Resource Forecasting for Intelligent Storage Planning
Author(s): Kirran Priya Nookala
Publication #: 2606042
Date of Publication: 11.10.2022
Country: India
Pages: 1-11
Published In: Volume 8 Issue 5 October-2022
DOI: https://doi.org/10.62970/IJIRCT.v8.i5.2606042
Abstract
Modern software development environments rely extensively on repository management platforms to organize, preserve, and distribute software components throughout the application lifecycle. Among these platforms, Sonatype Nexus is widely adopted in DevOps and Continuous Integration/Continuous Deployment (CI/CD) environments because of its capability to centrally manage binary artifacts, software libraries, container images, and application dependencies. As enterprise software development expands, repository storage utilization increases continuously, making storage capacity management a critical administrative responsibility. Although Sonatype Nexus provides automated repository cleanup mechanisms, these utilities are primarily designed to remove obsolete or unused artifacts and do not provide predictive insights into future repository growth. Since production-critical and frequently accessed artifacts must remain available, organizations require an effective mechanism to accurately forecast future storage requirements and support proactive infrastructure planning. To address this challenge, this paper presents a machine learning-based predictive framework using Univariate Linear Regression Analysis to estimate repository storage utilization from historical usage data. The proposed model derives a regression equation that captures the relationship between elapsed time and repository storage consumption, enabling accurate prediction of future storage requirements. The forecasting results enable administrators to proactively plan infrastructure expansion, optimize repository maintenance activities, and allocate storage resources more efficiently. Experimental evaluation demonstrates that the proposed framework accurately models repository growth trends, reduces administrative effort, minimizes the risk of storage exhaustion, improves repository availability, and supports effective infrastructure capacity planning for enterprise-scale DevOps environments. By enabling proactive storage forecasting, the proposed approach enhances operational reliability, improves infrastructure resource utilization, and ensures uninterrupted software development and deployment activities.
Keywords: Linear Regression, Forecasting, Prediction, Analytics, Storage, Repository, Nexus, Capacity, Utilization, Modeling, Machine Learning, DevOps, Artifacts, Trend Analysis, Regression, Optimization, Infrastructure, Automation, NXRM.
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