Kubernetes clusters, especially those at scale, can be vulnerable to resource exhaustion and performance degradation if proper resource limits and resilience mechanisms are not enforced. Failure to adhere to recommended limits can lead to several risks:
Resource Exhaustion: Without controlling the number of pods, containers, and nodes, a Kubernetes cluster can run out of available resources (CPU, memory), causing service disruptions, performance degradation, or even cluster-wide outages.
Forkbombing Attacks: Attackers or misconfigured applications can flood the cluster with an excessive number of pods, leading to a DoS attack or cluster paralysis. Commands like kubectl scale can be misused to deploy an excessive number of replicas, overwhelming the cluster.
Loss of Performance and Availability: Exceeding recommended cluster limits (e.g., more than 5000 nodes or 150,000 pods) can cause Kubernetes control plane components to become overwhelmed, leading to service unavailability and system crashes.
To mitigate these risks, it is essential to set resource limits, enforce autoscaling policies, and monitor resource usage.
