Critical Kubernetes Risks

And How to Detect Them at Enterprise Scale

In this guide, you’ll learn:

  • What determines a critical reliability risk
  • The most common critical reliability risks
  • Methods for detecting them at enterprise scale

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About the Authors

Jordan Pritchard

Director of Infrastructure & Site Reliability Engineering

Michael Kehoe

Architect of reliable, scalable infrastructure

Rodney Lester

Technical Lead, Reliability Pillar of Well Architected Program

Tammy Butow

Principal SRE

Jay Holler

Manager, Site Reliability Engineering

Ramin Keene

Founder

Complex Kubernetes systems can have a variety of potential points of failure, also known as reliability risks.

These include node failures, pod or container crashes, missing autoscaling rules, misconfigured load balancing or application gateway rules, pod crash loops, and more.

But how do you know which reliability risks are the most important? And how can you automatically detect them across an enterprise-scale Kubernetes deployment?

This guide will give you the tools to systematically find and fix risks, making your Kubernetes systems more reliable.

  • Incident classification: SEV descriptions and levels, and SEV and time-to-detection (TTD) timelines

  • Organization-wide critical service monitoring, including key dashboards and KPI metrics emails

  • Service ownership and metrics for organizations maintaining a microservices architecture

  • Effective on-call principles for site reliability engineers, including rotation structure, alert threshold maintenance, and escalation practices

  • Chaos Engineering practices to identify random and unpredictable behavior in your system

  • Monitoring and metrics to detect incidents caused by self-healing systems

  • Creating a high-reliability culture by listening to people in your organization

Over a decade of collective experience unleashing chaos at companies like

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