# Slicing-Based AI Service Provisioning on Network Edge

arXiv: https://arxiv.org/abs/2105.07052 · HTML: https://mlsanity.com/paper/2105.07052/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2021.

## Built on by (most cited first, 3 of 3)

- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)

## Abstract (arXiv)

Edge intelligence leverages computing resources on network edge to provide artificial intelligence (AI) services close to network users. As it enables fast inference and distributed learning, edge intelligence is envisioned to be an important component of 6G networks. In this article, we investigate AI service provisioning for supporting edge intelligence. First, we present the features and requirements of AI services. Then, we introduce AI service data management, and customize network slicing for AI services. Specifically, we propose a novel resource pooling method to jointly manage service data and network resources for AI services. A trace-driven case study demonstrates the effectiveness of the proposed resource pooling method. Through this study, we illustrate the necessity, challenge, and potential of AI service provisioning on network edge.