Autoscaling Kubernetes Deployments: A (Mostly) Practical Guide

Autoscaling Kubernetes Deployments is a flexible and rich option for ensuring stable performance when the load on the application changes over time.

• Factors to consider when sizing your Kubernetes application
• Horizontal vs Vertical autoscaling
• Selecting the right auto scaling metric for your application
• A Turing-complete autoscaler demo

The speaker highlights the unpredictability of workloads and spikes in traffic that can lead to latency problems and outages. Autoscaling is intended to solve these problems and Kubernetes provides good support for it. However, the ideal resource allocation depends on the workload and can be unpredictable. The speaker recommends selecting the right auto scaling metric for your application and provides a demo of a Turing-complete autoscaler.

Sizing a Kubernetes deployment can be tricky. How many pods should it have? How much CPU/memory is needed per pod? Is it better to use a small number of large pods or a large number of small pods? What’s the best way to ensure stable performance when the load on the application changes over time? Luckily for anyone asking these questions, Kubernetes provides rich, flexible options for autoscaling deployments. This session cover the following topics: - Factors to consider when sizing your Kubernetes application - Horizontal vs Vertical autoscaling - How, when, and why to use the Kubernetes custom metrics API - Practical demo: Autoscaling with application metrics from Prometheus, Linkerd, Pixie (request throughput/latency, number of shoes purchased in my web store) - Impractical demo: A Turing-complete autoscaler!Click here to view captioning/translation in the MeetingPlay platform!