How
good
would
it
be
if
your
data
centre
was
smart
enough
to
help
you
proactively
optimise
your
operations?
Well,
the
good
news
is
artificial
intelligence
(AI)
and
machine
learning
(ML)
technologies
are
making
digital
infrastructure
smarter
and
more
intuitive.
To
create
real
customer
value,
let’s
look
at
how
AI/ML
models
are
being
used
in
five
areas
to
realise
greater
data
centre
optimisation
and
efficiency,
and
enhance
user
experience.
1.
Energy
Efficiency
and
Sustainability
In
the
fight
against
climate
change,
businesses
want
to
reduce
the
environmental
impact
of
their
IT
infrastructure
and
become
carbon-neutral.
Sustainability
is
such
a
priority,
investors,
employees,
partners,
and
customers
consider
a
company’s
sustainability
rating
before
getting
involved
with
them.
A
low
power
usage
effectiveness
(PUE)
and
water
usage
effectiveness
(WUE)
can
address
both
energy
efficiency
and
sustainability
metrics.
AI/ML
models
optimise
a
data
centre’s
energy
efficiency
by:
-
Proactively
managing
the
PUE
and
assessing
the
impact
of
the
ongoing
manual
changes
in
the
operating
parameters
of
data
centre
assets. -
Identifying
the
best
set
of
operating
parameters
for
individual
or
group
assets
to
minimise
energy
consumption
while
achieving
the
desired
physical
impact
from
each
asset
(i.e.
maintaining
optimal
temperature,
humidity,
etc.). -
Predicting
and
optimising
WUE
to
operate
your
data
centre
in
a
more
sustainable
manner.
Meeting
global
data
centre
energy
demands
while
achieving
sustainability
objectives
presents
IT
operators
with
some
unique
challenges,
from
finding
clean
and
renewable
energy
sources
to
managing
power
and
cooling
through
adaptive
intelligent
control
systems.
At
Equinix,
we
are
advancing
a
bold
environmental
agenda
through
a
range
of
commitments,
which
include
reaching
our
goal
of
100%
clean
and
renewable
energy
usage
across
our
global
portfolio
(in
2021
we
reached
95%)
and
becoming
climate-neutral
in
our
global
operations
by
2030.
We
leverage
AI/ML
to
develop
PUE
optimisation
models
that
recommend
optimal
operating
parameters
for
assets
based
on
a
“state
of
the
asset”
digital
twin.
We
can
forecast
power
and
space
capacity
in
our
IBX
data
centres
and
ensure
customer
requirements
for
specific
megawatt
thresholds
are
met.
We
also
incorporate
greater
visibility
into
our
customers’
cabinet-level
energy
consumption
via
our
IBX
SmartView
DCIM
APIs.
The
direct
customer
benefit
is
they
can
move
their
IT
into
a
“ready-made”
sustainable
data
centre
with
a
digital
infrastructure
platform
that
gives
them
valuable
data,
helps
regulate
energy
usage
and
meet
sustainability
goals.
2.
Asset
Performance
Management
Asset
performance
management
(APM)
includes
data
capture,
integration,
visualisation
and
analytics
for
the
purpose
of
improving
the
reliability
and
availability
of
physical
assets.
AI/ML
models
for
APM
have
been
demonstrated
to:
-
Extend
the
useable
life
of
an
asset
by
proactively
detecting
and
fixing
asset
operating
parameters
that
may
reduce
its
usability
(e.g.
fans
changing
speed
too
frequently). -
Predict
when
an
asset
needs
maintenance
based
on
its
operating
conditions,
and
when
to
move
from
scheduled
to
predictive
maintenance
to
lower
costs
and
improve
customer
and
employee
satisfaction
by
reducing
unplanned
outages. -
Learn
normal
operating
conditions,
such
as
energy
usage,
for
individual
and
group
assets
and
then
identify
anomalous
operating
conditions
by
monitoring
real-time
data
streams.
Aging
or
poorly
functioning
assets
can
be
a
drain
on
power
and
the
cause
of
system
failures
and
user
dissatisfaction
if
not
quickly
identified.
