Real-Time Hardware Monitoring System Using Big Data Technology Stack
Jainil Jain, Surbhi Saraswat · Procedia Computer Science · 2026
The continuous monitoring of critical computer hardware is often a manual and time-consuming process. This work presents a solution by developing a comprehensive real-time log analysis system that leverages a powerful big data technology stack to automate and optimize the continuous monitoring of critical computer hardware parameters. It utilizes HWInfo logs to efficiently stream data through Apache Kafka, process and analyze it using Apache Spark, and finally store the structured information in Elasticsearch. This approach ensures seamless data ingestion, processing, and retrieval, allowing for fast and accurate access to key performance indicators. To enhance usability, the system features a dynamic dashboard for real-time visualizations and alert mechanisms with various visual cues to proactively notify users/system managers of anomalies. This entire functionality is designed upon a scalable and fault tolerant architecture that also capable of processing logs at scale. By identifying potential system bottlenecks, overheating issues, our approach allows users to make informed and data-driven decisions crucial for improving reliability and preventing critical system failures. Experimental evaluations demonstrate the system’s effectiveness in detecting CPU anomalies with the integrated ML model delivering 98.3% accuracy in predicting CPU core thermal throttling and the end-to-end pipeline latency under a second.