🚀 eAPI-Payments Scaling Simulation

TPS: 0

Total Requests: 0

Dropped: 0

Current Utilization: 0%

Expected Load: 0

Actual Load: 0

HPA Status: Stable

📊 Pod Status Overview

Total Pods

All created pods

Active Pods

Ready to serve requests

Desired Pods

Target replica count

Pending

Not ready / Removing

🚦 Traffic Controls

Configure incoming request patterns

Transactions per Second: 250

Response Duration (ms): 1000

Pool Size per Pod: 200

📈 Scaling Controls

Configure autoscaling behavior

Min Replicas: 6

Max Replicas: 20

Scale Up Threshold (%): 50

Scale Down Threshold (%): 30

Pod Readiness Time (s): 20

⚙️ System Controls

Configure simulation behavior and presets

🚀 Simulation Settings

Simulation Speed:

Allow Queue

Show Request Flow

🎯 Quick Presets

🔧 System Actions

Load Balancer

Legend

Low Load

Medium Load

High Load

Overloaded

Not Ready

🎯 What is HPA?

HPA automatically scales the number of pods based on observed metrics like CPU utilization, memory usage, or custom metrics.

📈 Scale Up Formula

desiredReplicas = ceil[currentReplicas × (currentValue / targetValue)]

Example: If you have 6 pods running at 75% utilization and your target is 50%:

currentReplicas: 6
currentValue: 75%
targetValue: 50%
Calculation: ceil[6 × (75/50)] = ceil[6 × 1.5] = ceil[9] = 9 pods

📉 Scale Down Logic

Pods are scaled down when utilization falls below the Scale Down Threshold for a sustained period. This prevents rapid oscillation.

⚙️ Key Parameters

Min Replicas: Minimum pods that must always run

Max Replicas: Maximum pods the system can create

Scale Up Threshold: Utilization % that triggers scaling up

Scale Down Threshold: Utilization % that triggers scaling down

🔄 Real-time Behavior

In this simulation, you can see HPA in action:

Watch how utilization changes affect pod counts
Observe the delay between scaling decisions and pod readiness
See how min/max constraints limit scaling behavior
Notice the difference between scale-up (formula-based) and scale-down (threshold-based) logic