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
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