October 29, 2021 • 25 mins
The ability to manage resources effectively can be either a defining point of success or a nail in the coffin for growing a business. That is definitely the case for IT and application-based companies. Oved Lourie joins us on this episode to discuss how to make the day-to-day handling of resource management a problem of the past. He explains the strategies being employed to give the right amount of resources at the right time. Whether that is allocating enough data to complete a demanding applications process, or even slowing it down if it doesn’t need it. With these key points from Oved, businesses can look at how to get the most mileage out of their systems, reduce cost and mitigate business risk.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Matt Eastwood (00:04):
If you can think or tell us a little bit
about what's governing the strategies that folks are actually putting
together around all this.
Oved Lourie (00:14):
I think this is where we can even disrupt the
AIOps space, because everybody wants to do automation. Every customer
I talk to every day is very focused on automating
as much as possible.
Matt Eastwood (00:33):
It's a busy world out there with new innovations each
day. Though it may seem like we live in a
golden age for technology in IT, it can have its
downsides. The scale of disruption affecting businesses from startups to
enterprise corporations is astounding. In IT, application portfolios are mind-
splittingly hefty. So how do we keep it all running
(00:56):
smooth? Hi, I'm Matt Eastwood, Senior VP of Enterprise IT
Research at IDC, and I'm one of the hosts of
Scaling AIOps, artificial intelligence for IT specialists and business outcomes,
a joint venture between IDC and IBM. Joining me is Stephen Elliot,
our analyst on the show and Group Vice President of I&
(01:17):
O Cloud Operations and DevOps.
Stephen Elliot (01:19):
In this episode, we're going to discuss resource optimization using
the power of artificial intelligence. We'll address some of the
hesitancy around adopting this evolving technology, as well as the
wonders it brings using some pretty standard principles.
Matt Eastwood (01:37):
We have spent the better half of the last year
and a half talking about the accelerated pace of transformation
that we're seeing among all types of customers and the
resulting continuum of applications and data that stretches from the
edge to the core. So, Stephen, I thought maybe before
we introduce our guest, could you spend a little bit
(01:58):
of time just talking about the pace of complexity in
our IT world and how you've seen that changing?
Stephen Elliot (02:05):
The pace and rate of technology change just continues to
accelerate, and it's accelerating for a number of reasons. First
and foremost, the use of multiple cloud architectures, the various
types of application architectures being used; legacy, container- based, microservice-
based, Kubernetes based. We're seeing different types of hardware and software-
(02:30):
defined infrastructure continuing to be deployed. And on top of all
the technology complexity, we're also seeing a significant amount of
process complexity. And then finally, we're seeing that complexity across
the organizational constructs of IT and the business are continuing to increase.
(02:51):
We're seeing a number of different themes that are driving
complexity across the ability to deliver business outcomes, and they're
derived from increased complexity of the technology architectures and stacks,
the processes, and of course, the ability of the skills
and how those skill sets and roles are organized across IT and
(03:13):
the business.
Matt Eastwood (03:17):
With that, I want to bring in our guest for
today's show. Oved Lourie is the Global Field CTO and
Worldwide Sales Engineering Leader of Turbonomic. Turbonomic is a game
changer in managing IT resource optimization, so let's get into it.
Oved Lourie (03:32):
Yeah. Thank you for having me here, Matt.
Matt Eastwood (03:34):
Great, Oved. Could you tell us a little bit about
Turbonomic and where you guys you came from and a little bit on
the history?
Oved Lourie (03:41):
It's interesting, because when we first started the company, what
we decided to do was take a top- down approach,
meaning let's first figure out what problem we're going to
solve. Then let's figure out how to solve that problem.
Really, if we step back, we looked at IT as
a whole and said, " Look, IT, the principle purpose is
(04:01):
to provide the platforms and the resources to continuously assure
the performance of the applications that run our businesses." And
when we look at how the industry's been tackling this
problem for years, what we've been doing is taking a bottoms-
up approach, which is collect as much data as possible,
and then use that data to solve problems. What that
(04:22):
means is we expect to fail at the one thing
we're supposed to do, provide the resources across all of
these different platforms that we're managing to continuously assure performance.
So in order to solve this problem, we had to
come up with an elegant way to do it. What
if we could find a way to continuously match the
(04:45):
demand of an application with the supply of its resources,
regardless of whether it runs on virtualization or in a
hyperscaler, AWS, Azure, Google, or on top of some of
these modern application architectures, Kubernetes? The artifact changes. The problem
remains the same. How do I continuously all the time
(05:06):
in real time match the demand of something with the
supply of something? And that's what we did, and that's what we do today.
Matt Eastwood (05:12):
And I think it's important, Oved, if you could just help us understand what
you mean by the term resource optimization.
