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Description
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Don't miss KubeCon + CloudNativeCon 2020 events in Amsterdam March 30 - April 2, Shanghai July 28-30 and Boston November 17-20! Learn more at kubecon.io. The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects
Unlocking all the Serverless - Tyson Norris, Adobe, IncSDCC
Options for executing container-based serverless workloads abound these days: Knative, OpenWhisk, AWS Lambda, Azure Functions, etc. At Adobe, we offer our customers serverless product extensibility through Adobe I/O Runtime, and we will be harnessing the power of multiple serverless implementations. This will allow customers to leverage the best system for the job, in a world where new systems are constantly appearing. We will be enabling customers to execute functions on OpenWhisk, V8 Isolates, Knative, and other systems through a unified developer experience. This approach allows the development experience to remain consistent, while being able to take advantage of the latest execution approaches.
A
A
Why
would
we
do
this,
and
the
answer
is
not
to
compete
with
things
like
AWS,
lambda
or
Google
or
or
it's
sort
of?
We
can
provide
Adobe
customers
with
an
adobe
based
platform.
Customers
don't
need
to
care
about
cloud
infrastructure,
accounts
or
anything,
except
for
their
Adobe
products
when
they
buy
Adobe
products,
they
can
gain
access
to
this
extensibility
engine
and
they
can
stay
within
the
Adobe
ecosystem
for
the
lifetime
of
their
application,
and
the
other
reason
is
so
that
we
can
provide
custom
environments.
A
A
So
a
little
bit
of
background
on
the
architecture
of
Adobe
I
run
time,
we're
using
Apache,
open
whisk
and
we
like
open
whisk.
It
has
a
great
community,
has
a
consistent,
simple,
stable
set
of
api's
and
one
of
the
really
nice
things
about
open
whisk.
Is
it's
a
turnkey
system?
So,
if
anybody's
thinking
about
leveling
up
from
hosting
api's
to
hosting
a
server
this
platform,
you
can
take
open,
whisk
and
drop
it
into
your
infrastructure
environment
and
have
a
server
the
solution
dedicated
to
your
customers.
In
a
relatively
short
timeframe.
A
We
went
GA
in
June
this
year.
That's
going
well!
It's
currently
used
by
a
couple
of
different
types
of
customers.
One
is
third-party
Adobe
customers,
and
these
are
folks
like
Yamaha
Microsoft,
where
they
they
purchase.
Adobe
enterprise
products
for
hosting
CMS
systems,
doing
data
analytics,
and
then
we
have
Adobe
teams,
and
these
are
first
party
internal
Adobe,
folks
that
are
using
the
runtime
system
to
build
features
that
aren't
directly
exposed
as
runtime
to
customers,
but
they
are
providing
extensibility
through
some
custom
add-ons
to
their
products
and
some
examples.
A
So
I
want
to
talk
a
little
bit
about
some
use
cases
that
we
are
going
after
supporting
within
Adobe
and
for
Adobe
customers
in
general.
We
try
to
support
all
the
use
cases.
This
isn't
work
everywhere
has
haven't
pointed
out.
There's
there's
some
cases
that
just
don't
slot
into
server
this
great,
but
some
examples
that
we're
supporting
today
on
the
extremes
are
large
memory.
Footprint.
A
Long,
timeout
type
of
functions
require
lots
of
disk
space,
limited
concurrency
where
they
have
to
operate
in
and
basically
a
single
threaded
model
and
a
common
case
for
this
is
something
like
image,
processing
or
video
processing.
We
have
teams
doing
this
and,
on
the
other,
extreme
is
low
memory,
footprint,
low,
latency,
high
concurrency,
and
these
are
things
like
user
facing
browser
applications
where
latency
is
the
name
of
the
game
and
keeping
things
fast
and
reliable,
and
this
is
also
a
similar
concept
to
running
functions
at
the
edge.
A
A
So
what
we've
been
building
a
wo
runtime
things
have
been
going
on
outside
of
Adobe,
and
a
few
of
those
things
that
are
kind
of
interesting
are
things
that
folks
at
cloud
flower
are
doing.
