Kubernetes pod scheduling requires balancing cost efficiency with resilience through strategic use of affinity rules, anti-affinity constraints, and topology spread configurations. The guide covers resource optimization techniques that can improve CPU utilization by 35-47% and memory utilization by 28-39%. Key strategies include using cascading constraints from strict at broad topology levels to flexible at narrow levels, implementing different patterns for various workload types (global services, stateful applications, microservices, cost-optimized services), and avoiding common pitfalls like overly strict anti-affinity rules and conflicting configurations. Proper implementation ensures high-performance applications remain available during infrastructure issues while optimizing resource usage.
Table of contents
Resource Optimization ConsiderationsResilience Engineering with Topology ControlsReal-World Implementation PatternsCommon Pitfalls and MisconfigurationsConclusionSort: