We'll explore how mounting complexity and a lack of multi-cloud services management maturity must be solved in order for businesses to grow and thrive as digital enterprises.
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Here are some excerpts:
Gardner: Paul, there’s a lot of
evidence that businesses are adopting cloud models at a rapid pace. There is
also lingering concern about the complexity of managing so many fast-moving
parts. We have legacy IT, private cloud, public cloud, software as a service (SaaS)
and, of course, multi-cloud. So as someone who tracks technology and its
consumption, how much has technology itself been tapped to manage this sprawl,
if you will, across hybrid IT?
 |
| Teich |
cloud business model. As you know, it takes a while for management technology to catch up with the actual compute technology and storage. So I think we are seeing that management is the tail of the dog, it’s getting wagged by the rest of it, and it just hasn’t caught up yet.
Gardner: Things have been moving so
quickly with cloud computing that few organizations have had an opportunity to step
back and examine what’s actually going on around them -- never mind properly react
to it. We really are playing catch up.
Teich: As we look at the options available, the cloud
giants -- the public cloud services -- don’t have much incentive to work
together. So you are looking at a market where there will be third parties stepping
in to help manage multi-cloud environments, and there’s a lag time between
having those services available and having the cloud services available and
then seeing the third-party management solution step in.
Gardner: It’s natural to see that a
specific cloud environment, whether it’s purely public like AWS or a hybrid
like Microsoft Azure and Azure Stack, want
to help their customers, but they want to help their customers all get to their
solutions first and foremost. It’s a natural thing. We have seen this before in
technology.
There are not
that many organizations willing to step into the neutral position of being ecumenical,
of saying they want to help the customer first, manage it all from the first.
As we look to how this might unfold, it seems to me that the previous
models of IT management -- agent-based, single-pane-of-glass, and unfortunately
still in some cases spreadsheets and Post-It notes -- have been brought to bear
on this. But we might be in a different ball game, Paul, with hybrid IT, that there’s just too
many moving parts, too much complexity, and that we might need to look at data-driven
approaches. What is your take on that?
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Teich: I think that’s exactly correct. One of the jokes in
the industry right now is if you want to find your stranded instances in the cloud,
cancel your credit card and AWS or Microsoft will be happy to notify you of all
of the instances that you are no longer paying for because your credit card
expired. It’s hard to keep track of this, because we don’t have adequate tools
yet.
When you are an IT manager and you have a lot of folks on public cloud services, you don't have a full picture.
That single
pane of glass, looking at a lot of data and information, is soon overloaded. When
you are an IT manager, you are at a mid-sized or a large corporation, you have
a lot of folks paying out-of-pocket right now, slapping a credit card down on
public cloud services, so you don’t have a full picture. Where you do have a
picture, there are so many moving parts.
I think we have to get past having a
screen full of data, a screen full of information, and to a point where we have
insight. And that is going to require
a new generation of tools, probably borrowing from some of the machine learning
evolution that’s happening now in pattern analytics.
Gardner: The timing in
some respects couldn’t be better, right? Just as we are facing this massive
problem of complexity of volume and velocity in managing IT across a hybrid
environment, we have some of the most powerful and cost-effective means to deal
with big data problems just like that.
Life in the infrastructure
Paul, before we go further let’s hear about you and your
organization, and tell us, if you would, what a typical day is like in the life
of Paul Teich?
Teich: At TIRIAS Research we are boutique industry analysts. By boutique we mean
there are three of us -- three principal analysts; we have just added a few senior
analysts. We are close to the metal. We live in the infrastructure. We are all
former engineers and/or product managers. We are very familiar with deep technology.
My day tends to be first, a lot of
reading. We look at a lot of chips, we look at a lot of service-level information,
and our job is to, at a very fundamental level, take very complex products and
technologies and surface them to business decision-makers, IT decision-makers,
folks who are trying to run lines of business (LOB) and make a profit. So we do
the heavy lifting on why new technology is important, disruptive, and
transformative.
Gardner: Thanks. Let’s
go back to this idea of data-driven and analytical values as applied to hybrid IT
management and complexity. If we can apply AI and machine learning to solve
business problems outside of IT -- in such verticals as retail, pharmaceutical,
transportation -- with the same characteristics of data volume, velocity, and
variety, why not apply that to IT? Is this a case of the cobbler’s kids having
no shoes? You would think that IT would be among the first to do this.
Dig deep, gain insight
Teich: The cloud giants have already implemented systems
like this because of necessity. So they have been at the front-end of that big
data mantra of volume, velocity -- and all of that.
To successfully train for the new
pattern recognition analytics, especially the deep learning stuff, you need a
lot of data. You can’t actually train a system usefully without presenting it
with a lot of use cases.
