论文标题
深层固定：深入强化学习有助于网络切片的资源分配
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
论文作者
论文摘要
网络切片使多个虚拟网络在相同的物理基础架构上运行，以支持5G及以后的各种用例。但是，这些用例具有非常多样化的网络资源需求，例如通信和计算以及各种性能指标，例如延迟和吞吐量。为了有效地将网络资源分配给切片，我们提出了深入的固定，以整合乘数的交替方向方法（ADMM）和深钢筋学习（DRL）。深层固定将网络切片问题分解为主问题和几个奴隶问题。主问题是基于凸优化解决的，从而通过DRL方法来处理从事最佳资源分配策略。提出的算法的性能通过网络模拟验证。
Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond. These use cases, however, have very diverse network resource demands, e.g., communication and computation, and various performance metrics such as latency and throughput. To effectively allocate network resources to slices, we propose DeepSlicing that integrates the alternating direction method of multipliers (ADMM) and deep reinforcement learning (DRL). DeepSlicing decomposes the network slicing problem into a master problem and several slave problems. The master problem is solved based on convex optimization and the slave problem is handled by DRL method which learns the optimal resource allocation policy. The performance of the proposed algorithm is validated through network simulations.
