The confluence of 5G networks, AI and machine learning, industrial IoT, and edge computing are driving the fourth industrial revolution – Industry 4.0. The impact of the industrial edge and how it is being transformed were among the topics at our SNIA Cloud Storage Technologies Initiative (CSTI) webcast “5G Industrial Private Network and Edge Data Pipelines.” If you missed it, you can view it on-demand along with the presentation slides in the SNIA Educational Library. In this blog, we are sharing and clarifying answers to some of the intriguing questions from the live event.
Q. What are some of the key challenges to support the agility and flexibility requirements of Industry 4.0?
A. The fourth industrial revolution aka Industry 4.0 aspires to fundamentally transform the flexibility, versatility and productivity of future smart factories. Key attributes of this vision include complex workloads to enable remote autonomous operation, which involves autonomous mobile robots and machines, augmented reality aided connected workers, wireless sensors, actuators and remote supervisory control systems, as shown in the diagram below. Machines in smart factories will no longer be stationary. To enable quick response to supply demand changes and enable mass customization (“batch size of one”), factory lines need to be quickly reconfigurable and need machines to move within a certain range. These AI-based, mobile autonomous robots and machines require high data through-put wireless networks and highly reliable sub-second latency for machine-to-machine control communications.
Q. What are some of the new 5G capabilities that are important to enable Industry 4.0?
A. Although cellular technology (2G through 4G) was primarily targeted to serve consumers via massive public networks, 5G enables standalone private networks and is designed to meet the needs of vertical industries. Not only does 5G provide faster data rates (up to 10x faster than 4G), but 5G also supports delivery of massive amounts of concurrent machine-to-machine connections. With 5G private networks, users can tune the network based on custom requirements of the factory. Providing enhanced design flexibility, network slicing establishes multiple logical/virtual networks to handle a wide variety of use cases, all coexisting on the same physical infrastructure. Here are a few of the transformational 5G developments, as shown in the diagram below.
Enhanced Mobile Broadband (eMBB) supports extremely high data rates (up to several Gb/s) and offers enhanced coverage beyond existing networks
Massive Machine Type Communication (mMTC) supports wide-area coverage, signal penetration through indoor structures, ubiquitous connectivity enabling hundreds of thousands of IoT devices per square kilometer and support for battery-saving low energy operation
Q. What does this all mean for storage?
A. There is a huge shift now that 50% of data is being created and processed at the edge. It’s really putting us at a tipping point (as demonstrated by the chart below). By the time we get to the year 2040, it is projected that there will be 364 times the amount of storage growth at the edge compared to data centers. Our mission as storage professionals is to start looking at how we place our technology and how we think about data and data movement within our organizations.
The bottom line: Storage and data strategies must include the edge.
The second webcast in our Storage Life on the Edge series is coming up on March 22, 2022 at 10:00 am Pacific time. This panel, moderated by Bill Martin, SNIA Compute, Memory, and Storage Initiative Chair, takes a deeper dive to focus on edge use cases in the computational storage space.
Our panelists Mayank Saxena from Sansung, Stephen Bates from Eideticom, and Tong Zhang from ScaleFlux will discuss edge to cloud use cases where storage and compute resources need to be deployed in practical topologies that deliver the very best in application performance. They’ll examine high performance edge data needs, database acceleration solutions, meeting retail chain challenges, and more. You won’t want to miss their panel discussion and the chance to ask your questions live.
Register here to attend. We’ll look forward to seeing you!
The post Our Storage Life on the Edge Webcast Series Continues…. first appeared on SNIA Compute, Memory and Storage Blog.
The SNIA Networking Storage Forum (NSF) kicked off our “Storage Life on the Edge” webcast series with a session on managing data from the edge to the cloud and back. We were fortunate to have a panel of experts, Dan Cummins, John Kim and David McIntyre to explain key considerations when managing and processing data generated at the edge. If you missed this introductory session, it’s available on-demand, along with the presentation slides at the SNIA Educational Library.
Our presenters spent a good percentage of time answering
questions from our live audience. Here are answers to them all.
Q. Could an application be deployed simultaneously at
near-edge, far edge and functional edge?
A. [Dan] Many of the applications are the outcomes that
customers need depending on the vertical market; whether that's manufacturing,
retail or and gas. They require processing that's distributed at each of these
locations so absolutely if we go back to that computer vision
use case you can see that part of the application or the
outcome includes an IoT stack that is deployed at the far right. That's
ingesting the data from the cameras (and/or sensors)
as well as an AI/ML streaming framework that in runtime is deployed to process
the model at the near edge. Also, applications do the retraining up in the
cloud. So, yes absolutely many of the outcomes actually are distributed
applications that are deployed simultaneously across the edge, the core, and
the cloud.
Q. Can the cloud ever be considered to be at the edge?
A. [Dan] “The edge” has a different meaning to many people.
A cloud is an edge. One of the trends that we're seeing is what's called the
“functional edge” or some people like to refer to that as the “IoT edge,” and
then you have the “far edge” and then something we defined as a “mirror edge,”
(a coordinated center in a cloud), but that cloud is an edge. Now, what we're
seeing is that the onset of private wireless or 5G for example has the ability
to be able to connect that far edge or that IoT edge directly to a cloud or
directly to a data center, but each of those eliminates the edge computing
locations in between. You can think of it like an always-on world with the
speed and the bandwidth that you would get with 5G, as it becomes ubiquitous
and allows you to connect processing in a cloud or core data center directly to
the IoT edge which is pretty compelling, but yes, cloud is an edge.
