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Resource Efficiency

The Resource Efficiency analysis helps optimize your Kubernetes cluster's resource utilization by identifying inefficiencies and providing optimization recommendations.

Overview

This analysis examines:

  • Resource Utilization: CPU, memory, and storage usage patterns
  • Right-sizing Opportunities: Over/under-provisioned workloads
  • Cost Optimization: Resource allocation vs. actual usage
  • Performance Bottlenecks: Resource constraints affecting performance
  • Waste Identification: Unused or underutilized resources

Key Metrics

Utilization Analysis

  • CPU utilization trends over time
  • Memory usage patterns and spikes
  • Storage consumption and growth
  • Network bandwidth utilization

Resource Requests vs. Usage

  • Over-requested resources
  • Under-requested resources leading to throttling
  • Request/limit ratios analysis
  • Quality of Service (QoS) class distribution

Efficiency Scores

  • Cluster Efficiency Score: Overall resource utilization rating
  • Workload Efficiency: Per-application resource efficiency
  • Node Efficiency: Per-node resource utilization
  • Cost Efficiency: Resource cost vs. value analysis

Optimization Recommendations

Right-sizing Suggestions

# Example optimization recommendation
apiVersion: apps/v1
kind: Deployment
metadata:
  name: example-app
spec:
  template:
    spec:
      containers:
      - name: app
        resources:
          requests:
            cpu: "200m"      # Recommended: reduce from 500m
            memory: "256Mi"  # Recommended: reduce from 512Mi
          limits:
            cpu: "400m"      # Recommended: reduce from 1000m
            memory: "512Mi"  # Keep current limit

Workload Optimization

  • Vertical Pod Autoscaler (VPA) recommendations
  • Horizontal Pod Autoscaler (HPA) configuration
  • Node sizing optimization
  • Storage class selection guidance

Cost Analysis

Resource Costing

  • Per-workload resource costs
  • Idle resource costs
  • Over-provisioning costs
  • Potential savings identification

Budget Optimization

  • Resource budget allocation
  • Cost alerts and thresholds
  • Multi-cluster cost comparison
  • Reserved capacity recommendations

Usage

# Full resource efficiency analysis
k8s-analyzer analyze --view resource-efficiency

# Focus on specific namespace
k8s-analyzer analyze --view resource-efficiency --namespace production

# Generate optimization report
k8s-analyzer report --template resource-optimization

# Export recommendations
k8s-analyzer optimize --format yaml > recommendations.yaml

Implementation Guide

Applying Recommendations

  1. Review the efficiency analysis results
  2. Validate recommendations in staging environment
  3. Implement changes gradually
  4. Monitor impact on performance and costs
  5. Iterate based on new usage patterns

Monitoring Efficiency

  • Set up regular efficiency assessments
  • Create alerts for efficiency thresholds
  • Track efficiency trends over time
  • Integrate with cost management tools

Integration

Monitoring Tools

  • Prometheus for metrics collection
  • Grafana for visualization
  • Cost management platforms
  • Cloud provider billing APIs

Automation

  • GitOps workflows for recommendation deployment
  • CI/CD pipeline integration
  • Automated right-sizing policies
  • Scheduled efficiency reports