The public clouds have this data. They
are operating social media services, large retail storefronts, and e-tail, for
example. As the public clouds became available to enterprises, the IT management
problem ballooned into a big data problem. I don’t think it was a big data
problem five or 10 years ago, but it is now.
That’s a big transformation. We haven’t
actually internalized what that means operationally when your internal IT department
no longer runs all of your IT jobs anymore.
We are generating big data and that means we need big data tools to go analyze it and to get that relevant insight.
That’s
the biggest sea change -- we are generating big data in the course of managing
our IT infrastructure now, and that means we need big data tools to go analyze
it, and to get that relevant insight. It’s too much data flowing by for humans
to comprehend in real time.
Gardner: And, of course, we are also talking about islands of
such operational data. You might have a lot of data in your legacy operations.
You might have tier 1 apps that you are running on older infrastructure, and you
are probably happy to do that. It might be very difficult to transition those specific
apps into newer operating environments.
You also have multiple SaaS and cloud
data repositories and logs. There’s also not only the data within those apps, but
there’s the metadata as to how those apps are running in clusters and what they
are doing as a whole. It seems to me that not only would you benefit from having
a comprehensive data and analytics approach for your IT operations, but you
might also have a workflow and process business benefit by being an uber
analyst, by being on top of all of these islands of operational data.
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To me, moving
toward a comprehensive intelligence and data analysis capability for IT is the
gift that keeps giving. You would then be able to also provide insight for an
uber approach to processes across your entire organization -- across the supply
chains, across partner networks, and back to your customers. Paul, do you also
see that there’s an ancillary business benefit to having that data analysis capability,
and not ceding it to your cloud providers?
Manage data, improve workflow
Teich: I do.
At one end of the spectrum it’s simply what do you need to do to keep the
lights on, where is your data, all of it, in the various islands and collections
and the data you are sharing with your supply chain as well. Where is the
processing that you can apply to that data? Increasingly, I think, we are
looking at a world in which the location of the stored data is more important
than the processing power.
The management of all the data you have needs to segue into visible workflows.
We have processing power pretty much
everywhere now. What’s key is moving data from place to place and setting up
the connections to acquire it. It means that the management of all the data you
have needs to segue into visible workflows.
Once I know what I have, and I am
managing it at a baseline effectively, then I can start to improve my processes.
Then I can start to get better workflows, internally as well as across my supply
chain. But I think at first it’s simply, “What do I have going on right now?”
As an IT manager, how can I rein in
some of these credit card instances, credit card storage on the public clouds, and
put that all into the right mix. I have to know what I know first -- then I can
start to streamline. Then I can start to control my costs. Does that make
sense?
Gardner: Yes, absolutely. And how can you know which people
you want to give even more credit to on their credit cards – and let them do
more of what they are doing? It might be very innovative, and it might be very
cost-effective. There might also be those wasting money, spinning their wheels,
repaving cow paths, over and over again.
If you don’t have the ability to make
those decisions with insight, without the visibility, and then further analyze it
as to how best to go about it – it seems to me a no-brainer.
It also comes at an auspicious time as
IT is trying to re-factor its value to the organization. If in fact they are no
longer running servers and networks and keeping the trains running on time,
they have to start being more in the business of defining what trains should be
running and then how to make them the best business engines, if you will.
If IT departments needs to rethink their
role and step up their game, then they need to use technologies like advanced hybrid IT management from vendors with a neutral perspective. Then they become the
overseers of operations at a fundamentally different level.
Data revelation, not revolution
IT needs to keep a handle on costs -- so you can understand which jobs are running where and how much more capacity you need.
Teich: I think
that’s right. It’s evolutionary stuff. I don’t think it’s revolutionary. I
think that in the same way you add servers to a virtual machine farm, as your
demand increases, as your baseline demand increases, IT needs to keep a handle
on costs -- so you can understand which jobs are running where and how much more
capacity you need.
One of the things they are missing with
random access to the cloud is bulk purchasing. And so at a very fundamental
level, helping your organization manage which clouds you are spending on by
aggregating the purchase of storage, aggregating the purchase of compute
instances to get just better buying power, doing price arbitrage when you can. To
me, those are fundamental qualities of IT going forward in a multi-cloud
environment.
They are extensions of where we are
today; it just doesn’t seem like it yet. They have always added new servers to increasing
internal capacity and this is just the next evolutionary step.
Gardner: It certainly makes sense that you would move as
maturity occurs in any business function toward that orchestration, automation
and optimization – rather than simply getting the parts in place. What you are describing
is that IT is becoming more like a procurement function and less like a
building, architecture, or construction function, which is just as powerful.
