Discovering Linux Kernel Subsystems in Use

Discovering Linux Kernel Subsystems in Use 🔗 **Source:** [Discovering Linux Kernel Subsystems in Use](https://www.youtube.com/watch?v=q3ZsHQ-wvb0) > [!note]+ Document Notes > tags: [linux-kernel, embedded-systems, workload-analysis] > author: Shuah Khan > source: YouTube > reference: https://www.youtube.com/watch?v=q3ZsHQ-wvb0 > date created: 2024-12-19 14:30 > > # 🏷️ Title: Discovering Linux Kernel Subsystems in Use >  ## 🧰 Action Inbox - [H1] Implement system information gathering at three points: before, during, and after workload execution — establish baseline metrics #testing - [H2] Set up event tracing for workload analysis using system-wide tracing capabilities — understand subsystem usage patterns #tracing - [H3] Use strace for fine-grained command analysis to identify specific system calls and resource usage — supplement high-level analysis #debugging ## 🧭 Table of Contents - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#️-notes) - [📚 References](#-references) - [🗣️ Paraphrase](#️-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-assessment) - [✅ Logical Validity](#-logical-validity) - [🛡️ DARVO Analysis](#-darvo-analysis) - [🎯 Conclusion](#-conclusion) ### 🔗 References - [Linux Kernel Documentation](https://kernel.org/doc/) - [OpenAPS Documentation](https://openaps.readthedocs.io/) ## 📝 Summary - Shuah Khan presents methods for discovering which Linux kernel subsystems are actively used by workloads - The approach combines system information gathering, event tracing, and fine-grained command analysis - Primary use case demonstrated through OpenAPS (Open Artificial Pancreas System) running on Raspberry Pi - Enables proper system sizing, kernel configuration, tuning, and regression testing for embedded and safety-critical applications - Bottom line: Understanding workload-kernel interactions is essential for automotive, medical, and other safety-critical embedded systems ## 📖 Main Content The presentation outlines a systematic approach to understanding how applications interact with the Linux kernel. This is particularly important for embedded systems where resource constraints and safety requirements demand precise understanding of system behavior. The methodology involves three phases of data collection: establishing baselines before workload execution, monitoring during execution, and validating cleanup after completion. This approach reveals both resource usage patterns and potential issues with applications that fail to properly clean up after themselves. The speaker demonstrates practical applications using OpenAPS, an open-source artificial pancreas system that manages insulin delivery for diabetic patients. This real-world example illustrates how the methodology applies to safety-critical embedded systems where understanding kernel interactions is crucial for regulatory compliance and system reliability. ## 🗒️ Notes #### ⏱️ [00:00](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=0) — [H1] Introduction and Problem Definition - Key idea: Understanding workload requirements is critical for embedded system design - Evidence: "sizing and memory and storage for a workload...especially when we are talking about Automotive talking about medical" #### ⏱️ [02:15](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=135) — [H2] Why Understanding Workloads Matters - Key idea: Four key reasons drive workload analysis in embedded systems - Evidence: "sizing...configure and tune the kernel...configure and tune the system...identify tests that we care about to avoid regressions" #### ⏱️ [03:45](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=225) — [H1] Three-Phase Data Collection Strategy - Key idea: Information gathering occurs at three critical points in workload lifecycle - Evidence: "before starting the workload while the workload is running and after stopping the workload" #### ⏱️ [04:30](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=270) — [H2] System Information Categories - Key idea: Five categories of system information provide comprehensive workload understanding - Evidence: "supported system calls...features...system parameters...scheduler...modules kernel modules" #### ⏱️ [06:00](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=360) — [H3] Tools for System Call Analysis - Key idea: Multiple tools enable comprehensive system call discovery and analysis - Evidence: "Ausyscall which is part of rdd tool set...checksyscalls script is part of the kernel" #### ⏱️ [08:15](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=495) — [H2] Workload Behavior Assessment - Key idea: Post-execution analysis reveals workload quality and cleanup behavior - Evidence: "good workload good well-behaving workload would probably set the system back to another state" #### ⏱️ [10:30](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=630) — [H3] Practical Tool Examples - Key idea: Command-line tools provide specific insights into system capabilities and usage - Evidence: "you can list the features you can also check...stack protector...memory leaks" #### ⏱️ [13:45](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=825) — [H2] Change Detection Through Differential Analysis - Key idea: Comparing system states reveals workload footprint and subsystem usage - Evidence: "we find differences so we can figure out what exactly is the workload footprint" #### ⏱️ [15:20](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=920) — [H1] Event Tracing for Workload Analysis - Key idea: System-wide event tracing captures comprehensive workload behavior beyond traditional debugging - Evidence: "tracing is not for just debugging...using tracing in this context for a very different purpose" #### ⏱️ [17:30](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=1050) — [H2] Mapping Events to Kernel Subsystems - Key idea: System calls and functions in traces map directly to specific kernel subsystems - Evidence: "if you see get timer calls...then we know that the timer subsystem is being used" #### ⏱️ [19:45](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=1185) — [H3] OpenAPS Case Study Results - Key idea: Real-world analysis of OpenAPS reveals specific module and subsystem usage patterns - Evidence: "we have seen a couple...three modules it does load and then unload" #### ⏱️ [21:15](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=1275) — [H2] Fine-Grained Analysis with strace - Key idea: strace enables detailed analysis of individual command execution and failure cases - Evidence: "we want to understand run as stress run various commands...what kind of system calls this particular command is using" #### ⏱️ [24:30](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=1470) — [H3] Command-Level Analysis Examples - Key idea: Individual OpenAPS commands reveal specific system interactions and failure modes - Evidence: "get insulin pump model...battery levels...pump history insulin temporary basal rates" #### ⏱️ [27:00](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=1620) — [H3] Practical Failure Analysis - Key idea: strace output reveals specific failure modes and device interaction problems - Evidence: "it cannot connect to...the pump...trying to open Dev SPI Dev and it cannot open that" #### ⏱️ [30:15](https://www.youtube.com/watch?v=q3ZsHQ-wvb0?t=1815) — [H2] Runtime Context and Hardware Integration - Key idea: Complete system view shows hardware-software interactions through kernel subsystems - Evidence: "SPI Dev driver...gpio...continuous glucose monitor and night Scout" ## 📚 References - [Linux Kernel Event Tracing Documentation](https://www.kernel.org/doc/html/latest/trace/events.html) - [OpenAPS Documentation](https://openaps.readthedocs.io/) - [strace Manual](https://man7.org/linux/man-pages/man1/strace.1.html) - [Raspberry Pi GPIO Documentation](https://www.raspberrypi.org/documentation/usage/gpio/) ## 🗣️ Paraphrase - Understanding which kernel subsystems your workload uses is essential for proper system sizing, configuration, and regression testing in embedded environments - A three-phase approach (before, during, after workload execution) combined with differential analysis reveals workload behavior and resource usage patterns - Event tracing and strace provide complementary views: high-level system behavior and detailed command-level interactions respectively ## 📰 Newsletter **Kernel Subsystem Discovery for Embedded Workloads** Understanding how your applications interact with the Linux kernel is crucial for embedded systems, especially in safety-critical domains like automotive and medical devices. Shuah Khan's methodology provides a systematic approach to workload analysis that goes far beyond traditional debugging. The three-phase data collection strategy—gathering system information before, during, and after workload execution—reveals not just what resources your application uses, but also whether it behaves properly by cleaning up after itself. Combined with event tracing and fine-grained analysis tools like strace, this approach enables precise system sizing, optimal kernel configuration, and targeted regression testing. The OpenAPS case study demonstrates real-world application, showing how this methodology identifies critical subsystems like SPI drivers and GPIO interfaces that must be monitored for regressions in medical device deployments. Resources: [Kernel Tracing Guide](https://www.kernel.org/doc/html/latest/trace/), [Embedded Linux Wiki](https://elinux.org/), [strace Examples](https://man7.org/linux/man-pages/man1/strace.1.html) ## ✅ Action Items #### DevRel - Developer advocacy: Present workload analysis methodology at embedded systems conferences - Education: Create workshop curriculum for kernel subsystem discovery techniques - Developer experience: Document integration patterns for tracing tools in CI/CD pipelines - Developer marketing: Develop case studies showcasing methodology in different industries - Community management: Facilitate discussions on workload analysis best practices #### OSPO - Policy & Governance: Establish guidelines for kernel subsystem monitoring in safety-critical applications - License Compliance: Review tracing tool licensing for embedded deployment scenarios - Security Management: Assess security implications of system-wide tracing in production environments - Software Inventory: Track kernel subsystem dependencies revealed through workload analysis - Developer Support: Provide training on embedded Linux workload analysis techniques - Community Engagement: Support upstream contributions of workload analysis improvements - Strategic Alignment: Align embedded Linux initiatives with organizational safety and compliance goals #### Community - Maintainers: Support kernel documentation improvements for workload analysis - Contributors: Encourage contributions to tracing infrastructure and analysis tools - Sponsors: Fund development of improved workload analysis capabilities - Ecosystem: Coordinate cross-project efforts for embedded system