Tag: Patching

Automating Windows Patching of EC2 Autoscaling Group Instances

Background

Dealing with Windows patching can be a royal pain as you may know.  At least once a month Windows machines are subject to system security and stability patches, thanks to Microsoft’s Patch Tuesday. With Windows 10 (and its derivatives), Microsoft has shifted towards more of a Continuous Delivery model in how it manages system patching. It is a welcome change, however, it still doesn’t guarantee that Windows patching won’t require a system reboot.

Rebooting an EC2 instance that is a member of an Auto Scaling Group (depending upon how you have your Auto Scaling health-check configured) is something that will typically cause an Elastic Load Balancing (ELB) HealthCheck failure and result in instance termination (this occurs when Auto Scaling notices that the instance is no longer reporting “in service” with the load balancer). Auto Scaling will of course replace the terminated instance with a new one, but the new instance will be launched using an image that is presumably unpatched, thus leaving your Windows servers vulnerable.

The next patch cycle will once again trigger a reboot and the vicious cycle continues. Furthermore, if the patching and reboots aren’t carefully coordinated, it could severely impact your application performance and availability (think multiple Auto Scaling Group members rebooting simultaneously). If you are running an earlier version of Windows OS (e.g. Windows Server 2012r2), rebooting at least once a month on Patch Tuesday is an almost certainty.

Another major problem with utilizing the AWS stock Windows AMIs with Auto Scaling is that AWS makes those AMIs unavailable after just a few months. This means that unless you update your Auto Scaling Launch Configuration to use the newer AMI IDs on a continual basis, future Auto Scaling instance launches will fail as they try to access an AMI that is no longer accessible. Anguish.

Automatically and Reliably Patch your Auto-Scaled Windows instances

Given the aforementioned scenario, how on earth are you supposed to automatically and reliably patch your Auto-Scaled Windows instances?!

One approach would be to write some sort of an orchestration layer that detects when Auto Scaling members have been patched and are awaiting their obligatory reboot, suspend Auto Scaling processes that would detect and replace perceived failed instances (e.g. HealthCheck), and then reboot the instances one-by-one. This would be rather painful to orchestrate and has a potentially severe drawback that cluster capacity is reduced by N-1 during the rebooting (maybe more if you don’t take into account service availability between reboots).

Reducing capacity to N-1 might not be a big deal if you have a cluster of 20 instances but if you are running a smaller cluster of something— say 4, 3, or 2 instances—then that has a significant impact to your overall cluster capacity. And, if you are running on an Auto Scaling group with a single instance (not as uncommon as you might think) then your application is completely down during the reboot of that single member. This of course doesn’t solve the issue of expired stock AWS AMIs.

Another approach is to maintain and patch a “golden image” that the Auto Scaling Launch Configuration uses to create new instances from. If you are unfamiliar with the term, a golden-image is an operating system image that has everything pre-installed, configured, and saved in a pre-baked image file (an AMI in the case of Amazon EC2). This approach requires a significant amount of work to make this happen in a reasonably automated fashion and has numerous potential pitfalls.

While it prevents a service outage by replacing the unavailable public AMI with a stock AMI, you still need a way to reliably and automatically handle this process. Using a tool like Hashicorp’s Packer can get you partially there, but you would still have to write a number of Providers to handle the installation of Windows Update and anything else you need to do in order to prep the system for imaging. In the end, you would still have to develop or employ a fair number of tools and processes to completely automate the entire process of detecting new Windows Updates, creating a patched AMI with those updates, and orchestrating the update of your Auto Scaling Groups.

A Cloud-Minded Approach

I believe that Auto Scaling Windows servers intelligently requires a paradigm shift. One assumption we have to make is that some form of configuration management (e.g. Puppet, Chef)—or at least a basic bootstrap script executed via cfn-init/UserData—is automating the configuration of the operating system, applications, and services upon instance launch. If configuration management or bootstrap scripts are not in play, then it is likely that a golden-image is being utilized. Without one of these two approaches, you don’t have true Auto Scaling because it would require some kind of human interaction to configure a server (ergo, not “auto”) every time a new instance was created.

