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

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.