Adaptive Models for  Infrastructure Resource Planning

Manni Megna Nookala

doi:10.71097/IJTAS.v17.i3.1351

Adaptive Models for Infrastructure Resource Planning

Author(s)	Manni Megna Nookala
Country	India
Abstract	Sonatype Nexus is a widely adopted repository management platform used in DevOps and Continuous Integration/Continuous Delivery (CI/CD) environments for the efficient storage, management, and retrieval of binary artifacts. It provides centralized management of build artifacts, software dependencies, and container images, enabling development teams to collaborate effectively while maintaining the integrity and security of the software supply chain. As software development activities continue to grow, a substantial volume of artifacts is uploaded daily to multiple repositories within each Nexus instance, resulting in continuous growth in storage utilization. Repository storage management is a critical responsibility of Nexus administrators. When repository storage exceeds the available disk capacity, the Nexus server may become unavailable, interrupting software development, build, and deployment activities and causing significant business disruption. Although Nexus provides automated cleanup tasks to remove obsolete artifacts, these mechanisms have inherent limitations because administrators must carefully distinguish between obsolete and actively used artifacts. Frequently accessed artifacts cannot be removed without affecting ongoing development processes, making proactive storage management a challenging task. Consequently, administrators lack an effective mechanism to accurately estimate future storage requirements and prevent unexpected repository outages.This paper presents a machine learning-based framework using Univariate Linear Regression Analysis to predict repository storage utilization from historical usage data. The proposed approach derives a regression equation that models the relationship between time and repository storage consumption, enabling accurate estimation of future storage requirements. The prediction model assists administrators in proactively planning storage expansion, scheduling repository maintenance, and preventing service interruptions caused by insufficient disk space. Experimental results demonstrate that the proposed framework provides reliable storage prediction with high accuracy, reduces administrative effort, improves repository availability, minimizes operational downtime, and supports effective capacity planning for enterprise-scale DevOps infrastructures.
Keywords	Sonatype Nexus Repository Manager, NXRM, Release Repository, Snapshot Repository, Docker registry, npm repository, Maven, Nuget, LDAP, Linear Regression Analysis.
Published In	Volume 17, Issue 3, March 2026
Published On	2026-03-05
DOI	https://doi.org/10.71097/IJTAS.v17.i3.1351

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International Journal of Technology and Applied Science

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Adaptive Models for Infrastructure Resource Planning

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