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Sunday, January 14, 2024

The Final AWS AutoScaling Group ASG Lab Package

Some time again I wrote a weblog and companion Cloud Formation templates for experimenting with the methods an ELB creation template could possibly be linked to an ASG. That iteration was primarily based on an ASG template designed to point out how you can kernel patch linux and reboot with out termination utilizing ASG Lifecycle hooks.

I had numerous enhancements I wished to make to this template set and this weblog represents that work.

The result’s actually the reply to the query “What could be a minimal, however production-useful working instance to be taught and experiment with AWS ASGs that use spot situations and correct lifecycle hooks?”

Because the final crew I managed needed to do all of our automation work for each Home windows and Linux, I wished the answer to work for each.

So right here is the online set of performance that the Ulitimate AWS AutoScaling Group Lab Package contains:

Beforehand Present Options

The next options have been made accessible in a earlier incarnation of this template at: https://MissionImpossibleCode.io/put up/asg-lifecycle-hook-for-linux-kernel-patching-with-a-reboot-in-aws-autoscaling-groups/

  • Create a “Launching” ASG lifecycle hook so Linux kernel patching may reboot earlier than well being checks begin (thereby avoiding early termination).
  • Permit an check net app to be put in to emulate an actual software server.
  • Maintainability Constructed-in: Allow patch updating of the cluster by merely updating the CloudFormation stack.
  • Non-compulsory Troubleshooting mode that units up SSM permissions and installs the SSM agent.
  • Assist Excessive Availability Solely (no scaling): Heat HA (1 Occasion – usable for functions that don’t help a number of nodes), HOT/HOT HA (2 occasion ASG – if software helps it)
  • “logit” operate to reveal script data to the console and customary logs (Linux: /var/log/messages, Home windows: Software log

Right here is the earlier article if you wish to be taught extra about it’s options and design – which incorporates comparability to different ASG patching strategies: ASG Lifecycle Hook for Linux Kernel Patching with a Reboot In AWS Autoscaling Teams

New Options (all for each Home windows and Linux):

  • Be a fantastic lab equipment for studying, but in addition be a nice place to begin for precise manufacturing implementations.
  • Home windows help for all earlier performance which was just for Linux. That is particularly essential in case your Home windows spin up and preliminary automation may exceed the default hook time of 60 minutes (ahhh, and time to make a customized AMI for that and to by no means use T2 situations – simply saying)
  • Maintainability Constructed-in: Permit CloudFormation to lookup the most recent AMI (together with on updates) AND allow an override to peg to a selected or customized AMI.
  • Non-compulsory set up of CodeDeploy in case the ASG is wired to it for code deployment.
  • Group CloudFormation parameters in a smart approach, somewhat than the default alphanumeric type.
  • Assist “Least Useful resource Creation” by solely creating AWS sources when they are going to be used – as an illustration not configuring SSM IAM permissions if the troubleshooting characteristic was not configured.
  • Assist Spot Cases and primary spot configuration parameters.
  • Assist non-Spot configurations (by setting On Demand Proportion Above Base to zero) – which additionally helps full on-demand ASGs that may choose from a number of occasion varieties to keep away from failure when a selected occasion is exhausted in an availability zone.
  • Assist Configurable Autoscaling (non-obligatory) and embody parameters for configuring it (step scaling insurance policies)
  • Assist TERMINATING lifecycle hooks and cleanup script for implementations that ought to do clear up or deregistration when the ASG scales in.
  • Assist three OS Patch Scopes: all patches, solely safety patches and no patching (for sooner testing of different issues)
  • State primarily based installs – solely set off installs if the specified software program is just not current already.
  • Constructed-in scaling testing by together with an non-obligatory sythentic CPU driving utility and SSM parameter to manage it. This lets you dial-in the CPU utilization load you need the ASG to be below and alter it after deployment to utterly validate the scaling parameters and smoothness. Following the “Least Useful resource Creation” precept, the sources to help this are solely deployed in the event you configure the potential.
  • Permit override of the fundamental, built-in Occasion Profile IAM Function that the template creates with one which already exists.
  • **Permit extension of userdata from an embedded script, native file or a file to obtain from s3://, http:// or https://

Minimal however Utterly Working Template

The CloudFormation template is purposely minimal with a purpose to extra clearly reveal the ideas of the answer. On the similar time it contains every part wanted and works. The method adheres to The Testable Reference Sample Manifesto

Examined With Each ASG Updatepolicy Settings

The parameter UpdateType defaults to “RollingThroughInstances” which units the UpdatePolicy to make use of AutoScalingRollingUpdate, however it may be modified to “ReplaceEntireASG” to set the UpdatePolicy to make use of AutoScalingReplacingUpdate. Though not examined with Lambda primarily based updates, they might be anticipated to work simply superb with this template.

