Cost optimization strategies in GCP - Mini Project: Build & Apply

Cost Optimization Strategies in GCP

📖 Scenario: You are managing cloud resources for a small company using Google Cloud Platform (GCP). The company wants to reduce its monthly cloud costs without affecting the performance of its applications.

🎯 Goal: Build a simple GCP resource configuration that demonstrates cost optimization by selecting appropriate machine types, setting up budget alerts, and enabling autoscaling.

📋 What You'll Learn

Create a dictionary named instances with three VM instances and their machine types

Add a variable named budget_limit set to 500 (representing $500 monthly budget)

Write a loop to create a list optimized_instances that includes only instances with machine types costing less than or equal to 2 vCPUs

Add a final configuration dictionary autoscaling_config with keys enabled set to true and max_instances set to 5

💡 Why This Matters

🌍 Real World

Cloud engineers often need to optimize resource usage and costs by selecting appropriate machine types, setting budgets, and enabling autoscaling.

💼 Career

Understanding cost optimization strategies in GCP is essential for roles like Cloud Engineer, DevOps Engineer, and Cloud Architect.

1

Create VM instances with machine types

Create a dictionary called instances with these exact entries: 'vm1': 'n1-standard-1', 'vm2': 'n1-standard-4', 'vm3': 'e2-medium'.

GCP

# Create the instances dictionary with specified VM names and machine types
# Your code here

Hint

Use a Python dictionary with VM names as keys and machine types as values.

2

Set the monthly budget limit

Add a variable called budget_limit and set it to 500 representing the monthly budget in dollars.

GCP

instances = {'vm1': 'n1-standard-1', 'vm2': 'n1-standard-4', 'vm3': 'e2-medium'}
# Set the budget_limit variable to 500
# Your code here

Hint

Just assign the number 500 to the variable budget_limit.

3

Filter instances by machine type vCPU count

Create a list called optimized_instances that includes only the instance names from instances whose machine types have 2 or fewer vCPUs. Use a for loop with variables vm and machine to iterate over instances.items(). Use this vCPU mapping: 'n1-standard-1': 1, 'n1-standard-4': 4, 'e2-medium': 2.

GCP

instances = {'vm1': 'n1-standard-1', 'vm2': 'n1-standard-4', 'vm3': 'e2-medium'}
budget_limit = 500
# Create optimized_instances list filtering by vCPU count <= 2
# Your code here

Hint

Use a dictionary for vCPU counts and a for loop to filter instance names.

4

Add autoscaling configuration

Add a dictionary called autoscaling_config with keys enabled set to true and max_instances set to 5.

GCP

instances = {'vm1': 'n1-standard-1', 'vm2': 'n1-standard-4', 'vm3': 'e2-medium'}
budget_limit = 500
vcpu_map = {'n1-standard-1': 1, 'n1-standard-4': 4, 'e2-medium': 2}
optimized_instances = []
for vm, machine in instances.items():
    if vcpu_map[machine] <= 2:
        optimized_instances.append(vm)
# Add autoscaling_config dictionary with enabled=true and max_instances=5
# Your code here

Hint

Create a dictionary with the specified keys and values.

Practice

1. Which of the following is a basic way to reduce costs in Google Cloud Platform (GCP)?

easy

A. Use the most powerful machines regardless of need

B. Increase the number of virtual machines

C. Use smaller or cheaper machine types

D. Keep all resources running 24/7

Solution

1. Step 1: Understand cost drivers in GCP

   Machine types with more CPU and memory cost more money.

2. Step 2: Choose cost-saving options

   Using smaller or cheaper machine types reduces the hourly cost.

3. Final Answer:

   Use smaller or cheaper machine types -> Option C

4. Quick Check:

   Smaller machines cost less [OK]

Hint: Pick smaller machines to save money quickly [OK]

Common Mistakes:

• Thinking more machines always save money
• Believing running all resources nonstop is cheaper
• Choosing the most powerful machines by default

2. Which of the following is the correct way to specify a preemptible VM in a GCP instance creation command?

easy

A. gcloud compute instances create my-vm --preemptible

B. gcloud compute instances create my-vm --no-preemptible

C. gcloud compute instances create my-vm --machine-type=preemptible

D. gcloud compute instances create my-vm --enable-preemptible=false

Solution

1. Step 1: Review gcloud syntax for preemptible VMs

   The flag to create a preemptible VM is --preemptible.

