Chapter 4: Cloud Compute Services – Virtual Machines (VMs), Scaling and Resource Management, Containerization Technologies (Docker, Kubernetes)

4.1 Introduction to Cloud Compute Services

Cloud compute services provide the backbone for processing workloads in the cloud. These services include virtual machines (VMs), containers, and orchestration platforms, enabling developers and enterprises to deploy, scale, and manage applications effectively. By leveraging these technologies, users can optimize performance, improve scalability, and reduce costs.

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4.2 Virtual Machines (VMs)

4.2.1 Definition and Characteristics

A Virtual Machine (VM) is an emulated computer system that runs on a hypervisor, simulating hardware functionality. It allows multiple operating systems (OS) to run on a single physical machine.

Key Features:

• Full OS environment

• Hardware-level isolation

• Persistent storage

• Independent system resources

4.2.2 VM Providers in Cloud

• Amazon EC2 (Elastic Compute Cloud) – AWS

• Azure Virtual Machines – Microsoft Azure

• Google Compute Engine – Google Cloud Platform

4.2.3 Example: Creating a VM on AWS EC2

1. Log in to AWS Console.

2. Navigate to EC2 service.

3. Launch instance.

4. Choose an Amazon Machine Image (AMI), e.g., Ubuntu Server.

5. Select instance type (e.g., t2.micro).

6. Configure settings, add storage, and launch.

Use Case: Hosting a web application, database server, or batch processing system.

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4.3 Scaling and Resource Management

4.3.1 What is Scaling?

Scaling refers to the ability to handle increased load by adjusting computing resources.

• Vertical Scaling (Scaling Up): Increasing the size (CPU, RAM) of a single VM.

• Horizontal Scaling (Scaling Out): Adding more instances (VMs) to distribute the load.

4.3.2 Auto-Scaling

Auto-Scaling automatically adjusts resources based on traffic demands.

Example:
AWS Auto Scaling can add EC2 instances during peak traffic and remove them during low traffic.

4.3.3 Load Balancing

A Load Balancer distributes traffic evenly across multiple instances to prevent any one instance from being overwhelmed.

Example:

• AWS Elastic Load Balancer (ELB)

• Azure Load Balancer

• Google Cloud Load Balancer

4.3.4 Resource Optimization Tools

• AWS Trusted Advisor

• Azure Cost Management

• GCP Operations Suite

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4.4 Containerization Technologies

4.4.1 What is Containerization?

Containerization packages applications and their dependencies into containers, ensuring consistent performance across environments.

Benefits:

• Lightweight and fast

• Better resource utilization

• Portability

• Rapid scaling

4.4.2 Docker

Docker is a popular platform for building and managing containers.

Key Concepts:

• Docker Image: Blueprint of the container.

• Docker Container: Running instance of an image.

• Dockerfile: Script to create Docker images.

Example: Dockerizing a Node.js App

# Dockerfile
FROM node:14
WORKDIR /app
COPY . .
RUN npm install
CMD ["node", "index.js"]

Commands:

docker build -t my-node-app .
docker run -p 3000:3000 my-node-app

4.4.3 Kubernetes (K8s)

Kubernetes is an open-source container orchestration platform developed by Google.

Key Components:

• Pods: Smallest deployable units.

• Services: Expose applications.

• Deployments: Manage replicas and updates.

• Nodes: Worker machines.

Benefits:

• Automated deployment and scaling

• Load balancing and self-healing

• Declarative configuration

Example: Deploying a Container using Kubernetes

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-node
  template:
    metadata:
      labels:
        app: my-node
    spec:
      containers:
      - name: my-node-container
        image: my-node-app
        ports:
        - containerPort: 3000

Commands:

kubectl apply -f deployment.yaml
kubectl get pods
kubectl expose deployment my-app --type=LoadBalancer --port=80 --target-port=3000

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4.5 Comparing VMs and Containers

Feature	Virtual Machines	Containers
OS	Full guest OS	Share host OS kernel
Start-up Time	Minutes	Seconds
Resource Usage	High	Low
Portability	Less Portable	Highly Portable
Use Case	Monolithic applications	Microservices

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4.6 Real-World Applications

• Netflix: Uses containers (Docker + Titus) for deploying microservices at scale.

• Airbnb: Uses Kubernetes to manage its growing infrastructure.

• Spotify: Migrated to Google Cloud using containers for efficient delivery.

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4.7 Summary

In this chapter, we explored:

• The concept and usage of Virtual Machines in cloud platforms.

• Techniques of scaling, auto-scaling, and load balancing.

• Containerization with Docker and orchestration with Kubernetes.

• Real-world examples of companies effectively using these technologies.

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4.8 Exercises

Objective Questions:

1. Which of the following is true about containers?
   a) They require a full OS
   b) They start slower than VMs
   c) They are lightweight and portable
   d) They do not support microservices

2. Vertical scaling means:
   a) Adding more nodes
   b) Reducing the VM size
   c) Increasing memory and CPU of a single VM
   d) Distributing load across containers

Short Answer Questions:

3. List two differences between VMs and containers.

4. Explain what a Dockerfile is and give a simple example.

5. What is Kubernetes, and why is it important?

Practical Task:

6. Create a Dockerfile for a Python Flask application. Build and run the container.

7. Write a Kubernetes deployment YAML file for your container and deploy it using kubectl.