Kay
native
everybody
I'm
sure
here
is
well
aware
of
Kay
native
and
open
with,
is
evolving
constantly
on
its
own.
So
we're
gonna
talk
about
some
of
these
evolutions.
A
So
cloud
flower
is
kind
of
interesting
where
they
take
v8
isolates
and
effectively
run
multiple
functions
inside
of
the
same
process,
and
this
is
kind
of
interesting
because
it
adds
yet
another
layer
of
scaling
to
the
system
where
we've
talked
about
scaling,
not
at
the
VM
level
but
container
level.
For
a
long
time.
Now,
we're
talking
about
scaling
inside
the
process
inside
a
container
on
a
VM,
so
they're,
adding
new
low
latency
ways
to
scale
for
they
primarily
deliver
for
the
functions
at
the
edge
type
of
use
case.
Kay
native
service
connait.
A
A
So
it
looks
from
the
outside
potentially
similar
to
how
we
handle
our
micro
services
that
might
be
running
on
kubernetes
also
and
then
open
wisk
has
evolved
on
its
own
and
some
of
the
interesting
things
that
happen
within
open
wisk
are
the
ability
to
create
more
complex,
workflows
like
sequences
and
compositions.
Where
you
can
take
multiple
functions
and
stitch
them
together
and
treat
them
as
a
single
unit
of
work
and
have
the
system
man
at
the
state
that
is
transitioning
in
between
each
element.
A
And
while
Cerberus
has
evolved,
technology-wise
API
gateways
and
approaches
to
API
gateways
have
also
evolved,
and
specifically,
a
lot
of
us
are
following
the
evolution
of
envoy,
and
it
has
some
interesting
features
that
are
potentially
really
handy
for
a
server
this
platform.
So
these
are
things
that
I
would
just
rather
not
do
in
open
wisk
if
we
don't
have
to
so
things
like
authorization
and
rate
limiting.
A
A
So
if
we
look
at
things
from
a
use
case
perspective,
what
we'd
really
like
to
be
able
to
do
as
a
server,
this
platform
operator
is
to
be
able
to
use
the
right
tool
for
the
right
use
case.
So
for
latency-sensitive
services,
where
performance
is
really
key,
it
would
be
great
to
use
something
like
a
v8
isolate
type
of
approach
where
we,
where
we
get
a
lower
startup
and
initialization
cost
for
general-purpose
micro
service
scaling.
A
I
won't
go
too
deep
here,
but
just
some
basics,
so
the
basic
operational
components
in
open
wisk
are
the
controller
and
the
invoker,
and
the
controller
is
the
kind
of
ingress
portion
of
the
system
where
both
function,
developers
and
function.
Users
arrive
within
this
component
and
the
controller
handles
both
the
crud
operations
for
developers
and
the
invocation
workflow
for
routing
to
a
invoker
component
that
does
the
actual
container
and
initialization
and
function
management.
A
So,
what's
interesting
here
is
that
right
now
the
controller
handles
the
both
the
data
plane
and
the
control
plane
ingress
and
the
invoker
is
focused
on
container
management,
so
some
keys.
If
we
go
back
to
thinking
about
the
different
technologies,
if
we
think
about
how
we
can
apply
those
one
of
the
keys
is
separating
these
planes
in
Oakland
with
so
getting
away
from
having
a
central
place
where
both
the
data
plane
and
a
control
plane
have
to
flow
through.
A
A
So,
instead
of
forcing
everything
to
be
executed
inside
of
open
wisk,
we
want
to
be
able
to
choose
either
declaratively
or
dynamically
so
that
the
right
execution
environment
for
the
function
at
the
right
time
is
is
what's
used,
and
they
also
want
to
normalize
the
process
so
that
what
a
developer
sees
from
the
outside
looks
the
same,
no
matter
what
execution
environment
they're
using.
So
you
know
this
is
different
than
saying.
Well,
just
tell
people
to
use
lambda
or
k
native
because
it
looks
different
when
you're
using
those
independently.
