Mai/.planning/phases/03-resource-management/03-03-SUMMARY.md

phase, plan, subsystem, tags, requires, provides, affects, tech-stack, key-files, key-decisions, patterns-established, duration, completed

phase	plan	subsystem	tags	requires	provides	affects	tech-stack	key-files	key-decisions	patterns-established	duration	completed
03-resource-management	03	resource-management	proactive-scaling hybrid-monitoring resource-management graceful-degradation	phase provides 03-01 Resource monitoring foundation phase provides 03-02 Hardware tier detection and classification	Proactive scaling system with hybrid monitoring and graceful degradation Integration between ModelManager and ProactiveScaler Pre-flight resource checks for model operations Performance tracking for scaling decisions	04-memory-management 05-conversation-engine	added patterns hybrid-monitoring proactive-scaling graceful-degradation stabilization-periods	created modified src/resource/scaling.py src/models/model_manager.py	Proactive scaling prevents performance degradation before it impacts users Hybrid monitoring combines continuous checks with pre-flight validation Graceful degradation completes current tasks before model switching Stabilization periods prevent model switching thrashing	Pattern 1: Hybrid monitoring with background threads and pre-flight checks Pattern 2: Graceful degradation cascades with immediate and planned switches Pattern 3: Performance trend analysis for predictive scaling decisions Pattern 4: Hysteresis and stabilization periods to prevent thrashing	15min	2026-01-27

Phase 3: Resource Management Summary

Proactive scaling system with hybrid monitoring, graceful degradation cascades, and intelligent stabilization periods for resource-aware model management

Performance

• Duration: 15 minutes
• Started: 2026-01-27T23:38:00Z
• Completed: 2026-01-27T23:53:00Z
• Tasks: 2
• Files modified: 2

Accomplishments

• Created comprehensive ProactiveScaler class with hybrid monitoring architecture combining continuous background monitoring with pre-flight checks
• Implemented graceful degradation cascades that complete current tasks before switching to smaller models
• Added intelligent stabilization periods (5 minutes for upgrades) to prevent model switching thrashing
• Integrated ProactiveScaler into ModelManager with seamless scaling callbacks and performance tracking
• Enhanced model selection logic to consider scaling recommendations and resource trends
• Implemented performance metrics tracking for data-driven scaling decisions

Task Commits

Each task was committed atomically:

1. Task 1: Implement ProactiveScaler class - 4d7749d (feat)
2. Task 2: Integrate proactive scaling into ModelManager - 53b8ef7 (feat)

Plan metadata: N/A (will be committed with summary)

Files Created/Modified

• src/resource/scaling.py - Complete ProactiveScaler implementation with hybrid monitoring, trend analysis, and graceful degradation
• src/models/model_manager.py - Enhanced ModelManager with ProactiveScaler integration, pre-flight checks, and performance tracking

Decisions Made

• Hybrid monitoring approach: Combined continuous background monitoring with pre-flight checks for comprehensive resource awareness
• Proactive scaling thresholds: Scale at 80% resource usage for upgrades, 90% for immediate degradation
• Stabilization periods: 5-minute cooldowns prevent model switching thrashing during volatile resource conditions
• Graceful degradation: Complete current tasks before switching models to maintain user experience
• Performance-driven scaling: Use actual response times and failure rates for intelligent scaling decisions

Deviations from Plan

None - plan executed exactly as written.

Issues Encountered

None - all implementation completed successfully with full verification passing.

User Setup Required

None - no external service configuration required.

Next Phase Readiness

Proactive scaling system is complete and ready for integration with memory management and conversation engine phases. The hybrid monitoring approach provides:

• Resource-aware model selection with tier-based optimization
• Predictive scaling based on usage trends and performance metrics
• Graceful degradation that maintains conversation flow during resource constraints
• Stabilization periods that prevent unnecessary model switching

The system maintains backward compatibility with existing ModelManager functionality while adding intelligent resource management capabilities.

---

Phase: 03-resource-management Completed: 2026-01-27