Oved Lourie (05:19):
Resource optimization is maybe not the right term. What we
talk about is application resource management. And if you think about it this
way, applications run well for two reasons. Number one, somebody
wrote really good code and it was architected well, and
we know that happens all the time. When it comes
(05:40):
to the second point, applications run well because they get
the resources they need when they need them. It's actually
very, very simple, just matching supply and demand. Think about the
taxi cab industry. You have a thousand cabs on one
corner and you have a thousand people three blocks away,
and never the two shall meet. And what ends up
(06:02):
happening is you have these incredible inefficiencies, so along comes
a company called Uber, and they're worth $ 50 billion for one
specific reason. It's because every single time you click the
button in the app, your car shows up in under
five minutes. Otherwise, people wouldn't use the app. That's what
makes them valuable is the fact that they can do
(06:23):
this in real time, match the demand to the supply.
Now, if I think about IT, what we typically do,
if you go back to the taxi cab example, we
have, for instance, a virtual machine that needs resources. And just
a few meters away, there's a physical machine that has
those resources. Today, what we do is we wait until
(06:46):
somebody sends out an alarm saying, " We need that taxi
cab, but we can't find it," and what we're talking
about is something different. What if software could understand exactly
where the demand was at any given time, understand exactly
where the supply was, make that decision so every time
(07:06):
any application, any container, any virtual machine needs those resources,
software just simply matches them up? Performance is a real
time problem, and that is where our advantage comes in,
because the system itself is continuously looking at the environment
and making the decisions about how to align the users
(07:29):
with the suppliers.
Matt Eastwood (07:33):
I'd like to just pull this back a little bit.
As we talk a little bit more about the value
proposition for Turbonomic, maybe you could also in doing that,
as you start to think about the dilemmas that these
folks are facing from this complexity, talk a little bit more
about the relationship between what's happening on the IT side
with the business, with the business owners. And in doing
(07:55):
that, how do you talk to people and how do
you relate the Turbonomic story to different parts of the organization?
Oved Lourie (08:02):
Yeah, it's interesting, because nobody ever wakes up in the
morning saying, " Today's the day I'm going to solve the
problem of how to assure performance." We just keep doing
what we've been doing for the last 20, 30 years. But
when we think about what value I get from solving
this problem and how that ties to directly back to
the business, if applications are so critical, then it's okay
(08:26):
to spend a little more money on them, to over-
provision our platforms, to buy more than we need, and
then still fail. So we throw money at this problem,
and then we still wait for IT to fail at
the one thing it's supposed to do. We take our
best people away from innovation projects, things that are actually
helping the business drive forward, so that they can go
(08:48):
figure out how to fix things that shouldn't have broken.
But most importantly, when somebody comes to your website and
they're looking to buy something, and they get to the
shopping cart and there's interference somewhere in the IT stack,
they're going to go to somebody else's website and buy
that same thing from somebody else. So it's absolutely critical
(09:11):
that we solve this problem of how to assure performance,
because the benefits are clear. If my applications aren't slow
because of resources, then my business is doing exactly what
it's supposed to do and maximizing the revenue that it's
supposed to be driving.
Matt Eastwood (09:29):
I'd like to get Stephen to weigh in here and
get your perspective on this, so if you can build
on what Oved's saying here and talk just a little bit
more about how this is changing the IT of the future
as you see it, and even how businesses will be
redefined by all of this.
Stephen Elliot (09:44):
A couple things stand out and are pretty common discussion
points. One is that there are a lot of folks
that have to think about the application of the analytics and resource
planning, as well as include the notion of automation. For
example, some of our conversations are around just simply increasing
(10:07):
the time to identify a problem, the time to resolve
a problem, and the time automating certain resource decisions to
help prevent problems. We've had other conversations with customers from
large IT organizations that also focus on, well, the who,
and what are the types of skills that I need
to think about defining the right process, automating that process,
(10:31):
or even considering not only full automation, but when are
there points in the process where maybe a human has
to get involved to move it forward? We've had other
conversations that think about understanding the type of analytic models
that are required to solve different types of outcomes. And
(10:52):
some of these, for example, are predictive capabilities to isolate
when a particular set of resources might inhibit the performance
of a service. Others are around rapid identification of an
issue. And then certainly in other conversations, closed- loop automation,
(11:14):
where you actually have autonomous operations or situational automation that
can drive a particular set of steps forward in a
fully automated, fully auto- remediated, self- healing type of manner.
We've seen all these types of use cases across our
conversations, and it's a combination of not only understanding the
(11:35):
technology, the type of analytics that you want to achieve
a particular benefit for, and then being very surgical about
that in the use case. But also then really recognizing
who are the right people that should be involved to
drive this particular use case forward on an ongoing basis,
to then continue to build upon the early successes that
(11:57):
these teams have to broaden out its adoption and to
drive even a higher return on investment.
Matt Eastwood (12:05):
There is so much to absorb there. We'll hear more
from Oved Lourie from Turbonomic in a minute.
You're listening to Scaling AIOps, a podcast by IDC and IBM for
industry leaders and professionals to better understand how AI is
reshaping the world around us. Again, I'm your host, Matt
(12:27):
Eastwood. I'm happy to be here along with my co-
host, Stephen Elliot.
Stephen Elliot (12:31):
We're diving into conversations with industry leaders in the field
of artificial intelligence who are at the forefront of some of the technology
shaping how we do IT today. So, if you're enjoying what
you've heard so far, we ask that you subscribe to
our show wherever you get your podcast. Now, back to
the conversation.
Matt Eastwood (12:53):
That's a perfect setup to this conversation on automation, and
I'd love to get Turbonomic's view of automation and what
gets you excited about the role that Turbonomic can play
in this area, and the future of automation as you
see it with the customers you're talking with.
Oved Lourie (13:09):
Yeah, no, it's something we're very, very passionate about, because
there are really two types of automation in this world.
There's decision automation and there's process automation. Decision automation is what is
the right thing to do at any time, all the
time, at scale in real time? What lever should I
pull to continuously make sure that everything is getting what
(13:32):
it needs? The process automation is the implementation of those
decisions. Now, when we built this company, this is not
something that suddenly popped up in 2008, this idea. In
fact, the idea goes all the way back to 1974
on a research paper written to use economic principles to
(13:53):
manage IT resources. The problem was that the world was
defined in these physical machines, where if something broke, I
had to send a human to pull a lever. I
had to wait for something to happen, and then I
could go and fix it. But the opportunity that virtualization presented
us with and then public cloud, and then microservices is
(14:16):
everything is now defined in software. I've exposed all the
knobs and levers of all the actions that I can
take; moving, starting, stopping, reconfiguring. I can actually use software
to pull those levers, and that's how our customers consume
the software today is first, you have to get folks
(14:38):
to agree that software is making the right decision. And it's not
a permutation model. It's not trying to figure out if
this happens, then that. It's a very simple system, because
what better way to manage demand and supply than using
economics? So all the software has to do is make
the right decision to match demand and supply, and then
(15:01):
how do we operationalize this decision automation? And this is
really where the paradigm shift occurs, because while the industry is
very focused on this bottoms- up approach, collect as much
data as possible, then try to figure out how to
automate things after conditions are met... And we never get
to the promised land, because you'll never get all the
(15:23):
right conditions in place, and you'll still have humans in
the loop trying to validate whether these decisions are right.
Is it the right time to run this runbook? What
we're doing here is we're saying software, everything below the
application, the resources can make the right decision. Software can
pull the lever, and what we're helping our customers with
(15:44):
is operationalizing this. This isn't Skynet. You don't drop this
thing in, makes a whole bunch of decisions, pulls a
whole bunch of levers, and then chaos happens. No, it's
a journey.
Matt Eastwood (15:56):
One of the exciting prospects of automation is, of course,
the simplification and the scale that it can help bring
to an environment, but one of the real hesitancies is around
giving up that control. I'm just wondering if you could
just make a few comments about where that resistance may
be coming from and how Turbonomic is helping folks get
over that.
Oved Lourie (16:15):
Yeah, no, it's a very interesting one. That's why I
was smiling as you asked this question, because who is
our competition to this? We don't really compete with any
one product on doing this. We compete with business as
usual, because there is a perception of control, which is
we are monitoring. We are looking at all these things.
(16:37):
This is our job. This is what we do. We
are trying to build the automation. It's very disruptive software,
because it is going to change the way that people
do things. If you are talking about it to somebody
and making them feel like this software is going to
replace them, well, that is the wrong approach. This isn't
(16:59):
that people aren't doing a good job. This is about
a problem that is beyond human scale. There is a
perception of control, but it's not really the reality. We
are not solving the problem of how to assure performance
today. Pick any data center in the world and you'll
see it. They're ignoring how many alerts every single day?
More than zero and less than infinity. Why? Because there's
(17:20):
so much noise coming out of the systems. So what
we're trying to say is, look, there's a better approach
that's going to free up people's time so that they
can work on innovative projects instead of trying to come
to work every day, waiting for some alarm to go
off, figuring out which lever to pull, move, start, stop
or reconfigure, and then doing it again, and again, and
(17:41):
again. You have to make people feel comfortable that this
isn't you just drop this thing in and it takes
over. This is additive. This solves a problem that people
can't solve and it brings all those groups together.
Matt Eastwood (17:55):
Yeah, I think one of the big themes and the message
there is to really help IT move from that reactive
state to being much more proactive. I'm just curious, Stephen,
if you have any thoughts on that in terms of
your conversations in the space.
Stephen Elliot (18:07):
Yeah, no, that's exactly right. I think, Matt, many of
the use case customer conversations we have are really at
a high level about taking that next step in maturity
across certain set of processes, across certain sets of teams.
For example, I had a recent conversation with a customer, and they
were thinking about just team collaborate across operations and development
(18:31):
and DevOps teams. They were also thinking about levels of
service availability and planned and unplanned downtime. And then there
was one executive opt- in who said, " Well, this service is
pretty critical. What about the impact on the customer experience?"
There were multiple layers of conversation going on, where they
started to see the value streams across what technology could
(18:56):
bring across the data, across the decision- making, across their
automation strategy, across the teams, and probably most importantly, that
all added up to a specific set of business discussions
and customer- centric discussions. And that's the next step, because
it is really, it is about the customer. It is
(19:18):
about the customer experience, or it should be. It all
stems from have great data, but have great technology that
can make sense of it, that can drive some automation,
and that you have teams and the right leadership in
place to then measure the different phases of automation that
drive a particular business outcome or an improved customer experience.
Matt Eastwood (19:44):
Let's bring the conversation just back up a click or
two. As we move to close this conversation out, just
curious first, Oved, from your perspective, and then I'll ask
Stephen the same question, what's next here? What's a piece
of advice that we could give to customers about where
we see the market heading? What might be coming up
(20:04):
next that they need to start to think about, they
need to start to prepare for?
Oved Lourie (20:07):
I might over- simplify it, but I don't think the
world is going to change in the software- defined space.
I think about network, for instance. Network is becoming more
and more defined in software. You think about SD-WAN, for
instance. All of a sudden, if I understand the demand
that needs to be shifted across my networks, I cannot
(20:29):
pull software levers to manage how to align that demand
with the supply of the networks. So, what about network?
That's coming very, very soon. The way that I look
at the world, especially in this space, is whatever comes
next, it's going to have exactly the same problem. But
for the first time in the history of IT, we
(20:52):
have a way to shift the paradigm from expecting failure
and just introducing the next great tool to help people
fix things faster or see what they're using to one
whereas we move into the future. Everybody is going to
need a control system. You can only have one. So
I'm hopeful that this will be the control platform, but
(21:16):
this is the only industry that doesn't have one. I
mean, I look at the nuclear power industry, for instance.
1979, humans ran around trying to pull levers to adjust
the coolant in the system and we had a partial
meltdown. Every nuclear power plant has a control system, but
IT is that last industry where we expect to have
(21:37):
an outcome that results in failure. So this is where
I see the future heading, is a world in which
we can expect to succeed in our single mission of
providing those platforms, whatever platform comes along to support the
applications that run our businesses.
Matt Eastwood (21:55):
Oh, that's really interesting. So, Stephen, any thoughts from you
on that, that you'd like to close on?
Stephen Elliot (22:00):
One of the things is I think a lot of
companies tend to get almost too over- excited about what
they think they can do, and then we have to say, "
Well, what's your real objective technically, and then what would
you like to do for the business?" In a recent
conversation, a customer said, " Well, I'd like to focus on
(22:20):
outage avoidance, avoiding outages through predictive, automated decision- making." That's
a pretty powerful statement, and it really set the tone
for the project and for the people involved. Another key
thing is really double- clicking on the analytic models that
(22:41):
are represented in this tool set. There's a lot of
folks that just maybe don't have necessarily a data scientist
background, and you don't have to, but you should have
a general context of what are the types of analytic
models involved and what will each bring to the table
(23:02):
in terms of information intelligence and certain automated, in some
cases, responses? So I think that the final piece here is
don't underestimate the importance of integration. Oftentimes, you'll have great
decisions being made that you might need to update other
(23:22):
teams on, particularly if it impacts that customer experience. The
value of integration becomes very, very critical for ongoing success
and building out the early successes that the teams often
find as they move forward.
Matt Eastwood (23:37):
Perfect. I think that's a great place to bring this
conversation to a close. I want to thank Oved for
joining Stephen and I today, and having this conversation around
the role that Turbonomic can play in AIOps and in
automation and really, where industry's heading and how important it
is for folks to think about all of these things
that we've been talking about today. Thanks, everybody, for joining
(23:59):
us today. Thank you, Oved, for joining Stephen and I.
Oved Lourie (24:02):
Thank you, Matt, for having us on. Really appreciate the time.
Matt Eastwood (24:06):
Thank you for listening to our show, Scaling AIOps, artificial
intelligence for IT specialists for business outcomes. Join us next
week in our final episode, where Stephen and I will
speak with Robert Barron from IBM to discuss some of
the work they're doing to make AIOps more proactive.
Stephen Elliot (24:23):
We're going to see under the hood of some of
the most fascinating technology IBM has put together, like the
time world- famous chess champion, Garry Kasparov, was unseated by
the supercomputer Deep Blue. If you've enjoyed this episode of
Scaling AIOps, we ask that you subscribe wherever you get
your podcast.
