Min/Max Recommendation
Data-driven HPA boundary suggestions for optimal replica ranges based on actual traffic patterns.
Overview
Min/Max Recommendation analyzes your actual traffic patterns and workload behavior to suggest optimal minReplicas and maxReplicas boundaries for HPA configurations. Setting these correctly prevents both over-scaling (wasting money) and under-scaling (causing downtime).
Instead of guessing at appropriate replica ranges, Wave uses historical data to recommend boundaries that match your actual traffic peaks, valleys, and growth trends—eliminating the trial-and-error of HPA tuning.
Why Better Than Manual HPA Tuning
| Manual HPA Tuning | Wave Min/Max Recommendation | |
|---|---|---|
| Boundary Setting | Guesswork based on limited observation or conservative estimates | Data-driven recommendations based on actual traffic patterns |
| Configuration Updates | Manual HPA manifest updates after incidents or traffic changes | Automatic recommendations as workload patterns evolve |
| Accuracy | Often set too high (wasting cost) or too low (risking downtime) | Optimized for both cost efficiency and reliability |
Key Outcomes & Benefits
Set optimal min/max based on actual traffic patterns
Avoid over-scaling waste from too-high maxReplicas
Eliminate trial-and-error HPA configuration testing
How It Works
1. Traffic Analysis
Analyzes historical traffic patterns including peaks, valleys, and growth trends.
2. Replica Correlation
Correlates traffic levels with actual replica counts needed to maintain SLOs.
3. Boundary Calculation
Calculates optimal minReplicas (baseline load) and maxReplicas (peak capacity) with buffers.
4. Recommendation Delivery
Provides actionable recommendations with one-click apply or manual HPA update.
Optimize HPA Boundaries with Data-Driven Insights
Stop guessing at replica ranges—get precise recommendations.
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