---
title: "Scheduling and autoscaling"
chapter: "07"
---

# Scheduling and autoscaling

Scaling is several feedback loops, not one magic switch.

## Pod placement

The scheduler compares requests with available capacity. Node selectors and
affinity attract Pods. Taints repel Pods unless they tolerate them. Topology
spread distributes replicas across failure domains. Hard rules can protect
isolation but also make Pods impossible to schedule.

## Pod scaling

The Horizontal Pod Autoscaler changes replica count from CPU, memory, or custom
metrics. Choose a metric that follows demand. Set minimums for availability and
maximums from downstream capacity. The Vertical Pod Autoscaler helps right-size
requests but may restart Pods in automatic modes.

## Node scaling

Cluster Autoscaler changes managed node-group size. Karpenter selects and
launches EC2 instances from Pod requirements. Auto Mode manages a
Karpenter-based capacity layer through NodePools and NodeClasses. Pod scaling
and node scaling are separate: new Pods can remain Pending while capacity
arrives.

## Capacity choices

Use On-Demand for reliable baseline capacity. Use Spot for replicated,
checkpointed, queue-driven, or retryable work. Diversify instance types and
zones. Graviton can improve price-performance when images support arm64.

## Feynman check

Requests are the shopping list the scheduler reads. If the list is wrong, no
autoscaler can buy the right shelves.