Building AI Service Repositories for On-Demand Service Orchestration in 6G AI-RAN
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908317296623616 |
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| author | Tang, Yun Zou, Mengbang Srinivasan, Udhaya Chandhar Umealor, Obumneme Kevogo, Dennis Scott, Benjamin James Guo, Weisi |
| author_facet | Tang, Yun Zou, Mengbang Srinivasan, Udhaya Chandhar Umealor, Obumneme Kevogo, Dennis Scott, Benjamin James Guo, Weisi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09647 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Building AI Service Repositories for On-Demand Service Orchestration in 6G AI-RAN Tang, Yun Zou, Mengbang Srinivasan, Udhaya Chandhar Umealor, Obumneme Kevogo, Dennis Scott, Benjamin James Guo, Weisi Artificial Intelligence Software Engineering 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. |
| title | Building AI Service Repositories for On-Demand Service Orchestration in 6G AI-RAN |
| topic | Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2504.09647 |