Create benchmarking workloads for comparison

Overview

Develop benchmarking workloads that can be executed on both experimental Kubernetes runners and hosted VM runners to enable standardized performance comparison and cost analysis.

Why This Matters

Standardized benchmarking workloads enable fair comparison between platforms by controlling for workload variability. This provides reliable data for cost-benefit analysis and performance validation.

Scope

1. Workload Design

   • Create synthetic workloads representing different job types
   • Design workloads for:
     • Short jobs (<5 minutes)
     • Medium jobs (5-30 minutes)
     • Long jobs (>1 hour)
     • Memory-intensive jobs
     • CPU-intensive jobs
     • I/O-intensive jobs

2. Workload Implementation

   • Implement workloads as reproducible CI/CD jobs
   • Ensure consistent execution across platforms
   • Document workload specifications
   • Create workload execution harness

3. Metrics Collection

   • Define metrics to collect from each workload
   • Implement metrics collection
   • Ensure metrics are comparable across platforms
   • Create reporting framework

4. Validation

   • Test workloads on both platforms
   • Validate reproducibility
   • Ensure metrics are accurate and comparable
   • Document workload behavior

Acceptance Criteria

• Benchmarking workloads designed and documented
• Workloads implemented as reproducible CI/CD jobs
• Workloads tested on both platforms
• Metrics collection validated
• Workload execution harness created
• Comparison reports generated