Simplifying Enterprise Multi-Cloud Complexity
Key Points
- Nden, Red Hat’s Global Chief Architect, and IBM Fellow Kyle Brown introduce a joint effort to simplify today’s complex, multi‑platform IT environments.
- IBM’s landscape exemplifies typical enterprise heterogeneity, with workloads spread across mainframe Z systems, multiple public clouds, on‑prem datacenters, virtualized environments, and edge devices.
- Kyle notes that this “Z‑cloud‑on‑prem‑virtual‑edge” mix mirrors the challenges most large organizations face, making it a common pain point for customers.
- The conversation will focus on the lessons learned from managing such distributed environments and how enterprises can effectively operate across them.
- They will outline how IBM and Red Hat, together with partners, can deliver integrated solutions and architectures that unify and streamline these diverse workloads.
Full Transcript
# Simplifying Enterprise Multi-Cloud Complexity **Source:** [https://www.youtube.com/watch?v=Au0_9W5BRlw](https://www.youtube.com/watch?v=Au0_9W5BRlw) **Duration:** 00:19:04 ## Summary - Nden, Red Hat’s Global Chief Architect, and IBM Fellow Kyle Brown introduce a joint effort to simplify today’s complex, multi‑platform IT environments. - IBM’s landscape exemplifies typical enterprise heterogeneity, with workloads spread across mainframe Z systems, multiple public clouds, on‑prem datacenters, virtualized environments, and edge devices. - Kyle notes that this “Z‑cloud‑on‑prem‑virtual‑edge” mix mirrors the challenges most large organizations face, making it a common pain point for customers. - The conversation will focus on the lessons learned from managing such distributed environments and how enterprises can effectively operate across them. - They will outline how IBM and Red Hat, together with partners, can deliver integrated solutions and architectures that unify and streamline these diverse workloads. ## Sections - [00:00:00](https://www.youtube.com/watch?v=Au0_9W5BRlw&t=0s) **Simplifying Complex Multi‑Cloud IT** - Red Hat’s chief architect and IBM’s CTO discuss how enterprises can streamline diverse workloads across mainframes, multi‑clouds, and on‑prem data centers into a unified, simpler architecture. ## Full Transcript
hello there everyone my name is nden I'm
the global Chief Architect leader in the
field CTO organization at take a guess
red hat today we're going to tell a
story about
simplification simplifying the
complexity of the it
landscape with me here today I have the
privilege to stand right next to Kyle
Brown from IBM Kyle thank you very much
dden hi I'm Kyle Brown I'm an IBM fellow
and I'm the CTO for the IBM CIO
office so the story here is really about
you know um how where it is today where
the technology landscape is today and
we're going to build it up you're seeing
the the boxes and lines but we're going
to as we tell the story we're going to
write the chapters write the text and at
the end of the day VOA you're going to
have a book on how it can be
simplified so Kyle when you look at the
journey that IBM has gone through is
going through what are some of the you
know the the platforms the environments
that you know IBM has and typic that is
very typical I would say of what we see
in the industry we are typical we're a
big company just like most other big
companies and so if you think about the
problem that we have we have for
instance workloads on z uh Z is at the
core of our business and we do a lot of
work work on IBM Z platforms but Z is
not where all of our workloads sit we
have Cloud workloads not just in the IBM
Cloud but in multiple clouds so we have
to deal with the fact that we have those
all over we have on Prim workloads in
our data centers that we also have to
think about as we're looking at all of
the different possibilities of the
different things that we're running
there what's more we also have workloads
that are virtualized that run in all of
these different places on Z on Prim on
cloud everywhere and then finally we do
have some Edge workloads particularly in
our content management we have devices
at the edge that we have to control too
and if you put this all together that
basically represents the problem that we
face at the CIO we have to live with all
of these different workloads and
managing all of these different
platforms here is what is fascinating
Kyle I know this is the IBM story but
then as you and I when we go out to meet
with customers you know to me this seems
very very typical of what customers tell
they have in their Enterprise you have
in you at a price absolutely as I've
talked to a number of customers and
we've talked about these very same set
of platforms they all agree that looks
exactly like what we have and so that
begins a conversation that we can start
to have around what did we learn about
how to live in an environment that's
distributed across many different
platforms like this so as we write this
book Kyle I'm going to take a twist it
you know at detour if you will right and
that is how about if we talk about how
IBM and red hat come together with the
solutions we have the approaches we have
the Technologies on how we can work not
only just between ourselves but with our
partners
to make lives better for our customers
why don't we tell that story sounds
great
so Kyle when you have this many
environments and these are you know they
come across as singular boxes but really
you many many many instances of these I
cannot even begin to imagine how do you
deal with that what is this thing you
know that I like the box here but what
goes in there Kyle that was the first of
the major decisions we ended up having
to make to really understand how to
manage something this disperate and that
is we had to have a common automation
strategy we realized that you can't just
manage all of these environments by
throwing people at the problem you have
to be able to automate the management
the installation the operation of all of
those different pieces of your puzzle
and so common automation is absolutely
critical to what you need to be able to
solve this problem and importantly to
build up any of the players that come on
top of that that's great uh automation
is uh you know we will later we will get
into AI the twool letter magic word I
would submit Kyle that it's important to
have an automation strategy before
thinking about an AI strategy so I'm
delighted you know so everyone watching
bear in mind if you're are talking about
