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aws-solutions-library-samples/guidance-for-ec2-spot-placement-score-tracker

EC2 Spot Placement Score Tracker

Author: Carlos Manzanedo Rueda ruecarlo@amazon.com

Introduction

Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices. Spot Placement Score (SPS) is a feature that helps AWS Spot customers by providing recommendations about which are the best suited AWS Region or Availability Zone to run a diversified configuration that adjusts to the customer requirements.

Spot capacity fluctuates. You can't be sure that you'll always get the capacity that you need. A Spot placement score indicates how likely it is that a Spot request will succeed in a Region or Availability Zone. Spot placement score provides a score from 1 to 9 of how successful your experience when using Spot instances would be on a set of regions.

This project automates the capture of Spot Placement Scores and stores SPS metrics in CloudWatch. Historic metrics can be then be visualized using CloudWatch Dashboards. CloudWatch can also be used to trigger Alarms and automation of events such as moving your workload to a region where capacity is available.

Spot can be used to optimize the scale, cost and execution time of Workloads such as Containers (K8s, EKS, ECS, etc), Loosely coupled HPC and high throughput computing (AWS Batch, Parallel Cluster), Data & Analytics using Spark, Flink, Presto, CICD, Rendering, and in general any workload that is retryable, scalable and stateless.

Spot instances can be managed through Auto Scaling Groups and EC2 Fleet, and controllers engines such as Karpenter. If the configuration of your workload follows Spot best practices, when a Spot instance receives a notification for termination, Auto Scaling Groups, EMR, Karpenter, etc, will automate the replacement of the instance from another Spot pool where there is capacity available. Even better! Allocation strategies such as capacity-optimized ,and price-capacity-optimized select the optimal pools to reduce the frequency of interruption and cost for your workload.

Spot placement Score considers takes as an input a diversified fleet. With this Spot Placement Score Tracker dashboards, you will be able to monitor and evaluate how to apply spot best practices and as a result optimize your workload to make the most of Spare capacity at scale. Some of the best practices you should consider are:

  • Increasing Instance Diversification. Adding instances from other sizes, and families.
  • Considering Flexibility in your workloads by selecting multiple Availability zones and if your workload allows, exploring the possibility of using multiple region
  • Considering running at times of the day when spare capacity is more available

The following graph shows one of the Spot Placement Score dashboards

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Architecture Diagram

The project provides Infrastructure as Code (IaaC) automation using CDK to deploy the infrastructure, IAM roles and policies required to run Lambda that gets executed every 5 minutes to collect the Spot Placement Scores of as many diversified configurations as needed.

img

The image above shows components deployed using CDK. If you are not familiar with CDK you can use the cloud 9 to proceed with the whole setup and installation here.

The CDK project sets up a few policies and roles to run with least privilege read access to all resources except for Cloudwach for which it needs to store metrics.

The diagram shows how the different steps are called:

  • First, Event Bridge cron functionality starts the execution of the spotPlacementScoresLambda every 5 minutes.
  • The lambda function, uses the environment variable config to fetch the YAML document that contains the dashboard.
  • The lambda decomposes all the requests and starts requesting one by one the queries to SPS
  • The responses are then used to create and store CloudWatch Metrics into Cloudwatch.
  • The CDK project did also read the YAML document before storing it into S3 and did use the project to preset the CloudWatch representation of the dashboards.

Important notes Spot Placement Score Limits imposed by AWS

Spot placement Score API's imposes a set of limits that you should be aware of:

  • Limit on number of Instances, vCPU, Memory for each request. This limit will be equivalent to the number of instances that you are already using in your account in a regular way, so that you can evaluate your current workload on different regions or AZ.
  • Limit on number of configurations. Spot Placement Score limits you to a few (10) diversified configurations. If you configure too many configurations you may find that the lambda will fail and will be limited to just query a few of the configurations. This will also be checked as part of the CDK deployment process.

The following log snippet shows one of this throttling limits in action:

botocore.exceptions.ClientError: An error occurred (MaxConfigLimitExceeded) when 
calling the GetSpotPlacementScores operation: You have exceeded your maximum allowed 
Spot placement configurations. You can retry configurations that you used within the 
last 24 hours, or wait for 24 hours before specifying a new configuration.

Steps to consider before Deployment: SPS Dashboard Configuration

The file sps_configuration.yaml provides an example configuration file that you can modify and adapt to your needs. This file will be used and deployed by the stack to the cloud and will be kept in S3 as the source configuration. The file uses a YAML format that follows a compatible schema as the one used by the Spot Placement Score call. You can find more information on the SPS API structure for python here

Before proceeding with the deployment of the dashboards CDK you will need to adapt the configuration file that defines the different Spot diversified configurations.

