I'm working on a learning project for IoT with AWS IoT Things and ESP32 using Arduino/C (no micro-python). While I have shadows and messages working well, the part I'm not sure about is the best approach to onboard new devices.
Currently the onboarding process is:
I create the Thing in the AWS Console
I create the certs
I save the certs to laptop
I copy cert contents into the Shadow.h then upload the sketch to the ESP32
This feels incredibly manual :(
Hypothetically how would a reseller of ESP32-based IoT devices automate the onboarding process? How can the Things and certs be automated?
Many thanks in advance
Ant
We're talking about provisioning devices in cloud.
If you (or your organization) is adding your own devices to your own cloud, then it's quite easy to automate. Steps 1 and 2 are the cloud-side part of provisioning - just install the required SDK-s and write a script in your favourite supported scripting language to do the dirty work. For steps 3 and 4 you just use the device's own Flash to store the device certificates. Espressif has a useful non-volatile storage system called NVS - it's fairly easy to use and supports Flash encryption (this bit could be more elegant, but it works). You can use their NVS Partition Generator to pre-create the required storage with the device's certs in it, then flash it into the device when setting it up. Device-side provisioning can be scripted together with cloud-side provisioning so you can do the whole thing in a single step. The Arduino IDE is not the tool to use, though. You just need the final program binaries, but everything else you need to create on your own.
If you're talking about a third party taking your device and provisioning it in their cloud, this is a bit more difficult (but not impossible). Presumably they need to do steps 1 & 2 on their own and you need to give them a way to configure their AWS endpoints and certificates on the device. So you need to build some interface which allows them to do it.
Related
My project is to develop an IoT system that uses sensors to collect data which can be monitored by the user using a mobile app. I want to use AWS for this project but since I am a beginner, I am confused on where to start. Do you have some tips or some chronological steps that I have to learn and do to be able to create this project?
Not a criticism, but an observation. If you don't know the conceptual steps to achieve this, understand this before you jump into any technology. If you can't do this right now without any AWS technology, jumping in with AWS is going to make your life 100x harder.
Break it down into key challenges.
IoT Sensors. Start with a single sensor. Build a POC with a Raspberry PI that sends data from the device to www.example.com
Build a listening service on your localhost. i.e. HelloWorldIoTDevice simply responds with a 200 OK, don't worry about the data and payloads right now
Save this to a database that simple shows 200 OK messages every time a successful message is received from the IoT device
Build a HelloWorldWebApp that reads this data from the database
Build an API that reads the data from the database
Build a HelloWorldMobileApp that reads data from the API
Build it in full - Add payloads, add authentication and authorisation, get a POC published on the web so it works end to end
Productionise it
Then you need to get actual data flowing. Conceptually this is a relatively simple thing. But in reality this requires an extremely in-depth understanding of technology layers across the entire stack which is extremely challenging if you are a beginner as this is an enormous learning curve.
Take a look at Ngrok to help with building these types of POCs, https://www.contradodigital.com/2016/04/09/access-localhost-internet/
Hope that provides a bit of guidance. Take one step at a time.
I have developed Django API which accepts images from livefeed camera using in the form of base64 as request. Then, In API this image is converted into numpy arrays to pass to machine learning model i.e object detection using tensorflow object API. Response is simple text of detected objects.
I need GPU based cloud instance where i can deploy this application for fast processing to achieve real time results. I have searched a lot but no such resource found. I believe google cloud console (instances) can be connected to live API but I am not sure how exactly.
Thanks
I assume that you're using GPU locally or wherever your Django application is hosted.
First thing is to make sure that you are using tensorflow-gpu and all the necessary setup for Cuda is done.
You can start your GPU instance easily on Google Cloud Platform (GCP). There are multiple ways to do this.
Quick option
Search for notebooks and start a new instance with the required GPU and
RAM.
Instead of the notebook instance, you can set up the instance separately if you need some specific OS and more flexibility on choosing the machine.
To access the instance with ssh simply add your ssh public key
to Metadata which can be seen when you open the instance details.
Setup Django as you would do on the server. To test it simply just debug run it on host 0 or 0.0.0.0 and preferred port.
You can access the APIs with the external IP of the machine which can be found out in the instance details page.
Some suggestions
While the first option is quick and dirty, it's not recommended to use that in production.
It is better to use some deployment services such as tensorflow-serving along with Kubeflow.
If you think that you're handling the inference properly itself, then make sure that you load balance the server properly. Use NGINX or any other good server along with gunicorn/uwsgi.