Q. If I’m using a tablet inside a connected car where is
the edge? Is it the tablet, the car or a nearby cellular base station?
A. [John] All of those could be different parts of the edge.
The tablet could be the functional edge, and the car may also be the functional
edge because they both have a sort of constricted or restricted domain in terms
of the hardware which is fairly fixed. You can update the software and use it
as your fairly dedicated functional processes, which don't really have any other
applications going on. And then the cellular base station could probably be
considered the far edge and you could also have a telco office acting as a
traffic processing center or small data center that could be the near-edge. Or
this information from the functional and far edge could then flow into the
cloud and the cloud could be that near-edge data center before some of the data
ultimately goes back to a core data center that's run by the enterprise or
hosted in a colocation facility.
[Dan] There is no one edge. What we're really talking about
is edge compute or where does processing of data take place and it takes place
everywhere and the applications are the outcomes that you need for that
processing. If you're in manufacturing, you need overall equipment
effectiveness. If you’re in retail you need fraud detection. Where to produce
that outcome, you're going to need to be able to operate in multiple locations
and so I really would define edge as the point where data is being acted on by
edge compute.
Q. How do edge storage needs differ from cloud or data
center storage needs?
A. [Dan] We touched a little bit on this. I was trying to
convey that depending on the location, much of the data that's generated from
the IoT edge is mostly streaming data and they have to get it to a point where
they can process the data. The streaming architectures are prevalent because as
you're transporting data between locations, processed data could have an intermittent
connectivity or a low bandwidth situation so typically you see either a
streaming data architecture or some sort of architecture that can handle that
disconnect and then re-establish and continue. Most of the data that's coming
in at the far edge is usually because of the streaming. It’s a store forward
and most of it is unstructured. When you start to get into upstream, where you
need to do more with the data like that near edge, I had explained that you
have kind of a mix of both these edge workloads which is working on this
unstructured data, as well as a mix of structured data for business
applications. So, you have a mix of edge and IT workloads. All of those are in
that data processing pipeline that is needed to provide a near real-time response.
It's not really out of band data, it's in-band data and then usually with a
core data center or cloud is where you're usually going to be operating on data
that is out of band that requires deep storage. So, the data center will
continue to have deep storage as well as the cloud where you need to expand and
you need longer term storage for in-home or for training models and things like
that. Typically, the further you go up the edge, locations all the way up to
the core, the more storage that you're going to need.
[David] One needs to be concerned from an application standpoint on a balancing of compute and storage resources or memory resources as well. So, latency inherent delays especially as performance requirements increase with applications require localized compute and storage resources at the point of creation of the data. The heavy lift of processing will still be done at the cloud where you can balance out racks and racks of compute resources along with arrays and racks of solid-state drives. However, as we expand out to the edge having point compute and storage resources that are tailored for specific applications perhaps we should call them edge aggregation points, basically collecting all that IoT data and you want to provide some level of real-time processing and analytics and decision-making out in the field, having those aggregation points allows you to do so. The other part of this, is how to optimize the performance against that data through caching, through providing persistent memory layer or higher performance storage flash memory. That's actually worth another discussion at a separate time. But you can start getting deep on how to optimize compute and storage resources and memory resources at the edge.
[Dan] One thing that is not obvious to folks trying to build out edge solutions is that customers care very much about cost. If you take a look at the amount of compute with the growth of compute at the edge, there's in aggregate more compute at the edge than there is in a core data center or cloud and if you want to reduce transport fees or if you want to reduce infrastructure sprawl you really want to be able to act on that data near the point of generation. John talked about how you would distribute the edge processing for AI and ML out to the edge. Doing so helps you filter the data so you don't have to move all the data to a cloud or core data center, therefore reducing your infrastructure sprawl and also reducing your cost for transport. You get the added benefit of more of a real-time response. This is why you see new technologies that are coming out that promote acting on data near the point of generation. If you take a look at AWS lambda functions, for example, or even some of the data management applications that are basically tagging the data as it comes in at the edge and they're caching it at the edge. Then they're using a centralized repository or distributed ledger where somebody can query that ledger and then distribute that query out to where the data exists.
Q. With that topic in mind does AI training ever happen
at any of the edge points or is this only reserved for cloud?
A. [John] The traditional answer would be no. AI training
only happens either in the core data center or in a cloud data center and at
the edge you only have inference. However, I don't think that's completely true
now. That's generally still true, because the training requires the most
compute power, the most CPUs or GPUs, the most storage, and networking
bandwidth, but I think increasingly there is more and more AI training that can
happen or is happening out there beyond the core data centers and clouds, especially
at near edge or the far edge.
If you're training your phone to recognize your fingerprint
or your face, I’m not sure if all of that has to go back to the data center in
order for the training models to be done. Everyone knows that the actual
inference for face or fingerprint recognition once you’ve trained it, is done
completely on the phone—it doesn’t need to contact the cloud or core data
centers to do that. Many of these phones have small GPUs built into their
chipset and they can recognize your fingerprint interface even if they don't
have any connectivity going at all - no network connectivity. But I would say
traditionally, most or all the training was in the data center. But some of it
has moved out to the near edge or the far edge.
[Dan] The only thing I would add on top of that is some
customers are disconnected from public clouds. Especially some government and
even some retailers. They have their own local data centers and they do a form
of federated learning in their edges.
Q. I believe there are other applications but we're just
talking about AI here. What are some of these other applications besides
artificial intelligence that are used in this environment?
