is there's a way in AWS to increase and decrease instances CPUs depending on pressure. I have been paying a lot of money for AWS statically increasing and decreasing instance cores when no clients are using it.
to be more specific, clients can upload an excel file and the software will do some calculations that will take time depending on the AWS instance cores. Having 2 cores will take 30 minutes to completion and having 96 cores will take only a couple of minutes.
Is there's a way to automatically increase the cores to 96 when the clients are using and uploading files to the website and automatically decrease the cores to 2 when no action is happening and clients are either not using the website or just using the website with current data and aren't taking a new action.
If not then can I possibly add a schedule in AWS to change the instance type. As an example run the instance on a 2 core type (ex: t2.large) and then change the instance type only from 1pm-6pm to 96 cores (ex: c5a.24xlarge) after that get it back to 2 cores?
I'm very new to AWS and devops in general, and I have been reading about AWS Autoscaling groups, but I'm not sure if this is the answer for my problem.
No, it is not possible to "scale CPU cores". (Commonly known as Vertical scaling.)
Instead, the recommended method is to add/remove parallel capacity based upon demand.
If you are using Amazon EC2, then you can launch more instances or terminate existing instances. This can be automated through Amazon EC2 Auto Scaling, which can monitor metrics (eg CPU Utilization) and then launch/terminate instances automatically. You would typically put a Load Balancer in front of these instances if they are web servers, or the instances might be 'worker nodes' that pull work from a queue.
If you are using containers (Docker, Kubernetes) then Amazon ECS/Amazon EKS can automatically add/remove tasks to meet demand for your application.
If you are using AWS Lambda functions, then they 'scale' by allowing multiple functions to run in parallel. Lambda functions typically exit when they have finished processing, so there is not charge when there is nothing to process.
These are all examples of Horizontal scaling, where capacity is added/removed in parallel.
Related
CPU metrics cannot be selected below 1 minute in Cloudwatch service. For example, how can I lower this period time to trigger the Autoscale scale faster? I just need to trigger the AutoScale instances in short time. (By the way, datapoints value 1 to 1)
the minimum granularity for the metrics that EC2 provides is 1 minute.
Source: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/viewing_metrics_with_cloudwatch.html
Would also say that if you need to scale that quickly, wouldn't the startup time be an issue anyway?
You are correct -- basic monitoring of an Amazon EC2 instance provides metrics over 5-minute periods. If you activate EC2 Detailed Monitoring, metrics are provided over 1-minute periods. Extra charges apply for Detailed Monitoring.
When launching a new instance via Amazon EC2 Auto-Scaling, it can take a few minutes for the new instance to launch and for the User Data script (if any) to run. Linux instances are quite fast, but Windows instances take a while on their first boot due to sysprep operations.
You mention that you want to react to a metric in less than one minute. I would suggest that this would not be an ideal way to trigger Auto-scaling. Sometimes a computer can be busy for a while, then can drop down again. Reacting too quickly to a high CPU load would cause the Auto-Scaling group to flap between adding instances and terminating instances. It is better to provision enough capacity for a reasonable amount of extra load and then gradually add more capacity as it is required over time.
If you have a need to react so quickly, then perhaps you should investigate using AWS Lambda to perform small amounts of work in a highly-parallel fashion rather than relying on Amazon EC2 instances.
In our ECS cluster setup with ASG Capacity provider, we have 5 EC2 instances and each instance can take around 20 tasks. So overall there are resources available to run 100 tasks. Now if we submit a service with 100 tasks, though there are enough resources, not all tasks are started parallely. I see tasks are coming up in batches of size 20 with a gap of 10 secs between each batch. I observed this from ECS Service Event logs. Any configuration which we can tweak to achieve complete parallelism.
This behavior is due to artificially controlled throughput (expressed in Tasks per Second - TPS) that the ECS service control plane imposes. There is a bursting concept in there (which is the reason for which you see this batch of tasks being launched and then a delta in seconds). The reasons for which these limits exist is to avoid being throttled in other parts of the services surface. These limits can be lifted if there is a strong need but the engineering team will need to validate the use case and expectations (see the point about hitting potentially other limits). The best way to address this discussion is by opening a ticket with AWS Support and explore your alternatives (based on your requirements).
I am running the ECS cluster that currently has 3 services running on T3 medium instance. Each of those services is running only one task which has a soft memory limit of 1GB, the hard limit is different for each (but that should not be the problem). I will always have enough memory to run one, new deployed task (new one will also take 1GB, and T3 medium will be able to handle it since it has 4GB total). After the new task is up and running, the old one will be stopped and I will have again 1GB free for the new deployment. I did similar to the CPU (2048 CPU, each task has 512, and 512 free for new deployments).
So everything runs fine now, but I am not completely satisfied with this setup for the future. What will happen if I need to add another service with another task? I need to deploy all existing tasks and to modify their task definitions to use less CPU and memory in order to run this new task (and new deployments). I am planning to get a reserved EC2 instance, so it will not be easy to swap the current EC2 instance with the larger one.
