Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability. However, the vast array of cloud native services and applications presents a new challenge in resource allocation for dynamic cloud computing environments. To tackle this challenge, we investigate a cloud native wireless architecture that employs container-based virtualization to enable flexible service deployment. We then study two representative use cases: network slicing and Multi-Access Edge Computing. To optimize resource allocation in these scenarios, we leverage deep reinforcement learning techniques and introduce two model-free algorithms capable of monitoring the network state and dynamically training allocation policies. We validate the effectiveness of our algorithms in a testbed developed using Free5gc. Our findings demonstrate significant improvements in network efficiency, underscoring the potential of our proposed techniques in unlocking the full potential of cloud native wireless networks.

翻译：云原生技术已深刻革新了5G演进及6G通信网络，提供了前所未有的运营自动化水平、灵活性和适应性。然而，云原生服务和应用种类繁多，对动态云计算环境下的资源分配提出了新挑战。为应对此问题，我们研究了一种基于容器虚拟化的云原生无线架构，能够支持灵活的服务部署。在此基础上，我们分析了两个典型应用场景：网络切片与多接入边缘计算。为了优化这些场景下的资源分配，我们利用深度强化学习技术，提出了两种无模型算法，能够监测网络状态并动态训练分配策略。我们在基于Free5gc搭建的测试平台上验证了算法有效性，实验结果表明网络效率显著提升，凸显了所提技术在充分发挥云原生无线网络潜力方面的巨大潜力。