# Atlas: Automate Online Service Configuration in Network Slicing

arXiv: https://arxiv.org/abs/2210.16902 · HTML: https://mlsanity.com/paper/2210.16902/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 65 references that are in the map.
- First posted on arXiv 30 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [Scalable Bayesian Optimization Using Deep Neural Networks](https://mlsanity.com/paper/1502.05700/) (2015)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [Maximizing acquisition functions for Bayesian optimization](https://mlsanity.com/paper/1805.10196/) (2018)
- [Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation](https://mlsanity.com/paper/2006.04296/) (2020)
- [Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA](https://mlsanity.com/paper/2002.00073/) (2020)
- [OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning](https://mlsanity.com/paper/2111.01616/) (2021)
- [EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning](https://mlsanity.com/paper/2003.12911/) (2020)
- [Constraint-Aware Deep Reinforcement Learning for End-to-End Resource Orchestration in Mobile Networks](https://mlsanity.com/paper/2110.04320/) (2021)
- [Sl-EDGE: Network Slicing at the Edge](https://mlsanity.com/paper/2005.00886/) (2020)
- [Network Slicing Management Technique for Local 5G Micro-Operator Deployments](https://mlsanity.com/paper/1906.10993/) (2019)

## Built on by (most cited first, 1 of 1)

- [AWaRe-SAC: Proactive Slice Admission Control under Weather-Induced Capacity Uncertainty](https://mlsanity.com/paper/2601.05978/) (2026)

## Abstract (arXiv)

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.