Least Privilege IAM

The IAM Roles and least privilege permissions are included in order that it’s clear what permissions are wanted and in order that situations should not have extra permissions than wanted to work together with their very own ASG. Two potential strategies for limiting the permissions are supplied. Utilizing the ASG identify within the Useful resource specification of the IAM is energetic. Utilizing a situation on a tag is supplied as a examined, however commented out various.

Maximizing ARN Flexibility for Template Reuse

The ASG arn within the IAM coverage with the SID “ASGSelfAccessPolicy” demostrates maximizing using intrinsic AWS variables through the use of them for AWS Partition (use in Gov cloud or China with out modification), AWS Account ID (use in any account) and AWS Area (use in any area with out modification).

Works With out ASG

If the userdata code can not retrieve it’s ASG tag it assumes that it isn’t in an ASG and all lifecycle hook actions are skipped. This permits the answer for use in non-ASG eventualities.

Patch Upkeep Constructed-in

Zero-downtime patching for the complete ASG is supported by updating the PatchRunDate within the cloudformation stack – the complete fleet will probably be changed with situations which can be updated on patching. The date is purposedly used to file an setting variable inside Userdata in order that the ASG Updatepolicy is aware of it ought to change all situations.

Scheduled ASG Patching

By merely scheduling a cloud formation replace command with an up to date date, the complete ASG will roll. Probably the most AWS cloudy approach to do this can be a scheduled CloudWatch Occasion that triggers a Lambda operate.

aws cloudformation replace-stack --stack-identify "your-asg-stack" --parameters ParameterKey=1OSPatchRunDate,ParameterValue=$(date '+%Y-%m-%d'),UsePreviousValue=false

Scheduling Occasion Availability

In case you are utilizing this template primarily for HA for an occasion, you may also think about using skeddly to set the ASG Desired and Minimal counts to zero for the hours that the occasion won’t be in use. This assumes that the put in software program has it’s state knowledge someplace else and that you simply use the termination monitoring to carry out any orderly software shutdown whether it is wanted.

Dynamic Extension of Userdata

Added in Model. 1.2.0 Permits extra script instructions throughout startup. That is parameterized for testing new variations and to allow one CloudFormation template codebase for use for a lot of totally different Autoscaling teams. It additionally permits you to use this template with out customizing it with the intention to take future updates with out headache. Home windows 2012 and earlier even have a userdata measurement restrict of 16Kb – this technique will get round that.

  1. “Embedded” makes use of the code proper on this template and doesn’t use exterior recordsdata in any respect.
  2. Enter a URL beginning with s3://. s3 permits simple non-public file storage.
  3. http:// or https:// to dynamically supply one throughout occasion provisioning. http/s allows utilization of git uncooked urls (whether or not public or non-public).
  4. Enter a file pathname on the native occasion. The file should be current within the location by the point Userdata processes (e.g. through a customized AMI)

For all exterior file sources, the occasion will need to have a community route and permission to any distant areas.

The code you write should be idempotent in order that it does the proper factor when run once more after a patching reboot.

There’s a easy instance at: https://gitlab.com/DarwinJS/ultimate-aws-asg-lab-kit/-/uncooked/grasp/CustomInstanceConfigurationScriptSample.sh_and_ps1

Monitoring and Metrics

Two monitoring and metrics values are recorded as metadata. You’ll be able to management what log file the is added to (or mute the log file) by altering the operate “logit”. Typically you need this to be a log file that’s collected by your log aggregation service (sumologic, loggly, and so on). Should you already acquire /var/log/cloud-init-output.log, you may mute the log file write to /var/log/messages.


The CloudFormation parameter PatchRunDate is:

  • saved on the occasion because the setting variable LAST_CF_PATCH_RUN in /and so on/profile.d/lastpatchingdata.sh
  • emited to /var/log/messages as “LAST_CF_PATCH_RUN: ”
  • added as a tag to each the ASG and all Ec2 situations

This date merely signifies the preliminary setup of the ASG or the final fleetwide compelled patch. It additionally serves to purposely change one thing in userdata in order that the complete fleet is compelled to get replaced once you run an replace and alter this date.


The date as of spin-up is:

  • saved on the occasion because the setting variable ACTUAL_PATCH_DATE in /and so on/profile.d/lastpatchingdata.sh emited to /var/log/messages as “ACTUAL_PATCH_DATE: ”

Cases that spin up because of autoscaling won’t have their patches restricted to the date expressed in LAST_CF_PATCH_RUN, so ACTUAL_PATCH_DATE tracks the date they have been truly patched.

