Introducing the capacity-optimized allocation strategy for Amazon EC2 Spot Instances

AWS announces the new capacity-optimized allocation strategy for Amazon EC2 Auto Scaling and EC2 Fleet. This new strategy automatically makes the most efficient use of spare capacity while still taking advantage of the steep discounts offered by Spot Instances. It’s a new way for you to gain easy access to extra EC2 compute capacity in the AWS Cloud.

This post compares how the capacity-optimized allocation strategy deploys capacity compared to the current lowest-price allocation strategy.

Overview

Spot Instances are spare EC2 compute capacity in the AWS Cloud available to you at savings of up to 90% off compared to On-Demand prices. The only difference between On-Demand Instances and Spot Instances is that Spot Instances can be interrupted by EC2 with two minutes of notification when EC2 needs the capacity back.

When making requests for Spot Instances, customers can take advantage of allocation strategies within services such as EC2 Auto Scaling and EC2 Fleet. The allocation strategy determines how the Spot portion of your request is fulfilled from the possible Spot Instance pools you provide in the configuration.

The existing allocation strategy available in EC2 Auto Scaling and EC2 Fleet is called “lowest-price” (with an option to diversify across N pools). This strategy allocates capacity strictly based on the lowest-priced Spot Instance pool or pools. The “diversified” allocation strategy (available in EC2 Fleet but not in EC2 Auto Scaling) spreads your Spot Instances across all the Spot Instance pools you’ve specified as evenly as possible.

As the AWS global infrastructure has grown over time in terms of geographic Regions and Availability Zones as well as the raw number of EC2 Instance families and types, so has the amount of spare EC2 capacity. Therefore it is important that customers have access to tools to help them utilize spare EC2 capacity optimally. The new capacity-optimized strategy for both EC2 Auto Scaling and EC2 Fleet provisions Spot Instances from the most-available Spot Instance pools by analyzing capacity metrics.

Walkthrough

To illustrate how the capacity-optimized allocation strategy deploys capacity compared to the existing lowest-price allocation strategy, here are examples of Auto Scaling group configurations and use cases for each strategy.

Lowest-price (diversified over N pools) allocation strategy

The lowest-price allocation strategy deploys Spot Instances from the pools with the lowest price in each Availability Zone. This strategy has an optional modifier SpotInstancePools that provides the ability to diversify over the N lowest-priced pools in each Availability Zone.

Spot pricing changes slowly over time based on long-term trends in supply and demand, but capacity fluctuates in real time. The lowest-price strategy does not account for pool capacity depth as it deploys Spot Instances.

As a result, the lowest-price allocation strategy is a good choice for workloads with a low cost of interruption that want the lowest possible prices, such as:

• Time-insensitive workloads
• Extremely transient workloads
• Workloads that are easily check-pointed and restarted

Example

The following example configuration shows how capacity could be allocated in an Auto Scaling group using the lowest-price allocation strategy diversified over two pools:

{
  "AutoScalingGroupName": "runningAmazonEC2WorkloadsAtScale",
  "MixedInstancesPolicy": {
    "LaunchTemplate": {
      "LaunchTemplateSpecification": {
        "LaunchTemplateName": "my-launch-template",
        "Version": "$Latest"
      },
      "Overrides": [
        {
          "InstanceType": "c3.large"
        },
        {
          "InstanceType": "c4.large"
        },
        {
          "InstanceType": "c5.large"
        }
      ]
    },
    "InstancesDistribution": {
      "OnDemandPercentageAboveBaseCapacity": 0,
      "SpotAllocationStrategy": "lowest-price",
      "SpotInstancePools": 2
    }
  },
  "MinSize": 10,
  "MaxSize": 100,
  "DesiredCapacity": 60,
  "HealthCheckType": "EC2",
  "VPCZoneIdentifier": "subnet-a1234567890123456,subnet-b1234567890123456,subnet-c1234567890123456"
}

In this configuration, you request 60 Spot Instances because DesiredCapacity is set to 60 and OnDemandPercentageAboveBaseCapacity is set to 0. The example follows Spot best practices and is flexible across c3.large, c4.large, and c5.large in us-east-1a, us-east-1b, and us-east-1c (mapped according to the subnets in VPCZoneIdentifier). The Spot allocation strategy is set to lowest-price over two SpotInstancePools.

First, EC2 Auto Scaling tries to make sure that it balances the requested capacity across all the Availability Zones provided in the request. To do so, it splits the target capacity request of 60 across the three zones. Then, the lowest-price allocation strategy allocates the Spot Instance launches to the lowest-priced pool per zone.

