Strategic Management of Hybrid Retrieval-Augmented Microservices for Long-Horizon Cloud Machine Learning.
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| Title: | Strategic Management of Hybrid Retrieval-Augmented Microservices for Long-Horizon Cloud Machine Learning. |
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| Authors: | Bansal, Deepak1, Arora, Yojna2, Singh, Hare Ram3, Sharma, Rashmi4, Chaturvedi, Rekha5 rekha.chaturvedi@jaipur.manipal.edu |
| Source: | International Journal of Performability Engineering. May2026, Vol. 22 Issue 5, p288-296. 9p. |
| Subjects: | Machine learning, Software architecture, Information filtering, Cloud computing, Scalability, Information retrieval |
| Abstract: | For long-horizon machine learning, there is a need to process large amounts of contextual data and perform reasoning over long sequences of time or knowledge. The traditional monolithic machine learning architectures have shown limitations in terms of scalability, accessibility of knowledge, and efficient utilization of resources in cloud computing. The paper aims to introduce a Hybrid Retrieval-Augmented Microservices Architecture (HRAMA), which can be used to enhance the efficiency of machine learning architectures in cloud computing. The hybrid retrieval mechanism integrates semantic vector similarity search with metadata-based filtering to improve the relevance of the extracted information. The architecture is based on a machine learning pipeline that is decomposed into independent microservices, which are then deployed using containerized cloud computing. The performance of the proposed architecture is validated using experimental results that show improved retrieval accuracy, system throughput, and scalability, along with reduced inference latency. The proposed HRAMA framework is efficient for long horizon cloud machine learning applications. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193880206 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.23940/ijpe.26.05.p6.288296 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 288 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Software architecture Type: general – SubjectFull: Information filtering Type: general – SubjectFull: Cloud computing Type: general – SubjectFull: Scalability Type: general – SubjectFull: Information retrieval Type: general Titles: – TitleFull: Strategic Management of Hybrid Retrieval-Augmented Microservices for Long-Horizon Cloud Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bansal, Deepak – PersonEntity: Name: NameFull: Arora, Yojna – PersonEntity: Name: NameFull: Singh, Hare Ram – PersonEntity: Name: NameFull: Sharma, Rashmi – PersonEntity: Name: NameFull: Chaturvedi, Rekha IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09731318 Numbering: – Type: volume Value: 22 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Performability Engineering Type: main |
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