By Deepthi Chelupati and Chad Schmutzer

AWS now offers Capacity Rebalancing for Amazon EC2 Auto Scaling, a new feature for proactively managing the Amazon EC2 Spot Instance lifecycle in an Auto Scaling group. Capacity Rebalancing complements the capacity optimized allocation strategy (designed to help find the most optimal spare capacity) and the mixed instances policy (designed to enhance availability by deploying across multiple instance types running in multiple Availability Zones). Capacity Rebalancing increases the emphasis on availability by automatically attempting to replace Spot Instances in an Auto Scaling group before they are interrupted by Amazon EC2.

In order to proactively replace Spot Instances, Capacity Rebalancing leverages the new EC2 Instance rebalance recommendation, a signal that is sent when a Spot Instance is at elevated risk of interruption. The rebalance recommendation signal can arrive sooner than the existing two-minute Spot Instance interruption notice, providing an opportunity to proactively rebalance a workload to new or existing Spot Instances that are not at elevated risk of interruption.

Capacity Rebalancing for EC2 Auto Scaling provides a seamless and automated experience for maintaining desired capacity through the Spot Instance lifecycle. This includes monitoring for rebalance recommendations, attempting to proactively launch replacement capacity for existing Spot Instances when they are at elevated risk of interruption, detaching from Elastic Load Balancing if necessary, and running lifecycle hooks as configured. This post provides an overview of using Capacity Rebalancing in EC2 Auto Scaling to manage your Spot Instance backed workloads, and dives into an example use case for taking advantage of Capacity Rebalancing in your environment.

EC2 Auto Scaling and Spot Instances – a classic love story

First, let’s review what Spot Instances are and why EC2 Auto scaling provides an optimal platform to manage your Spot Instance backed workloads. This will help illustrate how Capacity Rebalancing can benefit these workloads.

Spot Instances are spare EC2 compute capacity in the AWS Cloud available for steep discounts off On-Demand prices. In exchange for the discount, Spot Instances come with a simple rule – they are interruptible and must be returned when EC2 needs the capacity back. Where does this spare capacity come from? Since AWS builds capacity for unpredictable demand at any given time (think all 350+ instance types across 77 Availability Zones and 24 Regions), there is often excess capacity. Rather than let that spare capacity sit idle and unused, it is made available to be purchased as Spot Instances.

As you can imagine, the location and amount of spare capacity available at any given moment is dynamic and continually changes in real time. This is why it is extremely important for Spot customers to only run workloads that are truly interruption tolerant. Additionally, Spot workloads should be flexible, meaning they can be shifted in real time to where the spare capacity currently is (or otherwise be paused until spare capacity is available again). In practice, being flexible means qualifying a workload to run on multiple EC2 instance types (think big: multiple families, sizes, and generations), and in multiple Availability Zones, at any given time.

This is where EC2 Auto Scaling comes in. EC2 Auto Scaling is designed to help you maintain application availability. It also allows you to automatically add or remove EC2 instances according to conditions you define. We’ve continued to innovate on behalf of our customers by adding new features to EC2 Auto Scaling to natively support flexible configurations for EC2 workloads. One of these innovations is the mixed instances policy (launched in 2018), which supports multiple instance types and purchase options in a single Auto Scaling group. Another innovation is the capacity optimized allocation strategy (launched in 2019), an allocation strategy designed to locate optimal spare capacity for Spot Instances backed workloads. These features are aimed at supporting flexible workload best practices, and reacting to the dynamic shifts in capacity automatically.

The next level – moving from reactive to proactive Spot Capacity Rebalancing in EC2 Auto Scaling

The default behavior for EC2 Auto Scaling is to take a reactive approach to Spot Instance interruptions. This means that EC2 Auto Scaling attempts to replace an interrupted Spot Instance with another Spot Instance only after the instance has been shut down by EC2 and the health check fails. The reactive approach to interruptions works fine for many workloads. However, we have received feedback from customers requesting that EC2 Auto Scaling take a more proactive approach to handling Spot Instance interruptions.

Capacity Rebalancing in EC2 Auto Scaling is the answer to this request. Capacity Rebalancing is designed to take a proactive approach in handling the dynamic nature of EC2 capacity. It does this by monitoring for the EC2 Instance rebalance recommendation signal in addition to the “final” two-minute Spot Instance interruption notice. When a rebalance recommendation signal is detected, it automatically attempts to get a head start in replacing Spot Instances with new Spot Instances before they are shut down. In addition to attempting to maintain desired capacity through interruptions by launching replacement Spot Instances, Capacity Rebalancing gives customers the opportunity to gracefully remove Spot Instances from an Auto Scaling group by taking Spot Instances through the normal shut down process, such as deregistering from a load balancer and running terminating lifecycle hooks.

