Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language Model - Jingya Zheng; Gaofeng Tao; Shuxin Qin; Dan Wang; Zhongjun Ma (IEEE Access)
Storage resources are essential in heterogeneous multi-cloud environments. In response to the growing demand for efficient storage resource management (SRM) in these environments, this paper proposes an intent-based storage management (IBSM) system powered by a fine-tuned large language model (LLM). To overcome the limitations of existing methods, the IBSM system focuses on enhancing the controllability, completeness, and reliability of SRM in multi-cloud environments. Specifically, the IBSM system employs a dual-phase joint intent classification algorithm, which leverages a fine-tuned LLM to accurately identify user intents across diverse knowledge backgrounds. Additionally, the system constructs a collaborative intent decomposition method, which guarantees the integrity of intents. Furthermore, the system integrates an automated intent deployment mechanism that supports error recovery through checkpoints. Experimental results show that the system achieves a whole end-to-end (E2E) lifecycle for managing user intents. The E2E time is reduced by at least half compared to the manual approach, with an average of 50.14% dedicated to interactive tasks. Performance metrics for intent classification, including accuracy, precision, and recall, all exceed 90%. Moreover, the recovery time is reduced by an average of 30.6%. Therefore, the system provides a valuable solution for the autonomous management of multi-cloud resources.