Eclipse TMLL (Trace Server Machine Learning Library)

Eclipse TMLL provides users with pre-built, automated solutions that integrate general trace server analyses (e.g., CPU usage, memory, and interrupts) with machine learning models. This allows for more precise, efficient analysis without requiring deep knowledge in either trace server operations or ML. By streamlining the workflow, TMLL empowers users to identify anomalies, trends, and other performance insights without extensive technical expertise, significantly improving the usability of trace server data in real-world applications. 

Capabilities of TMLL 

• Anomaly Detection: TMLL employs unsupervised machine learning techniques, such as clustering and density-based methods, alongside traditional statistical approaches like Z-score and IQR analysis, to automatically detect outliers and irregular patterns in system behavior. This helps users quickly identify potential anomalies, such as unexpected spikes in CPU usage or memory leaks.
• Predictive Maintenance: Using time-series analysis, TMLL can forecast potential system failures or performance degradation. By analyzing historical data, the tool can predict when maintenance or adjustments will be necessary, helping users avoid costly downtime and improve system reliability.
• Root Cause Analysis: TMLL leverages supervised learning techniques to identify the underlying causes of performance issues. By training models on labelled trace data, users can determine which factors contribute to problems such as bottlenecks or system crashes, leading to faster resolution and more effective troubleshooting.
• Resource Optimization: Through a combination of classical optimization techniques and Reinforcement Learning (RL), TMLL helps users optimize system resources like CPU, memory, and disk I/O. This ensures efficient use of system resources and helps avoid unnecessary waste, while also adapting to changing workloads for better overall performance.
• Performance Trend Analysis: TMLL provides comprehensive tools to analyze long-term performance trends. By evaluating historical data and identifying patterns, users can detect performance shifts, regressions, or improvements over time, providing valuable insights for ongoing system optimization and future planning.