Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language Model
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)....
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10975014/ |
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| author | Jingya Zheng Gaofeng Tao Shuxin Qin Dan Wang Zhongjun Ma |
| author_facet | Jingya Zheng Gaofeng Tao Shuxin Qin Dan Wang Zhongjun Ma |
| author_sort | Jingya Zheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5545372d52f84f0f88e341ec61bd4e56 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5545372d52f84f0f88e341ec61bd4e562025-08-20T03:14:12ZengIEEEIEEE Access2169-35362025-01-0113727367275310.1109/ACCESS.2025.356320010975014Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language ModelJingya Zheng0https://orcid.org/0009-0007-6101-8529Gaofeng Tao1https://orcid.org/0009-0004-5006-6259Shuxin Qin2Dan Wang3Zhongjun Ma4https://orcid.org/0009-0006-5302-9773Shandong Future Network Research Institute, Jinan, ChinaShandong Future Network Research Institute, Jinan, ChinaPurple Mountain Laboratories, Nanjing, ChinaPurple Mountain Laboratories, Nanjing, ChinaShandong Future Network Research Institute, Jinan, ChinaStorage 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.https://ieeexplore.ieee.org/document/10975014/Autonomous managementcloud computingcloud-network integrationintent-based networkinglarge language modelmulti-cloud |
| spellingShingle | Jingya Zheng Gaofeng Tao Shuxin Qin Dan Wang Zhongjun Ma Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language Model IEEE Access Autonomous management cloud computing cloud-network integration intent-based networking large language model multi-cloud |
| title | Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language Model |
| title_full | Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language Model |
| title_fullStr | Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language Model |
| title_full_unstemmed | Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language Model |
| title_short | Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language Model |
| title_sort | intent based multi cloud storage management powered by a fine tuned large language model |
| topic | Autonomous management cloud computing cloud-network integration intent-based networking large language model multi-cloud |
| url | https://ieeexplore.ieee.org/document/10975014/ |
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