International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Infrastructure Storage Planning Through Predictive Modeling

Author(s) Rajani Gatta
Country India
Abstract Software repository management systems have become an integral 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 ability to manage diverse artifact types, including Maven dependencies, Docker container images, NuGet packages, and software libraries. The platform enhances artifact governance, facilitates collaboration among development teams, and strengthens 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 greater infrastructure demands. Consequently, repository capacity management has become a critical operational responsibility for administrators, who must continuously monitor storage utilization and perform maintenance activities to ensure uninterrupted repository services. Although Sonatype Nexus provides automated cleanup mechanisms for removing obsolete artifacts, these capabilities are limited to artifact deletion and do not provide predictive insights into future storage consumption. Since production-critical and frequently accessed artifacts cannot be removed without disrupting software development workflows, organizations require an intelligent and proactive approach to accurately forecast repository storage requirements, optimize infrastructure planning, and prevent unexpected service interruptions.
This paper proposes a data-driven storage capacity planning framework based on Univariate Linear Regression Analysis to forecast repository storage utilization using historical repository usage data. The proposed model identifies storage growth trends by deriving a regression equation that establishes the relationship between elapsed time and repository storage consumption. The resulting predictive model estimates future storage requirements with improved accuracy, enabling administrators to schedule infrastructure expansion, optimize repository maintenance activities, and allocate storage resources proactively. Experimental evaluation demonstrates that the proposed approach closely approximates actual repository growth patterns, reduces administrative effort, minimizes the risk of storage exhaustion, improves repository availability, and supports efficient 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 6, Issue 3, May-June 2024
Published On 2024-05-04
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.82413
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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