Building AI service repositories for on-demand service orchestration in 6G AI-RAN

Abstract

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 the proposed orchestrators generally over-simplify key orchestration factors compared to real edge computing environments. To fill these gaps, this paper systematically reviews and categorizes critical attributes influencing AI service orchestration in 6G AI-RAN 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 by up to 98%, and the necessity of infrastructure-specific profiling, paving the way for more production-ready service orchestration and provisioning frameworks.