Predicting Machine Failure in Data Centers Using Time-Series Analysis and LSTM Networks

Abstract

Data center reliability depends critically on predicting and preventing machine failures before they occur. This paper presents a novel Bidirectional Long Short-Term Memory (Bi-LSTM) architecture that processes multivariate time-series data to predict potential failures up to 24 hours in advance. Our model analyzes CPU usage, memory utilization, disk activity, network traffic, temperature, and power consumption patterns. Evaluation of production data demonstrates 90.5 percent accuracy, 92.3 percent precision, 89.1 percent recall, and an F1- score of 0.90 - significantly outperforming traditional approaches. The system processes streaming data with sub-100ms latency and reduces unplanned downtime by 30 percent. We provide detailed implementation guidance for production deployment, including data pipeline architecture, feature engineering approaches, and resource optimization strategies.