Atlas: Automate Online Service Configuration in Network Slicing

Document Type

Conference Proceeding

Publication Date

11-30-2022

Abstract

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.

Identifier

85144814978 (Scopus)

ISBN

[9781450395083]

Publication Title

Conext 2022 Proceedings of the 18th International Conference on Emerging Networking Experiments and Technologies

External Full Text Location

https://doi.org/10.1145/3555050.3569115

First Page

140

Last Page

155

Grant

2147623

Fund Ref

National Science Foundation

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