Lustre Performance Monitoring

[Jan-Willem]

Lustre Performance Monitoring and Benchmarking

Objective

Develop a Kubernetes-integrated solution for monitoring and benchmarking Lustre performance, including automated testing, alerting, and documentation.

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Requirements

• Benchmark Testing Suite:
  • Tests should run on each cluster node.
  • Tests must be executed within Kubernetes pods.
• Automated Monitoring and Alerting System:
  • Automatic scheduling and execution of tests.
  • Email notifications for test failures and critical metrics.
• Data Storage:
  • Test results must be stored persistently for analysis.
• Documentation:
  • Clear instructions on setup, execution, and troubleshooting.

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Definition of Done

Performance Specification

• Define and document key performance metrics:
  • Required read speeds.
  • Required write speeds.
  • Acceptable latency thresholds.
  • Performance consistency requirements.

Testing Infrastructure

• Test Configuration:
  • Select scripting language for test implementation (e.g., Python, Bash).
  • Define test execution frequency.
  • Choose a scheduling mechanism:
    • Evaluate systemd vs. crontab.
    • Consider Kubernetes CronJobs for pod-based tests.
• Benchmark Test Development:
  • Create node-level performance tests.
  • Develop Kubernetes pod-based tests.
  • Incorporate robust error handling and detailed logging.

Monitoring and Alerting

• Notification System:
  • Configure email alerts for test failures and threshold breaches.
  • Define alert thresholds and severity levels.
  • Implement a centralized logging mechanism for diagnostics.

Documentation

• Comprehensive Documentation:
  • Detailed test setup and configuration instructions.
  • Manual execution and validation guides.
  • Explanation of performance metrics and their implications.
  • Troubleshooting steps for common issues.

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Key Decision Points

1. Performance Testing Tools:
   • Evaluate tools like fio, IOR, MDTest, or custom scripts for suitability.
2. Monitoring and Alerting Framework:
   • Choose email notification services.
   • Explore integration with existing monitoring systems (e.g., Prometheus, Grafana).
   • Implement effective logging and tracking mechanisms.
3. Test Execution Strategy:
   • Determine the optimal frequency for performance tests.
4. Scripting Language Selection:
   • Assess scripting languages for performance, compatibility, and ease of maintenance.

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Artifacts

• Performance test scripts.
• Configuration files for tests and scheduling.
• Documentation covering setup, execution, and troubleshooting.
• Sample test results and baseline metrics for benchmarking.

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Success Criteria

• Fully automated Lustre performance testing integrated with Kubernetes.
• Defined and measurable performance benchmarks.
• Proactive failure detection with actionable alerts.
• Minimal testing impact on production workloads.

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Potential Challenges

• Variability in performance across cluster nodes.
• Network and storage inconsistencies affecting results.
• Ensuring a consistent test environment.
• Balancing testing load with production workload demands.

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