LLM-based policy generation for intent-based management of applications
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913235146375168 |
|---|---|
| author | Dzeparoska, Kristina Lin, Jieyu Tizghadam, Ali Leon-Garcia, Alberto |
| author_facet | Dzeparoska, Kristina Lin, Jieyu Tizghadam, Ali Leon-Garcia, Alberto |
| contents | Automated management requires decomposing high-level user requests, such as intents, to an abstraction that the system can understand and execute. This is challenging because even a simple intent requires performing a number of ordered steps. And the task of identifying and adapting these steps (as conditions change) requires a decomposition approach that cannot be exactly pre-defined beforehand. To tackle these challenges and support automated intent decomposition and execution, we explore the few-shot capability of Large Language Models (LLMs). We propose a pipeline that progressively decomposes intents by generating the required actions using a policy-based abstraction. This allows us to automate the policy execution by creating a closed control loop for the intent deployment. To do so, we generate and map the policies to APIs and form application management loops that perform the necessary monitoring, analysis, planning and execution. We evaluate our proposal with a use-case to fulfill and assure an application service chain of virtual network functions. Using our approach, we can generalize and generate the necessary steps to realize intents, thereby enabling intent automation for application management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10067 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM-based policy generation for intent-based management of applications Dzeparoska, Kristina Lin, Jieyu Tizghadam, Ali Leon-Garcia, Alberto Distributed, Parallel, and Cluster Computing Artificial Intelligence Formal Languages and Automata Theory Human-Computer Interaction Machine Learning Automated management requires decomposing high-level user requests, such as intents, to an abstraction that the system can understand and execute. This is challenging because even a simple intent requires performing a number of ordered steps. And the task of identifying and adapting these steps (as conditions change) requires a decomposition approach that cannot be exactly pre-defined beforehand. To tackle these challenges and support automated intent decomposition and execution, we explore the few-shot capability of Large Language Models (LLMs). We propose a pipeline that progressively decomposes intents by generating the required actions using a policy-based abstraction. This allows us to automate the policy execution by creating a closed control loop for the intent deployment. To do so, we generate and map the policies to APIs and form application management loops that perform the necessary monitoring, analysis, planning and execution. We evaluate our proposal with a use-case to fulfill and assure an application service chain of virtual network functions. Using our approach, we can generalize and generate the necessary steps to realize intents, thereby enabling intent automation for application management. |
| title | LLM-based policy generation for intent-based management of applications |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Formal Languages and Automata Theory Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2402.10067 |