Matt Eastwood (24:41):
I've been your host, Matt Eastwood.
Stephen Elliot (24:43):
And I'm Stephen Elliot.
Matt Eastwood (24:44):
And thank you for listening to episode two of Scaling
AIOps, a joint venture between IBM and IDC, and we'll see all
of you soon.
If you can think or tell us a little bit
about what's governing the strategies that folks are actually putting
together around all this.
Oved Lourie (00:14):
I think this is where we can even disrupt the
AIOps space, because everybody wants to do automation. Every customer
I talk to every day is very focused on automating
as much as possible.
Matt Eastwood (00:33):
It's a busy world out there with new innovations each
day. Though it may seem like we live in a
golden age for technology in IT, it can have its
downsides. The scale of disruption affecting businesses from startups to
enterprise corporations is astounding. In IT, application portfolios are mind-
splittingly hefty. So how do we keep it all running
(00:56):
smooth? Hi, I'm Matt Eastwood, Senior VP of Enterprise IT
Research at IDC, and I'm one of the hosts of
Scaling AIOps, artificial intelligence for IT specialists and business outcomes,
a joint venture between IDC and IBM. Joining me is Stephen Elliot,
our analyst on the show and Group Vice President of I&
(01:17):
O Cloud Operations and DevOps.
Stephen Elliot (01:19):
In this episode, we're going to discuss resource optimization using
the power of artificial intelligence. We'll address some of the
hesitancy around adopting this evolving technology, as well as the
wonders it brings using some pretty standard principles.
Matt Eastwood (01:37):
We have spent the better half of the last year
and a half talking about the accelerated pace of transformation
that we're seeing among all types of customers and the
resulting continuum of applications and data that stretches from the
edge to the core. So, Stephen, I thought maybe before
we introduce our guest, could you spend a little bit
(01:58):
of time just talking about the pace of complexity in
our IT world and how you've seen that changing?
Stephen Elliot (02:05):
The pace and rate of technology change just continues to
accelerate, and it's accelerating for a number of reasons. First
and foremost, the use of multiple cloud architectures, the various
types of application architectures being used; legacy, container- based, microservice-
based, Kubernetes based. We're seeing different types of hardware and software-
(02:30):
defined infrastructure continuing to be deployed. And on top of all
the technology complexity, we're also seeing a significant amount of
process complexity. And then finally, we're seeing that complexity across
the organizational constructs of IT and the business are continuing to increase.
(02:51):
We're seeing a number of different themes that are driving
complexity across the ability to deliver business outcomes, and they're
derived from increased complexity of the technology architectures and stacks,
the processes, and of course, the ability of the skills
and how those skill sets and roles are organized across IT and
(03:13):
the business.
Matt Eastwood (03:17):
With that, I want to bring in our guest for
today's show. Oved Lourie is the Global Field CTO and
Worldwide Sales Engineering Leader of Turbonomic. Turbonomic is a game
changer in managing IT resource optimization, so let's get into it.
Oved Lourie (03:32):
Yeah. Thank you for having me here, Matt.
Matt Eastwood (03:34):
Great, Oved. Could you tell us a little bit about
Turbonomic and where you guys you came from and a little bit on
the history?
Oved Lourie (03:41):
It's interesting, because when we first started the company, what
we decided to do was take a top- down approach,
meaning let's first figure out what problem we're going to
solve. Then let's figure out how to solve that problem.
Really, if we step back, we looked at IT as
a whole and said, " Look, IT, the principle purpose is
(04:01):
to provide the platforms and the resources to continuously assure
the performance of the applications that run our businesses." And
when we look at how the industry's been tackling this
problem for years, what we've been doing is taking a bottoms-
up approach, which is collect as much data as possible,
and then use that data to solve problems. What that
(04:22):
means is we expect to fail at the one thing
we're supposed to do, provide the resources across all of
these different platforms that we're managing to continuously assure performance.
So in order to solve this problem, we had to
come up with an elegant way to do it. What
if we could find a way to continuously match the
(04:45):
demand of an application with the supply of its resources,
regardless of whether it runs on virtualization or in a
hyperscaler, AWS, Azure, Google, or on top of some of
these modern application architectures, Kubernetes? The artifact changes. The problem
remains the same. How do I continuously all the time
(05:06):
in real time match the demand of something with the
supply of something? And that's what we did, and that's what we do today.
Matt Eastwood (05:12):
And I think it's important, Oved, if you could just help us understand what
you mean by the term resource optimization.