To learn how to better adjust your configurations keep reading the best practices section and understand how to get actionable insights based on your configuration that will help you optimize your workload.

Requirements

  • CDK
  • Python =>3.8
  • virtualenv
  • IAM Permissions run CDK stacks and request for Spot Placement Score
  • Docker

Installation

Deploying CDK project using Cloud9

The easier way to setup and deploy your environment is using Cloud9 following this instructions.

  • Create a Cloud9 environment :
  • On the console run the following commands:

1.- Create a Cloud9 environment in your account. Note: If you use a pre-existing cloud9 environment you may need to upgrade your python and npm.

2.- Execute the following commands on Cloud 9 (you can just copy and paste)

export VERSION=1.0.3
wget https://github.com/aws-solutions-library-samples/guidance-for-ec2-spot-placement-score-tracker/archive/refs/tags/v$VERSION.tar.gz -O ec2-spot-placement-score-tracker-v$VERSION.tar.gz
tar xzvf ec2-spot-placement-score-tracker-v$VERSION.tar.gz
cd $HOME/environment/guidance-for-ec2-spot-placement-score-tracker-$VERSION

Configuring the Cloud9 Setup before deployment

At this stage, you can check on the Cloud 9 editor and edit the configuration file at $HOME/environment/spot-placement-score-dashboard-cdk-v0.2.0/sps_configuration/sps_config.yaml We do provide an example file with a few passwords, but we also recommend checking the best practices below. Use those best practices to define the dashboards that are meaningful for your configuration.

Deploy dependencies

Once your configuration file is ready, we should install CDK and the rest of dependencies.

npm install -g --force aws-cdk
pip install virtualenv
virtualenv .env
source .env/bin/activate
pip install -r requirements.txt 

Bootstrap

Deploying AWS CDK apps into an AWS environment may require that you provision resources the AWS CDK needs to perform the deployment. These resources include an Amazon S3 bucket for storing files and IAM roles. We will also use that S3 bucket to upload our dashboard configuration. Execute the following command to bootstrap your environment:

cdk bootstrap

You can read more about the bootstrapping process here

Deploying the application & Dashboards

cdk deploy

Once deployed, go to your AWS console and visit the CloudWatch Dashboard section. The Dashboards are aggregated with a period of 15 minutes.

Cleanup

Once you are done, you can destroy the cdk deployment and delete the cloud9 environment. You can also delete the configuration by deleting the stack in CloudFormation.

cdk destroy

Note the user you run this with, should be able to create Cloud9 environments create deploy CloudFormation stacks, add extra IAM roles and have access to execute Spot Placement Score queries.

Configuration

The configuration file contains a YAML defined vector of dashboards. For example The following snippet shows how to configure two dashboards for two workloads. Each dashboard can define DefaultWidgetHeight and DefaultWidgetWidth to set the size of each individual chart. The maximum width of CloudWatch Grid is 24, so in this example below we will be creating rows of 2 charts of height 12. The Sps section defines a list of SPS configurations to evaluate.

- Dashboard: MySpsDashboard-for-Workload-A
  DefaultWidgetHeight: 12    # Default : 6
  DefaultWidgetWidth: 12     # Default : 6, Grid Maximum Width:24
  Sps:
    ...
- Dashboard: MySpsDashboard-for-Workload-B
  DefaultWidgetHeight: 12    # Default : 6
  DefaultWidgetWidth: 12     # Default : 6, Grid Maximum Width:24
  Sps:
    ...

Now that we know how to create more than one dashboard, let's check at the SPS section. The Sps section defines an array of SPS configurations. Each individual Sps section has a named SPS query. The request format is the same as the serialised version of the call to SPS API's. You can use as a reference the boto documentation here or the aws-cli for spot placement score Check the section JSON Syntax

Below is an example of a Dashboard with a single SPS chart. The configuration shows a dashboard for workload A with 2 charts per row. The first chart has a name Compute Xlarge and uses the schemas defined in the link above to diversify over instances c5.xlarge and similar sized instances from the compute instance family. Aside from the key ConfigurationName the rest of the parameters follows the schemas provided in the links above to target european regions up to 2000 vCPUs. Note that below the configuration Compute Xlarge, there is a second one for Compute 2Xlarge.