You can use redis for queue management. When someone calls the API, it is not necessary that GPU is available for the inference. It is fine not to use this when you have very less number of hits on the API per second. But when we think of scaling up, think of 50 requests per second which a single GPU can't handle at a time, we can use a queue system.
All the requests should directly go to redis first and the GPU takes the jobs required to be done from the queue. If required, you can always scale the GPU.
Google Cloud actually offers Cloud GPUs. If you are looking to perform higher level computations with your applications that require real-time capabilities I would suggest your look into the following link for more information.
https://cloud.google.com/gpu/
Compute Engine also provides GPUs that can be added to your virtual machine instances. Use GPUs to accelerate specific workloads on your instances such as Machine Learning and data processing.
https://cloud.google.com/compute/docs/gpus/
However, if your application requires a lot of resources you’ll need to increase your quota to ensure you have enough GPUs available in your project. Make sure to pick a zone where GPUs are available. If this requires much more computing power you would need to submit a request for an increase of your quota. https://cloud.google.com/compute/docs/gpus/add-gpus#create-new-gpu-instance
Since you would be using the Tensorflow API for your application on ML Engine I would advise you to take a look at this link below. It provides instructions for creating a Deep Learning VM instance with TensorFlow and other tools pre-installed.
https://cloud.google.com/ai-platform/deep-learning-vm/docs/tensorflow_start_instance
My company is currently evaluating hyperledger(fabric) and we're using it for our POC. It looks very promising and we're targeting rolling out to production in next few months.
We're targeting AWS as our production environment.
However, we're struggling to find good tutorial/practices/recommendations about operating hyperledger network in such environment.
I'm aware that Cello is aiming to solve/ease deploying/monitoring hyperledger network but i also read that its not production ready yet. Question is, should we even consider looking at Cello at this point?
If not, what are our alternatives? Docker swarm, kubernetes?
I also didn't find information about recommended instance types. I understand this is application and AWS specific but what are the minimal system requirements
(memory&CPU&network) for example for 'peer' node (our application is not network intensive, nor a lot of transactions will be submitted per hour/day, only few of them per day).
Another question is where to create those instances on AWS from geographical&decentralization point of view. Does it make sense all of them to be created in same region? Or, we must create instances running in different regions?
Tnx a lot.
Igor.
yes, look at Cello.. if nothing else it will help you see the aws deployment model.
really nothing special..
design the desired system, peers, orderer, gateways, etc..
then decide who many ec2 instance u need to support that.
as for WHERE (region).. depends on where the connecting application is and what kind of fault tolerance you need for your business model.
one of the businesses I am working with wants a minimum of 99.99999 % availability. so, multi-region is critical. its just another ec2 instance with sockets open from different hosts..
aws doesn't provide much in terms of support for hyperledger. they have some templates which allow you to setup the VMs initially, but that's stuff you can do yourself as well.
you are right, the documentation is very light and most of the time confusing. I got to the point where I can start from scratch with a brand new VM and got everything ready and deploy my own network definition and chaincode and have the scripts to do that.
IBM cloud has much better support for hyperledger however. you can design your network visually, you can download your connection profiles, deploy and instantiate chaincode, create and join channels, handle certificates, pretty much everything you need to run and support such a network. It's light years ahead of AWS. They even have a full CI / CD pipepline that you could replicate for your own project. if you look at their marbles demo, you'll see what i mean.
Cello is definitely worth looking at, with the caveat that it's incubation meaning, not real yet, not production ready and not really useful until it becomes a fully fledged product.
I'm working on a socket server that'll be deployed to AWS and so far we have the basic OTP application set up following a structure similarly to the sample project in Erlang in Practice, but we wanted to avoid having a global message router because that's not going to scale well.
Having looked through the OTP design guide on Distributed Applications and the corresponding chapters (Distribunomicon and Distributed OTP) in Learn You Some Erlang it seems the built-in distributed application mechanism is geared towards on-premise solutions where you have known hostnames and IPs and the cluster configuration is determined ahead of time, whereas in our intended setup the application will need to scale dynamically up and down and the IP addresses of the nodes will be random.
Sorry that's a bit of a long-winded build up, my question is whether there are design guidelines for distributed Erlang applications that are deployed to the cloud and need to deal with all the dynamic scaling?
Thanks,
There are a few possible approaches:
In Erlang and OTP in Action, one method presented is to use one or two central nodes with known domains or IPs, and have all the other nodes connect to this one to discover each other
Applications like https://github.com/heroku/redgrid/tree/logplex require having a central redis node where all Erlang nodes register themselves instead, and do membership management
Third party services like Zookeeper and whatnot to do something similar
Whatever else people may recommend
Note that unless you're going to need to protect your communication, either by switching the distribution protocol to use SSL, or by using AWS security groups and whatnot to restrict who can access your network.
