Adaptive Methods for Storage Capacity Optimization

Rajani Gatta

doi:10.70528/IJLRP.v5.i3.2277

Adaptive Methods for Storage Capacity Optimization

Author(s)	Rajani Gatta
Country	India
Abstract	Software repository management systems have become indispensable in modern software development by providing a centralized platform for storing, managing, and distributing software components throughout the software engineering lifecycle. Among these platforms, Sonatype Nexus is widely adopted in DevOps and Continuous Integration/Continuous Deployment (CI/CD) environments due to its ability to manage diverse artifact types, including Maven dependencies, Docker container images, NuGet packages, and software libraries. The platform enhances artifact governance, strengthens collaboration among development teams, and supports secure management of enterprise software assets. As organizations continuously generate and deploy large volumes of software artifacts, repository storage requirements increase substantially, making capacity management a critical operational challenge. Repository administrators are responsible for monitoring storage utilization and performing maintenance activities to remove obsolete artifacts and maintain adequate storage capacity. However, automated cleanup mechanisms primarily target unused artifacts and cannot accurately forecast future repository growth. Since production-critical and frequently accessed artifacts must remain available, organizations require a proactive mechanism for predicting storage requirements and preventing repository failures. 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 5, Issue 3, March 2024
Published On	2024-03-06
DOI	https://doi.org/10.70528/IJLRP.v5.i3.2277

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Adaptive Methods for Storage Capacity Optimization

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