Comprehensive guide to manual scaling, scale-in policies, and custom scaling options on Azure

Azure offers a variety of ways to scale your resources, whether through manual scaling, scale-in policies, or custom scaling options.

These mechanisms are essential for optimizing resource allocation, ensuring cost efficiency, and maintaining application performance in cloud environments.

Let’s review each of these scaling options and policies in more detail.

Manual Scaling in Azure

Manual scaling involves manually adjusting the number of resources or instances allocated to your Azure services based on your requirements.

This is useful when you have predictable workloads and can anticipate demand.

How to Perform Manual Scaling

Manual scaling can be done in multiple Azure services such as Virtual Machine Scale Sets (VMSS), App Services, Azure Kubernetes Service (AKS), and others.

Virtual Machine Scale Sets (VMSS)

1. Navigate to your VMSS in the Azure Portal.

2. In the Scaling tab, you can manually adjust the number of instances in your scale set.

3. Increase or decrease the instance count by entering the desired number of instances.

   • This adjustment will take effect immediately but will not trigger any automatic scaling rules.

Azure App Service

1. Go to your App Service in the Azure Portal.

2. Under the Scaling section, you can manually scale the number of instances in your App Service Plan (e.g., scale from 2 instances to 5).

3. Manual scaling is ideal when you want to maintain a specific number of instances without relying on autoscaling.

When to Use Manual Scaling

Predictable workloads

If you know traffic will be high or low during specific times (e.g., scheduled sales events or predictable off-peak hours).

Testing/Trial Periods

When you need to assess the performance of specific configurations.

Cost Control

Manual scaling can help control costs in the absence of fluctuating demand.

Scale-In Policies in Azure

Scale-in policies determine how Azure scales down resources, i.e., when and how resources are removed or deallocated in response to reduced demand.

Proper configuration of scale-in policies ensures that scaling down is done in a controlled manner, avoiding the loss of critical services.

Key Concepts of Scale-In Policies

Instance Priority

When scaling down, the system needs to decide which instances to deallocate first.

Azure allows you to specify the priority or set rules on which VMs or instances should be scaled in first.

For VMSS

The instance priority for scale-in is often defined by the Health of the instance.

If an instance has failed a health probe or is performing poorly, it may be chosen for scaling in first.

Scale-In Delay

When defining scaling policies, you can configure a delay or cool-down period for scaling in.

This prevents the system from scaling in too quickly when a temporary drop in usage happens.

The goal is to give the service enough time to stabilize after scaling out.

Types of Scale-In Behaviors

First-To-Scale-In

When scaling down, instances that are least healthy or have the lowest priority may be scaled in first.

For example, the least recently used instances (those that are the least active) can be prioritized for deallocation.

Priority/Ordering of Scale-In:

Azure allows you to use priority or other settings to control which instances get removed first.

This can be particularly useful for specific workloads that you want to keep alive longer (e.g., critical database instances).

Healthy Instance Retention

You can set policies to retain healthy instances for longer periods and only scale in those instances that are underperforming or idle.

Auto-Scale vs. Manual

Autoscaling services (like VMSS and App Services) typically have default scale-in behaviors but allow you to customize or modify them based on specific needs (e.g., prioritize scaling in based on instances' uptime or performance).

Configuring Scale-In Policies

VMSS

In Virtual Machine Scale Sets, when you configure autoscaling, you can define a scale-in policy as part of your autoscale settings.

1. Go to Scaling in your VMSS settings.

2. Under Autoscale settings, you can define the minimum and maximum instance counts, and set scale-in conditions based on metrics (e.g., CPU utilization).

3. Scaling rules for VMSS can specify how to handle scaling-in when CPU or memory is low.

Custom Scaling Options in Azure

Custom scaling options allow more fine-grained control over how your Azure services scale.

You can create scaling rules and metrics specific to your needs, making scaling operations more dynamic and responsive to actual performance data.

