Auto-Scaling: How We Handle Traffic Spikes
Auto-scaling automatically adjusts your infrastructure capacity in response to demand — adding servers when traffic increases and removing them when demand drops. It is one of the defining advantages of cloud infrastructure over fixed on-premise hardware.
Why Auto-Scaling Matters
Without auto-scaling, you must provision for peak load at all times — paying for capacity that sits idle 90% of the time. With auto-scaling, you pay for what you use, scale to meet real demand, and maintain performance during unexpected traffic spikes.
Horizontal vs Vertical Scaling
- Horizontal scaling (scale out): Adding more instances of a service — more web servers, more application containers. This is the cloud-native scaling model. Requires stateless application design.
- Vertical scaling (scale up): Increasing the size of an instance — more CPU, more RAM. Simpler but has limits and often requires downtime.
Auto-Scaling Types
- Reactive scaling: Scale in response to current metrics — CPU utilisation, memory, request count, queue depth. React as demand changes.
- Predictive scaling: Scale in advance based on historical patterns — increase capacity before a predictable Monday morning surge rather than reacting when it arrives
- Scheduled scaling: Pre-configure capacity changes at known times — scale up before a marketing email sends, scale down overnight
Application Requirements for Auto-Scaling
Applications must be stateless to scale horizontally — session state in memory, local files, or local caches break when traffic is routed to a new instance. We design applications with external session storage (Redis), object storage (S3) for files, and distributed caching from the outset.