Machine Learning Framework for  Resource Utilization Analysis

Manni Megna Nookala

doi:10.36948/ijfmr.2023.v05i05.82412

Machine Learning Framework for Resource Utilization Analysis

Author(s)	Manni Megna Nookala
Country	India
Abstract	Repository management platforms have become an essential 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 support for 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 sufficient 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 predict 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 support proactive infrastructure planning. To address this challenge, this paper presents a machine learning-based analytical framework using Univariate Linear Regression Analysis to predict repository storage utilization from historical usage data. The proposed model identifies storage growth patterns 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 results demonstrate that the proposed approach closely approximates 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 framework helps organizations maintain uninterrupted software development and deployment operations while improving resource utilization and operational reliability.
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 5, September-October 2023
Published On	2023-09-09
DOI	https://doi.org/10.36948/ijfmr.2023.v05i05.82412

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 3 Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Machine Learning Framework for Resource Utilization Analysis

Share this