DEVELOPMENT OF A LINUX REAL-TIME LATENCY MONITORING, STORAGE AND VISUALIZATION SYSTEM

This paper presented the development of a Linux-based real-time latency monitoring, storage, and visualization system using RTLA timerlat. The proposed architecture integrates RTLA-based latency measurement with a Python data collector, SQLite database, Flask backend, and web-based dashboard. This provides a continuous data path from operating-system-level latency measurement to persistent storage and graphical visualization. The experimental results demonstrate the practical operation of the developed system. The documented cyclictest experiment produced a minimum latency of 26 µs, an average latency of 973 µs, and a maximum latency of 16 ms. The project also demonstrated the use of the RTLA timerlat top and timerlat hist interfaces and the subsequent processing of latency information through collector.py, SQLite, app.py, and the dashboard. The normal- and high-latency experiments further demonstrated the ability of the system to classify and visualize different latency conditions through the web dashboard. The documented results show the complete processing sequence involving the collector, database, Python application, and dashboard for both conditions. The developed system therefore provides a practical foundation for continuous Linux latency observation without depending solely on manual inspection of command-line output. The current implementation focuses on measurement, collection, storage, threshold-based classification, and visualization, while machine-learning prediction, hardware performance-counter integration, virtualization analysis, and automated notification remain potential areas for future development.

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