Atlas: automate online service configuration in network slicing
Atlas: automate online service configuration in network slicing
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DOI：
10.1145/3555050.3569115
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Qiang Liu;Nakjung Choi;Tao Han
中科院分区：
文献类型：
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作者：
Qiang Liu;Nakjung Choi;Tao Han
网络切片实现了具有成本效益的切片定制，以支持异构应用和服务。然而，由于模拟器和真实的网络之间复杂的底层相关性和模拟与现实的差异，基于服务级别协议将跨域资源配置到端到端切片是具有挑战性的。在本文中，我们提出了阿特拉斯，在线网络切片系统，通过安全和样本高效的学习配置方法在三个相互关联的阶段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，以减少模拟到真实的差异，这是通过一个新的参数搜索方法的基础上贝叶斯优化。其次，我们离线训练的政策，在增强的模拟器通过一种新的离线算法与贝叶斯神经网络和并行汤普森采样。第三，我们在线学习策略在真实的网络与一种新的在线算法，安全的探索和高斯过程回归。我们在基于OpenAirInterface RAN、OpenDayLight SDN传输、OpenAir-CN核心网络和基于Docker的边缘服务器的端到端网络原型上实现了Atlas。实验结果表明，与最先进的解决方案相比，Atlas实现了63.9%和85.7%的遗憾减少资源使用和切片质量的经验，在在线学习阶段，分别。
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.