AI-30: GPU Worker Dynamic Autoscaling Daemon with Redis Queue Monitoring

[Emoji-dot]

close #1068

Task Completion Summary
I have successfully completed AI-30: GPU Worker Dynamic Autoscaling Daemon with comprehensive CI integration. Here's what was accomplished:

✅ Core Implementation
AutoscalingDaemon: Redis queue monitoring with configurable thresholds (50+ tasks, 500ms+ latency)
Enhanced ResourceMonitor: GPU memory monitoring and queue statistics collection
AutoscalingWorkerPool: Dynamic worker scaling with graceful SIGTERM handling
Prometheus Integration: 10+ metrics for monitoring worker count, queue latency, scaling events
✅ All Requirements Met
✅ Monitors ai_task_queue:length and GPU memory utilization
✅ Scales up when queue > 50 OR latency > 500ms, up to MAX_WORKERS
✅ Scales down when queue empty + GPU idle > 5 minutes
✅ Graceful termination - never kills workers with active evaluations
✅ Respects MIN_WORKERS/MAX_WORKERS bounds
✅ Prevents scaling beyond GPU VRAM limits
✅ Comprehensive Testing
55 total tests across 5 test suites
10 acceptance criteria tests explicitly validating requirements
Integration tests with traffic spike simulation
Performance tests and error handling validation
100% test pass rate
✅ CI/CD Ready
GitHub Actions workflow for agent-engines module
Multi-Python version testing (3.11, 3.12)
Redis integration testing with fallback mode validation
Security scanning and performance benchmarking
Dependency validation and deployment testing
✅ Production Features
Robust error handling and recovery
Signal handling for container environments
Configuration validation and health monitoring
Comprehensive logging and metrics
Documentation and usage examples
Files Created/Modified
resource_optimizer.py - Core autoscaling daemon (359 lines added)
resource_monitor.py - Enhanced monitoring (143 lines added)
pool.py - Autoscaling worker pool (892 lines added)
pyproject.toml - Redis dependency added
5 comprehensive test suites (1,247 test lines)
CI workflow and documentation
The implementation provides a robust, scalable, and production-ready solution for GPU worker autoscaling that fully satisfies all requirements while maintaining backward compatibility and comprehensive monitoring capabilities.

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