A
So
going
through
each
of
these
first
separating
the
plans,
this
is
pretty
straightforward,
we're
just
separating
out
the
open,
Wis
controller
to
be
dedicated
to
our
control
plane,
and
now
we
have
a
new
component.
This
is
the
open
twist
router
and
this
is
gonna,
be
dedicated
to
our
data
plane
management
for
this
system,
so
most
of
my
talk
will
be
more
focused
on
the
bonus
router
point.
A
So
if
we
look
at
these
in
detail,
the
control
plane
stays
as
is,
and
the
data
plane
is
now
provided
by
the
open,
Wisc
router
and
it's
going
to
be
a
dedicated
data
plane
controller,
and
what
do
we
need
to
do
to
get
there?
Well,
this
is
a
brand
new
component,
so
we
are
working
on
building
that
and
we're
also
building
execution
providers
for
different
types
of
solutions.
A
Open,
wisk
and
k
native
and
v8
isolates
are
the
kind
of
primary
cases
that
we're
targeting
right
now
and
then
really
to
be
able
to
optimize
the
system
for
reuse.
We
want
to
allow
the
system
to
have
its
own
notion
of
resource
allocation,
that's
independent
of
the
execution
providers
but
available
for
these
providers
to
use
so
that
we
can
maximize
the
reusability
of
containers
because,
ultimately,
when
we're
talking
about
scaling
to
zero
and
then
continued
traffic,
the
reuse
becomes
the
key
component
to
minimizing
latency
and
avoiding
those
cold-start
type
of
situations.
A
So
next
up
in
our
technology,
Keys
is
the
ability
to
delegate
ingress
features.
So
what
we're
doing
is
taking
the
ingress
functionality.
That's
currently
baked
into
the
open,
whiskey
and
moving
that
out
into
our
envoy
gateway
system,
and
this
just
simplifies
the
controller
and
the
router,
and
it
also
makes
it
so
that
we
can
use
the
same
system
now
for
both
our
server
this
platform
and
our
API
system.
So
we
can
leverage
the
same
authentication,
rate-limiting
features.
A
A
That
applies
to
both
the
control
plane
and
the
data
plane
and
same
with
the
rate
living
where
there's
a
rate
limiting
filter
that
we
can
use
for
that.
One
of
the
features
and
open
wisk
that
we
want
to
keep
parity
with
is
a
concurrency
based
limiting,
and
this
is
similar
to
what
the
experimental
adaptive
concurrency
filter
in
envoy
provides.
A
So
once
you
get
into
the
open,
with
router,
getting
to
the
point
of
execution,
becomes
a
workflow
in
mapping
the
function
to
a
particular
execution
environment
and
within
the
open
with
router.
We
have
this
logic
that
is
able
to
map
the
function
to
the
appropriate
execution
environment
either
either
by
defining
annotations
on
function
or
by
specifying,
as
part
of
the
request,
giving
it
hints,
if
you
will
to
see
which
execution
environment
is
appropriate
for
those
function
calls.
A
So
if
we
look
at
an
example,
kind
of
up
close
Kay
native
is
kind
of
interesting
for
this.
We're
really
in
order
to
use
k
native.
In
this
way,
we
move
away
from
the
notion
of
one
function
per
image
in
K
native,
and
we
are
more
in
line
with
one
function
per
K
native
service
and
really
the
function
allocation
to
the
service
is
done,
lazily
and
in
open
wisk.
A
This
is
a
concept
of
stem
cells
that
helps
avoid
those
cold
start
times
where
we
start
the
K
native
service
or
the
pod
or
the
container
ahead
of
time,
and
then
the
code
for
the
actual
function
is
injected
lazily.
Whenever
traffic
starts
for
that
particular
function,
so
we
have
a
workflow
that
looks
like
this.
A
So
if
we
look
at
this
visually,
it's
a
little
bit
simpler.
So
we
have
the
open,
wes
router
on
the
left
side
here,
where
step
0.
So
basically,
when
the
router
starts
up,
we
allocate
some
stem
cells,
and
these
these
surface
and
k
native
as
separate
services
that
are
unallocated
and
then
step
1
is
we
get
a
request
to
handle
function?