- Dashboard: MySpsDashboard-for-Workload-A
  DefaultWidgetHeight: 12    # Default : 6
  DefaultWidgetWidth: 12     # Default : 6, Grid Maximum Width:24
  Sps:
  # Second configuration this one for Compute 2xlarge
  - ConfigurationName: Compute Xlarge
    InstanceTypes:
    - c5.xlarge
    - c6i.xlarge
    - c5a.xlarge
    - c5d.xlarge
    ...
    RegionNames:
    - eu-west-1
    - eu-west-2
    ...
    SingleAvailabilityZone: False
    TargetCapacity: 2000
    TargetCapacityUnitType: vcpu
    
  # Second configuration this one for Compute 2xlarge
  - ConfigurationName: Compute 2Xlarge
    ...

Instead of using InstanceTypes we do recommend using InstanceRequirementsWithMetadata. This maps with requesting Diversification using Instance attributes rather than the AWS instance names. You can read more about Attribute Based Instance Selection We do strongly recommend to define your configurations using Attribute Based Instance Selection. By doing that you will have a simple configuration to maximise the diversification and instance types that your workload can use and that will consider new instances as they are released by AWS.

- Dashboard: MySpsDashboard-for-Workload-A
  DefaultWidgetHeight: 12    # Default : 6
  DefaultWidgetWidth: 12     # Default : 6, Grid Maximum Width:24
  Sps:
  # Second configuration this one for Compute 2xlarge
  - ConfigurationName: Compute Xlarge
    InstanceRequirementsWithMetadata:
      ArchitectureTypes:
      - x86_64
      InstanceRequirements:
        VCpuCount:
          Min: 32
        MemoryMiB:
          Min: 256
        AcceleratorCount:
          Max: 0
        BareMetal: excluded
        BurstablePerformance: excluded
        CpuManufacturers:
        - intel
        - amd
        InstanceGenerations:
        - current
        MemoryGiBPerVCpu:
          Min: 8
        SpotMaxPricePercentageOverLowestPrice: 50
    
  # Second configuration this one for Compute 2xlarge
  - ConfigurationName: Compute 2Xlarge
    ...

Advanced configurations

The configuration file, by default supports the definition of multiple dashboards, but still in some scenarios you may want to have multiple configuration files, or deploy multiple times a CloudFormation stack with a different name and a different configuration.

Creating a stack with a different configuration file

The default configuration file is stored in the sps_configuration/sps_config.yaml. You can point to any other file by using the context key sps-config in when launching cdk commands:

cdk deploy --context "sps-config=./my_sps_dashboard_configuration.yaml"

Creating and deploying multiple stacks on the same AWS account

In some situations you may want to deploy a two different configuration files simultaneously on the same account. You can do it by using the following command

cdk deploy --context "sps-config=./my_sps_dashboard_configuration.yaml" --context "stack-name=my-sps-demo" 

This will create a new Stack named my-sps-demo. To destroy/remove the stack you can use CloudFormation directly.

Dashboard Setup Best Practices

Checking what is the Spot Placement Score is definitely useful. You can use this project and Spot Interruption Dashboard to get understand and get the right observability for your workloads, but that's just the begining.

The goal when we set up SPS dashboard is to find actionable configurations that will help to improve the way that our workload provisions Spot capacity at the scale you need. The next steps will guide you on a set of steps to define your dashboard configuration.

  • Consider using a dashboard per workload. We will focus our attention at the workload level and will evaluate which other configurations can improve our current configurations.

  • Understand your workload requirements and find: (a) how many vCPUs you will need, (b) what is the minimum configuration that qualifies for your workload (c) can the workload be spread across AZ's ? (d) Which regions can your organization use, and which ones are you considering using in the future. Set the first configuration of the dashboard to be your current workload configuration defined in this step.

  • Decide which other configurations you'd like to compare your current one against and how that will increase diversification. Select up to 3 Configurations from the ones you think have more chances to increase your access to spare capacity. 3 or 4 is enough adding more configurations can make an analysis confusing ( and you can try others later).

  • To consider new configuration you can use a mix of these techniques such as: (a)using Attribute Instance Selection instead of a list of instances (b) Think of using instances of larger sizes, or smaller sizes if appropriate for your workload (c) Consider expanding over all Availability zones if you have not done it yet (and is appropriate for your workload)

  • Consider adding potential regions where your workload could run in the future. Think capacity pools may have seasonality, which you can use to run your time flexible workload at a different time, find the next region to expand on, or find where you'd run your Disaster Recovery regional workload copy.

  • Create the extra configurations in the same dashboard. Make sure the properties for RegionNames, SingleAvailabilityZone and TargetCapacity stay the same so you can compare the configurations like for like.

  • Adapt the dashboard DefaultWidgetWidth to define how many charts/configurations you want per row. For example if you have 4 configurations, you can set the DefaultWidgetWidth to 6 so that each row contains the 4 configurations side by side, making them easier to compare.

  • With the first row already configured, we will follow the same pattern in the second row. We can make a copy of all the configurations, and then change just one dimension. The idea is that we can use the row / column patter to identify configurations. For example we could chose the TargetCapacity dimension, copying all the previous configuration and then checking what would happen if our workload doubles in size, or if we could perhaps reduce in two and run two copies in different regions.