Cloud Monitoring overview in GCP - Step-by-Step Execution

Process Flow - Cloud Monitoring overview

Start: Setup Monitoring Workspace

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Collect Metrics from Resources

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Store Metrics in Time Series Database

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Analyze Metrics & Logs

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Create Dashboards & Alerts

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Respond to Alerts & Optimize

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End

This flow shows how Cloud Monitoring collects data, stores it, analyzes it, and helps you respond with alerts and dashboards.

Execution Sample

GCP

1. Enable Monitoring API
2. Create Workspace
3. Add VM instance to monitor
4. View metrics on dashboard
5. Set alert policy for CPU usage

This sequence sets up Cloud Monitoring, collects VM metrics, visualizes them, and creates alerts.

Process Table

Step	Action	System State Change	Output/Result
1	Enable Monitoring API	API enabled for project	Monitoring services ready
2	Create Workspace	Workspace created and linked	Central place for metrics
3	Add VM instance to monitor	VM metrics start streaming	Metrics appear in time series
4	View metrics on dashboard	Dashboard shows VM CPU, memory	Visual graphs displayed
5	Set alert policy for CPU usage	Alert triggers if CPU > 80%	Alerts ready to notify
6	CPU usage exceeds 80%	Alert fires	Notification sent to user
7	User responds to alert	Issue investigated and fixed	System performance restored
8	End	Monitoring cycle continues	Continuous visibility maintained

💡 Monitoring runs continuously; this trace shows initial setup and alert cycle.

Status Tracker

Variable	Start	After Step 3	After Step 5	After Step 6	Final
Monitoring API	Disabled	Enabled	Enabled	Enabled	Enabled
Workspace	None	Created	Created	Created	Created
VM Metrics	None	Streaming	Streaming	Streaming	Streaming
Dashboard	Empty	Empty	Populated	Populated	Populated
Alert Policy	None	None	Set	Triggered	Set

Key Moments - 3 Insights

Why do metrics only appear after adding the VM instance?

Metrics start streaming only after the VM is added to the workspace, as shown in execution_table step 3.

What causes the alert to trigger in step 6?

The alert triggers because CPU usage exceeded the threshold set in step 5, as seen in execution_table step 6.

Does monitoring stop after the initial setup?

No, monitoring runs continuously as noted in the exit_note and step 8 of the execution_table.

Visual Quiz - 3 Questions

Look at the execution table, what is the system state after step 3?

A VM metrics start streaming

B Dashboard shows VM CPU and memory

C Alert triggers if CPU > 80%

D API disabled for project

💡 Hint

Check the 'System State Change' column for step 3 in the execution_table.

At which step does the alert policy get set?

A Step 2

B Step 5

C Step 4

D Step 6

💡 Hint

Look for 'Set alert policy' in the 'Action' column in the execution_table.

If the VM was never added, what would happen to the dashboard?

A Alerts would trigger automatically

B It would show CPU usage graphs

C It would show empty or no metrics

D Workspace would be deleted

💡 Hint

Refer to variable_tracker for 'VM Metrics' and 'Dashboard' states after step 3.

Concept Snapshot

Cloud Monitoring overview:
- Enable Monitoring API to start
- Create a Workspace to collect data
- Add resources (like VMs) to monitor
- View metrics on dashboards
- Set alerts to notify on issues
- Monitoring runs continuously for visibility

Full Transcript

Cloud Monitoring starts by enabling the API and creating a workspace. Then you add resources like virtual machines to collect metrics. These metrics are stored and shown on dashboards for easy viewing. You can set alert policies to notify you when something needs attention, such as high CPU usage. When alerts trigger, you respond to fix issues. This cycle runs continuously to keep your cloud resources healthy and visible.