Capacity Rebalancing in EC2 Auto Scaling works best when combined with a few best practices. Let’s quickly review them:

  1. Be flexible. Capacity Rebalancing thrives on flexibility, and works best when using the EC2 Auto Scaling mixed instances policy and as many instance types and Availability Zones as possible. Remember to think big and qualify multiple families, sizes, and generations for your workload, and use all Availability Zones if possible.
  2. Use the capacity optimized allocation strategy. Capacity rebalance works optimally when combined with the capacity optimized allocation strategy and a flexible list of instance types and Availability Zones, because the goal is to find the optimal spare capacity to rebalance your workload on.
  3. Take advantage of termination lifecycle hooks (optional). Termination lifecycle hooks are powerful in case you need to perform any final tasks before shutdown.

Example tutorial – Web application workload

Now that you understand the best practices for taking advantage of Capacity Rebalancing in EC2 Auto Scaling, let’s dive into the example workload. In this scenario, we have a web application powered by 75% Spot Instances and 25% On-Demand Instances in an Auto Scaling group, running behind an Application Load Balancer. We’d like to maintain availability, and have the Auto Scaling group automatically handle Spot Instance interruptions and rebalancing of capacity.

The Auto Scaling group configuration looks like this (note the best practices of instance type and Availability Zone flexibility combined with the capacity optimized allocation strategy in the mixed instances policy):

{ "AutoScalingGroupName": "myAutoScalingGroup", "CapacityRebalance": true, "DesiredCapacity": 12, "MaxSize": 15, "MinSize": 12, "MixedInstancesPolicy": { "InstancesDistribution": { "OnDemandBaseCapacity": 0, "OnDemandPercentageAboveBaseCapacity": 25, "SpotAllocationStrategy": "capacity-optimized" }, "LaunchTemplate": { "LaunchTemplateSpecification": { "LaunchTemplateName": "myLaunchTemplate", "Version": "$Default" }, "Overrides": [ { "InstanceType": "c5.large" }, { "InstanceType": "c5a.large" }, { "InstanceType": "m5.large" }, { "InstanceType": "m5a.large" }, { "InstanceType": "c4.large" }, { "InstanceType": "m4.large" }, { "InstanceType": "c3.large" }, { "InstanceType": "m3.large" } ] } }, "TargetGroupARNs": [ "arn:aws:elasticloadbalancing:us-west-2:123456789012:targetgroup/my-targets/a1b2c3d4e5f6g7h8" ], "VPCZoneIdentifier": "mySubnet1,mySubnet2,mySubnet3"
}

Next, create the Auto Scaling group as follows:

aws autoscaling create-auto-scaling-group \ --cli-input-json file://myAutoScalingGroup.json

We also use a lifecycle hook to download logs before an instance is shut down:

aws autoscaling put-lifecycle-hook \ --lifecycle-hook-name myTerminatingHook \ --auto-scaling-group-name myAutoScalingGroup \ --lifecycle-transition autoscaling:EC2_INSTANCE_TERMINATING \ --heartbeat-timeout 300

In this example scenario, let’s say that the above config results in nine Spot Instances and three On-Demand instances being deployed in the Auto Scaling group, three Spot Instances, and one On-Demand instance in each Availability Zone. With Capacity Rebalancing enabled, if any of the nine Spot Instances receive the EC2 Instance rebalance recommendation signal, EC2 Auto Scaling will automatically request a replacement Spot Instance according to the allocation strategy (capacity optimized), resulting in 10 running Spot Instances. When the new Spot Instance passes EC2 health checks, it is joined to the load balancer and placed into service. Upon placing the new Spot Instance in service, EC2 Auto Scaling then proceeds with the shutdown process for the Spot Instance that has received the rebalance recommendation signal. It detaches the instance from the load balancer, drains connections, and then carries out the terminating lifecycle hook. Note that the instances may take time to shut down according to the load balancer deregistration delay and lifecycle hook heartbeat timeout settings. Once the terminating lifecycle hook is complete, EC2 Auto Scaling shuts down the instance, bringing capacity back to nine Spot Instances.

Conclusion

Consider using the new Capacity Rebalancing feature for EC2 Auto Scaling in your environment to proactively manage Spot Instance lifecycle. Capacity Rebalancing attempts to maintain workload availability by automatically rebalancing capacity as necessary, providing a seamless and hands-off experience for managing Spot Instance interruptions. Capacity Rebalancing works best when combined with instance type flexibility and the capacity optimized allocation strategy, and may be especially useful for workloads that can easily rebalance across shifting capacity, including:

  • Containerized workloads
  • Big data and analytics
  • Image and media rendering
  • Batch processing
  • Web applications

To learn more about Capacity Rebalancing for EC2 Auto Scaling, please visit the documentation.

To learn more about the new EC2 Instance rebalance recommendation, please visit the documentation.