---
phase: 03-resource-management
plan: 03
subsystem: resource-management
tags: [proactive-scaling, hybrid-monitoring, resource-management, graceful-degradation]

# Dependency graph
requires:
  - phase: 03-01
    provides: Resource monitoring foundation
  - phase: 03-02
    provides: Hardware tier detection and classification
provides:
  - 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
affects: [04-memory-management, 05-conversation-engine]

# Tech tracking
tech-stack:
  added: []
  patterns: [hybrid-monitoring, proactive-scaling, graceful-degradation, stabilization-periods]

key-files:
  created: [src/resource/scaling.py]
  modified: [src/models/model_manager.py]

key-decisions:
  - "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"

patterns-established:
  - "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"

# Metrics
duration: 15min
completed: 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*