Mounting complexity and a lack of multi-cloud services management maturity are forcing companies to seek new breeds of solutions so they can grow and thrive as digital enterprises.
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Here to report on how international companies must factor localization, data sovereignty and other regional cloud requirements into any transition to sustainable hybrid IT is Peter Burris, Head of Research at Wikibon. The discussion is moderated by Dana Gardner, principal analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: Peter, companies doing business or software development just in North
America can have an American-centric
view of things. They may lack an appreciation for the global
aspects of cloud computing models. We want to explore that today. How much more
complex is doing cloud -- especially hybrid cloud -- when you’re straddling
global regions?
Burris: There are advantages and disadvantages to thinking cloud-first
when you are thinking globalization first. The biggest advantage is that you
are able to work in locations that don’t currently have the broad-based
infrastructure that’s typically associated with a lot of traditional computing
modes and models.
 |
| Burris |
So, cloud and globalization can go together -- but it
dramatically increases the need for smart and forward-thinking approaches to
imagining, and then ultimately realizing, how those two go together, and what
hybrid architecture is going to be required to make it work.
Gardner: If you need to then focus more on the data issues --
such as compliance, regulation, and data sovereignty -- how is that different
from taking an applications-centric view of things?
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Burris: Most companies have historically taken an
infrastructure-centric approach to things. They start by saying, “Where do I have
infrastructure, where do I have servers and storage, do I have the capacity for
this group of resources, and can I bring the applications up here?” And if the
answer is yes, then you try to ultimately economize on those assets and build
the application there.
That runs into problems when we start thinking about privacy,
and in ensuring that local markets and local approaches to intellectual
property management can be accommodated.
But the issue is more than just things like the
General Data Protection Regulation (GDPR) in Europe, which is a series of regulations in the European Union (EU) that are intended to protect consumers from
what the EU would regard as inappropriate leveraging and derivative use of
their data.
It
can be extremely expensive and sometimes impossible to even conceive of
a global cloud strategy where the service is being consumed a few
thousand miles away from where the data resides, if there is any
dependency on time and how that works.
Ultimately,
the globe is a big place. It’s 12,000 miles or so from point A to the farthest
point B, and physics still matters. So, the first thing we have to worry about
when we think about globalization is the cost of latency and the cost of
bandwidth of moving data -- either small or very large -- across different
regions. It can be extremely expensive and sometimes impossible to even
conceive of a global cloud strategy where the service is being consumed a few
thousand miles away from where the data resides, if there is any dependency on
time and how that works.
So, the issues of privacy, the issues of local
control of data are also very important, but the first and most important
consideration for every business needs to be: Can I actually run the
application where I want to, given the realities of latency? And number two: Can
I run the application where I want to given the realities of bandwidth? This issue can
completely overwhelm all other costs for data-rich, data-intensive applications
over distance.
Gardner: As you are factoring your architecture, you need to
take these local considerations into account, particularly when you are
factoring costs. If you have to do some heavy lifting and make your bandwidth
capable, it might be better to have a local closet-sized data center, because
they are small and efficient these days, and you can stick with a private cloud
or on-premises approach. At the least, you should factor the economic basis for
comparison, with all these other variables you brought up.
Edge centers
Burris: That’s correct. In fact, we call them “edge centers.”
For example, if the application features any familiarity with Internet of Things (IoT), then there will likely be some degree of latency considerations
obtained, and the cost of doing a round trip message over a few thousand miles
can be pretty significant when we consider the total cost of how fast computing
can be done these days.
The first consideration is what are the impacts of latency for an application workload like IoT and is that intending to drive more automation into the system? Imagine, if you will, the businessperson who says, “I would like to enter into a new market expand my presence in the market in a cost-effective way. And to do that, I want to have the system be more fully automated as it serves that particular market or that particular group of customers. And perhaps it’s something that looks more process manufacturing-oriented or something along those lines that has IoT capabilities.”
The
goal is to bring in the technology in a way that does not explode the
administration, management, and labor cost associated with the
implementation.
The goal, therefore, is to bring in the technology in
a way that does not explode the administration, managements, and labor cost
associated with the implementation.
The other way you are going to do that is if you do introduce
a fair amount of automation and if, in fact, that automation is capable of
operating within the time constraints required by those automated moments, as
we call them.
If the round-trip cost of moving the data from a remote
global location back to somewhere in North America -- independent of whether it’s
legal or not – comes at a cost that exceeds the automation moment, then you
just flat out can’t do it. Now, that is the most obvious and stringent
consideration.