A. [Dan] The big one would be streaming analytics that's non-video to preserve safety or to preserve production or to take action. You want to take action where the data is being generated. In manufacturing we see this quite a bit where a manufacturing system is taking telemetry in from a set of sensors running analytics on it and then modifying their action based on those analytics. With respect to storage that's very small unstructured amounts of data that's highly compressible and doesn't require a lot of storage processing. But yes, real-time analytics.
[David] AI can encompass video stream and camera captures
for security purposes and that’s an additional application for smart cities and
any deployment or mass deployment of video for security. Another one that is
interesting is genomic sequencing where instead of sending the batch data up to
the cloud for processing it can actually be done localized to the doctor's
office. That's an emerging trend, where we're seeing a tremendous amount of
interest especially for analyzing in near real time patient data and keeping
that data private and localized where decision making can be made to benefit
that patient versus sending that data across the network.
[John] I would say there are a lot of apps. The most famous
apps that we've talked about are AI or use AI, but there are a lot of other
apps that run at the edge. A lot of them are for accessing information, sharing
information, or otherwise utilizing information. For people it could be as
simple as navigation which may include some AI elements, but navigation by
itself does not necessarily use AI for the basic navigation, nor do the shopping
apps for entering information ordering food. These are all things that can run
partly at the edge. Other examples would include doing diagnostics, such as
automotive diagnostics, factory diagnostics, or robot diagnostics, or social
media apps. Those are things with edge apps that may or may not use AI at the
edge, though they often still use AI at the core or in the cloud.
Q. We're seeing a lot of movement towards repatriation of
apps from cloud to edge data centers or even far edge compute locations. Do you
think part of that repatriation is being driven by not getting the storage
locations right and are we perhaps aiming at the wrong problem by repatriating
the app to an edge?
A. [Dan] The fundamental reason people are repatriating their edge applications is to get that processing near the point of data generation to improve overall response. Now the second half of that, is the way that applications were developed 10 years ago is completely different than applications developed today. You have a cloud native microservices container-based application environment with rich APIs and some sort of DevOps pipeline for delivery and continuous integration. The rise of containers and virtualization in cloud native application architectures are making it easier to repatriate as well, right? So, I think it's both those things.
Q. Do you ever see CapEx versus OpEx as part of that
decision making as well?
A. [Dan] Absolutely 110% and that's the point that I was trying to make earlier. There are two things I kept saying throughout this webcast. The first is don't overlook generating real-time responses. Work for your outcomes is obviously important, but customers are running businesses. The second is they're looking for ways to reduce costs. So, if you're pushing your processing out to your edge you're doing a number of things. You're actually reducing CapEx because you're now filtering the data or doing all the local processing and making decisions locally and taking actions locally and reducing the infrastructure for shipping all that data up to some centralized location. That reduces your capital expenditures because it's reducing your infrastructure sprawl.
In terms of OpEx and making it simple, that's a more
complicated response. But reducing operational expenses comes in many forms. At
the edge is one of them. It could be through how do you manage your
applications through the edge. Leverage of containers and Kubernetes for
example, we see the hyperscalers like Amazon, Azure and Google, AKS EKS and GKE
with these managed Kubernetes services in multi-cluster management they can
repatriate those container applications and move them using the same common tool
sets to the edge. So, things like that help reduce OpEx.
[John] I think a lot of the movement to the cloud and then
the subsequent repatriation has to do with balancing of OpEx and CapEx. A lot
of people started out looking at CapEx, and move to the cloud where there's no
CapEx, it's all OpEx, so initially it is much cheaper for your first year or
two to move things to the cloud because you're saying “I don't have to buy
these servers, I just pay monthly for what I’m using.” But then after you either
build up enough data or you have enough traffic or enough compute power or
enough data ingress and egress, then the monthly charges add up and grow as you
grow the amount of data or compute. And then suddenly people look and say “wow,
if I could repatriate this data and build a relatively efficient infrastructure
on premises, the total cost of CapEx and OpEx combined would be lower than
keeping it in the public cloud.” There are also some security or privacy
concerns because you can say well in the cloud perhaps, I can't control which
country it's in or which data center. I can't prove who has access or who
doesn't have access so that's another reason to possibly repatriate.
[Dan] This is something that's often overlooked. I told the
story earlier where we were talking to a customer who has self-driving
vehicles. They took a cloud-first strategy where they were doing all of their
inferencing. So, they're collecting all the sensor information from the
vehicle, including the video and shipping that to the cloud doing the
infrastructure in the cloud, then they’re processing and inferencing from the
cloud back down to the edge. We said to them “that's going to cost you a lot of
money” and they said “yes and it costs a lot of bandwidth for us to send all
this video upstream too.” So, what they
are having to do is transcode their video to a lower frame rate so that they
could save on transport fees. We convinced them they should probably take a
hybrid approach. You still do training and everything up in the cloud, but move
your inferencing on board with something you can use to filter the data. That
way you don't have to re-transcode the data so that you can maintain the
fidelity of your video images because you're only going to be sending about 30 percent
of that upstream and if you're going to send it upstream since you're over a
cellular network why don't you send that to some co-location provider or local
data centers where you have more points of presence. We want to look at helping
design solutions for their age, but really take a look at how they improve
their business outcomes, while also reducing CapEx and OpEx.
Q. Is the edge inherently less secure than the data
center?
A. [Dan] If you take a look at the compute outside the data that are not protected by the whole data center then inherently it is less secure. I read an article where it said 30% of all security breaches are at the far edge or through some external port. So, this is infrastructure that can be moved to a room, it could be spoofed, it could be tampered with. When you're deploying edge compute outside the walls of an IT data center, out there in the wild, you really need to pay attention to zero trust architectures. Around how do I provide my hardware attestation as well as software attestation. Can I provide cryptographic network isolation, protected transports and everything else? It is definitely a top concern.
Remember I said this was a series? I encourage you to
register for next two live Storage Life of the Edge webcasts:
The SNIA Networking
Storage Forum (NSF) kicked off our “Storage Life on the Edge” webcast
series with a session on managing data from the edge to the cloud and back. We
were fortunate to have a panel of experts, Dan Cummins, John Kim and David
McIntyre to explain key considerations when managing and processing data
generated at the edge. If you missed this introductory session, it’s available
on-demand, along with the presentation slides at the SNIA
Educational Library.
Our presenters spent a good percentage of time answering
questions from our live audience. Here are answers to them all.
Q. Could an application be deployed simultaneously at
near-edge, far edge and functional edge?
A. [Dan] Many of the applications are the outcomes that
customers need depending on the vertical market; whether that’s manufacturing,
retail or and gas. They require processing that’s distributed at each of these
locations so absolutely if we go back to that computer vision
use case you can see that part of the application or the
outcome includes an IoT stack that is deployed at the far right. That’s
ingesting the data from the cameras (and/or sensors)
as well as an AI/ML streaming framework that in runtime is deployed to process
the model at the near edge. Also, applications do the retraining up in the
cloud. So, yes absolutely many of the outcomes actually are distributed
applications that are deployed simultaneously across the edge, the core, and
the cloud.
Q. Can the cloud ever be considered to be at the edge?
A. [Dan] “The edge” has a different meaning to many people.
A cloud is an edge. One of the trends that we’re seeing is what’s called the
“functional edge” or some people like to refer to that as the “IoT edge,” and
then you have the “far edge” and then something we defined as a “mirror edge,”
(a coordinated center in a cloud), but that cloud is an edge. Now, what we’re
seeing is that the onset of private wireless or 5G for example has the ability
to be able to connect that far edge or that IoT edge directly to a cloud or
directly to a data center, but each of those eliminates the edge computing
locations in between. You can think of it like an always-on world with the
speed and the bandwidth that you would get with 5G, as it becomes ubiquitous
and allows you to connect processing in a cloud or core data center directly to
the IoT edge which is pretty compelling, but yes, cloud is an edge.
Q. If I’m using a tablet inside a connected car where is
the edge? Is it the tablet, the car or a nearby cellular base station?
A. [John] All of those could be different parts of the edge.
The tablet could be the functional edge, and the car may also be the functional
edge because they both have a sort of constricted or restricted domain in terms
of the hardware which is fairly fixed. You can update the software and use it
as your fairly dedicated functional processes, which don’t really have any other
applications going on. And then the cellular base station could probably be
considered the far edge and you could also have a telco office acting as a
traffic processing center or small data center that could be the near-edge. Or
this information from the functional and far edge could then flow into the
cloud and the cloud could be that near-edge data center before some of the data
ultimately goes back to a core data center that’s run by the enterprise or
hosted in a colocation facility.
[Dan] There is no one edge. What we’re really talking about
is edge compute or where does processing of data take place and it takes place
everywhere and the applications are the outcomes that you need for that
processing. If you’re in manufacturing, you need overall equipment
effectiveness. If you’re in retail you need fraud detection. Where to produce
that outcome, you’re going to need to be able to operate in multiple locations
and so I really would define edge as the point where data is being acted on by
edge compute.
Q. How do edge storage needs differ from cloud or data
center storage needs?
A. [Dan] We touched a little bit on this. I was trying to
convey that depending on the location, much of the data that’s generated from
the IoT edge is mostly streaming data and they have to get it to a point where
they can process the data. The streaming architectures are prevalent because as
you’re transporting data between locations, processed data could have an intermittent
connectivity or a low bandwidth situation so typically you see either a
streaming data architecture or some sort of architecture that can handle that
disconnect and then re-establish and continue. Most of the data that’s coming
in at the far edge is usually because of the streaming. It’s a store forward
and most of it is unstructured. When you start to get into upstream, where you
need to do more with the data like that near edge, I had explained that you
have kind of a mix of both these edge workloads which is working on this
unstructured data, as well as a mix of structured data for business
applications. So, you have a mix of edge and IT workloads. All of those are in
that data processing pipeline that is needed to provide a near real-time response.
It’s not really out of band data, it’s in-band data and then usually with a
core data center or cloud is where you’re usually going to be operating on data
that is out of band that requires deep storage. So, the data center will
continue to have deep storage as well as the cloud where you need to expand and
you need longer term storage for in-home or for training models and things like
that. Typically, the further you go up the edge, locations all the way up to
the core, the more storage that you’re going to need.
[David] One needs to be concerned from an application standpoint on a balancing of compute and storage resources or memory resources as well. So, latency inherent delays especially as performance requirements increase with applications require localized compute and storage resources at the point of creation of the data. The heavy lift of processing will still be done at the cloud where you can balance out racks and racks of compute resources along with arrays and racks of solid-state drives. However, as we expand out to the edge having point compute and storage resources that are tailored for specific applications perhaps we should call them edge aggregation points, basically collecting all that IoT data and you want to provide some level of real-time processing and analytics and decision-making out in the field, having those aggregation points allows you to do so. The other part of this, is how to optimize the performance against that data through caching, through providing persistent memory layer or higher performance storage flash memory. That’s actually worth another discussion at a separate time. But you can start getting deep on how to optimize compute and storage resources and memory resources at the edge.
