构建6G AI-RAN的AI服务仓库，实现按需编排与自动化部署。
Building AI Service Repositories for On-Demand Service Orchestration in 6G AI-RAN
- 用LLM辅助工具链自动打包、部署和性能分析AI服务。
- 案例验证显示人工编码量减少，需针对硬件做运行时调优。
- 适合6G网络架构师与AI服务开发者快速构建可部署系统。
6G AI-RAN中高效编排AI服务需要结构化、即插即用的AI服务仓库，以及适应无线接入、边缘和云层多种运行环境的编排方法。现有文献缺乏完整的仓库构建框架，且普遍忽略关键实际编排因素。本文系统识别并分类了影响6G网络中AI服务编排的关键属性，提出一个开源的LLM辅助工具链，实现服务打包、部署和运行时性能分析的自动化。通过Cranfield AI Service仓库案例研究验证，该工具链显著提升了自动化水平，减少了手动编码工作量，并揭示了基础设施特定性能分析的必要性，为更实用的编排框架奠定基础。
原文摘要 · Abstract (English)
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.
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