Anteon: Complete Guide to eBPF-based Kubernetes Monitoring and Performance Testing

⏱️ Estimated Reading Time: 15 minutes

Introduction

Anteon (formerly Ddosify) is a revolutionary open-source platform that combines eBPF-based Kubernetes monitoring with performance testing capabilities. Unlike traditional monitoring solutions that require code instrumentation or sidecars, Anteon automatically generates service maps and provides real-time insights into your Kubernetes clusters.

What Makes Anteon Special?

• Zero Code Changes: No instrumentation, restarts, or sidecars required
• Automatic Service Discovery: eBPF-based agent creates service maps automatically
• Integrated Performance Testing: Native load testing from 25+ countries
• Real-time Monitoring: Live CPU, memory, disk, and network metrics
• Intelligent Alerting: Slack integration for anomaly detection

Prerequisites

Before starting this tutorial, ensure you have:

• Kubernetes cluster (local or cloud-based)
• kubectl configured and connected to your cluster
• Helm 3.0+ installed
• Docker installed (for local testing)
• macOS/Linux environment

Part 1: Setting Up Local Kubernetes Environment

Step 1: Install Required Tools

Let’s start by setting up a local Kubernetes environment using Kind (Kubernetes in Docker):

#!/bin/bash
# install-k8s-tools.sh

echo "🚀 Installing Kubernetes tools for Anteon tutorial..."

# Install Docker if not present
if ! command -v docker &> /dev/null; then
    echo "Installing Docker..."
    brew install --cask docker
fi

# Install kubectl
if ! command -v kubectl &> /dev/null; then
    echo "Installing kubectl..."
    brew install kubectl
fi

# Install Helm
if ! command -v helm &> /dev/null; then
    echo "Installing Helm..."
    brew install helm
fi

# Install Kind
if ! command -v kind &> /dev/null; then
    echo "Installing Kind..."
    brew install kind
fi

echo "✅ All tools installed successfully!"

Step 2: Create Local Kubernetes Cluster

#!/bin/bash
# setup-kind-cluster.sh

echo "🎯 Creating Kind cluster for Anteon demo..."

# Create Kind cluster configuration
cat <<EOF > kind-config.yaml
kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
name: anteon-demo
nodes:
- role: control-plane
  kubeadmConfigPatches:
  - |
    kind: InitConfiguration
    nodeRegistration:
      kubeletExtraArgs:
        node-labels: "ingress-ready=true"
  extraPortMappings:
  - containerPort: 80
    hostPort: 8080
    protocol: TCP
  - containerPort: 443
    hostPort: 8443
    protocol: TCP
- role: worker
- role: worker
EOF

# Create the cluster
kind create cluster --config kind-config.yaml

# Verify cluster is running
kubectl cluster-info --context kind-anteon-demo

echo "✅ Kind cluster 'anteon-demo' created successfully!"

Part 2: Installing Anteon on Kubernetes

Step 1: Deploy Sample Applications

First, let’s deploy some sample applications to monitor:

#!/bin/bash
# deploy-sample-apps.sh

echo "🎯 Deploying sample applications for monitoring..."

# Create namespace
kubectl create namespace demo-apps

# Deploy a sample web application
cat <<EOF | kubectl apply -f -
apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-app
  namespace: demo-apps
spec:
  replicas: 3
  selector:
    matchLabels:
      app: web-app
  template:
    metadata:
      labels:
        app: web-app
    spec:
      containers:
      - name: web-app
        image: nginx:1.21
        ports:
        - containerPort: 80
        resources:
          requests:
            cpu: 100m
            memory: 128Mi
          limits:
            cpu: 500m
            memory: 512Mi
---
apiVersion: v1
kind: Service
metadata:
  name: web-app-service
  namespace: demo-apps
spec:
  selector:
    app: web-app
  ports:
  - port: 80
    targetPort: 80
  type: ClusterIP
EOF

# Deploy a database simulation
cat <<EOF | kubectl apply -f -
apiVersion: apps/v1
kind: Deployment
metadata:
  name: db-app
  namespace: demo-apps
spec:
  replicas: 1
  selector:
    matchLabels:
      app: db-app
  template:
    metadata:
      labels:
        app: db-app
    spec:
      containers:
      - name: db-app
        image: postgres:13
        env:
        - name: POSTGRES_DB
          value: testdb
        - name: POSTGRES_USER
          value: testuser
        - name: POSTGRES_PASSWORD
          value: testpass
        ports:
        - containerPort: 5432
        resources:
          requests:
            cpu: 100m
            memory: 256Mi
          limits:
            cpu: 1000m
            memory: 1Gi
---
apiVersion: v1
kind: Service
metadata:
  name: db-app-service
  namespace: demo-apps
spec:
  selector:
    app: db-app
  ports:
  - port: 5432
    targetPort: 5432
  type: ClusterIP
EOF

echo "✅ Sample applications deployed successfully!"
kubectl get pods -n demo-apps

Step 2: Install Anteon using Helm

#!/bin/bash
# install-anteon.sh

echo "🚀 Installing Anteon on Kubernetes..."