[Dan] One thing that is not obvious to folks trying to build out edge solutions is that customers care very much about cost. If you take a look at the amount of compute with the growth of compute at the edge, there’s in aggregate more compute at the edge than there is in a core data center or cloud and if you want to reduce transport fees or if you want to reduce infrastructure sprawl you really want to be able to act on that data near the point of generation. John talked about how you would distribute the edge processing for AI and ML out to the edge. Doing so helps you filter the data so you don’t have to move all the data to a cloud or core data center, therefore reducing your infrastructure sprawl and also reducing your cost for transport. You get the added benefit of more of a real-time response. This is why you see new technologies that are coming out that promote acting on data near the point of generation. If you take a look at AWS lambda functions, for example, or even some of the data management applications that are basically tagging the data as it comes in at the edge and they’re caching it at the edge. Then they’re using a centralized repository or distributed ledger where somebody can query that ledger and then distribute that query out to where the data exists.
Q. With that topic in mind does AI training ever happen
at any of the edge points or is this only reserved for cloud?
A. [John] The traditional answer would be no. AI training
only happens either in the core data center or in a cloud data center and at
the edge you only have inference. However, I don’t think that’s completely true
now. That’s generally still true, because the training requires the most
compute power, the most CPUs or GPUs, the most storage, and networking
bandwidth, but I think increasingly there is more and more AI training that can
happen or is happening out there beyond the core data centers and clouds, especially
at near edge or the far edge.
If you’re training your phone to recognize your fingerprint
or your face, I’m not sure if all of that has to go back to the data center in
order for the training models to be done. Everyone knows that the actual
inference for face or fingerprint recognition once you’ve trained it, is done
completely on the phone—it doesn’t need to contact the cloud or core data
centers to do that. Many of these phones have small GPUs built into their
chipset and they can recognize your fingerprint interface even if they don’t
have any connectivity going at all – no network connectivity. But I would say
traditionally, most or all the training was in the data center. But some of it
has moved out to the near edge or the far edge.
[Dan] The only thing I would add on top of that is some
customers are disconnected from public clouds. Especially some government and
even some retailers. They have their own local data centers and they do a form
of federated learning in their edges.
Q. I believe there are other applications but we’re just
talking about AI here. What are some of these other applications besides
artificial intelligence that are used in this environment?
A. [Dan] The big one would be streaming analytics that’s non-video to preserve safety or to preserve production or to take action. You want to take action where the data is being generated. In manufacturing we see this quite a bit where a manufacturing system is taking telemetry in from a set of sensors running analytics on it and then modifying their action based on those analytics. With respect to storage that’s very small unstructured amounts of data that’s highly compressible and doesn’t require a lot of storage processing. But yes, real-time analytics.
[David] AI can encompass video stream and camera captures
for security purposes and that’s an additional application for smart cities and
any deployment or mass deployment of video for security. Another one that is
interesting is genomic sequencing where instead of sending the batch data up to
the cloud for processing it can actually be done localized to the doctor’s
office. That’s an emerging trend, where we’re seeing a tremendous amount of
interest especially for analyzing in near real time patient data and keeping
that data private and localized where decision making can be made to benefit
that patient versus sending that data across the network.
[John] I would say there are a lot of apps. The most famous
apps that we’ve talked about are AI or use AI, but there are a lot of other
apps that run at the edge. A lot of them are for accessing information, sharing
information, or otherwise utilizing information. For people it could be as
simple as navigation which may include some AI elements, but navigation by
itself does not necessarily use AI for the basic navigation, nor do the shopping
apps for entering information ordering food. These are all things that can run
partly at the edge. Other examples would include doing diagnostics, such as
automotive diagnostics, factory diagnostics, or robot diagnostics, or social
media apps. Those are things with edge apps that may or may not use AI at the
edge, though they often still use AI at the core or in the cloud.
Q. We’re seeing a lot of movement towards repatriation of
apps from cloud to edge data centers or even far edge compute locations. Do you
think part of that repatriation is being driven by not getting the storage
locations right and are we perhaps aiming at the wrong problem by repatriating
the app to an edge?
A. [Dan] The fundamental reason people are repatriating their edge applications is to get that processing near the point of data generation to improve overall response. Now the second half of that, is the way that applications were developed 10 years ago is completely different than applications developed today. You have a cloud native microservices container-based application environment with rich APIs and some sort of DevOps pipeline for delivery and continuous integration. The rise of containers and virtualization in cloud native application architectures are making it easier to repatriate as well, right? So, I think it’s both those things.
Q. Do you ever see CapEx versus OpEx as part of that
decision making as well?
A. [Dan] Absolutely 110% and that’s the point that I was trying to make earlier. There are two things I kept saying throughout this webcast. The first is don’t overlook generating real-time responses. Work for your outcomes is obviously important, but customers are running businesses. The second is they’re looking for ways to reduce costs. So, if you’re pushing your processing out to your edge you’re doing a number of things. You’re actually reducing CapEx because you’re now filtering the data or doing all the local processing and making decisions locally and taking actions locally and reducing the infrastructure for shipping all that data up to some centralized location. That reduces your capital expenditures because it’s reducing your infrastructure sprawl.
