Team members:
Long Phan-Phuc Tran 1573950
Sahaj Bikram Malla 1563270

Group:
CloudComp44

This project demonstrates a Spring Boot application designed for deployment on Kubernetes with autoscaling, monitoring, and CI/CD integration as part of Cloud Computing course from Hochschule Fulda.

• REST API endpoints with custom metrics
• Prometheus metrics exposure via Actuator
• Docker containerization
• Unit and integration tests
• Kubernetes deployment-ready
• Autoscaling compatible

• Java 17
• Maven 3.8+
• Docker
• Kubernetes cluster (or Docker Desktop for local testing)
• Helm (for Prometheus/Grafana installation)

git clone https://github.com/longtran112/springboot-autoscale.git
cd springboot-app

mvn clean package

java -jar target/*.jar

# Hello endpoint
curl http://localhost:8080/

# API endpoint
curl http://localhost:8080/api

# Increment custom metric
curl http://localhost:8080/increment

# Health check
curl http://localhost:8080/actuator/health

# Prometheus metrics
curl http://localhost:8080/actuator/prometheus

# Run all tests
mvn test

# Run specific test class
mvn -Dtest=HelloControllerTest test

docker build -t longtran112/springboot-app:latest .

docker run -p 8080:8080 longtran112/springboot-app

The application is designed to be deployed to Kubernetes using the provided Terraform configuration.

• Horizontal Pod Autoscaler (HPA)
• Prometheus metrics scraping
• Liveness and readiness probes
• Resource limits and requests

The application exposes Prometheus metrics at:

http://<pod-ip>:8080/actuator/prometheus

To enable custom metrics:

1. Add dependencies in pom.xml:

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-registry-prometheus</artifactId>
</dependency>

2. Configure in application.properties:

management.endpoints.web.exposure.include=health,metrics,prometheus
management.endpoint.prometheus.enabled=true
management.metrics.export.prometheus.enabled=true

Two approaches are implemented: CPU-Based Scaling (HPA):

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
spec:
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 50
  minReplicas: 1
  maxReplicas: 10

HTTP Request-Based Scaling (KEDA + Prometheus): KEDA ScaledObject tracks HTTP requests per second:

triggers:
  - type: prometheus
    metadata:
      metricName: http_requests_total
      threshold: "100" # Scale if >100 req/sec/pod## CI/CD Integration

The application is ready for Jenkins CI/CD pipelines with:

• Docker image building
• Push to DockerHub and Artifactory
• Kubernetes deployment

1. Checkout source code
2. Run tests with mvn test
3. Build Docker image
4. Push to DockerHub
5. Push to Artifactory
6. Deploy to Kubernetes cluster

The application exposes metrics for:

• HTTP request timings
• JVM metrics
• Custom business metrics
• System resource usage

1. Spring Boot Statistics: ID 6756
2. JVM Micrometer: ID 4701
3. Kubernetes Cluster Monitoring: ID 3119