Azure Scaling Methods

Custom Metrics

Azure Monitor and Azure Application Insights can provide custom metrics that can be used as triggers for scaling.

For example, instead of relying solely on CPU or memory usage, you can use custom application metrics (e.g., request queue length, database response time, or error rate) to trigger scaling actions.

Steps to Configure Custom Metrics

Use Azure Monitor to create custom metrics or Application Insights to track specific application behavior.

Set up autoscale rules based on these metrics:

1. Scale Out: Increase instances when the number of requests per second exceeds a certain threshold.

2. Scale In: Decrease instances when the application queue length is low.

Scheduled Scaling

You can configure scheduled scaling for predictable workloads.

This feature allows you to scale resources based on predefined times and dates, which can be particularly useful for regular events like marketing campaigns, promotional sales, or routine maintenance.

How to Configure Scheduled Scaling

1. In Azure App Service or VMSS, go to the Scaling section and select Schedule scaling.

2. Specify the time, recurrence, and instance count to scale out or scale in based on your desired schedule.

Autoscaling Based on Multiple Metrics

Azure supports the ability to scale based on multiple metrics simultaneously, such as combining CPU utilization and memory usage, or scaling based on the average of multiple metrics.

This allows for more complex scaling scenarios, where, for example, a VM might scale out if either CPU or memory utilization exceeds a certain threshold.

Scaling Based on Predictive Analytics

With Azure Machine Learning, you can forecast demand for your application and use those predictions to inform scaling decisions.

Predictive scaling can help prevent bottlenecks and performance degradation during traffic spikes.

VMSS Customization

Virtual Machine Scale Sets allow users to define custom scaling behaviors and rules, such as defining instance health or priority during scale-in.

Health Probes:

Use health probes (such as HTTP or TCP checks) to ensure only healthy instances are kept in service during autoscaling.

Example of Custom Scaling

1. Scaling Based on Queue Length:

If your application uses a message queue (e.g., Azure Service Bus), you can scale your application based on the length of the queue.

If the queue length exceeds a certain threshold, more instances can be added to process the messages.

For instance, you might define an autoscale rule like:

2. Scale Out:

Add an instance when queue length exceeds 100 messages.

3. Scale In:

Remove an instance when queue length falls below 20 messages.

Best Practices for Scaling in Azure

Use Scale-In Policies Wisely

Ensure that scaling-in removes the correct instances to minimize downtime.

Prioritize healthy instances and critical workloads, particularly for stateful applications.

Avoid rapid scaling in to prevent potential service disruptions during dynamic load changes.

Monitor and Adjust

Continuously monitor scaling operations using Azure Monitor or Application Insights to ensure the scaling rules are effective and that the application behaves as expected.

Adjust scaling rules based on real-world usage patterns, rather than setting static rules that may not align with your workload’s dynamic needs.

Balance Between Vertical and Horizontal Scaling

Horizontal scaling (increasing or decreasing the number of instances) is generally preferred for stateless applications, while vertical scaling (adjusting the size of the instances) can be useful for stateful applications, like databases.

Azure provides both horizontal and vertical scaling capabilities, but the choice depends on workload characteristics.

Use Cool-Down Periods

Always set cool-down periods to avoid rapid scaling actions, which might lead to instability or resource over-allocation.

A cool-down period is essential to give time for the system to stabilize after scaling actions.

Test Your Scaling Strategy

Simulate traffic spikes, downtime, and failures to ensure that your scaling strategy (including scale-in policies) works as expected in production environments.

Testing ensures that scale-in doesn’t inadvertently disrupt essential services.

Summary

Azure provides a robust set of manual scaling, scale-in policies, and custom scaling options to help you optimize performance and control costs for your cloud resources.

Manual scaling is useful for predictable scenarios, while scale-in policies and custom scaling options offer automation and flexibility to respond dynamically to changing demands.

By leveraging Azure’s scaling features, you can ensure that your applications remain highly available, responsive, and cost-effective under varying loads.