We’ve
developed
anomaly
detection
models
that
predict
assets
with
anomalous
operating
conditions
to
improve
an
asset’s
lifetime,
reduce
costs
and
lower
energy
consumption.
These
optimisations
have
improved
customer
and
employee
satisfaction
by
contributing
to
our
industry-leading
reliability
(99.9999%
average
uptime)
in
our
data
centre
operations.
3.
Capacity
Management
and
Planning
As
business
requirements
change,
companies
are
dealing
with
limited
resources
to
address
fluctuating
compute,
storage
or
networking
capacity
needs,
and
over
or
under
provisioning
data
centre
capacity
for
IT
infrastructure
can
lead
to
greater
waste
and
increased
costs.
AI/ML
technologies
enable
you
to
efficiently
manage
and
plan
resources
to
maximise
revenue,
reduce
TCO,
and
be
more
sustainable.
They
also
provide
valuable
data
to:
-
Learn
space
layout
and
optimise
the
sellable/useable
space
in
a
data
centre
while
maintaining
the
constraints
around
temperature,
humidity,
etc. -
Plan
from
current
and
past
data
centre
power
usage
and
optimise
near-term
and
predict/prescribe
future
power
consumption.
We
use
AI/ML
modelling
to
assess
our
current
and
future
capacity
and
power
needs
by
proactively
anticipating
our
customers’
colocation
and
digital
infrastructure
needs.
4.
Security
Cyber-attacks,
security
breaches
and
data
leaks
continue
to
be
big
threats,
with
digital
leaders
looking
to
AI/ML
technologies
to
improve
data
centre
security
posture
by
closing
seen
and
unseen
gaps
in
security
controls.
You
can
protect
your
equipment
and
data
using
AI/ML
models
for
capturing:
-
Video
analytics
that
monitor
and
flag
suspicious
activities
around
your
data
centre
and
within
or
near
customer
cages
via
video
surveillance
analysis. -
Cage/cabinet
access
data
that
model
and
detect
anomalous
access
patterns
for
customer
access.
Data
centre
security
requires
physical
and
virtual
controls
to
cover
an
ever-expanding
attack
surface.
In
addition
to
our
advanced
security
equipment,
techniques
and
procedures,
AI/ML
models
that
analyse
video
streams
and
logs
from
our
equipment
in
our
IBX
data
centres
detect
anomalies,
making
our
data
centres
and
customers’
equipment
and
data
even
more
secure.
5.
Productivity
Improvements
Improving
data
centre
workflows
and
processes
using
AI/ML
technologies
increases
user
satisfaction
and
productivity,
and
provides
valuable
insights
for
optimisations
to
future-proof
data
centres.
AI/ML
models
have
been
shown
to
effectively:
-
Facilitate
incident
management
by
clustering
events
in
similar
topics
to
more
quickly
solve
new
incidents
based
on
past
learnings
and
best
practices. -
Reduce
data
centre
work
ticket
processing
time
by
parsing
details
of
customer
work
and
more
quickly
send
them
to
the
right
team.
Reducing
the
number
of
manual
processes
and
improving
productivity
through
AI/ML
techniques
results
in
faster
response
to
customers’
needs.
At
Equinix,
we’ve
eliminated
manual
approvals
for
approximately
two-thirds
of
transactions
by
workflow
automation,
expediting
our
business
processes
and
customer
response
time.
We
also
use
natural
language
processing
(NLP)
in
our
intelligent
automated
ticket
routing
as
a
proof-of-concept
in
our
Equinix
Smart
Hands
remote
data
centre
management.
By
quickly
classifying
support
requests
via
NLP,
we
can
improve
the
customer
experience
with
faster
responses.
At
Equinix,
we’ve
made
a
real
investment
in
AI/ML
technologies
and
techniques
to
optimise
our
data
centres
and
global
business
operations.
We
continue
to
progress
on
our
sustainability
goals
and
look
to
build
a
business
that
reflects
our
purpose
to
bring
the
world
together
on
our
platform
to
create
the
innovations
that
will
enrich
our
work,
life
and
planet.
