Hands-On Lab: Experiencing the AWS Cloud Value Proposition

Estimated Time: 30 minutes | Difficulty: Guided | Cloud Provider: AWS

This lab bridges the theoretical concepts of the AWS Cloud Value Proposition with practical, hands-on experience. By provisioning and immediately tearing down compute resources, you will directly experience the core tenets of cloud economics: agility, elasticity, and the transition from Capital Expenditures (CapEx) to Operating Expenses (OpEx).

Prerequisites

Before starting this lab, ensure you have the following:

An active AWS Account with Administrator or PowerUser IAM permissions.
The AWS CLI installed and configured on your local machine.
Basic familiarity with navigating a terminal/command prompt.
Conceptual understanding of Cloud Computing benefits (scale, elasticity, metered billing).

Learning Objectives

By completing this lab, you will be able to:

Demonstrate Agility: Provision a compute resource in seconds, illustrating the speed of cloud deployment compared to traditional on-premises hardware procurement.
Understand Cloud Economics: Experience metered, pay-as-you-go billing (OpEx) by launching a resource for a few minutes and terminating it.
Perform Safe Experimentation: Deploy disposable infrastructure that carries no long-term financial commitment.

CapEx vs. OpEx Concept Review

Before we begin, review the fundamental economic shift the cloud provides:

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Architecture Overview

In this lab, we will simulate a scenario where a university needs temporary computational horsepower for an experiment. Instead of buying a server, we will provision an EC2 instance, verify it is running, and terminate it.

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Step-by-Step Instructions

Step 1: Verify Identity and Configuration

To ensure you are operating in the correct account and region, we will verify your CLI configuration. This prevents accidental deployments in production environments.

bash

aws sts get-caller-identity
aws configure get region

📸 Screenshot: Take a screenshot of your terminal output showing your Account ID and chosen region (e.g., us-east-1).

▶Console alternative

Log into the AWS Management Console. Check the upper right corner of the navigation bar to verify your IAM User/Role and the currently selected Region.

Step 2: Experience Agility (Provisioning an Instance)

In traditional IT, acquiring a new server could take weeks. Here, we will provision a virtual server (Amazon EC2 instance) in seconds. We are using the latest Amazon Linux AMI and a t2.micro instance type.

bash

aws ec2 run-instances \
    --image-id resolve:ssm:/aws/service/ami-amazon-linux-latest/al2023-ami-kernel-6.1-x86_64 \
    --instance-type t2.micro \
    --tag-specifications "ResourceType=instance,Tags=[{Key=Name,Value=brainybee-lab-agility}]"

[!TIP] Look at the JSON output returned by this command. Locate the InstanceId (it will look something like i-0abcd1234efgh5678). Copy this ID to your clipboard; you will need it for the next steps.

▶Console alternative

Navigate to

EC2

in the AWS Console.

Click

Launch instance

Name the instance brainybee-lab-agility.

Select

Amazon Linux 2023 AMI

Select

t2.micro

as the instance type.

Under Key pair, select

Proceed without a key pair

Click

Launch instance

Checkpoints

Checkpoint 1: Verify Instance State

Let's prove that the resource was deployed quickly. Run the following command using the Instance ID you copied earlier.

bash

aws ec2 describe-instances \
    --instance-ids <YOUR_INSTANCE_ID> \
    --query "Reservations[*].Instances[*].State.Name" \
    --output text

Expected Result: The terminal should output the word running. If it says pending, wait 10 seconds and try again. This demonstrates the core value of Speed and Agility.

Step-by-Step Instructions (Continued)

Step 3: Experience Elasticity (Terminating the Instance)

Now that our hypothetical "university AI test" is complete, we no longer need the compute power. To stop paying for it, we will terminate the resource. This represents the Pay-As-You-Go and Elasticity pillars of the AWS Cloud.

bash

aws ec2 terminate-instances --instance-ids <YOUR_INSTANCE_ID>

📸 Screenshot: Take a screenshot of the output showing the CurrentState changing to shutting-down or terminated.

▶Console alternative

In the EC2 Console, go to

Instances

Select the checkbox next to brainybee-lab-agility.

Click the

Instance state

dropdown menu.

Select

Terminate instance

and confirm.

Clean-Up / Teardown

[!WARNING] Remember to run the teardown commands to avoid ongoing charges. Cloud economics only saves you money if you turn off resources you are no longer using.

Because we manually terminated the EC2 instance in Step 3, the primary teardown is already complete. However, always verify that no orphaned resources remain.

Run the following command to ensure the instance is fully terminated:

bash

aws ec2 describe-instances \
    --filters "Name=tag:Name,Values=brainybee-lab-agility" \
    --query "Reservations[*].Instances[*].State.Name" \
    --output text

If the output is terminated, your clean-up is successful. You will no longer be billed for this compute capacity.

Troubleshooting

Common Error	Cause	Fix
`Unable to locate credentials`	AWS CLI is not configured with your access keys.	Run `aws configure` and provide your Access Key ID and Secret Access Key.
`UnauthorizedOperation`	Your IAM user lacks permissions to create EC2 instances.	Ensure your account administrator attaches the `AmazonEC2FullAccess` policy or equivalent to your IAM user.
`InvalidParameterValue` for AMI	The SSM parameter for the AMI might not exist in your selected region.	Use the console to manually find the latest Amazon Linux 2023 AMI ID in your region, and replace the `--image-id` parameter.
`InvalidInstanceID.NotFound`	You did not replace `<YOUR_INSTANCE_ID>` with the actual ID.	Scroll up in your terminal to find the `i-xxxxxxxxxxxxxxxxx` value and run the command again.

Cost Estimate

This lab is designed to be highly cost-effective and illustrates the principle of metered billing:

Amazon EC2 (t2.micro): Eligible for the AWS Free Tier (750 hours/month).
If you are outside the Free Tier, running a t2.micro instance for 30 minutes costs approximately $0.005 (half a cent), highlighting the massive cost reduction for experimental workloads compared to purchasing physical hardware.