On top of that, these moments of automation necessitate
significant amounts of data being generated and captured. We have done model studies
where, for example, the cost of moving data out of a small wind farm can be 10
times as expensive. It can cost
hundreds of thousands of dollars a year to do relatively simple and
straightforward types of data analysis on the performance of that wind farm.
Process locally, act globally
It’s a lot better to have a local presence that can
handle local processing requirements against models that are operating against
locally derived data or locally generated data, and let that work be automated with only
periodic visibility into how the overall system is working closely. And that’s
where a lot of this kind of on-premise hybrid cloud
thinking is starting.
It gets more complex than in a relatively simple environment like
a wind farm, but nonetheless, the amount of processing power that’s necessary
to run some of those kinds of models can get pretty significant. We are going
to see a lot more of this kind of analytic work be pushed directly down to the
devices themselves. So, the Sense, Infer, and Act loop will occur very, very
closely in some of those devices. We will try to keep as much of that data as we
can local.
But there are always going to be circumstances when
we have to generate visibility across devices, we have to do local training of
the data, we have to test the data or the models that we are developing
locally, and all those things start to argue for sometimes much larger classes
of systems.
Gardner: It’s
a fascinating subject as to what to push down the edge given that the storage cost
and processing costs are down and footprint is down and what to then use the
public cloud environment or Infrastructure-as-a-Service (IaaS) environment for.
But before we go into any further, Peter, tell us about
yourself, and your organization, Wikibon.
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Burris: Wikibon is a research firm that’s affiliated with something known as TheCUBE.
TheCUBE conducts about 5,000 interviews per year with thought leaders at
various locations, often on-site at large conferences.
I came to Wikibon from Forrester Research, and before
that I had been a part of META Group, which was purchased by Gartner. I have a
longstanding history in this business. I have also worked with IT organizations,
and also worked inside technology marketing in a couple of different places. So,
I have been around.
Wikibon's objective is to help mid-sized to large
enterprises traverse the challenges of digital transformation. Our opinion is
that digital transformation actually does mean something. It's not just a set
of bromides about multichannel or omnichannel or being “uberized,” or anything
along those lines.
The difference between a business and a digital business is the degree to which data is used as an asset.
The difference between a business and a digital business
is the degree to which data is used as an asset. In a digital business, data
absolutely is used as a differentiating asset for creating and keeping
customers.
We look at the challenges of what does it mean to use
data differently, how to capture it differently, which is a lot of what IoT is about.
We look at how to turn it into business value, which is a lot of what big data
and these advanced analytics like artificial intelligence (AI), machine
learning and deep learning are all about. And then finally, how to create the
next generation of applications that actually act on behalf of the brand with a
fair degree of autonomy, which is what we call “systems of agency” are all
about. And then ultimately how cloud and historical infrastructure are going to
come together and be optimized to support all those requirements.
We are looking at digital business transformation as
a relatively holistic thing that includes IT leadership, business leadership,
and, crucially, new classes of partnerships to ensure that the services that are
required are appropriately contracted for and can be sustained as it becomes an
increasing feature of any company’s value proposition. That's what we
do.
Global risk and reward
Gardner: We
have talked about the tension between public and private cloud in a global
environment through speeds and feeds, and technology. I would like to elevate
it to the issues of culture, politics and perception. Because in recent years,
with offshoring and looking at intellectual property concerns in other
countries, the fact is that all the major hyperscale cloud providers are
US-based corporations. There is a wide ecosystem of other second tier providers,
but certainly in the top tier.
Is that something that should concern people when it
comes to risk to companies that are based outside of the US? What’s the level
of risk when it comes to putting all your eggs in the basket of a company
that's US-based?
Burris: There are two perspectives on that, but let me add
one more just check on this. Alibaba
clearly is one of the top-tier, and they are not based in the US and that may be
one of the advantages that they have. So, I think we are starting
to see some new hyperscalers emerge, and we will see whether or not one will
emerge in Europe.
I had gotten into a significant argument with a group
of people not too long ago on this, and I tend to think that the political
environment almost guarantees that we will get some kind of scale in Europe for
a major cloud provider.
If you are a US company, are you concerned about how
intellectual property is treated elsewhere? Similarly, if you are a non-US
company, are you concerned that the US companies are typically operating under
US law, which increasingly is demanding that some of these hyperscale firms be relatively
liberal, shall we say, in how they share their data with the government? This
is going to be one of the key issues that influence choices of technology over
the course of the next few years.
