AWS ECS Task Memory and CPU Allocation

Question

I'm looking for guidance on allocating memory for an ECS task. I'm running a Rails app for a client who wants to be as cheap as possible on server cost. I was looking at the medium server size that has 2 CPU and 4 gb memory.

Most of the time I'll only need 1 container running the rails server at a time. However, there are occasional spikes and I want to scale out another server and have the container deployed to it. When traffic slows down, I want to scale back down to the single server / task.

Here's where I need help:

What should I make my task memory setting be? 4GB? That would be the total on the box but doesn't account for system processes. I could do 3 GB, but then I'd be wasting some passionless free memory. Same question for the CPU... should I just make it 100%?

I don't want to pay for a bigger server, i.e. 16 GB to sit there and only have 1 container needed most of the time... such a waste.

What I want seems simple. 1 task per instance. When the instance gets to 75% usage, scale a new instance and deploy the task to the second. I don't get why I have to set task memory and CPU settings when it's a one-to-one ratio.

Can anyone give me guidance on how to do what I've described? Or what the proper task definition settings should be when it's meant to be one-to-one with the instance?

Thanks for any help.

--Edit--

Based on feedback, here's a potential solution:

Task definition = memory reservation is 3 GB and memory is 4 GB.

Ec2 medium nodes, Which have 4 GB

ECS Service autoscaling configured: - scale up (increase task count by 1) when Service CPU utilization is greater than 75%.
- scale down (decrease task count by 1) when Service CPU utilization is less than 25%.

ECS Cluster scaling configured: - scale up (increase ec2 instance count by 1) when cluster memory utilization is greater than 80%. - scale down (decrease ec2 instance count by 1) when cluster memory utilization is less than 40%.

Example: Starts with 1 EC2 instance running a task with 3 GB reservation. This is 75% cluster utilization.

When the service spikes and CPU utilization of the service jumps to greater than 75%, it will trigger a service scale. Now the task count is increased and the new task is asking for 3 GB again, which makes it a total of 6 GB but only 4 is available so the cluster is at 150% utilization.

This triggers the cluster scale (over 80%) which adds a new ec2 node to the cluster for the new service. When it's there, we're back down to 6GB demand / 8 GB available which is 75% and stable.

The scale down would happen the same.

Answer 1

For setting memory for containers, I would recommend using "memoryReservation": The soft limit of memory to your container and "memory": the hard limit on your container.

You can set "memoryReservation" to 3GB, which will ensure the other instance of the container does not land up on the same EC2 instance. The "memory" option will allow the container to swell up on memory when absolutely needed.

Ref:http://docs.aws.amazon.com/AmazonECS/latest/developerguide/task_definition_parameters.html

ECS right now does not support the flexibility to disable the deployment of same task twice on the same ec2 compute instance.

But you can hack your way by either blocking cpu/memory or externalizing a known port on you task.