an AI strategy take a pause ask yourself
how is your automation strategy how is
our automation strategy so great start
Kyle okay fine it's automated all over
the place what comes next so the next
decision we had to make after deciding
on a common automation strategy was we
had to come up with a common
containerization strategy because in
fact what we found is that most of the
workloads we have were all fitting into
a very small number of patterns meaning
that there was an awful lot of
similarity between hundreds of these
different workloads
and what we found is that by putting
them into common images and building a
common container approach we were able
to in the end move from having workloads
that were completely different across
these different platforms into workloads
that were very similar and the great
news is containerization Works across
all of these different environments now
you IR my curiosity here so did the
containerization strategy in recent
times influence the virtualization
strategy by any chance it absolutely did
for us and particularly what we're
looking at now are some of the newest
changes in open source and we are now
moving away from a strategy that let's
say was a pure virtualization strategy
to one that is instead more tightly
integrated with the back plane of the
containerization strategy not to mention
that the increasing costs of
virtualization have also moved us to
increase our amount of containerization
we have and move even more strongly into
a fully containerized environment so
those of you who are watching the video
who are dealing with any
challenges related to
virtualization bookmark this so that you
can come back and revisit that for your
Enterprise Great Kyle so we have the
operating environments we have automated
we have containerized there is some
critical element missing here data
absolutely can't have an i a CIO office
without data because everything runs on
data it's the fuel that runs the entire
organization now when I think about data
I have to think about it in three
different forms first of all there's
data at rest now what that means is
that's essentially everything that's
sitting in your databases you have to be
able to not only think about where you
want to store everything but how you
want to manage it what the standards are
around it and how you deal deal with it
then obviously you've already started
working on data in motion that's what
are the strategies you're going to have
around being able to make sure that you
understand how applications communicate
with each other what are the kinds of
approaches you're going to take be they
Q driven or event driven or apid driven
you have to work out all of those
different pieces it's the last one
that's a little bit unusual when we're
talking about data at rest and even when
we're talking about data at motion we're
usually talking about structured data
but that's not all of the data we have
we have lots and lots of unstructured
data or content and where that has
really become important in the last few
years is this is again the fuel that
drives AI being able to have a Content
strategy for your data especially one
that involves
vectorization and the ability to take
advantage of vector databases in
multiple different types of search
allows you to be able to effectively
take advantage of the new capabilities
of new AI models like large language
models that's great so now when I start
thinking about the consumers the
customers right the app you know we all
have our phones and there are different
interfaces the application is the phase
of the Enterprise to the customer so why
are we doing all this yes it is to
simplify but to simplify for whom It's
The End consumer right so I would submit
let me take a guess this is all about
the applications and all together I
would say especially the
containerization is really the platform
for application would you agree Kyle it
is and if this is the application layer
and that's where all of the magic
happens is inside of your applications
the rest of what we've seen beneath it
is how we support that layer of
applications now that can be both custom
applications or off-the-shelf
applications third party applications
they still need that support of the
underlying containerization and
automation layer and they need the
support of that data layer to be able to
do the work that they need to do and so
what we've done as part of this is we
have as we've talked about been on this
journey to not only containerize our
applications but to modernize our
applications to be able to take
advantage of this containerized
automated environment excellent so you
know V don't know who's going to be
watching the video there could be
several different roles right so you
could be a CIO you could be a decision
maker an influencer so what's in it for
you what you're seeing here is yes the
you know the hard truth is that the
technology landscape is complex but the
approach the solution and how we address
the challenges does not have to be you
can simplify it that's what we heard
from Kyle how this can and should be
simplified simplification leads to
standardization it leads to you know
easier management you know and then the
kpis that you have time to deploy
realizing value business value all of
those come along with the magic word is
simplification that's what you have
especially if you are a decision maker
in a position to you know justify
rationalize why we have what we have
from an IT
perspective on the other hand you could
could be an engineer you could be a
developer you could be an operator let's
talk about operators life is much easier
if the right things are automated so
that you can focus on you know what
cannot be automated and what should not
be frankly there needs to be that human
touch so you can actually focus on the
intelligence of the patterns that you're
seeing so that when you do root cause
analysis there is actually more meaning
to it you Leverage The automation you
Leverage The analytics to do it right
there are some that only the human brain
can do so that's what is in there for
operators hello developers let's talk
about this here and you know to do all
this you need somebody to actually do it
right and then write the code and um you
know think through the logic and you
know the algorithms and all of that what
is this layer Kyle that's the developer
tools layer and that's absolutely
critical to be able to take advantage of