# Add Anteon Helm repository
helm repo add anteon https://getanteon.github.io/anteon
helm repo update

# Create namespace for Anteon
kubectl create namespace anteon

# Install Anteon with custom values
cat <<EOF > anteon-values.yaml
# Anteon configuration
alaz:
  enabled: true
  image:
    tag: "latest"
  resources:
    requests:
      cpu: 100m
      memory: 200Mi
    limits:
      cpu: 500m
      memory: 1Gi

backend:
  enabled: true
  replicas: 1
  resources:
    requests:
      cpu: 200m
      memory: 512Mi
    limits:
      cpu: 1000m
      memory: 2Gi

frontend:
  enabled: true
  replicas: 1
  service:
    type: NodePort
    port: 8080

postgres:
  enabled: true
  auth:
    database: anteon
    username: anteon
    password: anteon123
EOF

# Install Anteon
helm install anteon anteon/anteon \
  --namespace anteon \
  --values anteon-values.yaml \
  --wait

echo "✅ Anteon installed successfully!"

# Check installation status
kubectl get pods -n anteon
kubectl get services -n anteon

Step 3: Access Anteon Dashboard

#!/bin/bash
# access-anteon-dashboard.sh

echo "🌐 Setting up access to Anteon dashboard..."

# Port forward to access the dashboard
kubectl port-forward -n anteon service/anteon-frontend 8080:8080 &

# Wait for port forward to establish
sleep 5

echo "✅ Anteon dashboard is now accessible at: http://localhost:8080"
echo "📊 Opening dashboard in browser..."

# Open in default browser (macOS)
open http://localhost:8080

Part 3: Exploring Anteon Features

Service Map Generation

Once Anteon is running, it automatically starts generating service maps using eBPF. You’ll see:

1. Real-time Service Discovery: All services in your cluster automatically appear
2. Traffic Flow Visualization: Inter-service communication patterns
3. Performance Metrics: Response times, error rates, and throughput
4. Dependency Mapping: Service dependencies and data flow

Real-time Monitoring Dashboard

The Anteon dashboard provides comprehensive monitoring capabilities:

#!/bin/bash
# generate-sample-traffic.sh

echo "🚦 Generating sample traffic for monitoring..."

# Function to generate HTTP traffic
generate_traffic() {
    local service_url=$1
    local requests=$2
    
    echo "Sending $requests requests to $service_url"
    
    for i in $(seq 1 $requests); do
        curl -s $service_url > /dev/null
        sleep 0.1
    done
}

# Get service endpoint
WEB_SERVICE_IP=$(kubectl get service web-app-service -n demo-apps -o jsonpath='{.spec.clusterIP}')

# Port forward to access the service
kubectl port-forward -n demo-apps service/web-app-service 8090:80 &
FORWARD_PID=$!

sleep 3

# Generate traffic patterns
echo "🔄 Generating normal traffic..."
generate_traffic "http://localhost:8090" 50

echo "🔄 Generating burst traffic..."
for i in {1..10}; do
    generate_traffic "http://localhost:8090" 10 &
done
wait

# Clean up port forward
kill $FORWARD_PID 2>/dev/null

echo "✅ Traffic generation completed!"

Part 4: Performance Testing with Anteon

Step 1: Create Load Testing Scenarios

#!/bin/bash
# create-load-test.sh

echo "🎯 Creating performance testing scenarios..."

# Create a load test configuration
cat <<EOF > load-test-config.json
{
  "name": "Web App Load Test",
  "description": "Testing web application performance",
  "scenarios": [
    {
      "name": "Basic Load Test",
      "duration": "2m",
      "load_type": "linear",
      "steps": [
        {
          "name": "Home Page Test",
          "method": "GET",
          "url": "http://web-app-service.demo-apps.svc.cluster.local",
          "timeout": "10s"
        }
      ],
      "load_settings": {
        "users": 50,
        "spawn_rate": 5
      }
    }
  ]
}
EOF

echo "✅ Load test configuration created!"

