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Tasks completed: 2/2 - Implemented ProactiveScaler class with hybrid monitoring - Integrated proactive scaling into ModelManager Proactive scaling system with hybrid monitoring, graceful degradation cascades, and intelligent stabilization periods. SUMMARY: .planning/phases/03-resource-management/03-03-SUMMARY.md
114 lines
4.5 KiB
Markdown
114 lines
4.5 KiB
Markdown
---
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phase: 03-resource-management
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plan: 03
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subsystem: resource-management
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tags: [proactive-scaling, hybrid-monitoring, resource-management, graceful-degradation]
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# Dependency graph
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requires:
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- phase: 03-01
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provides: Resource monitoring foundation
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- phase: 03-02
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provides: Hardware tier detection and classification
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provides:
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- Proactive scaling system with hybrid monitoring and graceful degradation
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- Integration between ModelManager and ProactiveScaler
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- Pre-flight resource checks for model operations
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- Performance tracking for scaling decisions
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affects: [04-memory-management, 05-conversation-engine]
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# Tech tracking
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tech-stack:
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added: []
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patterns: [hybrid-monitoring, proactive-scaling, graceful-degradation, stabilization-periods]
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key-files:
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created: [src/resource/scaling.py]
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modified: [src/models/model_manager.py]
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key-decisions:
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- "Proactive scaling prevents performance degradation before it impacts users"
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- "Hybrid monitoring combines continuous checks with pre-flight validation"
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- "Graceful degradation completes current tasks before model switching"
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- "Stabilization periods prevent model switching thrashing"
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patterns-established:
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- "Pattern 1: Hybrid monitoring with background threads and pre-flight checks"
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- "Pattern 2: Graceful degradation cascades with immediate and planned switches"
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- "Pattern 3: Performance trend analysis for predictive scaling decisions"
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- "Pattern 4: Hysteresis and stabilization periods to prevent thrashing"
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# Metrics
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duration: 15min
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completed: 2026-01-27
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---
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# Phase 3: Resource Management Summary
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**Proactive scaling system with hybrid monitoring, graceful degradation cascades, and intelligent stabilization periods for resource-aware model management**
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## Performance
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- **Duration:** 15 minutes
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- **Started:** 2026-01-27T23:38:00Z
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- **Completed:** 2026-01-27T23:53:00Z
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- **Tasks:** 2
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- **Files modified:** 2
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## Accomplishments
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- **Created comprehensive ProactiveScaler class** with hybrid monitoring architecture combining continuous background monitoring with pre-flight checks
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- **Implemented graceful degradation cascades** that complete current tasks before switching to smaller models
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- **Added intelligent stabilization periods** (5 minutes for upgrades) to prevent model switching thrashing
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- **Integrated ProactiveScaler into ModelManager** with seamless scaling callbacks and performance tracking
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- **Enhanced model selection logic** to consider scaling recommendations and resource trends
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- **Implemented performance metrics tracking** for data-driven scaling decisions
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## Task Commits
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Each task was committed atomically:
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1. **Task 1: Implement ProactiveScaler class** - `4d7749d` (feat)
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2. **Task 2: Integrate proactive scaling into ModelManager** - `53b8ef7` (feat)
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**Plan metadata:** N/A (will be committed with summary)
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## Files Created/Modified
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- `src/resource/scaling.py` - Complete ProactiveScaler implementation with hybrid monitoring, trend analysis, and graceful degradation
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- `src/models/model_manager.py` - Enhanced ModelManager with ProactiveScaler integration, pre-flight checks, and performance tracking
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## Decisions Made
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- **Hybrid monitoring approach**: Combined continuous background monitoring with pre-flight checks for comprehensive resource awareness
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- **Proactive scaling thresholds**: Scale at 80% resource usage for upgrades, 90% for immediate degradation
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- **Stabilization periods**: 5-minute cooldowns prevent model switching thrashing during volatile resource conditions
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- **Graceful degradation**: Complete current tasks before switching models to maintain user experience
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- **Performance-driven scaling**: Use actual response times and failure rates for intelligent scaling decisions
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## Deviations from Plan
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None - plan executed exactly as written.
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## Issues Encountered
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None - all implementation completed successfully with full verification passing.
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## User Setup Required
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None - no external service configuration required.
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## Next Phase Readiness
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Proactive scaling system is complete and ready for integration with memory management and conversation engine phases. The hybrid monitoring approach provides:
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- Resource-aware model selection with tier-based optimization
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- Predictive scaling based on usage trends and performance metrics
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- Graceful degradation that maintains conversation flow during resource constraints
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- Stabilization periods that prevent unnecessary model switching
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The system maintains backward compatibility with existing ModelManager functionality while adding intelligent resource management capabilities.
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---
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*Phase: 03-resource-management*
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*Completed: 2026-01-27* |