In terms of OpEx and making it simple, that’s a more
complicated response. But reducing operational expenses comes in many forms. At
the edge is one of them. It could be through how do you manage your
applications through the edge. Leverage of containers and Kubernetes for
example, we see the hyperscalers like Amazon, Azure and Google, AKS EKS and GKE
with these managed Kubernetes services in multi-cluster management they can
repatriate those container applications and move them using the same common tool
sets to the edge. So, things like that help reduce OpEx.
[John] I think a lot of the movement to the cloud and then
the subsequent repatriation has to do with balancing of OpEx and CapEx. A lot
of people started out looking at CapEx, and move to the cloud where there’s no
CapEx, it’s all OpEx, so initially it is much cheaper for your first year or
two to move things to the cloud because you’re saying “I don’t have to buy
these servers, I just pay monthly for what I’m using.” But then after you either
build up enough data or you have enough traffic or enough compute power or
enough data ingress and egress, then the monthly charges add up and grow as you
grow the amount of data or compute. And then suddenly people look and say “wow,
if I could repatriate this data and build a relatively efficient infrastructure
on premises, the total cost of CapEx and OpEx combined would be lower than
keeping it in the public cloud.” There are also some security or privacy
concerns because you can say well in the cloud perhaps, I can’t control which
country it’s in or which data center. I can’t prove who has access or who
doesn’t have access so that’s another reason to possibly repatriate.
[Dan] This is something that’s often overlooked. I told the
story earlier where we were talking to a customer who has self-driving
vehicles. They took a cloud-first strategy where they were doing all of their
inferencing. So, they’re collecting all the sensor information from the
vehicle, including the video and shipping that to the cloud doing the
infrastructure in the cloud, then they’re processing and inferencing from the
cloud back down to the edge. We said to them “that’s going to cost you a lot of
money” and they said “yes and it costs a lot of bandwidth for us to send all
this video upstream too.” So, what they
are having to do is transcode their video to a lower frame rate so that they
could save on transport fees. We convinced them they should probably take a
hybrid approach. You still do training and everything up in the cloud, but move
your inferencing on board with something you can use to filter the data. That
way you don’t have to re-transcode the data so that you can maintain the
fidelity of your video images because you’re only going to be sending about 30 percent
of that upstream and if you’re going to send it upstream since you’re over a
cellular network why don’t you send that to some co-location provider or local
data centers where you have more points of presence. We want to look at helping
design solutions for their age, but really take a look at how they improve
their business outcomes, while also reducing CapEx and OpEx.
Q. Is the edge inherently less secure than the data
center?
A. [Dan] If you take a look at the compute outside the data that are not protected by the whole data center then inherently it is less secure. I read an article where it said 30% of all security breaches are at the far edge or through some external port. So, this is infrastructure that can be moved to a room, it could be spoofed, it could be tampered with. When you’re deploying edge compute outside the walls of an IT data center, out there in the wild, you really need to pay attention to zero trust architectures. Around how do I provide my hardware attestation as well as software attestation. Can I provide cryptographic network isolation, protected transports and everything else? It is definitely a top concern.
Remember I said this was a series? I encourage you to
register for next two live Storage Life of the Edge webcasts:
Ever wonder what’s the difference between data privacy, data governance and data security? All of these terms are frequently (and mistakenly) used interchangeably. They are indeed related, particularly when it comes to keeping data in the cloud protected, private and secure, but the definitions and mechanics of executing on each are all quite different.
Join us on March 30, 2022 for another SNIA Cloud Storage Technologies Initiative (CSTI) “15 Minutes in the Cloud” session for an overview of what each of these terms means, how and where they intersect, and why each one demands adequate attention or you risk threatening the overall security of your data.
Presenting will be Thomas Rivera, CISSP, CIPP/US, CDPSE and Strategic Success Manager at VMware Black Carbon together with Eric Hibbard, CISSP-ISSAP, ISSMP, ISSEP, CIPP/US, CIPT, CISA, CDPSE, CCSK and Director, Product Planning – Storage Networking & Security, Samsung Semiconductor. As you see, our security experts have more credentials than the alphabet!
Register today for “15 Minutes in the Cloud: Data Privacy vs. Governance vs. Security.” We look forward to seeing you on March 30th.
Ever wonder what’s the difference between data privacy, data governance and data security? All of these terms are frequently (and mistakenly) used interchangeably. They are indeed related, particularly when it comes to keeping data in the cloud protected, private and secure, but the definitions and mechanics of executing on each are all quite different.
Join us on March 30, 2022 for another SNIA Cloud Storage Technologies Initiative (CSTI) “15 Minutes in the Cloud” session for an overview of what each of these terms means, how and where they intersect, and why each one demands adequate attention or you risk threatening the overall security of your data.
Presenting will be Thomas Rivera, CISSP, CIPP/US, CDPSE and Strategic Success Manager at VMware Black Carbon together with Eric Hibbard, CISSP-ISSAP, ISSMP, ISSEP, CIPP/US, CIPT, CISA, CDPSE, CCSK and Director, Product Planning – Storage Networking & Security, Samsung Semiconductor. As you see, our security experts have more credentials than the alphabet!
Ever wonder what’s the difference between data privacy, data governance and data security? All of these terms are frequently (and mistakenly) used interchangeably. They are indeed related, particularly when it comes to keeping data in the cloud protected, private and secure, but the definitions and mechanics of executing on each are all quite different.
Join us on March 30, 2022 for another SNIA Cloud Storage Technologies Initiative (CSTI) “15 Minutes in the Cloud” session for an overview of what each of these terms means, how and where they intersect, and why each one demands adequate attention or you risk threatening the overall security of your data.
