩放。自动缩放问题被建模为时间序列预测问题，该问题基于预期的流量需求来预测VNF实例的未来数量。我们评估通过商业网络运营商数据集训练的各种深度学习模型的性能，并研究通过集中式学习方法进行联合学习的利弊。此外，我们介绍了通过利用我们的MEC平台实现的，由AI驱动的Kubernetes编排原型，并在实际设置中评估了建议的深度学习模型的性能。我们评估通过商业网络运营商数据集训练的各种深度学习模型的性能，并研究通过集中式学习方法进行联合学习的利弊。此外，我们介绍了通过利用我们的MEC平台实现的AI驱动的Kubernetes编排原型，并在实际设置中评估了建议的深度学习模型的性能。我们评估通过商业网络运营商数据集训练的各种深度学习模型的性能，并研究通过集中式学习方法进行联合学习的利弊。此外，我们介绍了通过利用我们的MEC平台实现的，由AI驱动的Kubernetes编排原型，并在实际设置中评估了建议的深度学习模型的性能。