Both approaches (launch-time configuration vs. golden-image) have their pros and cons. I generally prefer launch-time configuration as it allows for more flexibility, provides for better governance/compliance, and enables pushing changes dynamically. But…(and this is especially true of Windows servers) sometimes launch-time configuration simply takes longer to happen than is acceptable, and the golden-image approach must be used to allow for a more rapid deployment of new Auto Scaling group instances.

Either approach can be easily automated using a solution like to the one I am about to outline, and thankfully AWS publishes new stock Windows Server AMIs immediately following every Patch Tuesday.  This means, if you aren’t using a golden-image, patching your instances is as simple as updating your Auto Scaling Launch Configuration to use the new AMI(s) and preforming a rolling replacement of the instances. Even if you are using a golden-image or applying some level of customization to the stock AMI, you can easily integrate Packer into the process to create a new patched image that includes your customizations.

The Solution

At a high level, the solution can be summarized as:

  1. An Orchestration Layer (e.g. AWS SNS and Lambda, Jenkins, AWS Step Functions) that detects and responds when new patched stock Windows AMIs have been released by Amazon.
  2. A Packer Launcher process that manages launching Packer jobs in order to create custom AMIs. Note: This step is only required If copying AWS stock AMIs to your own AWS account is desired OR if you want to apply customization to the stock AMI. Either use case requires that the custom images are available indefinitely. We solved this problem by creating a Packer Launcher process by creating an EC2 instance with a Python UserData script that launches Packer jobs (in parallel) to create copies of the new stock AMIs into our AWS account. Note: if you are using something like Jenkins, this could be handled by having Jenkins launch a local script or even a Docker container to manage launching Packer jobs.
  3. A New AMI Messaging Layer (e.g. Amazon SNS) to publish notifications when new/patched AMIs have been created
  4. Some form of an Auto Scaling Group Rolling Updater will be required to replace exiting Auto Scaling Group instances with new ones based on the Patched AMI.

Great news for anyone using AWS CloudFormation… CFT inherently supports Rolling Updates for Auto Scaling Groups! Utilizing it requires attaching an UpdatePolicy and adding a UserData or cfn-init script to notify CloudFormation when the instance has finished its configuration and is reporting as healthy (e.g. InService on the ELB). There are some pretty good examples of how to accomplish this using CloudFormation out there, but here is one specifically that AWS provides as an example.

If you aren’t using CloudFormation, all hope is not lost. With Hashicorp Terraform’s ever increasing popularity for deploying and managing AWS infrastructure as code, Terraform has still yet to implement a Rolling Update feature for AWS Auto Scaling Groups. There is a Terraform feature request from a few years ago for this exact feature, but as of today, it is not yet available, nor do the Terraform developers have any short-term plans to implement it. However, several people (including Hashicorp’s own engineers) have developed a number of ways to work around the lack of an integrated Auto Scaling Group Rolling Updater in Terraform. Here are a few I like:

Of course, you can always roll your own solution using a combination of AWS services (e.g. SNS, Lambda, Step Functions), or whatever tooling best fits your needs. Creating your own solution will allow you added flexibility if you have additional requirements that can’t be met by CloudFormation, Terraform, or other orchestration tool.

The following is an example framework for performing automated Rolling Updates to Auto Scaling Groups utilizing AWS SNS and AWS Lambda:

a.  An Auto Scaling Launch Config Modifier worker that subscribes to the New AMI messaging layer performs an update to the Auto Scaling Launch Configuration(s) when a new AMI is released. In this use case, we are using an AWS Lambda function to subscribe to an SNS topic. Upon notification of new AMIs, the worker must then update the predefined (or programmatically derived) Auto Scaling Launch Configurations to use the new AMI. This is best handled by using infrastructure templating tools like CloudFormation or Terraform to make updating the Auto Scaling Launch Configuration ImageId as simple as updating a parameter/variable in the template and performing an update/apply operation.

b.  An Auto Scaling Group Instance Cycler messaging layer (again, an Amazon SNS topic) to be notified when an Auto Scaling Launch Configuration ImageId has been updated by the worker.

