Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network

TL;DR

This paper proposes deep reinforcement learning algorithms to optimize resource allocation in cloud native wireless networks, enhancing efficiency in network slicing and edge computing scenarios.

Contribution

It introduces two novel model-free deep reinforcement learning algorithms tailored for dynamic resource management in cloud native wireless architectures.

Findings

Significant improvements in network efficiency demonstrated in testbed experiments.

Effective real-time monitoring and policy training for resource allocation.

Validation using a Free5gc-based testbed.

Abstract

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.