Using the example Spot prices shown in the following table, the resulting allocation is:

• 20 Spot Instances from us-east-1a (10 c3.large, 10 c4.large)
• 20 Spot Instances from us-east-1b (10 c3.large, 10 c4.large)
• 20 Spot Instances from us-east-1c (10 c3.large, 10 c4.large)

The cost for this Auto Scaling group is $1.83/hour. Of course, the Spot Instances are allocated according to the lowest price and are not optimized for capacity. The Auto Scaling group could experience higher interruptions if the lowest-priced Spot Instance pools are not as deep as others, since upon interruption the Auto Scaling group will attempt to re-provision instances into the lowest-priced Spot Instance pools.

Capacity-optimized allocation strategy

There is a price associated with interruptions, restarting work, and checkpointing. While the overall hourly cost of capacity-optimized allocation strategy might be slightly higher, the possibility of having fewer interruptions can lower the overall cost of your workload.

The effectiveness of the capacity-optimized allocation strategy depends on following Spot best practices by being flexible and providing as many instance types and Availability Zones (Spot Instance pools) as possible in the configuration. It is also important to understand that as capacity demands change, the allocations provided by this strategy also change over time.

Remember that Spot pricing changes slowly over time based on long-term trends in supply and demand, but capacity fluctuates in real time. The capacity-optimized strategy does account for pool capacity depth as it deploys Spot Instances, but it does not account for Spot prices.

As a result, the capacity-optimized allocation strategy is a good choice for workloads with a high cost of interruption, such as:

• Big data and analytics
• Image and media rendering
• Machine learning
• High performance computing

Example

The following example configuration shows how capacity could be allocated in an Auto Scaling group using the capacity-optimized allocation strategy:

{
  "AutoScalingGroupName": "runningAmazonEC2WorkloadsAtScale",
  "MixedInstancesPolicy": {
    "LaunchTemplate": {
      "LaunchTemplateSpecification": {
        "LaunchTemplateName": "my-launch-template",
        "Version": "$Latest"
      },
      "Overrides": [
        {
          "InstanceType": "c3.large"
        },
        {
          "InstanceType": "c4.large"
        },
        {
          "InstanceType": "c5.large"
        }
      ]
    },
    "InstancesDistribution": {
      "OnDemandPercentageAboveBaseCapacity": 0,
      "SpotAllocationStrategy": "capacity-optimized"
    }
  },
  "MinSize": 10,
  "MaxSize": 100,
  "DesiredCapacity": 60,
  "HealthCheckType": "EC2",
  "VPCZoneIdentifier": "subnet-a1234567890123456,subnet-b1234567890123456,subnet-c1234567890123456"
}

In this configuration, you request 60 Spot Instances because DesiredCapacity is set to 60 and OnDemandPercentageAboveBaseCapacity is set to 0. The example follows Spot best practices (especially critical when using the capacity-optimized allocation strategy) and is flexible across c3.large, c4.large, and c5.large in us-east-1a, us-east-1b, and us-east-1c (mapped according to the subnets in VPCZoneIdentifier). The Spot allocation strategy is set to capacity-optimized.

First, EC2 Auto Scaling tries to make sure that the requested capacity is evenly balanced across all the Availability Zones provided in the request. To do so, it splits the target capacity request of 60 across the three zones. Then, the capacity-optimized allocation strategy optimizes the Spot Instance launches by analyzing capacity metrics per instance type per zone. This is because this strategy effectively optimizes by capacity instead of by the lowest price (hence its name).

Using the example Spot prices shown in the following table, the resulting allocation is:

• 20 Spot Instances from us-east-1a (20 c4.large)
• 20 Spot Instances from us-east-1b (20 c3.large)
• 20 Spot Instances from us-east-1c (20 c5.large)

The cost for this Auto Scaling group is $1.91/hour, only 5% more than the lowest-priced example above. However, notice the distribution of the Spot Instances is different. This is because the capacity-optimized allocation strategy determined this was the most efficient distribution from an available capacity perspective.

Conclusion

Consider using the new capacity-optimized allocation strategy to make the most efficient use of spare capacity. Automatically deploy into the most available Spot Instance pools—while still taking advantage of the steep discounts provided by Spot Instances.

This allocation strategy may be especially useful for workloads with a high cost of interruption, including:

• Big data and analytics
• Image and media rendering
• Machine learning
• High performance computing

No matter which allocation strategy you choose, you still enjoy the steep discounts provided by Spot Instances. These discounts are possible thanks to the stable Spot pricing made available with the new Spot pricing model.

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Chad Schmutzer is a Principal Developer Advocate for the EC2 Spot team. Follow him on twitter to get the latest updates on saving at scale with Spot Instances, to provide feedback, or just say HI.