ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2 (2201.00393v4)

Abstract: Testing and debugging have become major obstacles for robot software development, because of high system complexity and dynamic environments. Standard, middleware-based data recording does not provide sufficient information on internal computation and performance bottlenecks. Other existing methods also target very specific problems and thus cannot be used for multipurpose analysis. Moreover, they are not suitable for real-time applications. In this paper, we present ros2_tracing, a collection of flexible tracing tools and multipurpose instrumentation for ROS 2. It allows collecting runtime execution information on real-time distributed systems, using the low-overhead LTTng tracer. Tools also integrate tracing into the invaluable ROS 2 orchestration system and other usability tools. A message latency experiment shows that the end-to-end message latency overhead, when enabling all ROS 2 instrumentation, is on average 0.0033 ms, which we believe is suitable for production real-time systems. ROS 2 execution information obtained using ros2_tracing can be combined with trace data from the operating system, enabling a wider range of precise analyses, that help understand an application execution, to find the cause of performance bottlenecks and other issues. The source code is available at: https://github.com/ros2/ros2_tracing.

• The paper presents an extensible tracing framework that integrates with key ROS 2 components, including message handling, callbacks, and lifecycle states.
• It leverages LTTng to achieve minimal overhead, with an end-to-end message latency increase of only 0.0033 ms, ensuring suitability for real-time applications.
• The framework facilitates comprehensive runtime analysis by merging OS-level and application-level traces to pinpoint performance bottlenecks and system issues.

Assessment of "ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2"

The paper "ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2" presents an innovative tracing solution tailored for the Robot Operating System 2 (ROS 2). It addresses the key challenges encountered during testing and debugging in robotic software development, particularly in terms of understanding complex, distributed, and real-time systems.

Key Contributions

1. Extensible Tracing Instrumentation: The authors propose an extensible framework for tracing various aspects of ROS 2, including message handling, callbacks, services, executor states, and lifecycle states. This extensibility enables users to adapt the tracing framework based on specific needs or to expand it for future use cases.
2. Minimal Overhead: Leveraging the Linux Trace Toolkit next-generation (LTTng) tracer, the framework introduces low overhead, crucial for maintaining the performance and real-time capabilities of robotic applications. The end-to-end message latency overhead is a mere 0.0033 ms, affirming its suitability for production systems.
3. Integration with ROS 2 Tools: By integrating trace functionality into ROS 2's orchestration and usability tools, such as the ros2 launch system, the framework supports seamless operation in distributed environments. This enables coordinated tracing across multiple nodes and even hosts.
4. Comprehensive Analysis Capability: The framework allows detailed runtime analysis by combining trace data from ROS 2 applications and the operating system. This facilitates identifying performance bottlenecks and debugging issues that traditional logging and debugging techniques might miss.

Analytical Observations

The paper delineates the inadequacies of conventional data recording tools like rosbag and standard logging, emphasizing their limitations in revealing execution context and detailed computation events that are crucial for real-time diagnosis. In contrast, the proposed framework with LTTng supports capturing structured, on-demand data across different software layers and the kernel, offering an evolved debugging experience akin to classical debuggers but with lesser intrusiveness.

The evaluation covered varying message sizes and publishing rates to assess the framework's impact on latency. The results demonstrate negligible latency impact across typical message sizes and rates, solidifying the approach's applicability in real-time scenarios where timing constraints are stringent.

Implications and Future Directions

The ros2_tracing framework addresses a significant gap in robotic systems analysis, providing a potentially transformative approach to understanding complex interaction and performance issues in real-time systems. This contribution is not only practical but also opens up new research avenues, such as refining ROS 2 executors and enhancing deterministic behavior analysis.

Looking forward, potential enhancements include expanding support for services and actions, further reducing overhead by selective tracepoint activation, and extending cross-platform tracing support. Additionally, incorporating online monitoring features could allow adaptive system adjustments in real-time, propelling toward autonomous, self-optimizing robotics systems. Furthermore, leveraging such tracing mechanisms might contribute to more robust real-time safety standards and frameworks across the industry.

Overall, ros2_tracing signifies a substantial step toward advanced instrumentation frameworks, enabling detailed introspection necessary for both academia and industry to push the boundaries of real-time robotic applications using ROS 2.