c.  An Auto Scaling Group Instance Cycler worker that will perform replacing the Auto Scaling Group instances in a safe, reliable, and automated fashion. For example, another AWS Lambda function that will subscribe to the SNS topic and trigger new instances by increasing the Auto Scaling Desired Instance count to a value of twice the current number of ASG instances.

d.  Once the scale-up event generated by the Auto Scaling Group Instance Cycler worker has completed and the new instances are reporting as healthy, another message will be published to the Auto Scaling Group Instance Cycler SNS topic indicating scale-up has completed.

e.  The Auto Scaling Group Instance Cycler worker will respond to the prior event and return the Auto Scaling group back to its original size which will terminate the older instances leaving the Auto Scaling Group with only the patched instances launched from the updated AMI. This assumes that we are utilizing the default AWS Auto Scaling Termination Policy which ensures that instances launched from the oldest Launch Configurations are terminated first.

NOTE: The AWS Auto Scaling default termination policy will not guarantee that the older instances are terminated first! If the Auto Scaling Group is spanned across multiple Availability Zones (AZ) and there is an imbalance in the number of instances in each AZ, it will terminate the extra instance(s) in that AZ before terminating based on the oldest Launch Configuration. Terminating on Launch Configuration age will certainly ensure that the oldest instances will be replaced first. My recommendation is to use the OldestInstance termination policy to make absolutely certain that the oldest (i.e. unpatched) instances are terminated during the Instance Cycler scale-down process.  Consult the AWS documentation on the Auto Scaling termination policies for more on this topic.

In Conclusion

Whichever solution you choose to implement to handle the Rolling Updates to your Auto Scaling Group, the solution outlined above will provide you with a sure-fire way to ensure your Windows Auto Scaled servers are always patched automatically and minimize the operational overhead for ensuring patch compliance and server security. And the good news is that the heavy lifting is already being handled by AWS Auto Scaling and Hashicorp Packer. There is a bit of trickery to getting the Packer configs and provisioners working just right with the EC2 Config service and Windows Sysprep, but there are a number of good examples out on github to get you headed in the right direction. The one I referenced in building our solution can be found here.

One final word of caution... if you do not disable the EC2Config Set Computer Name option when baking a custom AMI, your Windows hostname will ALWAYS be reset to the EC2Config default upon reboot. This is especially problematic for configuration management tools like Puppet or Chef which may use the hostname as the SSL Client Certificate subject name (default behavior), or for deriving the system role/profile/configuration.

Here is my ec2config.ps1 Packer provisioner script which disables the Set Computer Name option:

$EC2SettingsFile="C:\\Program
Files\\Amazon\\Ec2ConfigService\\Settin
gs\\Config.xml"
$xml = [xml](get-content
$EC2SettingsFile)
$xmlElement =
$xml.get_DocumentElement()
$xmlElementToModify =
$xmlElement.Plugins
foreach ($element in
$xmlElementToModify.Plugin)
{
if ($element.name -eq
"Ec2SetPassword")
{
$element.State="Enabled"
}
elseif ($element.name -eq
"Ec2SetComputerName")
{
$element.State="Disabled"
}
elseif ($element.name -eq
"Ec2HandleUserData")
{
$element.State="Enabled"
}
elseif ($element.name -eq
"Ec2DynamicBootVolumeSize")
{
$element.State="Enabled"
}
}
$xml.Save($EC2SettingsFile)

Hopefully, at this point, you have a pretty good idea of how you can leverage existing software, tools, and services—combined with a bit of scripting and automation workflow—to reliably and automatically manage the patching of your Windows Auto Scaling Group EC2 instances!  If you require additional assistance, are resource-bound for getting something implemented, or you would just like the proven Cloud experts to manage Automating Windows Patching of your EC2 Autoscaling Group Instances, contact 2nd Watch today!

 

Disclaimer

We strongly advise that processes like the ones described in this article be performed on a environment prior to production to properly validate that the changes have not negatively affected your application’s functionality, performance, or availability.

 This is something that your orchestration layer in the first step should be able to handle. This is also something that should integrate well with a Continual Integration and/or Delivery workflow.

 

-Ryan Kennedy, Principal Cloud Automation Architect, 2nd Watch