Introduction

Kubernetes provides automatic scheduling and resource management, but improper configurations, excessive pod deployments, and inefficient node utilization can lead to performance bottlenecks. Common pitfalls include failing to define proper resource requests and limits leading to resource contention, using strict affinity/anti-affinity rules causing pod scheduling delays, overloading nodes with high-priority pods causing imbalance, excessive replica scaling causing API server throttling, and improper garbage collection increasing pod eviction rates. These issues become particularly problematic in high-load environments where optimizing scheduling and resource utilization is critical for application reliability. This article explores common Kubernetes performance bottlenecks, debugging techniques, and best practices for optimizing pod scheduling and resource allocation.

Common Causes of Kubernetes Performance Issues

1. Improper Resource Requests Causing Resource Starvation

Failing to set proper resource requests and limits can lead to unbalanced node utilization.

Problematic Scenario

apiVersion: v1
kind: Pod
metadata:
  name: my-app
spec:
  containers:
  - name: app-container
    image: my-app:latest

Without resource requests and limits, Kubernetes may schedule too many pods on a single node.

Solution: Define Resource Requests and Limits

resources:
  requests:
    cpu: "500m"
    memory: "256Mi"
  limits:
    cpu: "1000m"
    memory: "512Mi"

Defining requests ensures proper node utilization and prevents over-scheduling.

2. Inefficient Pod Scheduling Due to Strict Affinity Rules

Using strict affinity rules can lead to pod scheduling delays.

Problematic Scenario

affinity:
  podAffinity:
    requiredDuringSchedulingIgnoredDuringExecution:
      - labelSelector:
          matchExpressions:
            - key: app
              operator: In
              values:
                - backend
        topologyKey: kubernetes.io/hostname

This forces all backend pods to run on the same node, leading to resource imbalance.

Solution: Use Preferred Instead of Required Affinity

affinity:
  podAffinity:
    preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 50
        podAffinityTerm:
          labelSelector:
            matchExpressions:
              - key: app
                operator: In
                values:
                  - backend
          topologyKey: kubernetes.io/hostname

Using `preferredDuringSchedulingIgnoredDuringExecution` allows Kubernetes to prioritize but not enforce affinity.

3. Overloading Nodes with High-Priority Pods Causing Imbalance

Pods with high priority can cause other workloads to be preempted excessively.

Problematic Scenario

apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: high-priority
value: 1000000

Assigning high priority without consideration can cause eviction storms.

Solution: Balance Priority Classes and Resource Requests

apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: medium-priority
value: 500000
preemptionPolicy: Never

Using balanced priorities and avoiding excessive preemption prevents cluster instability.

4. Excessive Replica Scaling Causing API Server Throttling

Rapidly scaling replicas can overwhelm the API server.

Problematic Scenario

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
spec:
  minReplicas: 1
  maxReplicas: 100

Setting `maxReplicas: 100` without gradual scaling can cause API server throttling.

Solution: Use Stepwise Scaling

behavior:
  scaleDown:
    stabilizationWindowSeconds: 300
    policies:
      - type: Percent
        value: 10
        periodSeconds: 60

Using gradual scaling prevents excessive API load.

5. Inefficient Garbage Collection Causing Frequent Pod Evictions

Misconfigured garbage collection settings can lead to excessive pod restarts.

Problematic Scenario

kube-controller-manager --terminated-pod-gc-threshold=5000

Setting `terminated-pod-gc-threshold=5000` can cause delayed cleanup of unused pods.

Solution: Adjust Garbage Collection Threshold

kube-controller-manager --terminated-pod-gc-threshold=100

Lowering the threshold ensures timely cleanup of terminated pods.

Best Practices for Optimizing Kubernetes Performance

1. Define Resource Requests and Limits

Ensure proper node utilization and prevent pod eviction storms.

Example:

requests:
  cpu: "500m"
  memory: "256Mi"

2. Use Preferred Affinity Instead of Strict Rules

Allow Kubernetes to make scheduling decisions dynamically.

Example:

preferredDuringSchedulingIgnoredDuringExecution

3. Balance Priority Classes

Prevent excessive pod preemption and resource contention.

Example:

preemptionPolicy: Never

4. Implement Stepwise Scaling

Prevent API server overload by scaling in gradual steps.

Example:

policies:
  - type: Percent
    value: 10
    periodSeconds: 60

5. Tune Garbage Collection for Efficient Cleanup

Optimize terminated pod retention to avoid excessive evictions.

Example:

kube-controller-manager --terminated-pod-gc-threshold=100

Conclusion

Performance degradation and resource exhaustion in Kubernetes often result from inefficient pod scheduling, improper resource requests, excessive pod scaling, unbalanced priority assignments, and misconfigured garbage collection settings. By defining proper resource limits, using balanced scheduling rules, implementing controlled scaling strategies, tuning garbage collection thresholds, and optimizing node utilization, developers can significantly improve Kubernetes cluster performance. Regular monitoring using `kubectl top`, `kube-state-metrics`, and `Prometheus` helps detect and resolve performance issues before they impact production workloads.