Oved Lourie (05:19):
Resource optimization is maybe not the right term. What we
talk about is application resource management. And if you think about it this
way, applications run well for two reasons. Number one, somebody
wrote really good code and it was architected well, and
we know that happens all the time. When it comes
(05:40):
to the second point, applications run well because they get
the resources they need when they need them. It's actually
very, very simple, just matching supply and demand. Think about the
taxi cab industry. You have a thousand cabs on one
corner and you have a thousand people three blocks away,
and never the two shall meet. And what ends up
(06:02):
happening is you have these incredible inefficiencies, so along comes
a company called Uber, and they're worth $ 50 billion for one
specific reason. It's because every single time you click the
button in the app, your car shows up in under
five minutes. Otherwise, people wouldn't use the app. That's what
makes them valuable is the fact that they can do
(06:23):
this in real time, match the demand to the supply.
Now, if I think about IT, what we typically do,
if you go back to the taxi cab example, we
have, for instance, a virtual machine that needs resources. And just
a few meters away, there's a physical machine that has
those resources. Today, what we do is we wait until
(06:46):
somebody sends out an alarm saying, " We need that taxi
cab, but we can't find it," and what we're talking
about is something different. What if software could understand exactly
where the demand was at any given time, understand exactly
where the supply was, make that decision so every time
(07:06):
any application, any container, any virtual machine needs those resources,
software just simply matches them up? Performance is a real
time problem, and that is where our advantage comes in,
because the system itself is continuously looking at the environment
and making the decisions about how to align the users
(07:29):
with the suppliers.
Matt Eastwood (07:33):
I'd like to just pull this back a little bit.
As we talk a little bit more about the value
proposition for Turbonomic, maybe you could also in doing that,
as you start to think about the dilemmas that these
folks are facing from this complexity, talk a little bit more
about the relationship between what's happening on the IT side
with the business, with the business owners. And in doing
(07:55):
that, how do you talk to people and how do
you relate the Turbonomic story to different parts of the organization?
Oved Lourie (08:02):
Yeah, it's interesting, because nobody ever wakes up in the
morning saying, " Today's the day I'm going to solve the
problem of how to assure performance." We just keep doing
what we've been doing for the last 20, 30 years. But
when we think about what value I get from solving
this problem and how that ties to directly back to
the business, if applications are so critical, then it's okay
(08:26):
to spend a little more money on them, to over-
provision our platforms, to buy more than we need, and
then still fail. So we throw money at this problem,
and then we still wait for IT to fail at
the one thing it's supposed to do. We take our
best people away from innovation projects, things that are actually
helping the business drive forward, so that they can go
(08:48):
figure out how to fix things that shouldn't have broken.
But most importantly, when somebody comes to your website and
they're looking to buy something, and they get to the
shopping cart and there's interference somewhere in the IT stack,
they're going to go to somebody else's website and buy
that same thing from somebody else. So it's absolutely critical
(09:11):
that we solve this problem of how to assure performance,
because the benefits are clear. If my applications aren't slow
because of resources, then my business is doing exactly what
it's supposed to do and maximizing the revenue that it's
supposed to be driving.
Matt Eastwood (09:29):
I'd like to get Stephen to weigh in here and
get your perspective on this, so if you can build
on what Oved's saying here and talk just a little bit
more about how this is changing the IT of the future
as you see it, and even how businesses will be
redefined by all of this.
Stephen Elliot (09:44):
A couple things stand out and are pretty common discussion
points. One is that there are a lot of folks
that have to think about the application of the analytics and resource
planning, as well as include the notion of automation. For
example, some of our conversations are around just simply increasing
(10:07):
the time to identify a problem, the time to resolve
a problem, and the time automating certain resource decisions to
help prevent problems. We've had other conversations with customers from
large IT organizations that also focus on, well, the who,
and what are the types of skills that I need
to think about defining the right process, automating that process,
(10:31):
or even considering not only full automation, but when are
there points in the process where maybe a human has
to get involved to move it forward? We've had other
conversations that think about understanding the type of analytic models
that are required to solve different types of outcomes. And
(10:52):
some of these, for example, are predictive capabilities to isolate
when a particular set of resources might inhibit the performance
of a service. Others are around rapid identification of an
issue. And then certainly in other conversations, closed- loop automation,
(11:14):
where you actually have autonomous operations or situational automation that
can drive a particular set of steps forward in a
fully automated, fully auto- remediated, self- healing type of manner.
We've seen all these types of use cases across our
conversations, and it's a combination of not only understanding the
(11:35):
technology, the type of analytics that you want to achieve
a particular benefit for, and then being very surgical about
that in the use case. But also then really recognizing
who are the right people that should be involved to
drive this particular use case forward on an ongoing basis,
to then continue to build upon the early successes that
(11:57):
these teams have to broaden out its adoption and to
drive even a higher return on investment.
Matt Eastwood (12:05):
There is so much to absorb there. We'll hear more
from Oved Lourie from Turbonomic in a minute.
You're listening to Scaling AIOps, a podcast by IDC and IBM for
industry leaders and professionals to better understand how AI is
reshaping the world around us. Again, I'm your host, Matt
(12:27):
Eastwood. I'm happy to be here along with my co-
host, Stephen Elliot.
