Performance efficiency pillar in Azure - Step-by-Step Execution

Process Flow - Performance efficiency pillar

Start: Define workload needs

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Choose right resources

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Implement scaling

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Monitor performance

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Optimize and adjust

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End

This flow shows how to efficiently use cloud resources by choosing, scaling, monitoring, and optimizing them.

Execution Sample

Azure

az vm create --name MyVM --size Standard_B2s --image UbuntuLTS
az monitor metrics alert create --name CPUAlert --resource MyVM --condition "Percentage CPU > 70" --action-scale-out
az vmss create --name MyVMScaleSet --instance-count 1 --vm-sku Standard_B2s
az monitor autoscale create --resource MyVMScaleSet --min-count 1 --max-count 5 --count 1
az monitor autoscale rule create --resource MyVMScaleSet --condition "Percentage CPU > 70" --scale out 1

This code creates a VM, sets a CPU alert, and configures auto-scaling to keep performance efficient.

Process Table

Step	Action	Resource State	Performance Metric	Result
1	Create VM with Standard_B2s size	VM created with 2 vCPUs, 4GB RAM	N/A	VM ready for workload
2	Set CPU alert at 70%	Alert configured	CPU usage monitored	Alert triggers if CPU > 70%
3	Configure auto-scale: min 1, max 5 instances	Scale set created	CPU usage monitored	Scale out when CPU > 70%
4	CPU usage rises to 75%	1 VM running	CPU = 75%	Alert triggers, scale out starts
5	Scale out adds 1 VM	2 VMs running	CPU drops to 50%	Performance improves
6	CPU usage stabilizes below 70%	2 VMs running	CPU = 50%	No further scaling
7	Monitor and optimize resources	Resource usage tracked	Performance stable	Costs controlled
Exit	Performance efficient state achieved	Optimal VM count	CPU within threshold	Workload runs smoothly

💡 CPU usage is stable below threshold and resources are optimized for performance and cost.

Status Tracker

Variable	Start	After Step 4	After Step 5	After Step 6	Final
VM Count	1	1	2	2	2
CPU Usage (%)	N/A	75	50	50	50
Alert Status	Off	Triggered	Resolved	Resolved	Resolved

Key Moments - 3 Insights

Why does the VM count increase when CPU usage goes above 70%?

Because the auto-scale rule triggers when CPU usage exceeds 70%, adding more VMs to handle the load, as shown in execution_table step 4 and 5.

Why does CPU usage drop after scaling out?

Adding more VMs shares the workload, reducing CPU usage per VM, as seen in execution_table step 5 and 6.

Why do we stop scaling out when CPU usage is below 70%?

Because the scaling condition is only triggered when CPU usage is above 70%, so when it drops below, no new VMs are added, shown in step 6.

Visual Quiz - 3 Questions

Look at the execution_table, what is the CPU usage percentage at step 4?

A 75%

B 50%

C 70%

D 85%

💡 Hint

Check the 'Performance Metric' column at step 4 in the execution_table.

At which step does the VM count increase from 1 to 2?

A Step 3

B Step 5

C Step 4

D Step 6

💡 Hint

Look at the 'Resource State' column in execution_table rows for VM count changes.

If the CPU alert threshold was set to 80% instead of 70%, when would scaling trigger?

A At CPU usage 75%

B At CPU usage 50%

C At CPU usage 85%

D Immediately after VM creation

💡 Hint

Refer to the alert condition in execution_sample and relate it to CPU usage in execution_table.

Concept Snapshot

Performance Efficiency Pillar in Azure:
- Choose right VM size and resources
- Set monitoring alerts (e.g., CPU > 70%)
- Configure auto-scaling rules
- Monitor performance metrics
- Scale out/in to optimize cost and performance
- Keep workload running smoothly

Full Transcript

The Performance Efficiency Pillar in Azure helps you use cloud resources smartly. First, you pick the right size for your virtual machines. Then, you set alerts to watch CPU usage. When CPU goes above a set limit, the system adds more VMs automatically. This sharing of work lowers CPU load and keeps your app fast. When CPU usage drops, scaling stops to save money. Monitoring and adjusting resources keeps your app running well without wasting cost.