I'm just learning Erlang so can't offer any practical advice of my own but it sounds like your situation might require a "Resource Discovery" type of approach as i've read about in Erlang & OTP in Action.
Erlware also have an application to help with this: https://github.com/erlware/resource_discovery
Other stupid answers in addition to Fred's smart answers include:
Using Route53 and targetting a name instead of an IP
Keeping an IP address in AWS KMS or AWS Secrets Manager, and connecting to that (nice thing about this is it's updatable without a rebuild)
Environment variables: scourge or necessary evil?
Stuffing it in a text file in an obscured, password protected s3 bucket
VPNs
Hardcoding and updating the build in CI/CD
I mostly do #2
I have a web app running on php, mysql, apache on a virtual windows server. I want to redesign it so it is scalable (for fun so I can learn new things) on AWS.
I can see how to setup an EC2 and dump it all in there but I want to make it scalable and take advantage of all the cool features on AWS.
I've tried googling but just can't find a simple guide (note - I have no command line experience of Linux)
Can anyone direct me to detailed resources that can lead me through the steps and teach me? Or alternatively, summarise the steps in an answer so I can research based on what you say.
Thanks
AWS is growing and changing all the time, so there aren't a lot of books to help. Amazon offers training that's excellent. I took their three day class on Architecting with AWS that seems to be just what you're looking for.
Of course, not everyone can afford to spend the travel time and money to attend a class. The AWS re:Invent conference in November 2012 had a lot of sessions related to what you want, and most (maybe all) of the sessions have videos available online for free. Building Web Scale Applications With AWS is probably relevant (slides and video available), as is Dissecting an Internet-Scale Application (slides and video available).
A great way to understand these options better is by fiddling with your existing application on AWS. It will be easy to just move it to an EC2 instance in AWS, then start taking more advantage of what's available. The first thing I'd do is get rid of the MySql server on your own machine and use one offered with RDS. Once that's stable, create one or more read replicas in RDS, and change your application to read from them for most operations, reading from the main (writable) database only when you need completely current results.
Does your application keep any data on the web server, other than in the database? If so, get rid of all local storage by moving that data off the EC2 instance. Some of it might go to the database, some (like big files) might be suitable for S3. DynamoDB is a good place for things like session data.
All of the above reduces the load on the web server to just your application code, which helps with scalability. And now that you keep no state on the web server, you can use ELB and Auto-scaling to automatically run multiple web servers (and even automatically launch more as needed) to handle greater load.
Does the application have any long running, intensive operations that you now perform on demand from a web request? Consider not performing the operation when asked, but instead queueing the request using SQS, and just telling the user you'll get to it. Now have long running processes (or cron jobs or scheduled tasks) check the queue regularly, run the requested operation, and email the result (using SES) back to the user. To really scale up, you can move those jobs off your web server to dedicated machines, and again use auto-scaling if needed.
Do you need bigger machines, or perhaps can live with smaller ones? CloudWatch metrics can show you how much IO, memory, and CPU are used over time. You can use provisioned IOPS with EC2 or RDS instances to improve performance (at a cost) as needed, and use difference size instances for more memory or CPU.
All this AWS setup and configuration can be done with the AWS web console, or command-line tools, or SDKs available in many languages (Python's boto library is great). After learning the basics, look into CloudFormation to automate it better (I've written a couple of posts about that so far).
That's a bit of the 10,000 foot high view of one approach. You'll need to discover the details of each AWS service when you try to use them. AWS has good documentation about all of them.
Depending on how you look at it, this is more of a comment than it is an answer, but it was too long to write as a comment.
What you're asking for really can't be answered on SO--it's a huge, complex question. You're basically asking is "How to I design a highly-scalable, durable application that can be deployed on a cloud-based platform?" The answer depends largely on:
The specifics of your application--what does it do and how does it work?
Your tolerance for downtime balanced against your budget
Your present development and deployment workflow
The resources/skill sets you have on-staff to support the application
What your launch time frame looks like.
I run a software consulting company that specializes in consulting on Amazon Web Services architecture. About 80% of our business is investigating and answering these questions for our clients. It's a multi-week long project each time.
However, to get you pointed in the right direction, I'd recommend that you look at Elastic Beanstalk. It's a PaaS-like service that abstracts away the underlying AWS resources, making AWS easier to use for developers who don't have a lot of sysadmin experience. Think of it as "training wheels" for designing an autoscaling application on AWS.