Strategic Management of Hybrid Retrieval-Augmented Microservices for Long-Horizon Cloud Machine Learning.

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Bibliographic Details
Title:	Strategic Management of Hybrid Retrieval-Augmented Microservices for Long-Horizon Cloud Machine Learning.
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]
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Database:	Engineering Source

Description
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]
ISSN:	09731318
DOI:	10.23940/ijpe.26.05.p6.288296