Premise
I'm trying to come up with the right choice of AWS construct for a containerized microservice (set of microservices in fact) deployment. The application will have an average load of 50% through the day and little to nothing during the night and at very specific times in the day(which is not always pre-determinable) there is a burst of high-volume requests. Also, it's not a super-busy set of microservices ( in other words, 2 instances of 1VCPU and 8GB RAM will just be fine )
The fargate compute option seems to be a better option for this type of a setup, except of course that
When my application has little or no load during the night, I will still be charged for the full 1VCPU and 8GB (which according to me is not true "pay as you use" as I might be using only 0.05 or 0.25 VCPU - hypothetical numbers )
The only way to get around this is to write some redefinition strategies myself: watch Cloudwatch events and recreate the Fargate tasks with lesser VCPU. However, it will have some extra overhead in terms of deployment time (even if I ensure staggered deployments, it still means a 'lot of work' each time there is a material event). Is there a better way to do this or is there a more 'truly' out of the box pay-as-you-use arrangement that can let you consume resources in a range continuously based on what you actually are using at that moment without having to jump through the hoop?
Lastly, the purist in me still cannot reconcile in theory the fact that a microservice isn't a 'task' really and use of a Fargate compute option doesn't sound intuitively right to me even if I could think of a microservice as an extreme case of a task running permanently Costwise, am I better off using EC2 as some options seem to get me a cost that is lesser than Fargate (I'm aware of the additional responsibility in maintaining/patching those EC2 instances )?
Related
I have set up a Task Definition with CPU maximum allocation of 1024 units and 2048 MiB of memory with Fargate being the launch type. When I looked at the costs it was way more expensive than I thought ($ 1.00 per day or $ 0.06 per hour [us-east-1]). What I did was to reduce to 256 units and I am waiting to see if the costs goes down. But How does the Task maximum allocation work? Is the task definition maximum allocation responsible for Fargate provisioning a more powerfull server with a higher cost even if I dont use 100%?
The apps in containers running 24/7 are NestJS application + apache (do not ask why) + redis and I can see that it has low CPU usage but the price is too high for me. Is the fargate the wrong choice for this? Should I go for EC2 instances with ECS?
When you run a task, Fargate provisions a container with the resources you have requested. It's not a question of "use up to this maximum CPU and memory," but rather "use this much CPU and memory." You'll pay for that much CPU and memory for as long as it runs, as per the AWS Fargate pricing. At the current costs, the CPU and memory you listed (1024 CPU units, 2048MiB), the cost would come to $0.04937/hour, or $1.18488/day, or $35.55/month.
Whether Fargate is the right or wrong choice is subjective. It depends what you're optimizing for. If you just want to hand off a container and allow AWS to manage everything about how it runs, it's hard to beat ECS Fargate. OTOH, if you are optimizing for lowest cost, on-demand Fargate is probably not the best choice. You could use Fargate Spot ($10.66/month) if you can tolerate the constraints of spot. Alternatively, you could use an EC2 instance (t3.small # $14.98/month), but then you'll be responsible for managing everything.
You didn't mention how you're running Redis which will factor in here as well. If you're running Redis on Elasticache, you'll incur that cost as well, but you won't have to manage anything. If you end up using an EC2 instance, you could run Redis on the same instance, saving latency and expense, with the trade off that you'll have to install/operate Redis yourself.
Ultimately, you're making tradeoffs between time saved and money spent on managed services.
I am new to AWS and recently set up a free t3.micro instance. My goal is to achieve a stable hosting of an Angular application with 2 spring boot services. I got everything working, but after a while, the spring boot services are not reachable anymore. When i redeploy the service it will run again. The spring boot services are packed as jar and after the deployment the process is started as a java process.
I thought AWS guarantees permanent availability out of the box. Do i need some more setup such as autoscaling to achieve the desired uptime of the services or is the t3.micro instance not suffienciently performant, so that i need to upgrade to a stronger instance to avoid the problem?
It depends :)
I think you did the right thing by starting with a small instance type and avoid over provisioning in the first place. T3 instance types are generally beneficial for 'burst' usage scenarios i.e. your application sporadically needs a compute spike but not a persistent one. T3 instance types usually work with credits based system, where you instance 'earns' credits when it is idle, and that buffer is always available in times of need (but only until consumed entirely). Then you need to wait for some time window again and earn the credits back.