Step 2: Run Performance Tests

Using Anteon’s CLI tool for load testing:

#!/bin/bash
# run-performance-test.sh

echo "🚀 Running performance tests with Anteon..."

# Install Anteon CLI (ddosify)
if ! command -v ddosify &> /dev/null; then
    echo "Installing Anteon CLI..."
    # For macOS
    brew tap getanteon/tap
    brew install ddosify
fi

# Run a simple load test
echo "🔄 Executing load test..."

ddosify -t http://localhost:8090 \
    -n 1000 \
    -d 60 \
    -m GET \
    -h "User-Agent: Anteon-LoadTest" \
    -o stdout-json

echo "✅ Performance test completed!"

Step 3: Advanced Testing Scenarios

#!/bin/bash
# advanced-load-test.sh

echo "🎯 Running advanced performance testing scenarios..."

# Create advanced test configuration
cat <<EOF > advanced-test.json
{
  "iteration_count": 100,
  "load_type": "waved",
  "duration": 300,
  "steps": [
    {
      "id": 1,
      "url": "http://localhost:8090/",
      "method": "GET",
      "headers": {
        "User-Agent": "Anteon-Advanced-Test"
      },
      "timeout": 5
    },
    {
      "id": 2,
      "url": "http://localhost:8090/api/health",
      "method": "GET",
      "headers": {
        "Accept": "application/json"
      },
      "timeout": 3
    }
  ]
}
EOF

# Run the advanced test
ddosify -config advanced-test.json

echo "✅ Advanced testing completed!"

Part 5: Monitoring and Alerting Setup

Step 1: Configure Slack Alerts

#!/bin/bash
# setup-alerts.sh

echo "🔔 Setting up alerting with Anteon..."

# Create alert configuration
cat <<EOF > alert-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: anteon-alerts
  namespace: anteon
data:
  alerts.yml: |
    alerts:
      - name: high_cpu_usage
        condition: cpu_usage > 80
        duration: 2m
        severity: warning
        message: "High CPU usage detected in cluster"
        
      - name: slow_response_time
        condition: response_time > 1000ms
        duration: 1m
        severity: critical
        message: "Slow response time detected"
        
      - name: error_rate_spike
        condition: error_rate > 5%
        duration: 30s
        severity: critical
        message: "Error rate spike detected"
    
    channels:
      slack:
        webhook_url: "YOUR_SLACK_WEBHOOK_URL"
        channel: "#alerts"
EOF

kubectl apply -f alert-config.yaml

echo "✅ Alert configuration applied!"
echo "📝 Update the Slack webhook URL in the configuration"

Step 2: Custom Metrics and Dashboards

#!/bin/bash
# setup-custom-metrics.sh

echo "📊 Setting up custom metrics and dashboards..."

# Create custom metric configuration
cat <<EOF > custom-metrics.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: anteon-custom-metrics
  namespace: anteon
data:
  metrics.yml: |
    custom_metrics:
      - name: business_transactions
        type: counter
        description: "Number of business transactions processed"
        
      - name: user_sessions
        type: gauge
        description: "Active user sessions"
        
      - name: payment_processing_time
        type: histogram
        description: "Payment processing duration"
        buckets: [0.1, 0.5, 1.0, 2.0, 5.0]
EOF

kubectl apply -f custom-metrics.yaml

echo "✅ Custom metrics configuration applied!"

Part 6: Best Practices and Optimization

Resource Optimization

#!/bin/bash
# optimize-anteon.sh

echo "⚡ Optimizing Anteon performance..."

# Update Anteon with optimized settings
cat <<EOF > anteon-optimized-values.yaml
alaz:
  resources:
    requests:
      cpu: 200m
      memory: 400Mi
    limits:
      cpu: 1000m
      memory: 2Gi
  
  # eBPF optimization settings
  config:
    sampling_rate: 0.1  # Sample 10% of traffic for large clusters
    buffer_size: 1024
    max_events_per_second: 10000

backend:
  replicas: 2  # High availability
  resources:
    requests:
      cpu: 500m
      memory: 1Gi
    limits:
      cpu: 2000m
      memory: 4Gi

postgres:
  persistence:
    enabled: true
    size: 20Gi
  resources:
    requests:
      cpu: 300m
      memory: 512Mi
    limits:
      cpu: 1000m
      memory: 2Gi
EOF

# Upgrade Anteon with optimized settings
helm upgrade anteon anteon/anteon \
  --namespace anteon \
  --values anteon-optimized-values.yaml

echo "✅ Anteon optimized for production!"

Security Best Practices

#!/bin/bash
# secure-anteon.sh

echo "🔒 Applying security best practices..."