Is there a way to spin up another EC2 instance for the same ECS cluster to handle bursts in my tasks? Also deployments, it's not a perfect scenario to have the ability to deploy only one task, and then wait for old to be killed in order to deploy the next one, without downtimes.
And biggest concern, what if I need new service and task, I need again to adjust all others in order to run a new one and deploy others, which is not very maintainable and what if I cannot lower CPU and memory more because I already reached the lowest point in order to run the task smoothly.
I was thinking about having another EC2 instance for the same cluster, that will handle bursts, deployments, and new services/tasks. But not sure if that's possible and if that's the best way of doing this. I was also thinking about Fargate, but this is much more expensive and I cannot afford it for now. What do you think? Any ideas, suggestions, and hints will be helpful since I am desperate to find the best way to avoid the problems mentioned above.
Thanks in advance!
So unfortunately, there is no out of the box solution to ensure that all your tasks run on min possible (i.e. one) instance. You can use our new feature called Capacity Providers (CP), which will allow you to ensure the minimum number of ec2 instances required to run all your tasks. The major difference between CP vs ASG is that CP gives more weight to task placement (where as ASG will scale in/out based on resource utilization which isn't ideal in your case).
However, it's not an ideal solution. Just as you said in your comment, when the service needs to scale out during a deployment, CP will spin up another instance, the new task will be placed on it and once it gets to Running state, the old task will be stopped.
But now you have an "extra" EC2 instance because there is no way to replace a running task. The only way I can think of would be to use a lambda function that drains the new instance, which will move all the service tasks to the other instance. CP will, after about 15 minutes, terminate this instance as there are no tasks are running on it.
A couple caveats:
CP are new, a little rough around the edges, and you can't
delete/modify them. You can only create or deactivate them.
CP needs an underlying ASG and they must have a 1-1 relationship
Make sure to enable managed scaling when creating CP
Choose 100% capacity target
Don't forget to add a default capacity strategy for the cluster
Minimizing EC2 instances used:
If you're using a capacity provider, the 'binpack' placement strategy minimises the number of EC2 hosts that are used.
However, there are some scale-in scenarios where you can end up with a single task running on its own EC2 instance. As Ali mentions in their answer; ECS will not replace this running task, but depending on your setup, it may be fairly easy for you to replace it yourself by configuring your task to voluntarily 'quit'.
In my case; I always have at least 2 tasks running per service. So I just added some logic to my tasks' healthchecks, so they report as unhealthy after ~6 hours. ECS will spot the 'unhealthy' task, remove it from the load balancer, and spin up a replacement (according to the binpack strategy).
Note: If you take this approach; add some variation to your timeout so you're less likely to have all of your tasks expire at the same time. Something like: expiry = now + timedelta(hours=random.uniform(5.5,6.5))
Sharing memory 'headspace' with soft-limits:
If you set both soft and hard memory limits; ECS will place your tasks based on the soft limit. If your tasks' memory usage varies with usage, it's fairly easy to get your EC2 instance to start swapping.
For example: Say you have a task defined with a soft limit of 900mb, and a hard limit of 1800mb. You spin up a service with 4 running instances. ECS provisions all 4 of these instances on a single t3.medium. Notice here that each instance thinks it can safely use up to 1800mb, when in fact there's very little free memory on the host server. When you hit your service with some traffic; each task tries to use some more memory, and your t3.medium is incapacitated as it starts swapping memory to disk. ECS does not recover from this type of failure very well. It notices that the task instances are no longer available, and will attempt to provision replacements, but the capacity provider is very slow to replace the swapping t3.medium.
My suggestion:
Configure your service to auto-scale based on memory usage (this will be a percentage of your soft-limit), for example: a target memory usage of 70%
Configure your tasks' healthchecks so that they report as unhealthy when they are nearing their soft-limit. This way, your tasks still have some headroom for quick spikes of memory usage, while giving your load balancer a chance to drain and gracefully replace tasks that are getting greedy. This is fairly easy to do by reading the value within /sys/fs/cgroup/memory/memory.usage_in_bytes.
I've now learnt that when I start a new EC2 instance it has a certain number of CPU credits due to which it's performance is high when it starts processing but gradually reduces over time as the credits run out. Past that point, the instance runs at which appears to be the baseline CPU utilisation rate. To numerate, when I started the EC2 instance (t2.nano), Cloudwatch reported around 80% CPU utilisation gradually decreasing down to 5%.
Now I'm happy to use one of the better instance types pending the instance limit request. But whilst that is in progress, I'd like to know whether the issue of reducing performance over time will still hold even with the better instance type?