Evaluating these two dates may help you perceive if in case you have developed a big number of patching dates as a result of autoscaling and may wish to roll the fleet to a regular date by updating the cloudformation with a brand new PatchRunDate.

Kicking Off The Template

Use the AWS CloudFormation console to launch the template – to see how subsequent updates will work, decide 4 situations and set TroubleShootingMode to true.

Testing Scaling Configuration with Artificial CPU Loading

You’ll be able to validate whether or not the next reply as designed:

  • Confirm designed scaling responsiveness and smoothness – up and down.
  • Confirm AZ scaling configuration.
  • Confirm Spot / On-demand occasion parameters are responded as designed together with occasion varieties, blended situations coverage, share spot, and so on.

Throughout deployment, make sure to enter a numeric worth for the 8DBGCPULoadPercentInitialValue parameter (Yeah sorry, I even like my variable names to be absolutely self documenting).

If you do not need scaling to happen instantly, set it low to one thing like 5.

If you don’t present a worth in any respect, Artificial CPU Loading is just not even setup as a result of this template follows a precept of “Least Configuration”.

After the template completes, you will see a brand new SSM parameter that’s named as “YourASGName-SyntheticCPULoad” because the parameter identify. Because the ASG identify is dynamically named will probably be prepended with some random characters.

Now you can differ the artificial CPU load utilizing the parameter and watch the CloudWatch alarms for scale out and scale in and watch the AutoScaling Group for scaling actions.

IMPORTANT! Don’t deploy the template with a worth that causes scale out after which neglect about it for a protracted interval or in a single day – you may bankrupt your organization with AWS billing expenses.

Observing Lifecycle Hooks in AWS Console

Within the EC2 Console open the Autoscaling group, on the “Lifecycle Hook” tab observe the ‘instance-patching-reboot’ hook is configured.

Additionally, earlier than the situations are in service you may see “Not but in service” within the “Exercise Historical past” tab and “Pending:wait” within the “Lifecycle” column of the “Cases” tab for every occasion. These will change to point the situations are in service as every occasion completes setup procedures.

The identical is true for observing the terminating hook.

Observing On Occasion Script Actions

All of the actions of this template may be noticed with out logging into the occasion through the use of the AWS console to view the system log for situations (Proper Click on Occasion => Occasion Settings => Get System Log) and scanning for the textual content “USERDATA_SCRIPT:”

The primary message will comprise “Processing userdata script on occasion:”. All of the messsages embody timestamps with the intention to observe issues like how lengthy a reboot took and the truth that in the event you don’t sleep the script, it retains processing for some time after the reboot command.

On Home windows you would want to retreive the Software log to observe launching and terminating hook actions.

Should you allow the debugging mode you will get an internet primarily based console immediate on each working techniques utilizing SSM Session Supervisor console.

Observing Logs on The Occasion

Should you want or wish to logon to the occasion for examination or troubleshooting, set the parameter TroubleShootingMode to ’true’. This permits SSM IAM permissions and installs the SSM agent on the situations to permit AWS Session Supervisor to logon utilizing SSH or WinRM. For linux, the log strains that you simply see within the AWS System Console will probably be within the CloudFormation log at: varlogcloud-init-output.log. For Home windows will probably be the Software log. For observing the termination hook SSM will depart the final obtained go browsing the display screen – so you may truly see the termination messages after the occasion is gone. On Linux you need to use:

tail -f /var/log/messages

On Home windows you need to use (it is usually a very good generic EventLog tailing operate):

Perform Tail ($logspec="Software",$pastmins=5,$laptop=$env:computername) {$lastdate=$(Get-date).addminutes(-$pastmins);whereas ($True) {$newdate=get-date;get-winevent $logspec -ComputerName $laptop -ea 0 | ? {$_.TimeCreated -ge $lastdate -AND $_.TimeCreated -le $newdate};$lastdate=$newdate;begin-sleep -milliseconds 330}}; Tail

Observing Pseudo Internet App

Should you set SetupPseudoWebApp to true, the next is finished: 1) A port 80 ingress is added to the default VPC safety group, 2) Apache is put in, 3) an apache house web page is created which publishes the patching and ASG particulars of the


Create Now in CloudFormation Console

This part particulars a number of the advanced journey that goes into making a easy and extremely purposeful sample. It’s most useful to those that want to architect on prime of this resolution.

I discover it very useful to enumerate structure heuristics of a sample because it helps with:

1. maintaining monitor of the structure that emerged from the 'design by constructing' effort.
2. my very own recollection of the worth of a sample when inspecting previous issues I've executed for a brand new resolution.
3. others shortly understanding the all of the factors of worth of an provided resolution - serving to information whether or not they wish to put money into studying the way it works.
4. facilitating customization or refactoring of the code by distinguishing objective designed components versus incidental components.