Not many people can make those hybrid IT procurement decisions without knowing a lot about the technology. Someone with just business acumen can’t walk in and make these decisions. I think this is an opportunity for IT to elevate itself and become even more essential to the businesses.
Not many people can make those hybrid IT procurement decisions without knowing a lot about the technology. Someone with just business acumen can’t walk in and make these decisions. I think this is an opportunity for IT to elevate itself and become even more essential to the businesses.
Teich: The opportunity is a lot like the Sabre airline scheduling system that nearly every airline
uses now. That’s a fundamental capability for doing business, and it’s separate
from the technology of Sabre. It’s the ability to schedule -- people and
airplanes – and it’s a lot like scheduling storage and jobs on compute instances.
So I think there will be this step.
But
to go back to the technology versus procurement, I think some element of that has
always existed in IT in terms of dealing with vendors and doing the volume
purchases on one side, but also having some architect know how to compose the
hardware and the software infrastructure to serve those applications.
Connect the clouds
We’re simply
translating that now into a multi-cloud architecture. How do I connect those
pieces? What network capacity do I need to buy? What kind of storage
architectures do I need? I don’t think that all goes away. It becomes far more
important as you look at, for example, AWS as a very large bag of services.
It’s very powerful. You can assemble it in any way you want, but in some
respect, that’s like programming in C. You have all the power of assembly
language and all the danger of assembly language, because you can walk up in
the memory and delete stuff, and so, you have to have architects who know how
to build a service that’s robust, that won’t go down, that serves your
application most efficiently and all of those things are still hard to do.
So,
architecture and purchasing are both still necessary. They don’t go away. I
think the important part is that the orchestration part now becomes as
important as deploying a service on the side of infrastructure because you’ve
got multiple sets of infrastructure.
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Gardner: For hybrid IT, it really has to be an enlightened procurement,
not just blind procurement. And the people in the trenches that are just buying
these services -- whether the developers or operations folks -- they don’t have
that oversight, that view of the big picture to make those larger decisions
about optimization of purchasing and business processes.
That
gets us back to some of our earlier points of, what are the tools, what are the
management insights that these individuals need in order to make those
decisions? Like with Sabre, where they are optimizing to fill every hotel room
or every airplane seat, we’re going to want in hybrid IT to fill every socket,
right? We’re going to want all that bare metal and all those virtualization
instances to be fully optimized -- whether it’s your cloud or somebody else’s.
It
seems to me that there is an algorithmic approach eventually, right? Somebody
is going to need to be the keeper of that algorithm as to how this all operates
-- but you can’t program that algorithm if you don’t have the uber insights
into what’s going on, and what works and what doesn’t.
What’s
the next step, Paul, in terms of the technology catching up to the management
requirements in this new hybrid IT complex environment?
Teich: People can
develop some of that experience on a small scale, but there are so many
dimensions to managing a multi-cloud, hybrid IT
infrastructure business model. It’s throwing off all of this metadata for
performance and efficiency. It’s ripe for machine learning.
We're
moving so fast right now that if you are an organization of any size,
machine learning has to come into play to help you get better economies
of scale.
In a strong sense, we’re moving so fast right now that if
you are an organization of any size, machine learning has to come into play to
help you get better economies of scale. It’s just going to be looking at a
bigger picture, it’s going to be managing more variables, and learning across a
lot more data points than a human can possibly comprehend.
We
are at this really interesting point in the industry where we are getting
deep-learning approaches that are coming online cost effectively; they can help
us do that. They have a little while to go before they are fully mature. But IT
organizations that learn to take advantage of these systems now are going to
have a head start, and they are going to be more efficient than their
competitors.
Gardner: At the end of
the day, if you’re all using similar cloud services then that differentiation
between your company and your competitor is in how well you utilize and
optimize those services. If the baseline technologies are becoming
commoditized, then optimization -- that algorithm-like approach to smartly moving
workloads and data, and providing consumption models that are efficiency-driven
-- that’s going to be the difference between a 1 percent margin and a 5 percent
margin over time.
The deep-learning difference
Teich: The important part to remember is that these
machine-training algorithms are somewhat new, so there are several challenges
with deploying them. First is the transparency issue. We don’t quite yet know how
a deep-learning model makes specific decisions. We can’t point to one aspect
and say that aspect is managing the quality of our AWS services, for example.
It’s a black box model.
We
can’t yet verify the results of these models. We know they are being efficient
and fast but we can’t verify that the model is as efficient as it could
possibly be. There is room for improvement over the next few years. As the
models get better, they’ll leave less money on the table.
We’re
also validating that when you build a machine-learning model that it’s covering
all the situations you want it to cover. You need an audit trail for specific
sets of decisions, especially with data that is subject to regulatory
constraints. You need to know why you made decisions.
