Custom Termination Policy for Auto Scaling
(Feel free to skip ahead if you are familiar with auto scaling groups and termination policies.)
A bit of a background first: When an auto scaling group is scaled down, an instance to be terminated needs to be picked. There are sevaral policies to choose from: oldest/newest instance, oldest launch configuration, closest to full instance hour and the default (most complex) one. A customer gets to choose one of these and that’s it.
If none of these suited you, there are some optios available:
Use termination protection to prevent some instances being picked up for down-scaling termination. (This has been recommended to me as one way by several AWS engineers.)
Run your own down-scale scheduler in an instance. This would effectively implement the downscaling logic itself, but with your own custom twist for choosing instances to terminate.
I consider the first one almost a kludge — an it’s a binary decider, so more nuanced policies are impossible with it anyway — while the second one has superb flexibility, it brings in a new potential SPOF and maintenance burden. So neither is a perfect solution.
Oh, and why would you want a custom termination policy? Well… maybe those instances host large caches, and you’d like to terminate instances with the least filled caches? Or you are running batch jobs from a queue, where some jobs run long and some short and when jobs are drained you’d want terminate only instances without jobs?
(You could run the downscale scheduler inside the auto scale group, but that forces you to open the can of distributed worms including leader worm election and all that. I would not want to go down that road. It is much easier to instead run the scheduler in an instance within an auto scale group set to one instance in size, ensuring it’ll get re-created. Either way, it requires an instance, a thing that I’d like to avoid for smaller groups and simpler custom termination policies.)
Downscaling in an auto scaling group works when a CloudWatch alarm is triggered, which in turn is set to trigger an scaling action that then changes the desired instance count.
Now… alarms can also send notifications to SNS topics,
SNS topics can trigger Lambda functions,
Lambda functions can be assigned IAM roles, and Lambda functions can access all AWS API functionality that the IAM role allows,
Ergo, I can move the custom termination policy code to a Lambda function without needing to run a separate instance.
With my head humming I started hacking at it and got a
proof-of-concept version running already a few hours later.
is alive works!
I’ll tell you how to actually set up this in a while, but first, here’s the Lambda code.
It’s a bit verbose. Using Underscore or Coffeescript surely would be more readable and/or compact. I’m not really a Node.js developer which probably also shows. This certainly is not production quality as it makes quite a bit of assumptions and has hard-coded values in it. This is a proof-of-concept code! Caveat emptor!
The code is straightforward:
exports.handler will get called, it’ll
extract auto scale group name from the SNS notification, then iterate
over running instances in the group and fetch a total time value via
HTTP from these instances (a custom CloudWatch metric would do as
well), finally picking the instance with the lowest total time
The Lambda function above will not in itself yet do anything. To run it, you need to first (assuming you already have auto scale group set up):
- Create IAM role with enough permissions to do auto scaling actions
- Create the Lambda function
- Create an SNS topic and add a subscription for it to call the Lambda function
Create CloudWatch alarms to send a notification to the SNS topic from above when you want a downscale to occur.
You will need multiple alarms set up for multiples of your cooldown period. For example, if you want to downscale if average CPU load is less than 25% for 10 minutes, you’ll have to set up the first alarm at 10 minutes, second one at 20 minutes, third at 30 minutes up to the maximum number of your scaling group (see below for details why).
I ran into some limitations of Lambda and CloudWatch when working on this:
Lambda functions can access instances only via public IP addresses —
PrivateIpAddressdoes not work. This is a bit of a security bother, so I’d actually suggest to push custom cloudwatch metrics from instances that the code would read without relying on public access.
(I’d like to refer to Lambda functions in security groups directly and use internal IP addresses to access AWS resources.)
I originally had the downscale handler set the triggering cloudwatch alarm to
INSUFFICIENT_DATAstate in a hope to make it re-trigger later again. It got stuck in a re-trigger race loop, getting repeatedly triggered (downscaling the group into 1 instance in one fell swoop).
I could not figure out a way within AWS services to cause a delay in re-triggering the downscale handler. It is possible to have delay queues in SQS, but no way to trigger Lambda functions based on SQS messages. If there ever is a way to trigger Lambda functions from SQS messages (or route SQS messages to a SNS topic) then it would be possible for the handler to inspect the original alarm state after a cooldown period and requiring only a single alarm.
(I’d like either a way to schedule Lambda call for later, to trigger Lambda functions via an SQS delay queue, or to specify to an alarm to retrigger after some cooldown delay if it is still in a trigger state.)
Lambda is currently available only in US East, US West (Oregon) and EU West regions, which may limit its usefulness at the moment. On the other hand, it should become available in other regions soon-ish.
Note that auto scaling apparently has some internal logic that causes it to inspect the alarm state after cooldown period, retriggering the scaling action multiple times as needed. An alarm, however, will send only a single notification to SNS topic, thus making the Lambda function edge-triggered on the alarm. (Auto scaling magic turns that into level-triggered action instead.)
Anyway that’s it — please drop a comment below if you find this useful!
On 21st April 2015 at AWS Summit Stockholm, to be precise. ↩
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