Cross-border compute concerns
We think there are three fundamental concerns that
every firm is going to have to worry about.
I mentioned one, the physics of cloud computing. That
includes latency and bandwidth. One computer science professor told me years
ago, “Latency is the domain of God, and bandwidth is the domain of man.” We may
see bandwidth costs come down over the next few years, but let's just lump
those two things together because they are physical realities.
The second one, as we talked about, is the idea of
privacy and the legal implications.
The third one is intellectual property control and
concerns, and this is going to be an area that faces enormous change over the course
of the next few years. It’s in conjunction with legal questions on contracting
and business practices.
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From our perspective, a US firm that wants to operate
in a location that features a more relaxed regime for intellectual property absolutely
needs to be concerned. And the reason why they need to be concerned is data is
unlike any other asset that businesses work with. Virtually every asset follows
the laws of scarcity.
Money, you can put it here or you can put it there.
Time, people, you can put here or you can put there. That machine can be
dedicated to this kind of wire or that kind of wire.
Data
is weird, because data can be copied, data can be shared. The value of
data appreciates as we us it more successfully, as we integrate it and
share it across multiple applications.
Scarcity is a dominant feature of how we think about
generating returns on assets. Data is weird, though, because data can be
copied, data can be shared. Indeed, the value of data appreciates as we use it
more successfully, as we use it more completely, as we integrate it and share
it across multiple applications.
And that is where the concern is, because if I have
data in one location, two things could possibly happen. One is if it gets
copied and stolen, and there are a lot of implications to that. And two, if there are rules and
regulations in place that restrict how I can combine that data with other
sources of data. That means if, for example, my customer data in Germany may
not appreciate, or may not be able to generate the same types of returns as my
customer data in the US.
Now, that sets aside any moral question of whether or
not Germany or the US has better privacy laws and protects the consumers
better. But if you are basing investments on how you can use data in the US,
and presuming a similar type of approach in most other places, you are absolutely
right. On the one hand, you probably aren’t going to be able to
generate the total value of your data because of restrictions on its use; and
number two, you have to be very careful about concerns related to data leakage
and the appropriation of your data by unintended third parties.
Gardner: There
is the concern about the appropriation of the data by governments, including
the United States with the PATRIOT
Act. And there are ways in which governments can access hyperscalers’
infrastructure, assets, and data under certain circumstances. I suppose there’s
a whole other topic there, but at least we should recognize that there's some
added risk when it comes to governments and their access to this data.
Burris: It’s a double-edged sword that US companies may be
worried about hyperscalers elsewhere, but companies that aren't necessarily
located in the US may be concerned about using those hyperscalers because of
the relationship between those hyperscalers and the US government.
These concerns have been suppressed in
the grand regime of decision-making in a lot of businesses, but that doesn’t
mean that it’s not a low-intensity concern that could bubble up, and perhaps, it’s one of
the reasons why Alibaba is growing so fast right now.
All
hyperscalers are going to have to be able to demonstrate that they can
protect their clients, their customers’ data, utilizing the regime that
is in place wherever the business is being operated.
All
hyperscalers are going to have to be able to demonstrate that they can, in
fact, protect their clients, their customers’ data, utilizing the regime that
is in place wherever the business is being operated. [The rationale] for basing
your business in these types of services is really immature. We have made
enormous progress, but there’s a long way yet to go here, and that’s something
that businesses must factor as they make decisions about how they want to
incorporate a cloud strategy.
Gardner: It’s difficult enough given the variables and
complexity of deciding a hybrid cloud
strategy when you’re only factoring the technical issues. But,
of
course, now there are legal issues around data sovereignty, privacy, and
intellectual
property concerns. It’s complex, and it’s something that an IT
organization, on its own, cannot juggle. This is something that cuts
across all the different parts
of a global enterprise -- their legal, marketing, security, risk
avoidance and
governance units -- right up to the board of directors. It’s not just a
willy-nilly
decision to get out a credit card and start doing cloud computing on any
sustainable basis.
Burris: Well, you’re right, and too frequently it is a willy-nilly decision
where a developer or a business person says, “Oh, no sweat, I am just going
to grab some resources and start building something in the cloud.”
I can remember back in the mid-1990s
when I would go into large media companies to meet with IT people to talk about
the web, and what it would mean technically to build applications on the web. I would encounter
30 people, and five of them would be in IT and 25 of them would be in legal.
