I'm trying to determine if it makes sense to switch our hosting to EC2 from a dedicated dreamhost server, and if so, what EC2 instance type I should choose to get a good idea of the cost prior to switching. I would like to go low and then bump up if need be.
Current Usage:
dedicated server with 4 GB RAM and 4 CPUs
average disk usage: 783 MB
average bandwidth: 8.5 GB
This is really all the info I get from our dreamhost control panel, so hopefully it's enough to provide some recommendations on where to start.
Using the calculator located here, I'm leaning towards a t2.xlarge. Is that too much? not enough?
It is not possible for anyone to recommend the 'correct' instance type. This is because it depends on the operation of your particular application. It might be CPU-intensive, RAM-intensive, network-heavy, highly parallel, etc.
Some applications might need to handle occasional spikes of traffic, whereas other applications might be relatively consistent in their load.
The correct way to determine your 'best' instance type is to run tests that simulate the expected application load. If you can create an automated test, then you could run it against many different instance types and compare the performance vs cost.
Also, many applications are designed to be able to run across multiple instances, so it would be better to test various quantities of servers as well as their instance type.
You might also consider using Amazon EC2 Auto Scaling, which gives the ability to automatically add/remove servers based upon workload. This means that you could use much more powerful instances, but automatically turn some of them off during less-used periods. This affects the cost calculation because the more-powerful instances are more expensive, but you won't be using them all the time.
Then, you could also consider using Amazon EC2 Spot Instances, which can be up to 90% less cost but might be terminated when the demand for such instances is higher. You can also combine On-Demand and Spot Instances to give additional capacity at a lower cost.
(Spot and Auto Scaling are only really applicable if you are using more than one instance to host your application.)
And finally, if your application only requires one instance, you could also consider using Amazon Lightsail that combines the price for instance type and network bandwidth to make the price more predictable.
Bottom line: It depends!
One final word: Most companies consider switching to AWS not purely on a cost basis ("if it makes sense to switch our hosting to EC2 from a dedicated dreamhost server"), but rather on the breadth of features that AWS offers that are not available in a traditional server hosting service. If all you need is "a server", it's probably easiest to consider Amazon LightSail or keep whatever is currently working for you. The cost saving with AWS won't be dramatic (or it might not even be cheaper!), but it will offer you a lot more capabilities if you ever grow beyond just requiring "a server".
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what will be the best option to choose b/w less number of large instances or more number of the small instance when the performance is concerned, as the cloudwatch (load balancing and scaling) will be used if the traffic floods on the servers.
AWS is all about ELASTICITY
There is no need to provision large instances when not needed and burn out money.
There can be many instances when your CPU on one goes high and the next large instance you created remains under-utilized.
You should have medium instances to small w.r.t the tier you require (Memory Intensive, CPU, or Network) and scale those instances with properly written policies.
As long as the userdata, ami is stable you can spawn many instances within minutes making sure you are not spending way too much and saving every Penny.
SCALE WHEN NEEDED HORIZONTALLY
This is heavily dependent on your application.
I agree with Faisal Nizam's intuition of favoring horizontal scaling. However, there are many applications that will not run very well on small instances.
For example, Elastic recommends to have Elasticsearch cluster nodes with 64GB of RAM. Similar reasoning can be applied to many other data related applications, where it can be beneficial if a single instance is able to keep large data chunks in memory.
I would recommend to find the ideal instance size for your application, and from there scale horizontally.
Each EC2 has also some overhead, so you need to find a balance between large & costly instances vs. a lot and small instances with overhead.
(As of today) To vertically scale up/scale down an EC2 server, it needs to be shut down and spun back up - something to keep in mind before deciding to go for it.
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.
Our website is getting slow and we are in need of an upgrade.
We are currently AWS and have 1 micro ec2 instance that proved effective while our website had less traffic. Now when we get more traffic, our site is getting slower.
We can't seem to settle an argument.
Which would be better:
Adding multiple additional micro/small instances and have them managed either by nginx or amazon cloud computing
OR
Upgrading our micro instance into a large/xlarge instance.
which would be more effective considering the tasks to be performed by the server are simple, and considering the total amount of ram and processing power is similar. 1 big, or many small?
Thanks
Tough to say -
Option #2 is going to be the easiest to do, turn your server off, resize it, turn it back on get more capacity just by paying more money. Easy to do, but maybe not the best long-term solution. What will you do when traffic continues to increase (either constantly or at certain times) and there are no more gains to be had simply by picking a bigger box?
Option #1 is going to be more work, but ultimately maybe a better strategy.
First of all, you didn't say if you have a constant need for more throughput, or if it is certain times of the day/week/month/year when the capacity is needed - if that is the case, multiple EC2 instances with auto-scale groups setup to respond to increases and decreases in demand by turning on additional instances as needed and then turning them off as demand decreases is a cost-effective option.