Stephen Elliot (12:31):
We're diving into conversations with industry leaders in the field
of artificial intelligence who are at the forefront of some of the technology
shaping how we do IT today. So, if you're enjoying what
you've heard so far, we ask that you subscribe to
our show wherever you get your podcast. Now, back to
the conversation.
Matt Eastwood (12:53):
That's a perfect setup to this conversation on automation, and
I'd love to get Turbonomic's view of automation and what
gets you excited about the role that Turbonomic can play
in this area, and the future of automation as you
see it with the customers you're talking with.
Oved Lourie (13:09):
Yeah, no, it's something we're very, very passionate about, because
there are really two types of automation in this world.
There's decision automation and there's process automation. Decision automation is what is
the right thing to do at any time, all the
time, at scale in real time? What lever should I
pull to continuously make sure that everything is getting what
(13:32):
it needs? The process automation is the implementation of those
decisions. Now, when we built this company, this is not
something that suddenly popped up in 2008, this idea. In
fact, the idea goes all the way back to 1974
on a research paper written to use economic principles to
(13:53):
manage IT resources. The problem was that the world was
defined in these physical machines, where if something broke, I
had to send a human to pull a lever. I
had to wait for something to happen, and then I
could go and fix it. But the opportunity that virtualization presented
us with and then public cloud, and then microservices is
(14:16):
everything is now defined in software. I've exposed all the
knobs and levers of all the actions that I can
take; moving, starting, stopping, reconfiguring. I can actually use software
to pull those levers, and that's how our customers consume
the software today is first, you have to get folks
(14:38):
to agree that software is making the right decision. And it's not
a permutation model. It's not trying to figure out if
this happens, then that. It's a very simple system, because
what better way to manage demand and supply than using
economics? So all the software has to do is make
the right decision to match demand and supply, and then
(15:01):
how do we operationalize this decision automation? And this is
really where the paradigm shift occurs, because while the industry is
very focused on this bottoms- up approach, collect as much
data as possible, then try to figure out how to
automate things after conditions are met... And we never get
to the promised land, because you'll never get all the
(15:23):
right conditions in place, and you'll still have humans in
the loop trying to validate whether these decisions are right.
Is it the right time to run this runbook? What
we're doing here is we're saying software, everything below the
application, the resources can make the right decision. Software can
pull the lever, and what we're helping our customers with
(15:44):
is operationalizing this. This isn't Skynet. You don't drop this
thing in, makes a whole bunch of decisions, pulls a
whole bunch of levers, and then chaos happens. No, it's
a journey.
Matt Eastwood (15:56):
One of the exciting prospects of automation is, of course,
the simplification and the scale that it can help bring
to an environment, but one of the real hesitancies is around
giving up that control. I'm just wondering if you could
just make a few comments about where that resistance may
be coming from and how Turbonomic is helping folks get
over that.
Oved Lourie (16:15):
Yeah, no, it's a very interesting one. That's why I
was smiling as you asked this question, because who is
our competition to this? We don't really compete with any
one product on doing this. We compete with business as
usual, because there is a perception of control, which is
we are monitoring. We are looking at all these things.
(16:37):
This is our job. This is what we do. We
are trying to build the automation. It's very disruptive software,
because it is going to change the way that people
do things. If you are talking about it to somebody
and making them feel like this software is going to
replace them, well, that is the wrong approach. This isn't
(16:59):
that people aren't doing a good job. This is about
a problem that is beyond human scale. There is a
perception of control, but it's not really the reality. We
are not solving the problem of how to assure performance
today. Pick any data center in the world and you'll
see it. They're ignoring how many alerts every single day?
More than zero and less than infinity. Why? Because there's
(17:20):
so much noise coming out of the systems. So what
we're trying to say is, look, there's a better approach
that's going to free up people's time so that they
can work on innovative projects instead of trying to come
to work every day, waiting for some alarm to go
off, figuring out which lever to pull, move, start, stop
or reconfigure, and then doing it again, and again, and
(17:41):
again. You have to make people feel comfortable that this
isn't you just drop this thing in and it takes
over. This is additive. This solves a problem that people
can't solve and it brings all those groups together.
Matt Eastwood (17:55):
Yeah, I think one of the big themes and the message
there is to really help IT move from that reactive
state to being much more proactive. I'm just curious, Stephen,
if you have any thoughts on that in terms of
your conversations in the space.
Stephen Elliot (18:07):
Yeah, no, that's exactly right. I think, Matt, many of
the use case customer conversations we have are really at
a high level about taking that next step in maturity
across certain set of processes, across certain sets of teams.
For example, I had a recent conversation with a customer, and they
were thinking about just team collaborate across operations and development
(18:31):
and DevOps teams. They were also thinking about levels of
service availability and planned and unplanned downtime. And then there
was one executive opt- in who said, " Well, this service is
pretty critical. What about the impact on the customer experience?"