They were very concerned about what it meant to put intellectual property in a
digital format up on the web, because of how it could be misappropriated or how
it could lose value. So, that class of concern -- or that type of concern -- is
minuscule relative to the broader questions of cloud computing, of the grabbing
of your data and holding it a hostage, for example.
There are a lot of considerations that
are not within the traditional purview of IT, but CIOs need to start thinking
about them on their own and in conjunction with their peers within the business.
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Gardner: We’ve
certainly underlined a lot of the challenges. What about solutions? What can organizations do
to prevent going too far down an alley that’s dark and misunderstood, and
therefore have a difficult time adjusting?
How do we better rationalize for cloud
computing decisions? Do we need better management? Do we need better visibility
into what our organizations are doing or not doing? How do we architect with
foresight into the larger picture, the strategic situation? What do we need to
start thinking about in terms of the solutions side of some of these issues?
Cloud to business, not business to cloud
Burris: That’s a huge question, Dana. I can go on for the
next six hours, but let’s start here. The first thing we tell senior executives
is, don’t
think about bringing your business to the cloud -- think about bringing the
cloud to your business. That’s the most important thing. A lot of companies
start by saying, “Oh, I want to get rid of IT, I want to move my business to
the cloud.”
It’s like many of the mistakes that
were made in the 1990s regarding outsourcing. When I would go back and do
research on outsourcing, I discovered that a lot of the outsourcing was not
driven by business needs, but driven by executive compensation schemes,
literally. So, where executives were told that they would be paid on the basis of
return in net assets, there was a high likelihood that the business was
going to go to outsourcers to get rid of the assets, so the
executives could
pay themselves an enormous amount of money.
Think
about how to bring the cloud to your business, and to better manage
your data assets, and don't automatically default to the notion that
you're going to take your business to the cloud.
The same type of thinking pertains here
-- the goal is not to get rid of IT assets since those assets, generally
speaking, are becoming less important features of the overall proposition of
digital businesses.
Think instead about how to bring the
cloud to your business, and to better manage your data assets, and don’t
automatically default to the notion that you’re going to take your business to
the cloud.
Every decision-maker needs to ask himself or herself, “How can I get the cloud experience wherever the data demands?” The goal of the cloud experience, which is a very, very powerful concept, ultimately needs to be able to get access to a very rich set of services associated with automation. We need visible pricing and metering, self-sufficiency, and self-service. These are all the experiences that we want out of cloud.
Every decision-maker needs to ask himself or herself, “How can I get the cloud experience wherever the data demands?” The goal of the cloud experience, which is a very, very powerful concept, ultimately needs to be able to get access to a very rich set of services associated with automation. We need visible pricing and metering, self-sufficiency, and self-service. These are all the experiences that we want out of cloud.
What we want, however, are those experiences wherever the data requires it, and that’s what’s driving hybrid cloud. We call it “true private cloud,” and the idea is of having a technology stack that provides a consistent cloud experience wherever the data has to run -- whether that’s because of IoT or because of privacy issues or because of intellectual property concerns. True private cloud is our concept for describing how the cloud experience is going to be enacted where the data requires, so that you don’t just have to move the data to get to the cloud experience.
Weaving IT all together
The third thing
to note here is that ultimately this is going to lead to the most complex
integration regime we’ve ever envisioned for IT. By that I mean, we are going
to have applications that span Software-as-a-Service (SaaS), public cloud, IaaS
services, true private cloud, legacy applications, and many other types of
services that we haven’t even conceived of right now.
And understanding how to weave all of
those different data sources, and all those different service sources, into
coherent application framework that runs reliably and providers a continuous
ongoing service to the business is essential. It must involve a degree of
distribution that completely breaks most models. We’re thinking about
infrastructure, architecture, but also, data management, system management, security
management, and as I said earlier, all the way out to even contractual
management, and vendor management.
The arrangement of resources for the
classes of applications that we are going to be building in the future are
going to require deep, deep, deep thinking.
That leads to the fourth thing, and that
is defining the metric we’re going to use increasingly from a cost standpoint. And it is time.
As the costs of computing and bandwidth continue to drop -- and they will
continue to drop -- it means ultimately that the fundamental cost determinant
will be, How long does it
take an application to complete? How long does it take this transaction to
complete? And that’s not so much a throughput question, as it is a question of,
“I have all these multiple sources that each on their own are contributing some
degree of time to how this piece of work finishes, and can I do that piece of
work in less time if I bring some of the work, for example, in-house, and run
it close to the event?”