analysis standards #### WordPress - Core: Apply workload analysis principles to WordPress performance optimization - Core Performance: Use tracing techniques to identify performance bottlenecks - Community: Share embedded WordPress deployment best practices - Documentation: Document WordPress resource usage patterns for embedded deployments ## 🧪 Hypotheses - Systematic workload analysis reduces embedded system resource over-provisioning by 20-30% - Three-phase data collection identifies 90% of critical kernel subsystems for regression testing - Fine-grained command analysis reveals failure modes not apparent in high-level system monitoring - Differential analysis between workload phases accurately identifies misbehaving applications ### 📊 Methodology - Controlled experiments comparing resource usage before/after workload analysis implementation - Regression testing effectiveness measurement across identified vs. non-identified subsystems - Comparative analysis of strace vs. system-wide tracing for different types of applications ### ❓ Questions - How does this methodology scale to multi-container or microservice architectures? - What additional tracing mechanisms would enhance automotive and medical device analysis? - How can this approach be automated for continuous integration pipelines? ### 🔮 Future Research - Integration with container runtime analysis for cloud-native embedded systems - Machine learning approaches to automatically classify subsystem criticality - Real-time workload analysis for adaptive resource management ### 💡 Implications - Practice: Embedded developers need systematic approaches to understand kernel interactions - Policy: Safety-critical systems require documented subsystem dependency analysis - Security: System-wide tracing raises privacy and security considerations in production - Education: Engineering curricula should include workload analysis methodologies - Funding: Investment needed in automated workload analysis tooling and infrastructure ### 🧠 Learning Styles Assessment Reading level estimate: 12th grade (technical presentation with domain-specific terminology) #### 👀 Visual learners - Present: System architecture diagrams, flowcharts, command output examples - Improve: Interactive subsystem dependency visualizations, timeline charts #### 🔊 Aural learners - Present: Conference presentation format with speaker explanations - Improve: Narrated command demonstrations, audio walkthrough guides #### 🗣️ Verbal learners - Present: Technical terminology definitions, analogies to software engineering processes - Improve: Glossary of embedded systems terms, comparison tables #### 🧩 Kinesthetic learners - Present: Hands-on OpenAPS analysis, command-line tool demonstrations - Improve: Lab exercises, interactive tracing workshops #### 🧠 Logical learners - Present: Three-phase methodology structure, systematic analysis approach - Improve: Decision trees for tool selection, cause-effect diagrams #### 🧑‍🤝‍🧑 Social learners - Present: Conference Q&A session, community discussion opportunities - Improve: Collaborative analysis projects, peer review processes #### 🧘‍♀️ Solitary learners - Present: Technical documentation references, individual analysis exercises - Improve: Self-assessment checklists, independent study guides #### 🌿 Naturalistic learners - Present: Real-world medical device example, practical applications - Improve: Environmental impact considerations, sustainability connections ### 📊 Accessibility Assessment #### Screen reader semantics - Present: Clear heading structure, technical content organization - Improve: Alt text for diagrams, ARIA labels for data presentations #### Keyboard navigation - Present: Standard video controls, text-based content - Improve: Skip links for technical sections, keyboard shortcuts documentation #### Contrast and readability - Present: High contrast slides, clear font choices - Improve: Ensure code examples meet contrast requirements #### Text resizing and reflow - Present: Scalable presentation format - Improve: Responsive documentation layout for technical content #### Media and captions - Present: Video presentation available - Improve: Detailed captions including technical terms, transcript with timestamps #### Forms and interactions - Present: Basic video interface - Improve: Interactive code examples with accessible controls ### 🌍 DEIB Assessment #### Cultural breadth - Present: Technical content applicable globally - Improve: Examples from different geographical embedded system deployments #### Inclusive language - Present: Professional technical terminology - Improve: Define acronyms, explain domain-specific concepts #### Neurodiversity support - Present: Structured presentation format, clear section breaks - Improve: Multiple content formats, processing time accommodations #### Representation - Present: Technical focus on methodology - Improve: Diverse use case examples, varied speaker representation #### Psychological safety - Present: Professional conference environment - Improve: Beginner-friendly entry points, question encouragement ### ✅ Logical Validity #### Strong reasoning - Deductive strength: Strong - methodology follows logically from embedded system requirements - Inductive strength: Good - OpenAPS case study supports general applicability - Causal reasoning: Clear - workload analysis directly enables better system design - Analogical strength: Effective - medical device example illustrates broader principles #### Fallacies - Appeal to authority: