Deep Reinforcement Learning Based Resource Allocation for Cloud Native
  Wireless Network
Lin Wang Jiasheng Wu Yue Gao Jingjing Zhang
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.