Mathematical Framework for Storage Capacity Estimation

Rajani Gatta

doi:10.71097/IJSAT.v15.i4.11338

Mathematical Framework for Storage Capacity Estimation

Author(s)	Rajani Gatta
Country	India
Abstract	Software repository management systems have become an essential component of modern software development by providing a centralized platform for storing, managing, and distributing software artifacts throughout the software engineering lifecycle. Among these platforms, Sonatype Nexus is widely adopted in DevOps and Continuous Integration/Continuous Deployment (CI/CD) environments because of its capability to manage diverse artifact types, including Maven dependencies, Docker container images, NuGet packages, and software libraries. The platform strengthens artifact governance, enhances collaboration among development teams, and improves the security and reliability of enterprise software supply chains. As software development activities continue to expand, the volume of artifacts stored across repository instances increases significantly, resulting in rapid storage growth and escalating infrastructure demands. Consequently, repository capacity management has become a critical administrative responsibility, requiring continuous monitoring of storage utilization to ensure uninterrupted software delivery. Although Sonatype Nexus provides automated cleanup mechanisms for removing obsolete artifacts, these capabilities are limited to artifact deletion and do not support accurate prediction of future storage requirements. Since production-critical and frequently accessed artifacts must remain available, organizations require an intelligent and proactive solution to forecast repository growth, optimize infrastructure planning, and prevent unexpected service interruptions. This paper presents a data-driven framework for repository storage capacity planning using Univariate Linear Regression Analysis. The proposed approach analyzes historical repository utilization data to derive a regression equation that models the relationship between elapsed time and storage consumption. The resulting predictive model accurately estimates future repository storage requirements, enabling administrators to plan infrastructure expansion, optimize maintenance activities, and allocate storage resources proactively. Experimental results demonstrate that the proposed framework closely follows actual repository growth trends, improves prediction accuracy, reduces administrative effort, minimizes the risk of storage exhaustion, enhances repository availability, and supports effective infrastructure capacity planning in enterprise-scale DevOps environments.
Keywords	Linear Regression, Forecasting, Prediction, Analytics, Storage, Repository, Nexus, Capacity, Utilization, Modeling, Machine Learning, DevOps, Artifacts, Trend Analysis, Regression, Optimization, Infrastructure, Automation, NXRM.
Published In	Volume 15, Issue 4, October-December 2024
Published On	2024-11-09
DOI	https://doi.org/10.71097/IJSAT.v15.i4.11338

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Mathematical Framework for Storage Capacity Estimation

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