So
the net is, once you are training a machine-learning model,
Once
you are training a machine-learning model, you have to keep retraining
it over time. Your model is not going to do the same thing as your
competitor's model.
you have to keep
retraining it over time. Your model is not going to do the same thing as your
competitor's model. There is a lot of room for differentiation, a lot of room
for learning. You just have to go into it with your eyes open that, yeah, occasionally
things will go sideways. Your model might do something unexpected, and you just
have to be prepared for that. We’re still in the early days of machine
learning.
Gardner: You raise an
interesting point, Paul, because even as the baseline technology services in
the multi-cloud era become commoditized, you’re going to have specific, unique,
and custom approaches to your own business’ management.
Your
hybrid IT optimization is
not going to be like that of any other company. I think getting that machine-learning
capability attuned to your specific hybrid IT panoply of resources and assets
is going to be a gift that keeps giving. Not only will you run your IT better,
you will run your business better. You’ll be fleet and agile.
If
some risk arises -- whether it’s a cyber security risk, a natural disaster
risk, a business risk of unintended or unexpected changes in your supply chain or
in your business environment -- you’re going to be in a better position to
react. You’re going to have your eyes to the ground, you’re going to be well
tuned to your specific global infrastructure, and you’ll be able to make good
choices. So I am with you. I think machine learning is essential, and the
sooner you get involved with it, the better.
Before
we sign off, who are the vendors and some of the technologies that we will look
to in order to fill this apparent vacuum on advanced hybrid IT management? It
seems to me that traditional IT management vendors would be a likely place to
start.
Who’s in?
Teich: They are a likely place to start. All of them are starting
to say something about being in a multi-cloud environment, about being in a
multi-cloud-vendor environment. They are already finding themselves there with
virtualization, and the key is they have recognized that they are in a
multi-vendor world.
There are some start-ups, and I can’t name them specifically
right now. But a lot of folks are working on this problem of how do I manage hybrid IT: In-house IT,
and multi-cloud orchestration, a lot of work going on there. We haven’t seen a
lot of it publicly yet, but there is a lot of venture capital being placed.
I think this is the next step, just like PCs came in the
office, smartphones came in the office as we move from server farms to the
clouds, going from cloud to multi-cloud, it’s attracting a lot of attention. The
hard part right now is nailing whom to place your faith in. The name brands
that people are buying their internal IT from right now are probably good near-term
bets. As the industry gets more mature, we’ll have to see what happens.
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Gardner: We did hear a vision described on this from Hewlett Packard Enterprise (HPE) back in June
at their Discover event
in Las Vegas. I’m expecting to hear quite a bit more on something
they’ve been calling New Hybrid IT
Stack
that seems to possess some of the characteristics we’ve been describing, such
as broad visibility and management.
So at least one of the long-term IT management vendors is
looking in this direction. That’s a place I’m going to be focusing on, wondering
what the competitive landscape is going to be, and if HPE is going to be in the
leadership position on hybrid IT management.
Teich: Actually, I
think HPE is the only company I’ve heard from so far talking at that level.
Everybody is voicing some opinion about it, but from what I’ve heard, it does
sound like a very interesting approach to the problem.
Microsoft actually constrained their view on Azure Stack to
a very small set of problems, and is actively saying, “No, I don’t.” If you’re
looking at doing virtual machine migration and taking advantage of multi-cloud
for general-purpose solutions, it’s probably not something that you want to do
yet. It was very interesting for me then to hear about the HPE Project New Hybrid IT Stack and what HPE is planning to do there.
Gardner: For
Microsoft, the more automated and constrained they can make it, the more likely
you’d be susceptible or tempted to want to just stay within an Azure and/or Azure
Stack environment. So I can appreciate why they would do that.
Before we sign off, one other area I’m going to be keeping
my eyes on is around orchestration of containers, Kubernetes, in particular. If you follow
orchestration of containers and container usage in multi-cloud environments, that’s
going to be a harbinger of how the larger hybrid IT management demands are
going to go as well. So a canary in the coal mine, if you will, as to where
things could get very interesting very quickly.
The place to be
Teich: Absolutely.
And I point out that the Linux Foundation’s CloudNativeCon in early
December 2017 looks like the place to be -- with nearly everyone in the server
infrastructure community and cloud infrastructure communities signing on. Part
of the interest is in basically interchangeable container services. We’ll see
that become much more important. So that sleepy little technical show is going
to be invaded by “suits,” this year, and we’re paying a lot of attention to it.
Gardner: Yes, I agree. I’m
afraid we’ll have to leave it there. Paul, how can
our listeners and readers best follow you to gain more of your excellent
insights?
Teich: You can
follow us at www.tiriasresearch.com, and also we
have a page on Forbes Tech, and you can
find us there.
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