Presenting will be Thomas Rivera, CISSP, CIPP/US, CDPSE and Strategic Success Manager at VMware Black Carbon together with Eric Hibbard, CISSP-ISSAP, ISSMP, ISSEP, CIPP/US, CIPT, CISA, CDPSE, CCSK and Director, Product Planning – Storage Networking & Security, Samsung Semiconductor. As you see, our security experts have more credentials than the alphabet!
Register today for “15 Minutes in the Cloud: Data Privacy vs. Governance vs. Security.” We look forward to seeing you on March 30th.
Earlier this month, the SNIA Cloud Storage Technologies Initiative (CSTI) presented a live webcast called “High Performance Storage at Exascale” where our HPC experts, Glyn Bowden, Torben Kling Petersen and Michael Hennecke talked about processing and storing data in shockingly huge numbers. The session raises some interesting points on how scale is quickly being redefined and what was cost compute prohibitive a few years ago for most, may be in reach for all sooner than expected.
Is HPC a rich man’s game? The scale appears to have increased dramatically over the last few years. Is the cost increasing to the point where this has only for wealthy organizations or has the cost decreased to the point where small to medium-sized enterprises might be able to indulge in HPC activities?
[Torben] I would say the answer is both. To build these really super big systems you
need you need hundreds of millions of dollars because the sheer cost of infrastructure goes beyond anything that we've seen in the past, but on the other hand you also see HPC systems in the most unlikely places, like a web retailer that mainly sells shoes. They had a Lustre system driving their back end and HPC out-competed a standard NFS solution. So, we see this going
in different directions. Price has definitely gone down significantly; essentially the cost of a large storage system now is the same as it was 10 years ago. It's just that now it's 100 times faster and 50 times larger. That said, it's not it's not cheap to do any of these things because of the amount of hardware you need.
[Michael] We are seeing the same thing. We like to say that these types of HPC systems are more like a time machine that show you what will show up in the general enterprise world a few years after. The cloud space is a prime example. All of the large HPC parallel file systems are now being adopted in the cloud so we get a combination of the deployment mechanisms coming from the cloud world with the scale and robustness of the storage software infrastructure. Those are married together in very efficient ways. So, while not everybody will
build a 200 petabyte or flash system for those types of use cases the same technologies and the same software layers can be used at all scales. I really believe that this is a like the research lab for what will become mainstream pretty quickly. On the cost side, another aspect that we haven't covered is this old notion that tape is dead, disk is dead, and always the next technology is replacing the old ones. That hasn't happened, but certainly as new technologies
arrive and cost structures change you get shifts in dollars per terabyte or dollars per terabyte per second which is more the HPC metric. So, how do we get in QLC drives to lower the price of flash and then build larger systems out of that? That’s also technology explorations done at this level and then benefit everybody.
[Glyn] Being the consultant of the group, I guess I should say it depends. It depends on how you want to define HPC. So, I've got a device on my desk in front of me at the moment that I can fit in the palm of my hand it has more than a thousand graphics GPU cores in it and so
that costs under $100. I can build a cluster of 10 of those for under $1,000. If you look back five years, that would absolutely be classified as HPC based on the amount of cores and amount of processing it can do. So, these things are shrinking and becoming far more affordable and far more commodity at the low end meaning that we can put what was traditionally sort of a
an HPC cluster and run it on things like Raspberry Pi's at the edge somewhere. You can absolutely get the architecture and what was previously seen as that kind of parallel batch processing against many cores for next to nothing. As Michael said it's really the time machine and this is where we’re catching up with what was an HPC. The big stuff is always going to cost the big bucks, but I think it's affordable to get something that you can play on and work as an HPC system.
We also had several questions on persistent memory. SNIA covers this topic extensively. You can access a wealth of information here. I also encourage you to register for SNIA’s 2022 Persistent Memory + Computational Storage Summit which will be held virtually May 25-26. There was also interest in CXL (Compute Express Link, a high speed cache-coherent interconnect for processors, memory and accelerators). You can find more information on that in the SNIA Educational Library.
Earlier this month, the SNIA Cloud Storage Technologies Initiative (CSTI) presented a live webcast called “High Performance Storage at Exascale” where our HPC experts, Glyn Bowden, Torben Kling Petersen and Michael Hennecke talked about processing and storing data in shockingly huge numbers. The session raises some interesting points on how scale is quickly being redefined and what was cost compute prohibitive a few years ago for most, may be in reach for all sooner than expected.
Is HPC a rich man’s game? The scale appears to have increased dramatically over the last few years. Is the cost increasing to the point where this has only for wealthy organizations or has the cost decreased to the point where small to medium-sized enterprises might be able to indulge in HPC activities?
[Torben] I would say the answer is both. To build these really super big systems you
need you need hundreds of millions of dollars because the sheer cost of infrastructure goes beyond anything that we’ve seen in the past, but on the other hand you also see HPC systems in the most unlikely places, like a web retailer that mainly sells shoes. They had a Lustre system driving their back end and HPC out-competed a standard NFS solution. So, we see this going
in different directions. Price has definitely gone down significantly; essentially the cost of a large storage system now is the same as it was 10 years ago. It’s just that now it’s 100 times faster and 50 times larger. That said, it’s not it’s not cheap to do any of these things because of the amount of hardware you need.
