Edge Computing: Data, Devices, and Networks
Key Points
- Edge computing means locating processing workloads as close as possible to where data is generated and actions are taken, rather than relying solely on centralized clouds.
- The raw data actually originates from human interactions and the equipment we use, making the “edge” the true source of information.
- Even when workloads run at the edge, aggregated analytics and trend analysis typically continue in a hybrid (private‑or‑public) cloud environment.
- Network providers are turning their infrastructure into “network edge” platforms, leveraging 5G to deliver compute capabilities directly to factories, warehouses, retail stores, banks, hotels, and other on‑premise sites.
- There are two main edge computing forms: edge servers—dedicated IT hardware like racks or industrial PCs—and edge devices—originally purpose‑built machines (e.g., robots, turbines, vehicles) that now embed compute power and evolve from traditional IoT devices.
Full Transcript
# Edge Computing: Data, Devices, and Networks **Source:** [https://www.youtube.com/watch?v=cEOUeItHDdo](https://www.youtube.com/watch?v=cEOUeItHDdo) **Duration:** 00:10:37 ## Summary - Edge computing means locating processing workloads as close as possible to where data is generated and actions are taken, rather than relying solely on centralized clouds. - The raw data actually originates from human interactions and the equipment we use, making the “edge” the true source of information. - Even when workloads run at the edge, aggregated analytics and trend analysis typically continue in a hybrid (private‑or‑public) cloud environment. - Network providers are turning their infrastructure into “network edge” platforms, leveraging 5G to deliver compute capabilities directly to factories, warehouses, retail stores, banks, hotels, and other on‑premise sites. - There are two main edge computing forms: edge servers—dedicated IT hardware like racks or industrial PCs—and edge devices—originally purpose‑built machines (e.g., robots, turbines, vehicles) that now embed compute power and evolve from traditional IoT devices. ## Sections - [00:00:00](https://www.youtube.com/watch?v=cEOUeItHDdo&t=0s) **Defining Edge Computing and Data Origins** - The speaker explains edge computing as positioning workloads close to the human‑ and equipment‑generated data sources, outlines how that data eventually returns to hybrid clouds for analytics, and highlights network providers’ role in delivering compute at the network edge. ## Full Transcript
what is edge computing we define edge
computing as being about placing
workloads as close to the edge to where
the data is being created and where
actions are being taken as possible so
let's think about that for a little bit
where does data come from we often think
about data existing in the cloud where
we might do analytics and AI activities
that it processes that data but that's
really not where the data originally was
created the data is created really by us
as human beings in our world in the
varmints that we operate in the places
where we do work it comes from us in our
interactions with the equipment that we
use as we're performing various tasks it
comes from the equipment itself and it's
produced as a byproduct of our use of
that equipment so let's take this down a
little bit further if we want to make
use of the edge and we want a place
workload there we have to start by
thinking about what data ends up coming
back to the cloud and when we talk about
clouds let's talk about both private and
public clouds and not distinguish those
because frankly where we put that data
where we end up processing that data for
things like aggregate analytics trend
analysis is still likely to occur in the
cloud in the hybrid cloud now it turns
out the network providers are also
looking at the world of networking the
facilities that they provide and how
they can bring workloads into the
network itself so we've been labeled at
the network edge that's sort of how they
refer to it themselves oftentimes you'll
hear the term edge being used by the
network providers as being about their
own network 5g opens up the opportunity
for us to communicate into the premises
where work is performed on to the
factory floor into the distribution
centers into the warehouses into the
retail stores into banks hotels you name
it there is an opportunity for us to
introduce compute capacity into those
environments
and communicate with that through 5-u
networks now there's two kinds of edge
computing capabilities that we often
find in these environments one is what
we call an edge server an edge server
you can think of as basically a piece of
IT equipment it could be a half rack of
you know maybe four or eight blades it
could be an industrial PC but it's a
piece of equipment that was built for
the purpose of computing IT workloads
now another place where we can perform
work in the edge in the on-premise
locations or in what we think of as edge
devices an edge device is interesting
because what it is first and foremost is
a piece of equipment that was built for
some purpose it could be an assembly
machine it could be a turbine engine it
could be a robot it could be a car they
were built first and foremost to perform
those functions they just so happened to
have compute capacity on it and in fact
what we've seen over the last few years
is that many of the pieces of equipment
that we had before that we referred to
as IOT devices now have grown up and
we've seen the addition of a more and
more compute capacity on these devices a
car let's take a car for example the
average car today
has 50 CPUs on it almost all new
industrial equipment have compute
capacity built into that equipment and
the thing is that these computers are
being opened up they oftentimes run
Linux they have the ability for us to