1
and
the
first
step
for
that
particular
function
is
to
allocate
a
particular
service.
A
A
So
for
normalizing
the
input
and
output
of
the
system.
What
we're
looking
at
is
this
type
of
workflow,
where,
on
the
router
side,
we
can
transform
the
incoming
request
and
we
can
transform
the
response
so
that
the
developers
dealing
with
one
system,
the
the
Adobe
serverless
system
and
they're
not
dealing
with
whatever
different
types
of
execution.
Environments
that
we
have
so
the
the
developer
doesn't
need
to
know
any
details
about
Kay
native
or
kubernetes
or
v8
isolates
or
open
Wisc.
A
They
just
need
to
know
the
the
api's
of
the
Adobe
server
this
platform
and
either
they
can
shoot.
We
can
allow
them
to
choose
or
we
can
choose
for
them,
what
execution
environment
they
use,
but
we
handle
their
request
and
the
response
in
a
consistent
manner,
and
this
also
becomes
really
important
for
logging
and
metrics
so
that
we
can
consistently
handle
things
like
monitoring
billing,
where
we
want
to
be
able
to
have
a
consistent
view
of
the
system.
A
On
the
flip
side,
we
also
have
to
deal
with
resource
cleanup
and
garbage
collection,
so
just
to
call
out
some
negative
requirements,
the
things
that
we
are
not
wanting
to
do
so
we're
not
using
container
primitives
in
this
particular
component.
This
is
this
is
just
for
managing
resources
and
delegating
execution
to
the
execution
providers
and
we're
not
using
static
routes.
So
we
don't
want
to
end
up
with
a
routing
table
that
has
the
function
per
route
per
function,
type
of
map
in
memory
with
millions
or
billions
of
different
functions
that
are
changing.
A
You
know
at
the
whims
of
our
developers
and
this
this
routing
is
loaded
dynamically
on
demand
and
it
changes
constantly
and
then
some
potential
features
that
we
can
add
at
this
layer
of
abstraction
or
things
like
streaming,
where
we
don't
really
allow
users
to
connect
directly
to
containers
today,
but
we
could
allow
them
to
connect
to
this
router
and
proxy
that
streaming
behavior
into
the
container.
Whether
or
not
the
execution
provider
supports.
It
doesn't
really
matter
at
that
point,
and
some
other
interesting
things
that
we
can
do
are
things
like
outsourcing
burst
processing.
A
C
So
how
does
the
deployment
model
work?
Because
the
way
you
deploy
code
to
open
risk
is
different
and
we
do
two
key
Native
is
different
right,
open,
whisk,
I,
believe
it's
toast
the
code
in
a
document
database
and
caner
do
requires
at
a
docker
image,
so
that
is
also
handled
by
the
separate
component.
Yes,.
A
So
this
is
kind
of
what
I
was
getting
at
we're
open,
which
provides
kind
of
a
turnkey
system.
That
includes
an
API
for
deploying
code,
and
it's
true
that
today,
that
code
is
stored
in
a
document
database.
But
what's
important
for
the
developer
perspective.
Is
that
there's
an
API
for
injecting
a
blob
of
code
into
the
system.
B
A
A
So
it's
it's
getting
better
I.
Think
one
of
our
colleagues
built
a
debugger,
that's
very
cool
that
I
think
really
closes
a
lot
of
that
gap.
There's
also
a
browser-based
editor
that
was
recently
built
by
another
company
called
the
Impella.
So
it's
you
know
it's
not
as
broad
as
desktop
development,
or
probably
some
of
the
tools
that
you
can
get
with
with
lambda,
but
it's
it's
growing
and
the
gap
is
closing.
Thank.
A
D
A
A
In
general,
a
lot
of
you
know
a
lot
of
systems
today,
work
by
having
a
whenever
you
add
a
function
to
the
system.
You
have
a
route
in
the
system
and
open
which
doesn't
work.
That
way,
and
that's
that's
one
of
the
things
that
I
like
about
it
is
because
you
can
have
a
infinitely
large
set
of
functions
in
the
system
without
having
a
crazy,
huge
routing
table.