any of the capabilities we've talked
about beneath it now for us what we
found is that there were several
different pieces of this developer tool
ler that were really important first of
all we had to be able to essentially get
a handle on the explosion that we had of
cicd environments one of the things
about architecture that has happened
over the last few years is that very
deeply distributed architectures things
like microservices architectures have
become very popular microservices
architectures are great but they have
this kind of interesting side effect in
that they make the number of
cicd platforms you have multiply like
rabbits they're
everywhere and it becomes very important
to set some standards and to especially
build tools that can help your teams to
be able to work more effectively in that
kind of Highly distributed environment
and so so what we ended up doing is we
ended up putting together a common cicd
layer supporting a number of other tools
including things like developer metrics
and including other things like uh now
we're getting into AI code assistance
all of which are supported by a common
set of underlying tools that give you
the ability to use the different pieces
of this platform excellent so I would
act you know yes we have been talking
about customers the external consumer
the external customers but frankly from
my perspective developers are the
internal customers so if we make these
environments easy to use for the
developer so that a minute of the
developers time realizes value thanks to
the underlying platforms and the
efficiencies that are built in the
developers can actually produce more
rapidly and be more relevant to the
features that the customers are looking
for that's another reason why you know
IBM is doing all of this right so that
the developers can actually you know get
value out of the environments they are
in and we live in a world today if the
developers don't get what they need
they're going to go west they're going
to go elsewhere and then do things where
they actually have the environments they
like would you agree Kyle I completely
agree and speaking of our friends the
developers uh the one thing that
Everyone likes now and that everyone is
incorporating into it seems every
application in our entire portfolio is
AI which is our last B bucket that we
have here now the interesting thing
about AI is that it follows a lot of the
principles that we've been talking about
through these other layers you have to
have a strategy for how you're going to
deal with AI you have to have an
environment in which you're going to be
running your llms and in which you're
going to be especially gaining access to
those piles of data that are important
not just for training and fine-tuning
but also for things like the rag pattern
and you need to be able to get to um
your applications running here through
things like the react pattern to allow
you to be able to not just have large
language models play interesting tricks
with language but to be able to make
them do things that are useful to your
business and so that's the last of the
layers that we've seen be really
important here is you have to be able to
have a common standardized way of doing
things so that everyone doesn't go off
and do things in their own way which can
create not only interesting problems in
being able to manage your portfolio but
especially ethical and security problems
now ai can actually write code too right
so how would you distinguish between
what would AI come out with versus what
the developer still needs to write it's
not like the developers don't have to
write code anymore there is a balance
there right absolutely there's there's a
deep division that we have here in that
uh let's say a lot of people think that
AI can write all of your code uh but the
only people that think that are the
people who actually aren't writing the
code uh instead what we found is AI can
be a great helper in writing code you
still have to have very good
specifications for it you still have to
know what the outcomes are that you want
from it in other words you have to be
able to specify what you want from your
tests you have to be able to specify
what you want in terms of being able to
describe the architecture of the outcome
and so that's why what we've seen as
part of the developers tools and as part
of the tools that we're building on top
of developers tools what is instead a
partnership rather than AI taking over
the entire uh Business of Being a
developer or being a manager
or being an operator is part of any of
the pieces of this puzzle excellent so
you're the CTO to the CIO right at IBM
so what does the you know what does the
ceso have to say about it Kyle right
where does security come in I have to
say that uh security is at top of mind
for all of us all the time and so it's
something that cuts across all of these
different layers we have to think about
our physical security that we have we
have to think about what it means to be
securing our operating systems and our
containers we have to think about data
security we have to think about
application Level security all of these
different pieces are something where
security enters into the equation and we
have to balance that as part of the
overall approach that we're trying to
set up that's fantastic as I look at the
you know the different personas who
could be watching the video we talked
about the decision makers we talked
about the Engineers both the developers
and The Operators there is one segment
that we haven't spoken about those are
the next generation of innovators from
Academia from The Faculty from the you
know the students so those of you who
are you know in high school and graduate
schools and so on you are the next
generation of the workforce there is a
lot going on here guys and girls for you
because what you're seeing here is not
just the technology of today you're
seeing how this is positioning for the
emerging technologies that are coming
around and also how we got here history
is very important in understanding where
we are going so what you heard in this
book that we wrote just in the last few
minutes is where we were starting with
the Z how we Evol to on-prem
virtualization to the cloud going to the
edge and how we have continued to grow
vertically leading up to artificial
intelligence thanks for watching before
you leave please please remember to
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