Autoscaling
AssessTechniques
Automatic adjustment of running replica counts based on demand or resource signals.
Why it's here
Placed in Assess: 2 article(s) of evidence from 1 source(s), led by research-stage coverage, with 0 in the last 30 days. Confidence 32%.
Evidence (2)
- 8The New Stack·7/10/2026framework_updateWhat AWS learned about zonal failures from running Kubernetes at massive scale
AWS describes how operating Amazon EKS across millions of clusters led it to redesign control-plane resilience for zonal impairments. The company says the key lesson was static stability: during a zone issue, systems should stop reacting, preserve capacity, and route traffic away from the impaired zone instead of triggering cascading failures.
- 6The New Stack·6/23/2026researchKubernetes teams trust automation for delivery but hesitate on CPU and memory tuning
A survey of 321 enterprise Kubernetes practitioners found strong trust in automated deployment and rollback, but much lower willingness to let systems auto-apply CPU and memory resource changes. The gap matters more as AI inference workloads on Kubernetes raise the cost of overprovisioning and make manual rightsizing harder to sustain at scale.