In addition, having multiple instances running - preferable in different availability zones, gives you fault-tolerance - when your big instance in #1 goes down, your website is down - if you have many small instances running across 2 or 3 availability zones, you can continue to function if one or more or your instances goes down, and even if AWS availability zone goes offline (rare, but it happens).
Besides the options above, without knowing anything about your application - other things you can do - move some static assets to S3 and/or use AWS cloudfront (or other CDN) to offload some of the work - this is often a cheap and easy way to get more out of an existing box.
I am currently using spot instances managed with auto-scaling groups. However, ASG has a number of shortcomings for use with spot instances. For example, it cannot launch instances of a different instance type if the current type is experiencing a price spike across all availability zones. It can't even re-distribute the number of running instances across zones (if one zone has a price spike, you're down 30% in the number of running instances.)
Are there any software solutions that I could run which would replace built-in AWS Auto-Scaling Groups? I've heard of SpotInst and Batchly, but I do not trust them. Basically, I think their business plan involves being bought out and killed by Amazon, like what happened to ClusterK. The evidence for this is the bizarre pricing policies and other red flags. I need something that I can self-host and depend on.
AWS recently released Auto Scaling for Spot Fleets which seems to fit your use case pretty well. You can define the cluster capacity in terms of vCPU that you need, choose the instance types you'd like to use and their weights and let AWS manage the rest.
They will provision spot instances at their current market price up to a limit you can define per instance type (as before), but integrating Auto Scaling capabilities.
You can find more information here.
https://aws.amazon.com/blogs/aws/new-auto-scaling-for-ec2-spot-fleets/
It's unlikely that you're going to find something that takes into account everything you want. But because everything in Amazon is an API, so you can write that yourself. There are lots of ways to do that.
For example, you could write a small script (bash, ruby, python etc) that shells out the AWS CLI to get the price, then shells out to launch boxes. For bonus points, use the native AWS SDK library instead of shelling out. (That will be slightly easier to handle errors, etc.) For even more bonus points, open source it, and hope that other people to improve on it!
This script can run on your home computer, or on a t1.micro for $5/month. Or you could write it in node.js, and run it on Lambda for pennies per month.
Here at Spotinst, these are exactly the problems we built Elastigroup to solve.
Elastigroup enables running simultaneously on as many instance types and availability zones (within a region) as you’d like. This is coupled with several things to maintain production availability:
Our algorithm makes live choices for the best Spot markets in terms of price and availability.
When an interruption happens, we predict it about 15 minutes in advance and take all the necessary steps to ensure (and insure) the capacity of your group.
In the extreme case that none of the markets have Spot availability, we simply fall back to an on-demand instance.
We have a great relationship with AWS and work closely with both their technical and business teams to provide our joined customers with the best experience possible. As we manage resources inside your own AWS account, I wouldn’t put the relationship between us as a concern, to begin with.
For my spark application I'm trying to determine whether I should be using 10 r3.8xlarge or 40 r3.2xlarge. I'm mostly concerned with shuffle performance of the application.
If I go with r3.8xlarge I will need to configure 4 worker instances per machine to keep the JVM size down. The worker instances will likely contend with each other for network and disk I/O if they are on the same machine. If I go with 40 r3.2xlarge I will be able to allocate a single worker instance per box, allowing each worker instance to have its own dedicated network and disk I/O.
Since shuffle performance is heavily impacted by disk and network throughput, it seems like going with 40 r3.2xlarge would be the better configuration between the two. Is my analysis correct? Are there other tradeoffs that I'm not taking into account? Does spark bypass the network transfer and read straight from local disk if worker instances are on the same machine?
Seems you have the answer already : it seems like going with 40 r3.2xlarge would be the better configuration between the two.
Recommend you go through aws well architect.
General Design Principles
The Well-Architected Framework identifies a set of general design principles to
facilitate good design in the cloud:
Stop guessing your capacity needs: Eliminate guessing your
infrastructure capacity needs. When you make a capacity decision before
you deploy a system, you might end up sitting on expensive idle resources
or dealing with the performance implications of limited capacity. With
cloud computing, these problems can go away. You can use as much or as
little capacity as you need, and scale up and down automatically.
Test systems at production scale: In a traditional, non-cloud
environment, it is usually cost-prohibitive to create a duplicate
environment solely for testing. Consequently, most test environments are
not tested at live levels of production demand. In the cloud, you can create
a duplicate environment on demand, complete your testing, and then
decommission the resources. Because you only pay for the test
environment when it is running, you can simulate your live environment
for a fraction of the cost of testing on premises.
refer:
AWS Well-Architected Framework