deploy containerize workloads onto these
devices which means that now that
becomes a place where we can do work
that we couldn't do before let's say for
example you've got a video camera built
into an assembly machine an assembly
machine that's making parts maybe it's
making metal boxes of some sort you can
put a camera on that you can put
analytic on that camera that now looks
at the quality that that machine is
producing now it's very common that a
lot of these operating environments also
have edge
servers again remember these things are
pieces of IT equipment so it might be a
half rack sitting on a factory floor
that is today being used to model the
production processes or to monitor for
production optimization and whether the
production is being performed as
efficiently and as with as much yield as
we want the same thing may occur in a
distribution center managing all of the
conveyor belts and all the stackers and
the sorters and the things that are used
in a distribution center so these are
places where work can be performed and
servers on the other hand being pieces
of IT equipment oftentimes are much
bigger so it's common that if we're
going to have a containerized workload
that we're trying to manage into these
spaces that will run that container on a
docker run time without the benefit that
kubernetes brings to the table
whereas an image server nothing that we
have the capacity to rent kubernetes but
more importantly we have the need the
need to get elastic scale and high
availability and continuous availability
out of the workloads that are ployed on
these edge servers because frankly
they're being used on behalf of many are
these edge devices so with that in mind
we can start to think about what happens
in these environments and how do we
manage these environments how do we make
sure the right workloads are placed in
the right place at the right time first
of all we think about what we've done in
the cloud we know that in the cloud is
important to build workloads as
containers this is something that we
have developed for scaling and
efficiency and consistency that almost
all of the public cloud providers and
certainly most of the private cloud
suppliers now enable with kubernetes
running in the cloud we can take that
same concept we can use it to package
the workloads and manage distribution
out into these edge computing scenarios
secondly because these things are often
built for use in hybrid cloud scenarios
where we have built hybrid cloud
management we can begin to reuse those
concepts as a taking
for handling the distribution of those
containers into these edge locations but
there's several problems one of them is
just think about the volumes the numbers
of devices out there we estimate there's
about 15 billion edge devices in the
marketplace today and that that grows to
about 55 billion by 2022 and there's
some estimates that will grow to about
150 billion by 2025 if that's true that
means that every enterprise out there
will have literally tens of thousands
hundreds of thousands maybe even
millions of devices that they have to
manage from their central operations we
have to have management techniques that
are able to distribute we're closed into
these places at massive scale
without requiring individual
administrators going out and assigning
those workloads to individual devices we
also have an issue of diversity these
devices come in many different forms
they have different purposes they have
different utility they make different
assumptions about their footprint but
also what operating system they use what
kinds of work they're going to perform
on these devices
finally security is an issue in these
environments these devices out here on
the edge exist outside the boundaries of
the IT data center they don't have the
protections that we typically associate
with the hybrid cloud environments
physical protection the uniformity the
consistencies that we look for typically
when we certify a security in these
kinds of environments we have to now
think about how do we ensure that
workloads don't get tampered with when
they're deployed out to these systems
how do we make sure the machine itself
if it does get tampered with it's
something that we can detect and respond
to remediate we have to make sure that
the data that we associate with these
workloads is properly protected not only
from the fact that this data may be
moved back into the network through the
network and into the cloud but also
because on the move in itself represents
a point of village
vulnerability if we can move the
workloads to the edge and avoid have to
move sensitive data back to other
locations then we actually reduce the
potential for people to find attacks on
that data so all these things together
are the things that will on the one hand
inhibit the use of edge computing but on
the other hand become an opportunity and
opportunity for vendors to introduce
management controls that are able to
handle that diversity and the dynamism
the ability to protect data in the right
places or at the right time and finally
to build an ecosystem which of course is
just as important as everything else so
just to wrap this all up it is important
that we acknowledge that the edge
computing world is growing this is going
to grow rapidly it will have as much
impact in the world of enterprise
computing as mobile phones did in the
world of consumer computing can be think
about the changes that have occurred as
a consequence of the mobile phones
you're going to see as much change occur
in enterprise computing as a consequence
of edge computing that we saw over the
last 10 years with mobile computing so
this is a world that's growing this is a
world that has lots of interesting
complexity to it but where if we can
solve these issues how will yield an
enormous amount of value to our
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