When you think about enterprise processing power, you probably think about supercomputers. Or, you
might visualize a data center with racks of servers going from ceiling to floor.
But as any mobile user probably knows, these devices have lots of processing power.
This means they can handle the analysis of data directly. This power shift has
the potential to change the way we work with technology we use every day.
In this video, I'll explain an exciting new emerging capability Samsung and IBM
are working on together to develop the ability to use containerization--at the edge on Android.
As an example, I'll use a mobile phone. But what makes processing on the edge so important?
Well, it doesn't require data to be sent across the network to a centralized cloud
to be analyzed. Instead, it brings applications closer to the data, which enables faster decision
making and smarter data collection. With a device like this Samsung Galaxy mobile phone,
edge computing helps us close that last mile between tech and the people that use it.
Modern mobile phones also have several beneficial functions as edge platforms. That includes high-res
cameras, microphones, accelerometer sensors and geospatial capabilities. Not to mention they
have significant processing power, 5G connectivity and advanced security suites like Samsung Knox.
So, let's start with an example of how, when combined with AI, devices on the edge can provide
rapid results right in someone's hands. Let's say, for example, I'm a plant safety inspector.
My job a lot easier if I could offload routine inspection work to AI-based visual analysis tools.
But these visual analysis tools don't have to be hosted in some faraway data center. That's where
a really smart phone and camera comes in. In my job as plant inspector, I can take applications
built in the cloud and have them sent directly to my device from a centralized management tool.
So I walk into the plant and for my initial inspection, start up an app and start recording
what I see. I could then immediately apply the AI model running on my device as I work in the plant.
That takes the guesswork out of my role and uses tech like AI where it can make
the biggest difference. Once the inspection is done, the results can be sent back to the cloud.
That way, they can be stored for historical purposes and potential model training data.
But how do we get our applications out to the edge and onto the device we use every day in a
way that is scalable and secure? One answer is containers. But what is the container in
the first place? And why might we want to run these on something like this Samsung phone?
In simple language, a container is a single executable package of software that includes
an application and all the dependencies it needs to run. That means the application, its scripts and
libraries are all together in one place. So in order to take advantage of containers on Samsung
devices at the edge, we use the Docker runtime. So how do containers compare with virtual machines?
With virtual machines, each instance includes a separate operating system. On the other hand,
containers run of the operating system level. So they share an operating system kernel.
That means containers can spin up really fast with little overhead per instance.
There are other advantages to running containers on these devices at the edge as well as across the
cloud. But I'll focus on four main benefits. The first, easier deployment: Containers are portable,
lightweight and easy to distribute through an orchestration platform like Kubernetes
without having to rebuild the core application each time. Number two, easier development:
Using containers means we can tap into cloud native developer skills. That means workloads
in AI on the cloud can be moved to the edge using existing development skills. Number three,
application performance: Because they are sharing OS level resources across containers in real time,
devices can quickly adjust and maintain application resource requirements as demand
rises and declines. And finally, number four: security. While containers might share the OS
kernel, it appears each container that has own operating system since it can access
only what was installed within the container itself. This isolates processes to control
what each application has access to and prevent access to other data or resources on a device.
Now, with lots of applications going to lots of devices, this could quickly get pretty complex.
But luckily we can use software to set policies to help. So instead of needing someone working
at a particular location to manage which applications go on which device for which tasks,
the IT team can centralize this. With preset policies, they can automate app management.
In our example, we can set the software policy so the container with AI application is automatically
sent to the plant safety inspector's mobile device. With the right models for that plant,
they're inspecting that day--without anyone in the plant managing this process.
This simplifies the management and orchestration of our applications and helps us to bring tech out
of the cloud and onto the devices we use every day in a scalable and secure way.
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