Cost optimization strategies in GCP - Step-by-Step Execution

Process Flow - Cost optimization strategies

Identify Resources

↓

Analyze Usage Patterns

↓

Select Optimization Techniques

↓

Implement Changes

↓

Monitor Costs

↓

Repeat Cycle for Continuous Savings

This flow shows how to find cloud resources, check how they are used, pick ways to save money, apply them, watch costs, and keep improving.

Execution Sample

GCP

1. List all active resources
2. Check usage and idle times
3. Choose reserved instances or autoscaling
4. Apply changes
5. Monitor billing dashboard

This sequence shows steps to reduce cloud costs by managing resources and usage.

Process Table

Step	Action	Input/Condition	Result/Output	Next Step
1	List all active resources	Current cloud environment	Resources identified: VM, Storage, DB	2
2	Check usage and idle times	Resource list	Idle VM found (30% unused)	3
3	Choose optimization technique	Idle VM detected	Decide to use autoscaling and reserved instances	4
4	Apply changes	Optimization plan	Autoscaling enabled, reserved instances purchased	5
5	Monitor billing dashboard	After changes	Cost reduced by 25%	End
End	Stop	Optimization cycle complete	Savings confirmed	N/A

💡 Optimization cycle ends after monitoring confirms cost savings

Status Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
Resources	Unknown	VM, Storage, DB	Same	Same	Same
Idle VM %	Unknown	30%	30%	0% (autoscaling)	0%
Optimization Technique	None	None	Autoscaling + Reserved Instances	Applied	Applied
Cost	Baseline	Baseline	Baseline	Reduced by 25%	Reduced by 25%

Key Moments - 2 Insights

Why do we check usage and idle times before choosing optimization?

Because knowing which resources are idle helps decide if autoscaling or reserved instances will save money, as shown in step 2 and 3 of the execution_table.

What happens if we skip monitoring costs after applying changes?

We might miss if the changes actually save money or cause issues. Step 5 shows monitoring confirms savings, so skipping it risks losing control.

Visual Quiz - 3 Questions

Look at the execution_table, what is the idle VM percentage after step 2?

A 0%

B 30%

C 50%

D 100%

💡 Hint

Check the 'Idle VM %' variable in variable_tracker after Step 2

At which step do we apply autoscaling and reserved instances?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look at the 'Apply changes' action in execution_table step 4

If no idle VMs were found, how would the optimization technique change?

A Focus on other resources like storage or databases

B Skip optimization

C Still use autoscaling and reserved instances

D Increase VM sizes

💡 Hint

Idle VM detection in step 2 guides technique choice in step 3

Concept Snapshot

Cost Optimization Strategies in GCP:
1. Identify all active resources.
2. Analyze usage to find idle or underused resources.
3. Choose techniques like autoscaling or reserved instances.
4. Apply changes and monitor costs.
5. Repeat regularly for continuous savings.

Full Transcript

Cost optimization in cloud means finding resources you use, checking how much you use them, picking ways to save money like autoscaling or reserved instances, applying those changes, and watching your bills to make sure you save money. This process repeats to keep costs low.

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