Reviews run for a minimum of one week. The outcome of the review is decided on this date. This is the last day to make comments or ask questions about this review.
Eclipse TMLL (Trace Server Machine Learning Library)
Performance analysis of software systems is a critical step in both development and maintenance. Among the current state-of-the-art methods for monitoring a system’s behavior during runtime, tracing stands out as a precise way to collect detailed information. Tools like Trace Compass, an open-source software developed and maintained by Ericsson, allow users to analyze collected trace data using a wide variety of analyses and visualizations. These tools help users explore system behavior, detect performance regressions, and identify anomalies during runtime.
Building on Trace Compass, Trace Server is an independent, open-source project that provides a robust infrastructure for trace analysis without requiring users to interact with a graphical interface. Instead, it enables programmatic access to trace data and analyses, offering greater flexibility and automation in software systems.
Incorporating Machine Learning (ML) into trace analysis offers significant benefits by enabling more advanced and automated insights. ML techniques can process vast amounts of trace data efficiently, identifying patterns, trends, and anomalies that may be difficult to detect with manual or traditional methods. By leveraging algorithms for anomaly detection, predictive maintenance, and root cause analysis, users gain a deeper understanding of their system's performance. This leads to faster identification of issues, proactive system management, and more informed decision-making, ultimately improving overall system reliability and optimization.
Core Problem
While both Trace Compass and Trace Server offer extensive analysis capabilities, users often face two major challenges:
- Knowledge of Trace Server: Conducting trace analyses such as CPU usage, memory, disk I/O, and synchronization monitoring requires a deep understanding of how to interact with Trace Server. Users must be familiar with specific trace categories to interpret the data and identify areas of interest, making it difficult for those without this background.
- Machine Learning Expertise: To leverage advanced, automated analyses through ML techniques—such as anomaly detection or regression analysis—users need considerable expertise in ML. For example, finding anomalies in CPU usage requires familiarity with statistical techniques or more advanced ML methods, which many users may lack.
Eclipse Trace Server Machine Learning Library (TMLL) provides an automated pipeline that applies machine learning techniques to the analyses derived from the trace server. The goal of TMLL is to simplify the process of performing both primitive trace analyses and complementary machine learning-based investigations.
Eclipse TMLL features:
- Automated Trace Data Analysis: Provide a streamlined pipeline for analyzing trace data from the trace server using both traditional methods and machine learning techniques.
- Machine Learning Integration: Incorporate multiple machine learning techniques (supervised, unsupervised, reinforcement learning, etc.) for tasks like anomaly detection, predictive maintenance, and resource optimization.
- Modular and Flexible Design: Allow users to plug in different modules (e.g., anomaly detection, trend analysis) tailored to specific system performance analysis needs, similar to libraries like PyCaret1.
- User-Friendly API: Offer a simple, intuitive interface for users with minimal ML or trace analysis expertise, making it easy to apply sophisticated analysis methods programmatically.
- Comprehensive System Insights: Provide a range of outputs such as performance trends, anomaly alerts, root cause identification, and optimization recommendations to help users manage and improve system performance.
- Extensibility and Customization: Enable developers and system administrators to extend the library by adding custom analysis modules or integrating with other performance monitoring tools.
- Visualization Capabilities: Include built-in methods for visualizing trace analysis results, such as heatmaps, time-series plots, or performance trend charts.
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.
None that we are aware of.
The Eclipse TMLL is contributed to the Trace Compass Incubator since it right now is working with the Trace Server implementation from the incubator. It will use the Trace Server Protocol. Therefore the fit is natural at the moment. However, if an AI group forms, it may migrate or be couple to that one.
Much of the implementation is still needed, at the moment we have anomaly detection, the scope shows the long term road-map.
EclipseSource, Arm, Ericsson, Polytechnique Montreal, Renessas, AMD, ST Micro.
https://github.com/kavehshahedi/tmll
- Log in to post comments
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.
- Log in to post comments