Computational Intelligence for Resource Consumption Prediction

Rajani Gatta

doi:10.70528/IJLRP.v4.i5.2276

Computational Intelligence for Resource Consumption Prediction

Author(s)	Rajani Gatta
Country	India
Abstract	Repository management platforms have become an indispensable component of modern software engineering by providing a centralized environment for storing, managing, and distributing software packages throughout the software development 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 multiple artifact formats, including Maven packages, Docker images, software libraries, and application dependencies. Its centralized architecture streamlines artifact management, enhances collaboration among development teams, and strengthens governance of enterprise software assets. As organizations continuously generate and publish large volumes of software artifacts across multiple repositories, storage utilization increases significantly, creating substantial challenges in repository administration and infrastructure capacity management. Repository administrators must continuously monitor storage growth and perform maintenance activities to ensure adequate storage availability. However, delayed maintenance or inadequate capacity planning can lead to repository outages, disrupting build pipelines, deployment workflows, and other mission-critical software engineering operations. Although Sonatype Nexus provides built-in cleanup mechanisms to automate repository maintenance, these utilities primarily remove obsolete or unused artifacts and do not support prediction of future repository growth. Since frequently accessed and production-critical artifacts must remain available, storage management becomes increasingly complex as repository utilization expands. Consequently, administrators require reliable analytical techniques to forecast future storage requirements and facilitate 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 identifies storage growth patterns by deriving a regression equation that captures the relationship between elapsed time and repository storage consumption. The resulting predictive model estimates future storage requirements with improved accuracy, enabling administrators to plan infrastructure expansion, optimize repository maintenance activities, and allocate storage resources proactively. Experimental results demonstrate 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 framework assists organizations in maintaining uninterrupted software development and deployment operations while improving storage utilization, infrastructure reliability, and overall operational efficiency.
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 4, Issue 5, May 2023
Published On	2023-05-05
DOI	https://doi.org/10.70528/IJLRP.v4.i5.2276

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Computational Intelligence for Resource Consumption Prediction

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