This relationship between increasing
distribution of work, increasing distribution of data, and the role that time
is going to play when we think about the event that we need to manage is going
to become a significant architectural concern.
The fifth issue, that really places
an enormous strain on IT is how
we think about backing up and restoring data. Backup/restore has been an
afterthought for most of the history of the computing industry.
As we start to build these more complex
applications that have more complex data sources and more complex services -- and
as these applications increasingly are the basis for the business and the
end-value that we’re creating -- we are not thinking about backing up devices
or infrastructure or even subsystems.
We
are thinking about what does it mean to backup,
even more importantly, applications and even businesses. The issue
becomes associated more with restoring. How do we restore applications
in business across this
incredibly complex arrangement of services and data locations and
sources?
There's
a new data regime that's emerging to support application development.
How's that going to work -- the role the data scientists and analytics
are going to play in working with application developers?
I listed five areas that are going
to be very important. We haven’t even talked about the new regime that’s
emerging to support application development and how that’s going to work. The
role the data scientists and analytics are going to play in working with
application developers – again, we could go on and on and on. There is a wide
array of considerations, but I think all of them are going to come back to the
five that I mentioned.
Gardner: That’s an excellent overview. One of the common themes that I keep hearing from you, Peter, is that there is a
great unknown about the degree of complexity, the degree of risk, and a lack of
maturity. We really are venturing into unknown territory in creating
applications that draw on these resources, assets and data from these different
clouds and deployment models.
When you have that degree of unknowns, that lack of
maturity, there is a huge opportunity for a party to come in to bring in new
types of management with maturity and with visibility. Who are some of the
players that might fill that role? One that I am familiar with, and I think I
have seen them on theCUBE is Hewlett Packard
Enterprise (HPE) with what they call Project
New Hybrid IT Stack. We still don’t know too much about it. I have
also talked about Cloud28+, which is an
ecosystem of global cloud environments that helps mitigate some of the concerns
about a single hyperscaler or a handful of hyperscale providers. What’s the
opportunity for a business to come in to this problem set and start to solve it? What
do you think from what you’ve heard so far about Project New Hybrid IT Stack at HPE?
Key cloud players
Burris: That’s a great question, and I’m going to answer it in three parts.
Part number one is, if we look back historically at the emergence of TCP/IP, TCP/IP killed the
mini-computers. A lot of people like to claim it was microprocessors, and there
is an element of truth to that, but many computer companies had
their own proprietary networks. When companies wanted to put those networks
together to build more distributed applications, the mini-computer companies
said, “Yeah, just bridge our network.” That was an unsatisfyingly
bad answer
for the users. So along came Cisco, TCP/IP, and they flattened out all
those mini-computer networks, and in the process flattened the
mini-computer
companies.
HPE was one of the few survivors because they embraced TCP/IP much earlier than anybody else.
We
are going to need the infrastructure itself to use deep learning,
machine learning, and advanced technology for determining how the
infrastructure is managed, optimized, and economized.
The second
thing is that to build the next generations of more complex applications -- and
especially applications that involve capabilities like deep learning or machine
learning with increased automation -- we are going to need the infrastructure
itself to use deep learning, machine learning, and advanced technology for determining how
the infrastructure is managed, optimized, and economized. That is an absolute
requirement. We are not going to make progress by adding new levels of complexity and
building increasingly rich applications if we don’t take full advantage of the
technologies that we want to use in the applications -- inside how
we run our infrastructures and run our subsystems, and do all the things we
need to do from a hybrid cloud
standpoint.
Ultimately, the companies are going to step up and
start to flatten out some of these cloud options that are emerging. We will need companies
that have significant experience with infrastructure, that really
understand the problem. They need a lot of experience with a lot of different
environments, not just one operating system or one cloud platform. They will
need a lot of experience with these advanced applications, and have both the
brainpower and the inclination to appropriately invest in those capabilities so
they can build the type of platforms that we are talking about. There are not a
lot of companies out there that can.
There are few out there, and certainly HPE with its
New Stack initiative is one of them, and we at Wikibon are especially excited
about it. It’s new, it’s immature, but HPE has a lot of piece parts that will
be required to make a go of this technology. It’s going to be one of the most
exciting areas of invention over the next few years. We really look forward to
working with our user clients to introduce some of these technologies and innovate with
them. It’s crucial to solve the next generation of problems that the world faces;
we can’t move forward without some of these new classes of hybrid technologies
that weave together fabrics that are capable of running any number of different
application forms.
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