For your current problem, I think first approach can be to get an idea of the current usage by going through the 'Monitoring' tab on the EC2 instance details page. This will help you understand if the needs are more compute related or i/o related and then you can choose an appropriate instance type from :
https://aws.amazon.com/ec2/instance-types
Next step could also be to profile your application and understand the memory, compute utilisation better. AWS does guarantee availability/durability of resources, but how you consume those resources is more of an application thing, which AWS does not guarantee/control
For your ideas around, autoscaling and availability, it again depends on what your needs are in terms of cost, outages in AWS data centres etc. To have a reliable production setup, you could consider them, but not something really important in the first place.
I have a web service running on several EC2 boxes. Based on the Cloudwatch latency metric, I'd like to scale up additional boxes. But, given that it takes several minutes to spin up an EC2 from an AMI (with startup code to download the latest application JAR and apply OS patches), is there a way to have a "cold" server that could instantly be turned on/off?
Not by using AutoScaling. At least not, instant in the way you describe. You could make it much faster however, by making your own modified AMI image where you place the JAR and the latest OS patches. These AMI's can be generated as part of your build pipeline. In that case, your only real wait time is for the OS and services to start, similar to a "cold" server.
Packer is a tool commonly used for such use cases.
Alternatively, you can mange it yourself, by having servers switched off, and start them by writing some custom Lambda scripts that gets triggered by Cloudwatch alerts. But since stopped servers aren't exactly free either, i would recommend against that for cost reasons.
Before you venture into the journey of auto scaling your infrastructure and spending time/effort. Perhaps you should do a little bit of analysis on the traffic pattern day over day, week over week and month over month and see if it's even necessary? Try answering some of these questions.
What was the highest traffic ever your app handled, How did the servers fare given the traffic? How was the user response time?
When does your traffic ramp up or hit peak? Some apps get traffic during business hours while others in the evening.
What is your current throughput? For example, you can handle 1k requests/min and two EC2 hosts are averaging 20% CPU. if the requests triple to 3k requests/min are you able to see around 60% - 70% avg cpu? this is a good indication that your app usage is fairly predictable can scale linearly by adding more hosts. But if you've never seen traffic burst like that no point over provisioning.
Unless you have a Zynga like application where you can see large number traffic at once perhaps better understanding your traffic pattern and throwing in an additional host as insurance could be helpful. I'm making these assumptions as I don't know the nature of your business.
If you do want to auto scale anyway, one solution would be to containerize your application with Docker or create your own AMI like others have suggested. Still it will take few minutes to boot them up. Next option is the keep hosts on standby but and add those to your load balancers using scripts ( or lambda functions) that watches metrics you define (I'm assuming your app is running behind load balancers).
Good luck.
We are running 6 Java Spring Boot based microservice projects in one AWS t2.large(2 CPU & 8GB RAM) EC2 machine. When we see the CPU and RAM utilization of this EC2 machine, CPU is underutilized, hardly hits 1% but the RAM usage is always above 85%.
Now we want to add 2 more microservice. We definitely can't deploy these 2 services in the EC2 which we already have as the RAM will be exhausted. So, we need to add one more EC2 machine or we need to upgrade t2.large to t2.xlarge(4 CPU & 16GB RAM) to deploy the 2 new services but we also want to be cost-effective.
We are studying some of the AWS articles and it says that running ECS services will be cost-effective as the billing is based on how much CPU and RAM is being used(reference - ECS Fargate Pricing). As our CPU is always underutilized, we are thinking that ECS Fargate pricing policy will reduce our cost.
So, 3 questions with respect to cost saving for the above case,
If we prefer ECS, will that be really cost-effective than adding one more EC2 instance as ECS pricing is based on how much resources are utilized?
If we need to deploy 8 projects in ECS, will that create 8 EC2 instances or we can still use 1 or 2 EC2 machines for all 8 projects deployment?
Should we completely re-think about our deployment process in a different way?
If we prefer ECS, will that be really cost-effective than adding one more EC2 instance as ECS pricing is based on how much resources are utilized?
This is worded a little awkward, but I think what you're asking is will Fargate be more cost effective than standard EC2 instances. That, I think, is pretty difficult for us to determine for you, though you should be able to do some quick estimations based on your real-world usage and compare that with the cost of the smallest necessary instance type.
To throw another option in the mix, consider Reserved Instances, as they come at a pretty steep discount if you're willing to reserve them for some period of time. From the linked doc, you'd be looking at somewhere around a 40% discount (compared to on-demand) for a 1 year reservation, and 60% for a 3 year reservation.
Additionally, consider different instance types, specifically the memory optimized instances r4 and x1. These let you purchase more memory and less cpu.
If we need to deploy 8 projects in ECS, will that create 8 EC2 instances or we can still use 1 or 2 EC2 machines for all 8 projects deployment
Assuming you're not referring to Fargate here, the answer is ... it depends; it depends on how you design your system. You can host as many containers on an instance as you want (assuming you have the resources) -- but whether or not that's what you want from a high-availability / disaster scenario is for you to decide.
Should we completely re-think about our deployment process in a different way?
This will be answered when you come to a conclusion on your first question.
Currently i'm building a chat application base on NodeJs
So i considered choose which is the best instance type for our server?
Because AWS have a lot of choice: General purpose, compute optimize, memory optimize ....
Could you please give me advise :(
You can read this - https://aws.amazon.com/blogs/aws/choosing-the-right-ec2-instance-type-for-your-application/
Actually it doesn't matter what hosting you chose -AWS, MS Azure, Google Compute Engine etc...
If you want to get as much as you can from your servers and infrastructure, you need to solve your current task.
First of all decide how many active users at the same time you will get in closest 3-6 months.
If there will be less than 1000k active users (connections) per second - I think you can start from the smallest instance type. You should check how you can increase CPU/RAM/HDD(or SSD) of your instance.
SO when you get more users you will have a plan how to speed up your server.
And keep an eye on your server analytics - CPU/RAM/IO utilizations when you are getting more and more users.
The other questions if you need to pass some certifications related to security restrictions...
Since you are not quite sure where to start with, I would recommend to start with General Purpose EC2 instance for production from M category (M3 or M4). You can start with smaller instance type like m3.medium.
Note: If its an internal chat application with low traffic you can even consider T series EC2 instances.
The important part here is not to try to predict the capacity needs. Instead you can start small with general purpose EC2 instance and down the line looking at the resource consumption of EC2 instance you can do a proper capacity planning. Since you can both Scale the instances Horizontally and Vertically, it will require to trade of the instance type also considering Cost and timely load requirements before selecting the scaling unit of EC2 instance.
One of the approach I'm following is as follows
Start with General Purpose Instance (Unless I'm confident that there are special needs such as Networking, IO & etc.)
Do a load test(Without Autoscaling for a single EC2 instance) of the application by changing the number of users and find out the limits (How many users can a single EC2 instance can handle).
After analyzing the Memory, CPU & IO utilization, you can also consider shifting to a different EC2 category or stick with the same type. (Lets say CPU goes to its limit but memory is hardly used, you can consider using C series instances).
Scale the EC2 instance vertically by moving to the next size (e.g m3.medium to m3.large) and carry out the load tests to find out its limits.
After repeating step, 3 and 4 you can find an optimal balance between Cost and Performance.
Lets take 3 instance types with cost as X for the lowest selected (Since increasing the EC2 size in one unit, makes the cost doubles)
m3.medium - can serve 100 users, cost X
m3.large - can serve 220 users, cost 2X
m3.xlarge - can serve 300 users. cost 3X
Its an easy choice to select m3.large as the EC2 instance size since it can serve 110 per X cost.
However its not straight forward for some applications where you need to decide the instance type based on your average expected load.
Setup autoscaling and load balancing to horizontally scale the EC2 instances to handle load above average.
For more details, refer the Architecting for the Cloud: Best Practices whitepaper.
I would recommend starting with a T2.micro Linux instance. Watch the CPU usage in CloudWatch. Once the CPU usage starts to exceed 50% to 75%, or free memory gets low, or disk I/O gets saturated, switch to the next larger instance.
T2.micro Linux instances are (for the most part) free. Read the fine print. T2.micro instances are burstable which means that you can get good performance from a small instance.
Unless your chat application has a huge customer / transaction base, you (probably) won't need the other instance types.