# scalings.xyz
> An interactive autoscaling simulator for Kubernetes HPA, AWS ASG, and GCP MIG.

## What this tool does
Lets engineers configure autoscaling parameters, pick a traffic pattern, and run
a time-series simulation to see if their scaling policy handles the load.

Supported platforms:
- Kubernetes Horizontal Pod Autoscaler (HPA)
- AWS Auto Scaling Group (ASG)
- GCP Managed Instance Group (MIG)
- Custom / Generic autoscaler

## How to use programmatically
Construct a URL: scalings.xyz/#config=<base64-encoded-json>
Add &autorun=true to auto-run the simulation on load.

The config parameter is a base64-encoded JSON object matching the SimulationConfig schema:
```json
{
  "version": 1,
  "name": "My Simulation",
  "platform": "kubernetes-hpa",
  "simulation": { "duration": 600, "tick_interval": 1 },
  "scaling": {
    "min_replicas": 2, "max_replicas": 50,
    "scale_up_threshold": 70, "scale_down_threshold": 30,
    "capacity_per_replica": 100, "startup_time": 30,
    "scale_up_step": 1, "scale_down_step": 1
  },
  "advanced": {
    "metric_observation_delay": 15, "cooldown_scale_up": 60,
    "cooldown_scale_down": 300, "node_provisioning_time": 120,
    "cluster_node_capacity": 20, "pods_per_node": 10, "pod_failure_rate": 0,
    "graceful_shutdown_time": 10, "cost_per_replica_hour": 0.05
  },
  "chaos": {
    "pod_failure_rate": 0, "random_seed": 0,
    "failure_events": [{"time": 120, "count": 3}]
  },
  "traffic": {
    "pattern": "spike",
    "params": { "base_rps": 200, "spike_rps": 2000, "spike_start": 120, "spike_duration": 60 }
  }
}
```

## Traffic patterns available
- steady: Constant RPS (params: rps)
- gradual: Linear ramp (params: start_rps, end_rps)
- spike: Sudden jump (params: base_rps, spike_rps, spike_start, spike_duration)
- wave: Sinusoidal oscillation (params: base_rps, amplitude, period)
- step: Discrete steps (params: steps[{rps, duration}])
- custom: User-defined time series (params: series[{t, rps}])

## Config schema
See the YAML schema at /docs or in the simulator interface.
Config files use the extension: simulation.scalings.yaml

## Who should use this
DevOps engineers, SREs, platform teams, anyone tuning autoscaling policies.
Useful for capacity planning, incident post-mortems, and scaling policy validation.

## Export capabilities
1. Source Config Export: Save/share simulation setup as YAML
2. Target Config Export: Generate deployable manifests (K8s HPA YAML, AWS CloudFormation, GCP Terraform)