  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.
