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It sometimes makes sense to treat edge computing not as a generic category but as two distinct types of architectures: cloud edge and device edge.
Most people talk about edge computing as a singular type of architecture. But in some respects, it makes sense to think of edge computing as two fundamentally distinct types of architectures: Device edge and cloud edge.
Although a device edge and a cloud edge operate in similar ways from an architectural perspective, they cater to different types of use cases, and they pose different challenges.
Here’s a breakdown of how device edge and cloud edge compare.
First, let’s briefly define edge computing itself.
Edge computing is any type of architecture in which workloads are hosted closer to the “edge” of the network — which typically means closer to end-users — than they would be in conventional architectures that centralize processing and data storage inside large data centers.
#cloud #edge computing #cloud computing #device edge #cloud edge
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Most of the companies in today’s era are moving towards cloud for their computation and storage needs. Cloud provides a one shot solution for all the needs for services across various aspects, be it large scale processing, ML model training and deployments or big data storage and analysis. This again requires moving data, video or audio to the cloud for processing and storage which also has certain shortcomings compared to do it at the client like
If you look at other side, cloud have their own advantages and I will not talk about them right now. With all these in mind, how about a hybrid approach where few requirements can be moved to the client and some remain on the cloud. This is where EDGE computing comes into picture. According to Wiki here is the definition of the same
Edge has a lot of use cases like
Look at Gartner hype cycle for emerging technologies. Edge is gaining momentum.
There are many platforms in the market specialised in edge deployments right from cloud solutions like azure iot hub, aws greengrass …, open source like _kubeedge, edgeX-Foundary _and third party like Intellisite etc.
I will focus this article on using one of the platforms for building an “Attendance platform” on the edge using facial recognition. I will add as many links as possible for your references.
Let us start with taking the first step and defining the requirements
Choosing the right platform from so many options was a bit tricky. For the POC, we looked at few pieces in the platform
There were other metrics as well but these were on top of our mind. Azure IoT looked pretty good in terms of above evaluation. We also looked at Kubeedge which provided deployments on Kubernetes on the edge. It is open source and looked promising. Looking at many components (cloud and edge) involved with maintenance overhead, we decided not to move ahead with open source. We were already using Azure cloud for other cloud infra, this also made our work a little more easier in choosing this platform. This also helped
Leading platform players
Azure IoT hub provided 2 main components. One is the cloud component responsible for managing the deployments on edge and collection of data from them. The other is the edge component consisting of
I will not go into the details, you can find more details here about the Azure IoT edge. To give a brief, Azure edge requires modules as containers which can to be pushed to the edge. The edge device first needs to be registered with the IoT Hub. Once the Edge agent connects with the hub, you can push your modules using a deployment.json file. The container runtime that Azure Edge uses is moby.
We used Azure IoT free tier which was sufficient for our POC. Check the pricing here
As per the requirements of the POC, this is what we came up with
The solution consists of various containers which are deployment on the edge as well as few cloud deployments. I will talk about each components in details as we move ahead.
As part of the POC, we assumed 2 sites where attendance needs to be taken at multiple gates. To simulate, we created 4 ubuntu machine. This is the ubuntu desktop image we used. For attendance, we created a video containing still photos of few filmstars and sportsperson. These videos will be used for attendance in order to simulate the cameras, one for each gate.
It captures IP camera feed and pushed the frames for consumption
The module was configured to use a lot of environment variables, one being sampling rate of the video frames. Processing all frames require high memory and CPU, so it is always advisable to drop frames to reduce cpu load. This can be done in either camera module or inferencing module.
#deep-learning #edge-computing #azure #edge
Building upon topics in his talk at QCon London, Peter Bourgon answers questions about edge computing, distributed data, and the complexity of synchronization.
#distributed systems #relational databases #edge computing #architecture #database #edge #cloud #development #architecture & design #devops #article