Auto scaling groups in AWS and Azure with scenarios and example
Auto Scaling in AWS and Azure can be used in a variety of scenarios to dynamically adjust the number of instances based on demand or other metrics.
Auto Scaling groups are useful in a number of scenarios where the demand for computing resources is unpredictable or changes frequently. Here are a few examples:
Web applications:
Auto Scaling is particularly useful for web applications that experience spikes in traffic, as it can quickly increase the number of instances to handle the increased load and then reduce the number of instances when demand subsides.
Batch processing:
Auto Scaling can be used for batch processing workloads where the amount of work to be done is not consistent. For example, a batch processing job might require a large number of instances for a short period of time, and then a smaller number of instances at other times.
High availability:
Auto Scaling can help ensure high availability by automatically launching replacement instances if an instance fails. This helps minimize downtime and ensure that the application remains available to users.
Cost optimization:
By automatically adjusting the number of instances based on demand, Auto Scaling can help optimize costs by ensuring that you only pay for the resources you need. This is especially important for organizations that need to maintain high levels of performance while controlling costs.
Common scenarios and examples of how auto scaling might be used in each platform as below
Scaling based on demand:
In this scenario, the number of instances is increased or decreased based on the load on the application. For example, during periods of high traffic, additional instances can be launched to handle the increased demand, and then terminated when traffic subsides.
AWS Example:
An Auto Scaling group is created with a minimum size of 2 instances and a maximum size of 10 instances. A CloudWatch Alarm is configured to trigger a scaling policy when the average CPU utilization across the instances exceeds 70%. When the alarm is triggered, the policy will launch additional instances until the group reaches its maximum size.
Azure Example:
An Azure Autoscale profile is created with a minimum size of 2 instances and a maximum size of 10 instances. A rule is created to increase the number of instances when the average CPU utilization of the instances exceeds 70%. When the rule is triggered, Azure Autoscale will launch additional instances until the profile reaches its maximum size.
Scaling based on schedule:
In this scenario, the number of instances is increased or decreased based on a schedule. For example, you might want to increase the number of instances during normal business hours and then reduce the number of instances during off-hours.
AWS Example:
An Auto Scaling group is created with a minimum size of 2 instances and a maximum size of 10 instances. A scheduled action is created to increase the size of the group to 10 instances during business hours, and then decrease the size of the group to 2 instances during off-hours.
Azure Example:
An Azure Autoscale profile is created with a minimum size of 2 instances and a maximum size of 10 instances. A time-based rule is created to increase the number of instances to 10 during business hours, and then decrease the number of instances to 2 during off-hours.
These are just a few examples of how auto scaling can be used in AWS and Azure. In both platforms, there are many options and configurations available to meet a variety of scaling requirements.
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