Predictive Scaler

Service: predictive-scaler · Port: 8089 · Database schema: forecasting

Autoscalers react after load arrives. predictive-scaler looks ahead: it forecasts each workload's load from its history and proposes scaling decisions so capacity is ready before demand peaks. You, or an authorized AI agent, approve them.

How forecasting works

The scaler uses Holt's linear trend method (double exponential smoothing), implemented in Go with no ML framework dependencies. It tracks the metric's level and trend:

Level:    L(t) = α × y(t) + (1 − α) × (L(t−1) + T(t−1))
Trend:    T(t) = β × (L(t) − L(t−1)) + (1 − β) × T(t−1)
Forecast: ŷ(t+h) = L(t) + h × T(t)

• α (0–1) sets how quickly the level follows new data; β (0–1) does the same for the trend.
• The prediction interval is ŷ ± 1.96 × RMSE, a 95% band.
• A workload needs at least 24 data points before its first forecast.
• Supported metrics: cpu_usage_pct, memory_usage_pct and request_rate.

For workloads with strong daily or weekly patterns, set FORECAST_SEASONALITY to use the seasonal (triple exponential smoothing) variant.

From forecast to decision

When a forecast shows a workload's utilization will leave its comfortable range within the forecast horizon, the scaler proposes a scaling decision with the recommended replica count, the reason, and its confidence.

Approving a decision

A decision can be approved in Analytics → Scaling Decisions, through the API, or by an AI agent with permission (the Cost Optimizer's approve_scaling_decision tool). On approval:

1. If the workload has a HorizontalPodAutoscaler, the scaler raises the HPA's minReplicas to the recommended value, so the HPA and the forecast work together rather than against each other. Otherwise, it sets the Deployment's replicas directly.
2. If the extra replicas need more nodes than are available, it asks nodes-manager to add capacity.
3. It marks the decision applied, recording who approved it and when.

When the forecast peak has passed, the scaler proposes a follow-up decision to return to the original level.

Domain model

predictive-scaler data model

Forecast

cluster_id,namespace,workload,metric

algorithm:holtholt_winters

points:[]{ timestamp, value, lower_bound, upper_bound }

confidence,horizon_mins

generated_at

ScalingDecision

id,forecast_id,cluster_id,namespace,workload

current_replicas,recommended_replicas

reason,confidence

status:pendingapprovedappliedrejected

decided_by,executed_at

REST API

Method	Path	Description
GET	/api/v1/forecasts	Latest forecasts (?cluster_id=&namespace=&workload=).
POST	/api/v1/forecasts/generate	Forecast one workload now.
GET	/api/v1/scaling-decisions	Scaling decisions (?status=pending).
POST	/api/v1/scaling-decisions/{id}/approve	Approve and apply.
POST	/api/v1/scaling-decisions/{id}/reject	Reject ({ "reason": "…" }).
GET	/healthz	Health check.

Permissions

rules:
  - apiGroups: ["autoscaling"]
    resources: ["horizontalpodautoscalers"]
    verbs: ["list", "get", "patch", "update"]
  - apiGroups: ["apps"]
    resources: ["deployments", "deployments/scale"]
    verbs: ["list", "get", "patch"]

Configuration

Variable	Default	Description
DATABASE_URL	—	PostgreSQL connection (reads k8s-monitor's metric history).
HOLT_WINTERS_ALPHA	0.3	Level smoothing factor.
HOLT_WINTERS_BETA	0.1	Trend smoothing factor.
FORECAST_SEASONALITY	—	Season length (for example 24h or 168h) to enable seasonal forecasting.
FORECAST_HORIZON_MINS	60	How far ahead to forecast.
NODES_MANAGER_BASE_URL	http://nodes-manager:8115	For adding node capacity.
PORT	8089	HTTP port.