Azure Scale Out on Memory Keeps Flapping: A Deep Dive into the Issue and Solutions
The Problem: Azure Scale Out Not Scaling In
Imagine your Azure web app is designed to scale out based on memory utilization. It works perfectly when traffic surges, adding more instances to handle the load. But here's the catch: when the load drops, the app struggles to scale back in. It keeps adding instances, even when memory usage is low, leading to wasted resources and unnecessary costs. This behavior, known as flapping, can be a real pain for developers and sysadmins.
Scenario and Code Example:
Let's say we have a web app using Azure App Service with autoscaling enabled. Our autoscale configuration might look something like this:
{
"profiles": [
{
"name": "Default",
"capacity": {
"default": 1,
"minimum": 1,
"maximum": 10
},
"autoscale": {
"enabled": true,
"rules": [
{
"name": "Memory",
"metricTrigger": {
"metricName": "Percentage CPU",
"metricNamespace": "Microsoft.Compute/virtualMachines",
"metricResourceUri": "/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/microsoft.web/sites/{siteName}/virtualmachines/vm",
"timeGrain": "PT1M",
"statistic": "Average",
"timeWindow": "PT5M",
"threshold": 80
},
"scaleAction": {
"direction": "Increase",
"type": "ChangeCount",
"count": 1
},
"cooldown": "PT1M"
},
{
"name": "Memory Down",
"metricTrigger": {
"metricName": "Percentage CPU",
"metricNamespace": "Microsoft.Compute/virtualMachines",
"metricResourceUri": "/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/microsoft.web/sites/{siteName}/virtualmachines/vm",
"timeGrain": "PT1M",
"statistic": "Average",
"timeWindow": "PT5M",
"threshold": 20
},
"scaleAction": {
"direction": "Decrease",
"type": "ChangeCount",
"count": 1
},
"cooldown": "PT1M"
}
]
}
}
]
}
This configuration scales the app out if the CPU usage goes above 80% and scales back in if it falls below 20%. However, the problem arises when the app is scaling back in. Even with low memory usage, the app might keep adding instances, leading to flapping.
Understanding the Cause of Flapping
The primary culprit is usually the way the autoscaling rules are set up. Here's a breakdown:
- Too short cooldown time: Setting a short cooldown period (like 1 minute in our example) prevents the autoscaler from evaluating the current situation properly. It might see a momentary spike in memory usage and trigger scaling out, even though it was caused by a temporary event.
- Incorrect metric: Relying solely on CPU usage might not be a good indicator of scaling needs. Other factors like memory, network, or disk usage could provide a better picture of the app's actual resource consumption.
- Aggressive scaling: The "count" parameter for scaling actions dictates how many instances are added or removed. A high value, like 1, can lead to rapid scaling out and make it difficult to recover quickly when load drops.
- Insufficient resource limits: If the app has insufficient memory or CPU allocated to it, even slight increases in load can trigger scaling out.
- External factors: Issues like database bottlenecks, network latency, or application code inefficiencies can artificially inflate resource usage, causing the autoscaler to react unnecessarily.
Solutions to Stop the Flapping
- Extend cooldown time: Increase the cooldown period for your autoscale rules to give the autoscaler more time to gather data and make informed decisions. A cooldown of 5-10 minutes might be a good starting point.
- Use multiple metrics: Instead of relying solely on CPU usage, incorporate other metrics like memory, disk usage, and network bandwidth to get a more holistic view of resource consumption. This allows the autoscaler to make more accurate scaling decisions.
- Adjust scaling actions: Decrease the "count" parameter for scaling actions to add or remove instances gradually. For example, instead of adding 1 instance at a time, try adding 0.5 or 0.25 instances.
- Optimize resource allocation: Ensure your app has enough resources to handle peak load without triggering unnecessary scaling. Monitor your app's resource utilization and adjust settings like memory or CPU limits as needed.
- Diagnose external factors: Investigate potential bottlenecks in databases, network connections, or your application code to rule out any issues that could be causing artificial resource spikes.
- Consider a "cool-down" scale rule: Implement a scale rule that decreases the number of instances when all metrics are below a certain threshold for a specified duration. This can help to prevent the autoscaler from adding new instances unnecessarily during periods of low load.
Conclusion
Azure autoscaling is a powerful tool for managing your application's resources dynamically. However, it's important to fine-tune the configuration to avoid the issue of flapping. By understanding the common causes and implementing appropriate solutions, you can ensure your application scales effectively and efficiently, maximizing resource utilization and minimizing unnecessary costs.