Would I require a dedicated host setup if I wish to ensure I get consistent CPU utilisation? The only problem I can see here is that I'm running a SQS worker queue and Elastic Beanstalk allows us to easily setup a worker environment which reads messages from the queue. From what I've read and from looking at the configuration options available in Elastic Beanstalk, I don't think I'll be able to launch instances into a dedicated host directly. Most of my reading has lead me to believe that I'll have to learn how to use a VPC. Would that be correct?
So I guess my questions are - would simply increasing the instance type to a more powerful instance guarantee consistent CPU utilisation performance or is a dedicated host required and if so, is it possible to set up one with Elastic Beanstalk or would it have to be setup manually and if it is set up manually can it be configured to work with an SQS queue automatically?
If you want consistent CPU performance, you should avoid the burstable performance instances (the T2 family). All other families of instances (M5, C5, etc) will have consistent CPU performance over time. You can use any instance family with Elastic Beanstalk. No need for a dedicated host.
When a Ec2 Container Engine cluster is created, it creates a Compute Engine managed instance group to manage the created instances. These instances are from Ec2 service, which means, they are Virtual machines.
But we know that containers represent a new way to deploy containers based on operating-system-level virtualization rather than hardware virtualization
like VMs that are heavyweight and non-portable, isn't a contradiction? correct me if I'm wrong.
We use containers because they are extremely fast (either in boot time or tasks execution) compared to VMs, and they save a lot of space storage. So if we have one node(vm) that can supports 4 containers max, our clients can rapidly lunch 4 containers, but beyond this number, Ec2 autoscaler will need to lunch a new node(vm) to support upcoming containers, which incurs some tasks delay.
Is it impossible to launch containers over physical machines?
And what do you recommend for running critical time execution tasks?
I believe you are working under an erroneous assumption that ECS scales the virtual machines ("container instances" -- the instances where containers will run) directly with task demand.
If that were true, you would have a point, because the cluster would be sluggish and unresponsive any time insufficient container instance resources were not immediately available.
ECS doesn't do that, the presence of the Auto Scaling Group notwithstanding.
Depending on the Amazon EC2 instance types you use in your clusters, and quantity of container instances you have in a cluster, your tasks have a limited amount of resources that they can use when they are run. ECS monitors the resources available in the cluster to work with the schedulers to place tasks. If your cluster runs low on any of these resources, such as memory, you will eventually be unable to launch more tasks until you add more container instances, reduce the number of desired tasks in a service, or stop some of the running tasks in your cluster to free up the constrained resource. (emphasis added)
http://docs.aws.amazon.com/AmazonECS/latest/developerguide/cloudwatch_alarm_autoscaling.html
So, no... it doesn't launch the new tasks slowly when you are out of capacity. It doesn't launch them at all.
But don't get ahead of me.
The link above explains, with examples, how scaling of the virtual machines (container instances) is designed to actually work.
Of course, you don't have to make them adaptively scalable at all. You can go with your physical server model (note: I say physical server model -- meaning a fixed, inelastic pool of resources, on always-running virtual machines, since virtual machines is what EC2 provides), and just choose how many instances you wait to have running at all times, essentially emulating physical servers. If you wanted, say, 8 container instances, the "auto scaling group" would maintain exactly 8 at all times, creating replacements if, say, one of them experienced a hardware failure. That "auto" accomplishment would be maintaining the status quo. And, of course, in this configuration, you could manually reconfigure from 8 to, say, 12 and the "auto" accomplishment would be that you'd automatically get 4 new ones to add to the existing 8.
But the idea of how the service is ideally used is that your group of virtual machines scales up and down by rules you devise, to anticipate the resources needed by future tasks -- or a future lack of tasks.
In the example given, memory reservation is the trigger:
When the memory reservation of your cluster rises above 75% (meaning that only 25% of the memory in your cluster is available to for new tasks to reserve), the alarm triggers the Auto Scaling group to add another instance and provide more resources for your tasks and services.
It triggers the addition of more container instances so that you always have whatever you have determined to be the appropriate threshold of surplus capacity already online by the time you need it.
Of course, memory is just one resource, and 75% is just an arbitrary threshold chosen for the example.
Auto Scaling Groups can scale on a variety of triggers -- the phrase of the moon, the price trends in the stock market, whatever is appropriate to anticipating your desired amount of surplus capacity and can be quantified and monitored can be used... but this service does not scale itself directly by the actual attempt to launch a new task when the task can't be launched due to insufficient resources.
Herein lies the flaw in your original argument.
Why virtual machines? Simply enough, because when you destroy a virtual machine because the capacity is not expected to be needed, you stop paying for it.
In this light, perhaps you'll agree that this is not a weakness, it's a strength. Physical servers never stop costing you when you are not using them.
You don't need to pay anything at all for capacity you will not be needing with VMs -- you only have to pay for the capacity you're using plus the amount you need to keep immediately available to handle anticipated demand.
You can have as much idle surplus immediately ready as you are willing to pay for, or you can maximize savings by allowing as little surplus capacity as you are comfortable with being able to access without delay.