I particularly just like the mannequin of utilizing Constraints, Necessities, Desirements, Applicability, Limitations and Options because it helps point out the optimization of the outcome with out stating every part as a “requirement”. This mannequin can be extra open to emergent structure components that come from the construct effort itself.

  • Requirement: (Happy) Idempotent coding – doesn’t assume something in regards to the put in / configured state of a given merchandise. This contains elemental automation utilities like AWS CLI. This permits the code to work:
    • On a broader set of distros / editions.
    • On an AMI that has been ready from scratch with out commonplace AWS tooling.
    • With a number of move processing when an occasion is rebooted (already carried out steps are skipped or lead to no adjustments).
  • Requirement: (Happy) Assist each Home windows and Linux (yum packaging) in all performance.
  • Requirement: (Happy) Deal with full patching or simply safety patching.
  • Requirement: (Happy) Assist spot situations.
  • Requirement: (Happy) Distinctive inside naming tied to stack identify in order that it may be deployed many instances for a number of, parallel deployments.
  • Requirement: (Happy) Least Useful resource Creation – solely create AWS sources or do occasion installs if they are going to be utilized by the particular template launch. (e.g. Offering an IAM Occasion Profile Function disables the built-in function creation)
  • Requirement: (Happy) Assist terminating lifecycle hooks to set off cleanup / deregister throughout scale in.
  • Requirement: (Happy) State Based mostly Reboot – solely reboot if reboot detection code exhibits that it’s truly wanted.
  • Requirement: (Happy) Precompile .NET bytecode after patching to make sure that new and patched .NET assemblies don’t decelerate manufacturing operations.
  • Desirement: (Happy) Write code in lowest frequent denominator so it may be wrapped in different orchestrators. Therefore that is executed in CloudFormation, which may be encapsulated into different automation techniques.
  • Desirement: (Happy) Constructed-in scaling testing via driving artificial CPU load throughout all situations within the ASG with the potential to alter it on demand.
  • Desirement: (Happy) Manage CloudFormation parameters in teams to make a extra wise interactive template deployment expertise.
  • Desirement: (Happy) Self-documentation by exposing all assist data as CloudFormation parameter descriptions – growing usability for each interactive use and automatic use through integration of documentation as feedback.
  • Desirement: (Happy) Construct in ASG scaling testing for studying and for manufacturing configuration validation.
  • Desirement: (Happy) Permit IAM Occasion Profile Function override with exterior function
  • Desirement: (Happy) Automated newest AWS constructed AMI lookup with override to peg the AMI.
  • Desirement: (Happy) Assist Gov ARNs with out code modification.
  • Desirement: (NOT Happy) It will be good if the answer may work with a set patch baseline to permit full DevOps setting promotion strategies utilizing a recognized, model pegged set of patches.
  • Limitation: The patch stage is dynamic and never a set baseline. When scaling happens the most recent situations could have patching updated with their spin-up date. These newer patches won’t have been examined with the appliance.
    • Countermeasure: Should you combine automated QA testing with the provisioning of a brand new occasion, you can catch issues with patching after they occur or by working a separate nightly construct of the server tier againt the most recent patches.
  • Limitation: If it’s essential design for a number of or many reboots, you would need to do customized code to make sure userdata may decide up within the correct spot after every reboot.
    • Countermeasure: This case is precisely what cfn-init is for, if in case you have not beforehand used it, you may learn up on how you can implement it inside the sample on this put up.
  • Applicability: Should you already launch a per-ASG AMI to your personal causes (often velocity of scaling), then merely making certain that AMI takes into consideration your required patching frequency is a greater resolution. You would shorten your AMI launch cycle to one thing like month-to-month in order that passable patching occurs as a part of the prevailing launch course of. This has the facet advantage of model pegging your patching stage and permitting it to be a part of your growth and automatic QA and be ensured that manufacturing runs on a examined patch stage.
    • Various: If in case you have an current lengthy AMI launch cycle (larger than 6 months), you can mix it with the dynamic patching resolution provided right here to maintain the cycle lengthy (to maintain the price and logistics of managing outdated AMIs to a minimal if that may be a excessive precedence).
  • Various: Essential Vulnerability Response If in case you have an pressing sufficient patching state of affairs, you could want to briefly use this sample to do dynamic patching when you don’t usually help it.
  • Limitation: This demo template depends on the default VPC safety group being added to the situations and on it having default settings which permit web entry. If in case you have the default VPC safety group nulled out (a fantastic safety observe!) or different networking configuration that limits web entry, you have to to replace the template in order that it has outbound web entry in your setting

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