There were multiple layers of conversation going on, where they
started to see the value streams across what technology could
(18:56):
bring across the data, across the decision- making, across their
automation strategy, across the teams, and probably most importantly, that
all added up to a specific set of business discussions
and customer- centric discussions. And that's the next step, because
it is really, it is about the customer. It is
(19:18):
about the customer experience, or it should be. It all
stems from have great data, but have great technology that
can make sense of it, that can drive some automation,
and that you have teams and the right leadership in
place to then measure the different phases of automation that
drive a particular business outcome or an improved customer experience.
Matt Eastwood (19:44):
Let's bring the conversation just back up a click or
two. As we move to close this conversation out, just
curious first, Oved, from your perspective, and then I'll ask
Stephen the same question, what's next here? What's a piece
of advice that we could give to customers about where
we see the market heading? What might be coming up
(20:04):
next that they need to start to think about, they
need to start to prepare for?
Oved Lourie (20:07):
I might over- simplify it, but I don't think the
world is going to change in the software- defined space.
I think about network, for instance. Network is becoming more
and more defined in software. You think about SD-WAN, for
instance. All of a sudden, if I understand the demand
that needs to be shifted across my networks, I cannot
(20:29):
pull software levers to manage how to align that demand
with the supply of the networks. So, what about network?
That's coming very, very soon. The way that I look
at the world, especially in this space, is whatever comes
next, it's going to have exactly the same problem. But
for the first time in the history of IT, we
(20:52):
have a way to shift the paradigm from expecting failure
and just introducing the next great tool to help people
fix things faster or see what they're using to one
whereas we move into the future. Everybody is going to
need a control system. You can only have one. So
I'm hopeful that this will be the control platform, but
(21:16):
this is the only industry that doesn't have one. I
mean, I look at the nuclear power industry, for instance.
1979, humans ran around trying to pull levers to adjust
the coolant in the system and we had a partial
meltdown. Every nuclear power plant has a control system, but
IT is that last industry where we expect to have
(21:37):
an outcome that results in failure. So this is where
I see the future heading, is a world in which
we can expect to succeed in our single mission of
providing those platforms, whatever platform comes along to support the
applications that run our businesses.
Matt Eastwood (21:55):
Oh, that's really interesting. So, Stephen, any thoughts from you
on that, that you'd like to close on?
Stephen Elliot (22:00):
One of the things is I think a lot of
companies tend to get almost too over- excited about what
they think they can do, and then we have to say, "
Well, what's your real objective technically, and then what would
you like to do for the business?" In a recent
conversation, a customer said, " Well, I'd like to focus on
(22:20):
outage avoidance, avoiding outages through predictive, automated decision- making." That's
a pretty powerful statement, and it really set the tone
for the project and for the people involved. Another key
thing is really double- clicking on the analytic models that
(22:41):
are represented in this tool set. There's a lot of
folks that just maybe don't have necessarily a data scientist
background, and you don't have to, but you should have
a general context of what are the types of analytic
models involved and what will each bring to the table
(23:02):
in terms of information intelligence and certain automated, in some
cases, responses? So I think that the final piece here is
don't underestimate the importance of integration. Oftentimes, you'll have great
decisions being made that you might need to update other
(23:22):
teams on, particularly if it impacts that customer experience. The
value of integration becomes very, very critical for ongoing success
and building out the early successes that the teams often
find as they move forward.
Matt Eastwood (23:37):
Perfect. I think that's a great place to bring this
conversation to a close. I want to thank Oved for
joining Stephen and I today, and having this conversation around
the role that Turbonomic can play in AIOps and in
automation and really, where industry's heading and how important it
is for folks to think about all of these things
that we've been talking about today. Thanks, everybody, for joining
(23:59):
us today. Thank you, Oved, for joining Stephen and I.
Oved Lourie (24:02):
Thank you, Matt, for having us on. Really appreciate the time.
Matt Eastwood (24:06):
Thank you for listening to our show, Scaling AIOps, artificial
intelligence for IT specialists for business outcomes. Join us next
week in our final episode, where Stephen and I will
speak with Robert Barron from IBM to discuss some of
the work they're doing to make AIOps more proactive.
Stephen Elliot (24:23):
We're going to see under the hood of some of
the most fascinating technology IBM has put together, like the
time world- famous chess champion, Garry Kasparov, was unseated by
the supercomputer Deep Blue. If you've enjoyed this episode of
Scaling AIOps, we ask that you subscribe wherever you get
your podcast.
Matt Eastwood (24:41):
I've been your host, Matt Eastwood.
Stephen Elliot (24:43):
And I'm Stephen Elliot.
Matt Eastwood (24:44):
And thank you for listening to episode two of Scaling
AIOps, a joint venture between IBM and IDC, and we'll see all
of you soon.
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