Analytical Approaches for Resource Consumption Forecasting

Rajani Gatta

doi:10.71097/IJAIDR.v13.i1.2020

Analytical Approaches for Resource Consumption Forecasting

Author(s)	Rajani Gatta
Country	India
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 ability to centrally manage binary artifacts, software libraries, container images, and application dependencies. Repository administrators must continuously monitor storage utilization and perform maintenance activities to ensure sufficient storage availability. Failure to perform timely maintenance or expand storage capacity can result in repository outages, disrupting software builds, deployment pipelines, and other mission-critical software engineering operations. Although Sonatype Nexus provides automated repository cleanup mechanisms, these utilities are primarily designed to remove obsolete or unused artifacts and do not support prediction of 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 allow administrators to proactively schedule infrastructure expansion, optimize repository maintenance activities, and allocate storage resources efficiently. Experimental evaluation demonstrates that the proposed framework closely models actual 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 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.
Published In	Volume 13, Issue 1, January-June 2022
Published On	2022-06-02
DOI	https://doi.org/10.71097/IJAIDR.v13.i1.2020

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Analytical Approaches for Resource Consumption Forecasting

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