2. Step 2: Identify the correct command

   gcloud compute instances create my-vm --preemptible uses --preemptible correctly to enable preemptible VM.

3. Final Answer:

   gcloud compute instances create my-vm --preemptible -> Option A

4. Quick Check:

   Use --preemptible flag to create preemptible VM [OK]

Hint: Look for --preemptible flag to enable preemptible VMs [OK]

Common Mistakes:

• Using --no-preemptible which disables preemptible
• Confusing machine-type with preemptible flag
• Using incorrect flag names

3. Consider this Python snippet using GCP SDK to list VM instances and filter by machine type for cost optimization:

instances = [
  {"name": "vm1", "machine_type": "n1-standard-1"},
  {"name": "vm2", "machine_type": "n1-highmem-4"},
  {"name": "vm3", "machine_type": "n1-standard-1"}
]
cheap_vms = [vm["name"] for vm in instances if vm["machine_type"] == "n1-standard-1"]
print(cheap_vms)

What is the output?

medium

A. ['vm2']

B. ['vm1', 'vm3']

C. ['vm1', 'vm2', 'vm3']

D. []

Solution

1. Step 1: Understand the list comprehension filter

   The code selects VMs where machine_type equals 'n1-standard-1'.

2. Step 2: Identify matching VMs

   vm1 and vm3 have 'n1-standard-1', vm2 does not.

3. Final Answer:

   ['vm1', 'vm3'] -> Option B

4. Quick Check:

   Filter by machine_type 'n1-standard-1' gives vm1 and vm3 [OK]

Hint: Filter list by machine_type to find cheaper VMs [OK]

Common Mistakes:

• Including vm2 which has a different machine type
• Returning empty list by mistake
• Confusing keys in dictionary

4. You wrote this command to create a preemptible VM but it keeps creating normal VMs:

gcloud compute instances create cheap-vm --machine-type=n1-standard-1 --enable-preemptible=false

What is the error and how to fix it?

medium

A. The VM name is invalid; rename it

B. The machine type is wrong; use a cheaper type

C. The command is missing zone specification

D. The flag disables preemptible; use --preemptible instead

Solution

1. Step 1: Analyze the preemptible flag usage

   The flag --enable-preemptible=false disables preemptible VMs.

2. Step 2: Correct the flag to enable preemptible

   Use --preemptible without false to create preemptible VM.

3. Final Answer:

   The flag disables preemptible; use --preemptible instead -> Option D

4. Quick Check:

   Use --preemptible to enable preemptible VMs [OK]

Hint: Use --preemptible flag alone to enable preemptible VMs [OK]

Common Mistakes:

• Using --enable-preemptible=false disables preemptible
• Changing machine type instead of flag
• Ignoring flag syntax errors

5. A company wants to optimize costs by automatically stopping VMs when not in use during nights and weekends. Which GCP service or strategy best supports this goal?

hard

A. Use Cloud Scheduler to trigger Cloud Functions that stop/start VMs on schedule

B. Manually stop VMs every night using the console

C. Use preemptible VMs only without scheduling

D. Increase VM size to reduce runtime

Solution

1. Step 1: Identify automation options for VM management

   Cloud Scheduler can run tasks on a schedule, and Cloud Functions can execute commands like stopping VMs.

2. Step 2: Match automation to cost optimization goal

   Automating stop/start reduces costs by not running VMs when unused.

3. Final Answer:

   Use Cloud Scheduler to trigger Cloud Functions that stop/start VMs on schedule -> Option A

4. Quick Check:

   Automate VM stop/start with Cloud Scheduler and Functions [OK]

Hint: Automate VM stop/start with Cloud Scheduler and Functions [OK]

Common Mistakes:

• Relying on manual stopping which is error-prone
• Using preemptible VMs without scheduling
• Increasing VM size which raises costs