# Create network policies
cat <<EOF | kubectl apply -f -
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: anteon-network-policy
  namespace: anteon
spec:
  podSelector:
    matchLabels:
      app: anteon
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          name: anteon
    ports:
    - protocol: TCP
      port: 8080
  egress:
  - {}
EOF

# Create RBAC policies
cat <<EOF | kubectl apply -f -
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: anteon-readonly
rules:
- apiGroups: [""]
  resources: ["pods", "services", "nodes"]
  verbs: ["get", "list", "watch"]
- apiGroups: ["apps"]
  resources: ["deployments", "replicasets"]
  verbs: ["get", "list", "watch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: anteon-readonly-binding
subjects:
- kind: ServiceAccount
  name: anteon
  namespace: anteon
roleRef:
  kind: ClusterRole
  name: anteon-readonly
  apiGroup: rbac.authorization.k8s.io
EOF

echo "✅ Security policies applied!"

Part 7: Troubleshooting and Maintenance

Common Issues and Solutions

#!/bin/bash
# troubleshoot-anteon.sh

echo "🔧 Anteon troubleshooting toolkit..."

# Function to check Anteon health
check_anteon_health() {
    echo "📊 Checking Anteon component health..."
    
    # Check pod status
    kubectl get pods -n anteon
    
    # Check service status
    kubectl get services -n anteon
    
    # Check recent events
    kubectl get events -n anteon --sort-by='.lastTimestamp'
    
    # Check logs
    echo "📋 Recent logs from Anteon components:"
    kubectl logs -n anteon -l app=anteon-backend --tail=50
}

# Function to restart Anteon components
restart_anteon() {
    echo "🔄 Restarting Anteon components..."
    
    kubectl rollout restart deployment -n anteon
    kubectl rollout status deployment -n anteon
}

# Function to check eBPF capability
check_ebpf() {
    echo "🔍 Checking eBPF capability..."
    
    # Check if eBPF is supported
    if kubectl get nodes -o jsonpath='{.items[*].status.nodeInfo.kernelVersion}' | grep -q "3."; then
        echo "⚠️  Warning: Old kernel version detected. eBPF features may be limited."
    else
        echo "✅ Kernel version supports eBPF"
    fi
}

# Run diagnostics
check_anteon_health
check_ebpf

echo "✅ Troubleshooting completed!"

Backup and Recovery

#!/bin/bash
# backup-anteon.sh

echo "💾 Creating Anteon backup..."

# Create backup directory
mkdir -p anteon-backup-$(date +%Y%m%d)
BACKUP_DIR="anteon-backup-$(date +%Y%m%d)"

# Backup Helm values
helm get values anteon -n anteon > $BACKUP_DIR/helm-values.yaml

# Backup custom configurations
kubectl get configmaps -n anteon -o yaml > $BACKUP_DIR/configmaps.yaml
kubectl get secrets -n anteon -o yaml > $BACKUP_DIR/secrets.yaml

# Backup database (if using external DB)
kubectl exec -n anteon $(kubectl get pods -n anteon -l app=postgres -o jsonpath='{.items[0].metadata.name}') -- \
    pg_dump -U anteon anteon > $BACKUP_DIR/database-backup.sql

echo "✅ Backup created in $BACKUP_DIR"

Conclusion

Anteon provides a powerful, comprehensive solution for Kubernetes monitoring and performance testing. Key benefits include:

Key Takeaways

1. Zero-Friction Monitoring: No code changes or sidecars required
2. Automatic Discovery: eBPF-based service map generation
3. Integrated Testing: Built-in performance testing capabilities
4. Real-time Insights: Live metrics and intelligent alerting
5. Production-Ready: Scalable architecture with security best practices

Next Steps

• Scale to Production: Apply optimization and security configurations
• Integrate CI/CD: Automate performance testing in your pipelines
• Custom Dashboards: Create business-specific monitoring views
• Advanced Analytics: Leverage Anteon’s data for capacity planning

Resources

• Official Documentation
• GitHub Repository
• Community Discord
• Anteon Guru AI Assistant

Clean Up

#!/bin/bash
# cleanup.sh

echo "🧹 Cleaning up tutorial resources..."

# Remove Anteon
helm uninstall anteon -n anteon
kubectl delete namespace anteon

# Remove sample applications
kubectl delete namespace demo-apps

# Remove Kind cluster
kind delete cluster --name anteon-demo

echo "✅ Cleanup completed!"

---

⚠️ Disclaimer: Anteon should only be used for testing systems you own. Always follow responsible testing practices and comply with your organization’s policies.