# Building AI Service Repositories for On-Demand Service Orchestration in 6G AI-RAN

arXiv: https://arxiv.org/abs/2504.09647 · HTML: https://mlsanity.com/paper/2504.09647/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [End-to-End Edge AI Service Provisioning Framework in 6G ORAN](https://mlsanity.com/paper/2503.11933/) (2025)
- [Joint Optimization of DNN Inference Delay and Energy under Accuracy Constraints for AR Applications](https://mlsanity.com/paper/2208.01860/) (2022)
- [Resource-Efficient Generative AI Model Deployment in Mobile Edge Networks](https://mlsanity.com/paper/2409.05303/) (2024)

## Built on by (most cited first, 2 of 2)

- [KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence](https://mlsanity.com/paper/2507.08164/) (2025)
- [Agentic Assistant for 6G: Turn-based Conversations for AI-RAN Hierarchical Co-Management](https://mlsanity.com/paper/2602.13868/) (2026)

## Abstract (arXiv)

Efficient orchestration of AI services in 6G AI-RAN requires well-structured, ready-to-deploy AI service repositories combined with orchestration methods adaptive to diverse runtime contexts across radio access, edge, and cloud layers. Current literature lacks comprehensive frameworks for constructing such repositories and generally overlooks key practical orchestration factors. This paper systematically identifies and categorizes critical attributes influencing AI service orchestration in 6G networks and introduces an open-source, LLM-assisted toolchain that automates service packaging, deployment, and runtime profiling. We validate the proposed toolchain through the Cranfield AI Service repository case study, demonstrating significant automation benefits, reduced manual coding efforts, and the necessity of infrastructure-specific profiling, paving the way for more practical orchestration frameworks.