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Difficulty: MediumConfigure Azure Monitor Autoscale Rules and Metrics

You are designing autoscale rules for several Azure solutions to optimize performance and prevent rapid oscillation (flapping). Match each Azure Monitor autoscale configuration setting on the left to the corresponding workload requirement it satisfies on the right.

  • Time aggregation set to Maximum for CPU PercentageTrigger scale-out if any single virtual machine instance in the scale set becomes overloaded.
  • Cool-down period set to 2020 minutes for scale-in rulesAvoid reducing capacity too quickly after load spikes, allowing active tasks to complete.
  • Metric set to Service Bus queue message countEnsure scaling occurs based on the total backlog of work waiting in the queue.
  • Time aggregation set to Average for CPU PercentageScale based on the overall, balanced resource utilization across all instances in the scale set.

Answer

Time aggregation set to Maximum for CPU Percentage matches with triggering scale-out if any single instance is overloaded. Cool-down period set to 2020 minutes for scale-in rules matches with avoiding reducing capacity too quickly. Metric set to Service Bus queue message count matches with scaling based on the total queue backlog. Time aggregation set to Average for CPU Percentage matches with scaling based on the overall, balanced resource utilization.
The correct pairings align each autoscale configuration parameter with its direct operational outcome. Time aggregation set to Maximum ensures that scale-out occurs if a single instance is overloaded. The cool-down period delays scale-in actions to prevent flapping. The Service Bus queue message count reflects the total workload backlog. Time aggregation set to Average measures overall, balanced pool utilization.

Step-by-Step Solution

1
Analyze the impact of Time Aggregation Maximum on autoscale behavior.
Maximum looks at the peak value among all instances, which correlates directly with detecting if any single instance is overloaded.
This matches the requirement to prevent individual instance degradation.
2
Evaluate the function of the scale-in cool-down period.
The cool-down period prevents the autoscale engine from performing another scale-in action for a set time (e.g., 2020 minutes) after a scaling event.
This is used to stabilize the system and allow active tasks to finish without immediate scale-down.
3
Identify the metric suitable for queue backlog workloads.
Service Bus queue message count represents the work queue backlog.
This matches the workload's scaling requirement based on pending work.
4
Determine the application of Time Aggregation Average.
Average computes the mean value across all instances, representing balanced workload pressure.
This is suitable for standard web apps where traffic is evenly distributed.

Key Concept

Azure Monitor Autoscale rule metric parameters and aggregation types are configured to match specific workload scaling patterns and prevent flapping.
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Topics

Configure Azure Monitor Autoscale Rules and Metrics

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