Request-based auto scaling in GCP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is request-based auto scaling in cloud infrastructure?

Request-based auto scaling automatically adjusts the number of running instances based on the number of incoming requests to handle traffic efficiently.

beginner

Which Google Cloud service commonly uses request-based auto scaling?

Google Cloud Run uses request-based auto scaling to scale container instances up or down depending on the number of requests it receives.

beginner

How does request-based auto scaling help save costs?

It scales down instances when there are fewer requests, so you only pay for the resources you actually use, avoiding waste.

intermediate

What happens if the number of requests suddenly spikes in request-based auto scaling?

The system quickly adds more instances to handle the increased load, preventing slowdowns or failures.

beginner

Name one key metric used in request-based auto scaling.

The number of incoming requests per second is a key metric to decide when to scale instances up or down.

What triggers request-based auto scaling in Google Cloud Run?

A Disk space usage

B CPU temperature

C Number of incoming requests

D User login count

Request-based auto scaling responds to the number of incoming requests to adjust instances.

What is a benefit of request-based auto scaling?

A Always runs maximum instances

B Ignores traffic changes

C Requires manual scaling

D Saves cost by scaling down when idle

It saves cost by reducing instances when there are fewer requests.

Which GCP service is best known for request-based auto scaling of containers?

A Cloud Run

B Cloud Storage

C BigQuery

D Cloud SQL

Cloud Run automatically scales container instances based on request load.

If request traffic drops to zero, what does request-based auto scaling do?

A Scales instances down to zero

B Keeps all instances running

C Increases instances

D Sends alert to admin

It scales down instances to zero to save resources when no requests come in.

What metric is NOT typically used in request-based auto scaling?

A Requests per second

B CPU usage

C Number of requests

D Incoming traffic

Request-based auto scaling focuses on request counts, not CPU usage.

Explain how request-based auto scaling works in Google Cloud Run.

Think about how the system reacts to more or fewer users accessing your app.

Describe the benefits of using request-based auto scaling for a web application.

Consider what happens when many users visit or leave your site.

Practice

1. What does request-based auto scaling primarily adjust in a cloud service?

easy

A. The number of server instances based on incoming requests

B. The amount of storage space available

C. The network bandwidth allocation

D. The security settings of the service

Solution

1. Step 1: Understand the purpose of request-based auto scaling

   Request-based auto scaling changes resources based on the number of incoming requests to handle load efficiently.

2. Step 2: Identify what is adjusted

   The main resource adjusted is the number of server instances to match demand.

3. Final Answer:

   The number of server instances based on incoming requests -> Option A

4. Quick Check:

   Scaling adjusts instances = A [OK]

Hint: Auto scaling changes instance count, not storage or security [OK]

Common Mistakes:

• Confusing scaling with storage or bandwidth changes
• Thinking it adjusts security settings
• Assuming it changes fixed resources regardless of requests

2. Which of the following is the correct way to set the minimum number of instances in a GCP service YAML configuration for request-based auto scaling?

easy

A. min-instances: 2

B. minInstances: 2

C. minimum_instances: 2

D. min_instances: 2

Solution

1. Step 1: Recall GCP YAML syntax for scaling parameters

   GCP uses kebab-case for configuration keys like min-instances.

2. Step 2: Identify the correct key format

   The correct key to set minimum instances is min-instances, not camelCase or underscored.

3. Final Answer:

   min-instances: 2 -> Option A

4. Quick Check:

   Use kebab-case keys like min-instances [OK]

Hint: Use kebab-case keys like min-instances in GCP YAML [OK]

Common Mistakes:

• Using camelCase or underscores instead of kebab-case
• Misspelling the key name
• Confusing min-instances with max-instances

3. Given this snippet of a GCP service configuration for request-based auto scaling:

concurrency: 10
min-instances: 1
max-instances: 5

What happens when the service receives 50 simultaneous requests?

medium

A. The service scales down to 0 instances to save cost

B. The service stays at 1 instance because min-instances is set

C. The service scales up to 10 instances because concurrency is 10

D. The service scales up to 5 instances to handle the load

Solution

1. Step 1: Understand concurrency and instance limits

   Concurrency 10 means each instance can handle 10 requests at once. Max-instances is 5, min-instances is 1.

2. Step 2: Calculate needed instances for 50 requests

   50 requests / 10 concurrency = 5 instances needed, which matches max-instances.

3. Final Answer:

   The service scales up to 5 instances to handle the load -> Option D

4. Quick Check:

   Requests/concurrency = instances needed = 5 [OK]

Hint: Divide requests by concurrency, respect max-instances limit [OK]

Common Mistakes:

• Ignoring max-instances limit
• Confusing concurrency with max-instances
• Assuming scaling down when load increases

4. You configured a GCP service with:

concurrency: 5
min-instances: 2
max-instances: 4

But the service never scales above 2 instances even under heavy load. What is the most likely cause?

medium

A. The concurrency value is too high, limiting scaling

B. The max-instances value is too low to allow scaling beyond 2

C. The min-instances value is preventing scaling down

D. The service needs a restart to apply scaling changes

Solution

1. Step 1: Analyze max-instances and min-instances settings

   Max-instances is set to 4, min-instances is 2. Scaling cannot exceed max-instances.

2. Step 2: Understand why scaling stops at 2

   If the service never goes above 2 instances, max-instances might be set too low or misinterpreted.

3. Final Answer:

   The max-instances value is too low to allow scaling beyond 2 -> Option B

4. Quick Check:

   Max-instances limits max scale = B [OK]

Hint: Check max-instances to allow scaling beyond min-instances [OK]

Common Mistakes:

• Confusing min-instances with max-instances
• Assuming concurrency limits scaling up
• Forgetting to check applied config values

5. You want to optimize cost and performance for a GCP service that experiences unpredictable traffic spikes. Which configuration best balances fast scaling up and cost savings during idle times?

hard

A. Set concurrency to 1, min-instances to 0, max-instances to 10

B. Set concurrency to 50, min-instances to 5, max-instances to 5

C. Set concurrency to 10, min-instances to 1, max-instances to 10

D. Set concurrency to 100, min-instances to 10, max-instances to 20

Solution

1. Step 1: Understand concurrency and instance settings impact

   Lower concurrency means more instances needed; min-instances controls baseline cost; max-instances limits scale.

2. Step 2: Evaluate options for balancing cost and performance

   Set concurrency to 10, min-instances to 1, max-instances to 10 uses moderate concurrency (10), low min-instances (1) to save cost, and high max-instances (10) to handle spikes.

3. Final Answer:

   Set concurrency to 10, min-instances to 1, max-instances to 10 -> Option C

4. Quick Check:

   Moderate concurrency + low min + high max = balanced scaling [OK]

Hint: Moderate concurrency with low min and high max balances cost and spikes [OK]

Common Mistakes:

• Setting concurrency too low causing many instances
• Setting min-instances too high increasing cost
• Setting max-instances too low limiting scaling