论文标题
地图集：在网络切片中自动化在线服务配置
Atlas: Automate Online Service Configuration in Network Slicing
论文作者
论文摘要
网络切片实现了具有成本效益的切片定制，以支持异质应用和服务。但是，由于复杂的基础相关性以及模拟器与真实网络之间的模拟与真实性的差异，将跨域资源配置为基于服务级别协议的端到端切片是具有挑战性的。在本文中，我们提出了一个在线网络切片系统Atlas，该系统通过在三个相互关联的阶段中通过安全和样本的学习对配置方法来自动化切片的服务配置。首先，我们设计了一个基于学习的模拟器来减少SIM卡之间的差异，该差异是通过基于贝叶斯优化的新参数搜索方法来完成的。其次，我们通过使用贝叶斯神经网络和平行的汤普森采样的新颖的离线算法来脱机在增强模拟器中训练该政策。第三，我们在线通过安全探索和高斯流程回归的新颖的在线算法学习真实网络中的政策。我们在基于OpenAirInterface Ran，OpenDaylight SDN Transport，OpenAir-CN Core Network和基于Docker的Edge Server的端到端网络原型上实现地图集。实验结果表明，与最先进的解决方案相比，Atlas分别在在线学习阶段的资源使用率和经验质量分别获得了63.9％和85.7％的遗憾。
Network slicing achieves cost-efficient slice customization to support heterogeneous applications and services. Configuring cross-domain resources to end-to-end slices based on service-level agreements, however, is challenging, due to the complicated underlying correlations and the simulation-to-reality discrepancy between simulators and real networks. In this paper, we propose Atlas, an online network slicing system, which automates the service configuration of slices via safe and sample-efficient learn-to-configure approaches in three interrelated stages. First, we design a learning-based simulator to reduce the sim-to-real discrepancy, which is accomplished by a new parameter searching method based on Bayesian optimization. Second, we offline train the policy in the augmented simulator via a novel offline algorithm with a Bayesian neural network and parallel Thompson sampling. Third, we online learn the policy in real networks with a novel online algorithm with safe exploration and Gaussian process regression. We implement Atlas on an end-to-end network prototype based on OpenAirInterface RAN, OpenDayLight SDN transport, OpenAir-CN core network, and Docker-based edge server. Experimental results show that, compared to state-of-the-art solutions, Atlas achieves 63.9% and 85.7% regret reduction on resource usage and slice quality of experience during the online learning stage, respectively.
