International Journal For Multidisciplinary Research

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Enhancing Business Resilience: Predicting Hard Disk Failures with Machine Learning for Efficient Resource Management

Author(s) Rajasee Thakre, Shruti Kulkarni, Anushka Kulkarni, Jayesh Suryawanshi
Country India
Abstract In today's data-driven business landscape, maintaining the resilience of digital infrastructure is paramount. One of
the most critical components of this infrastructure is the hard disk drive (HDD). The potential for HDD failures poses a
significant risk to data integrity and operational continuity. To address this challenge, this paper presents an innovative
approach to enhancing business resilience through the predictive analysis of hard disk drive failures using machine learning
techniques. Our research leverages machine learning algorithms to predict HDD failures, enabling organizations to proactively
manage resources and mitigate potential disruptions. By harnessing historical data, system behavior patterns, and SelfMonitoring, Analysis, and Reporting Technology (S.M.A.R.T.) metrics, our model can accurately forecast when an HDD is
likely to fail. This predictive capability empowers organizations to optimize resource allocation, reduce downtime, and enhance
data security.
Keywords Hard Disk Drive, Predictive Modeling, Resource Efficiency, Downtime Reduction
Field Engineering
Published In Volume 5, Issue 5, September-October 2023
Published On 2023-09-17
Cite This Enhancing Business Resilience: Predicting Hard Disk Failures with Machine Learning for Efficient Resource Management - Rajasee Thakre, Shruti Kulkarni, Anushka Kulkarni, Jayesh Suryawanshi - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.6521
DOI https://doi.org/10.36948/ijfmr.2023.v05i05.6521
Short DOI https://doi.org/gssfpt
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