[Michael] We are seeing the same thing. We like to say that these types of HPC systems are more like a time machine that show you what will show up in the general enterprise world a few years after. The cloud space is a prime example. All of the large HPC parallel file systems are now being adopted in the cloud so we get a combination of the deployment mechanisms coming from the cloud world with the scale and robustness of the storage software infrastructure. Those are married together in very efficient ways. So, while not everybody will
build a 200 petabyte or flash system for those types of use cases the same technologies and the same software layers can be used at all scales. I really believe that this is a like the research lab for what will become mainstream pretty quickly. On the cost side, another aspect that we haven’t covered is this old notion that tape is dead, disk is dead, and always the next technology is replacing the old ones. That hasn’t happened, but certainly as new technologies
arrive and cost structures change you get shifts in dollars per terabyte or dollars per terabyte per second which is more the HPC metric. So, how do we get in QLC drives to lower the price of flash and then build larger systems out of that? That’s also technology explorations done at this level and then benefit everybody.
[Glyn] Being the consultant of the group, I guess I should say it depends. It depends on how you want to define HPC. So, I’ve got a device on my desk in front of me at the moment that I can fit in the palm of my hand it has more than a thousand graphics GPU cores in it and so
that costs under $100. I can build a cluster of 10 of those for under $1,000. If you look back five years, that would absolutely be classified as HPC based on the amount of cores and amount of processing it can do. So, these things are shrinking and becoming far more affordable and far more commodity at the low end meaning that we can put what was traditionally sort of a
an HPC cluster and run it on things like Raspberry Pi’s at the edge somewhere. You can absolutely get the architecture and what was previously seen as that kind of parallel batch processing against many cores for next to nothing. As Michael said it’s really the time machine and this is where we’re catching up with what was an HPC. The big stuff is always going to cost the big bucks, but I think it’s affordable to get something that you can play on and work as an HPC system.
We also had several questions on persistent memory. SNIA covers this topic extensively. You can access a wealth of information here. I also encourage you to register for SNIA’s 2022 Persistent Memory + Computational Storage Summit which will be held virtually May 25-26. There was also interest in CXL (Compute Express Link, a high speed cache-coherent interconnect for processors, memory and accelerators). You can find more information on that in the SNIA Educational Library.
Earlier this month, the SNIA Cloud Storage Technologies Initiative (CSTI) presented a live webcast called “High Performance Storage at Exascale” where our HPC experts, Glyn Bowden, Torben Kling Petersen and Michael Hennecke talked about processing and storing data in shockingly huge numbers. The session raises some interesting points on how scale is quickly being redefined and what was cost compute prohibitive a few years ago for most, may be in reach for all sooner than expected.
Q. Is HPC a rich man’s game? The scale appears to have increased dramatically over the last few years. Is the cost increasing to the point where this is only for wealthy organizations or has the cost decreased to the point where small to medium-sized enterprises might be able to indulge in HPC activities?
A. [Torben] I would say the answer is both. To build these really super big systems you need hundreds of millions of dollars because the sheer cost of infrastructure goes beyond anything that we’ve seen in the past, but on the other hand you also see HPC systems in the most unlikely places, like a web retailer that mainly sells shoes. They had a Lustre system driving their back end and HPC out-competed a standard NFS solution. So, we see this going in different directions. Price has definitely gone down significantly; essentially the cost of a large storage system now is the same as it was 10 years ago. It’s just that now it’s 100 times faster and 50 times larger. That said, it’s not cheap to do any of these things because of the amount of hardware you need.
A. [Michael] We are seeing the same thing. We like to say that these types of HPC systems are more like a time machine that show you what will show up in the general enterprise world a few years after. The cloud space is a prime example. All of the large HPC parallel file systems are now being adopted in the cloud so we get a combination of the deployment mechanisms coming from the cloud world with the scale and robustness of the storage software infrastructure. Those are married together in very efficient ways. So, while not everybody will build a 200 petabyte or flash system for those types of use cases the same technologies and the same software layers can be used at all scales. I really believe that this is like the research lab for what will become mainstream pretty quickly. On the cost side, another aspect that we haven’t covered is this old notion that tape is dead, disk is dead, and always the next technology is replacing the old ones. That hasn’t happened, but certainly as new technologies arrive and cost structures change you get shifts in dollars per terabyte or dollars per terabyte per second which is more the HPC metric. So, how do we get in QLC drives to lower the price of flash and then build larger systems out of that? That’s also technology explorations done at this level and then benefit everybody.
A. [Glyn] Being the consultant of the group, I guess I should say it depends. It depends on how you want to define HPC. So, I’ve got a device on my desk in front of me at the moment that I can fit in the palm of my hand it has more than a thousand graphics GPU cores in it and so that costs under $100. I can build a cluster of 10 of those for under $1,000. If you look back five years, that would absolutely be classified as HPC based on the amount of cores and amount of processing it can do. So, these things are shrinking and becoming far more affordable and far more commodity at the low end meaning that we can put what was traditionally sort of an HPC cluster and run it on things like Raspberry Pi’s at the edge somewhere. You can absolutely get the architecture and what was previously seen as that kind of parallel batch processing against many cores for next to nothing. As Michael said it’s really the time machine and this is where we’re catching up with what was an HPC. The big stuff is always going to cost the big bucks, but I think it’s affordable to get something that you can play on and work as an HPC system.
We also had several questions on persistent memory. SNIA covers this topic extensively. You can access a wealth of information here. I also encourage you to register for SNIA’s 2022 Persistent Memory + Computational Storage Summit which will be held virtually May 25-26. There was also interest in CXL (Compute Express Link, a high speed cache-coherent interconnect for processors, memory and accelerators). You can find more information on that in the SNIA Educational Library.
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