Auto scaling policies and events

What are the different types of scaling policies available in AWS Auto Scaling?

AWS Auto Scaling provides three primary types of scaling policies: Target Tracking Scaling, Step Scaling, and Simple Scaling. Target Tracking Scaling adjusts the capacity based on a specific metric like CPU usage or network input/output. Step Scaling adjusts the capacity based on a set of scaling adjustments, defining how to scale in response to changing metrics. Simple Scaling adjusts the capacity based on a single scaling adjustment in response to an alarm.

Can you explain how a cooldown period works in the context of auto-scaling?

The cooldown period is a temporary block to prevent Auto Scaling from launching or terminating additional instances. This allows the instances to warm-up (in the case of scale-out) or to finish handling ongoing requests (in the case of scale-in) before a new scaling activity is initiated. This helps in stabilizing the instance count during fluctuations in usage patterns.

What is the difference between scaling in and scaling out in AWS Auto Scaling?

Scaling out refers to adding more instances to an auto scaling group to handle an increase in load, while scaling in means removing instances, often when demand decreases. Scaling out provides more computing power to handle traffic, and scaling in helps reduce costs by terminating unneeded instances.

How does AWS Auto Scaling maintain high availability and fault tolerance?

AWS Auto Scaling helps maintain high availability and fault tolerance by monitoring applications and automatically adjusting capacity. It ensures the number of instances increases during demand spikes to maintain performance and decreases automatically during lulls to minimize costs. Auto Scaling can also distribute instances across multiple Availability Zones to maintain performance even if one zone has an outage.

What is a predictive scaling policy in AWS Auto Scaling, and how does it differ from reactive scaling?

Predictive scaling uses machine learning to analyze historical load metrics data and predict future traffic, including daily and weekly patterns, thereby preemptively scaling resources before demand changes occur. This differs from reactive scaling, which adjusts resources in response to real-time changes in metrics.

Can scheduled scaling actions be implemented with AWS Auto Scaling, and what are their benefits?

Yes, scheduled scaling actions can be set in AWS Auto Scaling to automatically adjust the number of EC2 instances to meet the predicted demand for specific times, such as known peak business hours or a planned sale. This pre-emptive action allows for handling predictable load changes without delay.

How do auto-scaling groups use health checks?

Auto Scaling groups use health checks to determine the state of each instance within the group. If an instance fails a health check, it’s considered unhealthy, and Auto Scaling marks it for termination and replaces it with a new, healthy instance, thereby maintaining the desired capacity and ensuring high availability.

How can an AWS Solutions Architect optimize cost with auto-scaling policies for a workload with varying demand?

An AWS Solutions Architect can optimize costs by using a combination of various auto-scaling policies, such as Target Tracking for average load conditions, Scheduled Scaling for known high and low traffic patterns, and Spot Instances to bid for extra capacity at lower prices. Additionally, they can optimize instance types for better cost efficiency within the auto-scaling group.

Describe an instance where you would prefer using strict Step Scaling over Target Tracking Scaling?

Step Scaling may be preferred when a workload has abrupt, large changes in load, and there is a desire to manage scaling actions in a finely tuned manner based on the magnitude of the load change. This allows precise control over the scaling response to metric fluctuations.

What role do CloudWatch alarms play in auto-scaling?

CloudWatch alarms are used to trigger auto-scaling actions based on the defined metrics exceeding or dropping below defined thresholds. The alarms monitor metrics like CPU utilization, network usage, or custom metrics, and when those metrics breach a specified threshold, they signal the auto-scaling group to execute a scaling policy.

Is it possible to have multiple auto-scaling policies for a single auto-scaling group?

Yes, a single Auto Scaling group can have multiple scaling policies. This is useful for handling different scaling scenarios such as a rapid increase in demand or a slow growth trend. Each policy can be triggered by different CloudWatch alarms based on the need of the application workload.

How does Auto Scaling integrate with Elastic Load Balancing, and why is this integration beneficial?

Auto Scaling integrates with Elastic Load Balancing (ELB) by automatically distributing incoming application traffic across all instances within its group. The integration is beneficial because it helps ensure the uniform distribution of traffic to healthy instances, improving the fault tolerance of the application and providing a seamless experience to end-users even during scaling events.