I have created few AWS EC2 instances, however, sometimes, my data throughput (both for upload and download) are becoming highly limited on certain servers.
For example, typically I have about 15-17 MB/s throughput from instance located in US West (Oregon) server. However, sometimes, especially when I transfer a large amount of data in a single day, my throughput drops to 1-2 MB/s. When it happens on one server, the other servers have a typical network throughput (as previously expect).
How can I avoid it? And what can cause this?
If it is due to amount of my data upload/download, how can I avoid it?
At the moment, I am using t2.micro type instances.
Simple answer, don't use micro instances.
AWS is a multi-tenant environment as such resource are shared. When it comes to network performance, the larger instance sizes get higher priority. Only the largest instances get any sort of dedicated performance.
Micro and nano instances get the lowest priority out of all instances types.
This matrix will show you what priority each instance size gets:
https://aws.amazon.com/ec2/instance-types/#instance-type-matrix
Related
I have create a AWS EC2 instance to run a computation routine that works for most cases, however every now and then I get an user that needs to run a computation routine that crashes my program due to lack of RAM.
Is it possible to scale the EC2 instance's RAM and or vCPUs if required or if certain threshold (say when 80% of RAM is used) is reached. What I'm trying to avoid is keeping and unnecessary large instance and only scale resources when needed.
It is not possible to adjust the amount of vCPUs or RAM on an Amazon EC2 instance.
Instead, you must:
Stop the instance
Change the Instance Type
Start the instance
The virtual machine will be provisioned on a different 'host' computer that has the correct resources matched to the Instance Type.
A common approach is to scale the Quantity of instances to handle the workload. This is known as horizontal scaling and works well where work can be distributed amongst multiple computers rather than making a single computer 'bigger' (which is 'Vertical Scaling').
The only exception to the above is when using Burstable performance instances - Amazon Elastic Compute Cloud, which are capable of providing high amounts of CPU but only for limited periods. This is great when you have bursty needs (eg hourly processing or spiky workloads) but should not be used when there is a need for consistent high workloads.
I deployed the Machine Learning classification model in AWS EC2 (UBUNTU)instance successfully. I am able to access the instance "http://ec2-18-191-31-0.us-east-2.compute.amazonaws.com" and predictions are working fine only for few minutes. After that I or my colleagues are not able to access this. Getting an error "cannot connected to the server".
Security group that I crated as attached.
t2.micro instances are not suitable for any long running calculations. They are burstable. This means that their performance can be sustained only for short periods of time, e.g., sudden, short lived spikes in CPU usage. On top of that they have only 1 GB of RAM which limits its usefulness in machine learning.
For calculations, you could consider Compute optimized or Memory optimized instances. Obviously, these instance types are not free, but they are suited for calculations.
You can change instance type if you want and test with other, more power types. What you are describing indicates that your t2.micro exhausts all its RAM and/or CPU burst credits after few minutes and it freezes.
You can use CloudWatch Metrics for EC2 to monitor your instances and observer its CPU utilization and other metrics which can help you determine what exactly is causing the backlog. You can also monitor RAM and disc usage but this requires CloudWatch Agent setup on the instance.
I have an EC2 and RDS in the same region US East(N. Virginia) but both resources are in different zones; RDS in us-east-1a and EC2 in us-east-1b.
Now the question is that if I put both resources within the same zone then would it speed up the data transfer to/from DB? I receive daily around 20k-30k entries from app to this instance.
EDIT
I read here that:
Each Availability Zone is isolated, but the Availability Zones in a region are connected through low-latency links.
Now I am wondering if these low-latency links are very minor or should I consider shifting my resources in the same zone to speed up the data transfer?
Conclusion
As discussed in answers and comments:
Since I have only one instance of EC2 and RDS, failure of one service in a zone will affect the whole system. So there is no advantage to keeping them in a separate zone.
Even though zones are connected together with low-latency links but there is still some latency which is neglectable in my case.
There is also a minor data transfer charge of USD 0.01/GB between EC2 and RDS in different zones.
What are typical values for Interzone data transfers in the same region?
Although AWS will not guarantee, state, or otherwise commit to hard numbers, typical measurments are sub 10 ms, with numbers around 3 ms is what I have seen.
How does latency affect data transfer throughput?
The higher the latency the lower the maximum bandwidth. There are a number of factors to consider here. An excellent paper was written by Brad Hedlund.
Should I worry about latency in AWS networks between zones in the same region?
Unless you are using the latest instances with very high performance network adapters (10 Gb or higher) I would not worry about it. The benefits of fault tolerance should take precendence except for the most specialized cases.
For your use case, database transactions, the difference between 1 ms and 10 ms will have minimal impact, if at all, on your transaction performance.
However, unless you are using multiple EC2 instances in multiple zones, you want your single EC2 instance in the same zone as RDS. If you are in two zones, the failure of either zone brings down your configuration.
There are times where latency and network bandwidth are very important. For this specialized case, AWS offers placement groups so that the EC2 instances are basically in the same rack close together to minimize latency to the absolute minimum.
Moving the resources to the same AZ would decrease latency by very little. See here for some unofficial benchmarks. For your use-case of 20k reads/writes per day, this will NOT make a huge difference.
However, moving resources to the same AZ would significantly increase reliability in your case. If you only have 1 DB and 1 Compute Instance that depend on each other, then there is no reason to put them in separate availability zones. With your current architecture, a failure in either us-east-1a or us-east-1b would bring down your project. Unless you plan on scaling out your project to have multiple DBs and Compute Instances, they should both reside in the same AZ.
According to some tests, i can see like 600 microseconds (0.6 ms) latency between availability zones, inside the same region. A fiber has 5 microseconds delay (latency) per km, and between azs there is less than 100km, hence the result matches.
I need some understanding on how to do capacity planning for AWS and what kind of infrastructure components to use. I am taking the below example.
I need to setup a nodejs based server which uses kafka, redis, mongodb. There will be 250 devices connecting to the server and sending in data every 10 seconds. Size of each data packet will be approximately 10kb. I will be using the 64bit ubuntu image
What I need to estimate,
MongoDB requires atleast 3 servers for redundancy. How do I estimate the size of the VM and EBS volume required e.g. should be m4.large, m4.xlarge or something else? Default EBS volume size is 30GB.
What should be the size of the VM for running the other application components which include 3-4 processes of nodejs, kafka and redis? e.g. should be m4.large, m4.xlarge or something else?
Can I keep just one application server in an autoscaling group and increase as them as the load increases or should i go with minimum 2
I want to generally understand that given the number of devices, data packet size and data frequency, how do we go about estimating which VM to consider and how much storage to consider and perhaps any other considerations too
Nobody can answer this question for you. It all depends on your application and usage patterns.
The only way to correctly answer this question is to deploy some infrastructure and simulate standard usage while measuring the performance of the systems (throughput, latency, disk access, memory, CPU load, etc).
Then, modify the infrastructure (add/remove instances, change instance types, etc) and measure again.
You should certainly run a minimal deployment per your requirements (eg instances in separate Availability Zones for High Availability) and you can use Auto Scaling to add extra capacity when required, but simulated testing would also be required to determine the right triggers points where more capacity should be added. For example, the best indicator might be memory, or CPU, or latency. It all depends on the application and how it behaves under load.
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.