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Machine Learning For Anomaly Detection In CPU Performance: Improving Reliability In Data Centers

Year: 2025

Type: article

Abstract: Data centers have relied heavily on machine learning to increase the reliability and performance of the data center operations by identifying and mitigating CPU performance anomalies. Data centers therefore must frequently maintain highly critical systems that cannot underperform or fail. This paper discusses machine learning techniques used for anomaly detection in CPU performance and how they enhance system reliability, prevent down time, and improve the operational efficiency. This paper presents a variety of machine learning algorithms, their ability to identify anomalies, and their practical use in the data centers to manage the resources better and improve performance monitoring.

Source: International Journal For Multidisciplinary Research

DOI: 10.36948/ijfmr.2025.v07i02.41479

Authors: Manoj Chowdary Lingam, Aravind Barla

Institutions: The University of Texas at Dallas, University of Central Missouri

Language: English

Topic: Software System Performance and Reliability

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4409772338

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