►
Description
A presentation of the Hyperledger Telecom Special Interest Group on July 13, 2023 featuring Moshe Shadmon, Founder and CEO at AnyLog. Moshe discusses how they are using Hyperledger Fabric and shows a demo.
AnyLog is a software agent deployed on edge nodes. These nodes become members of a decentralized, self-managed and secure network that is optimized to manage distributed edge data and resources - using virtualization, applications extract real time insight from data distributed at the edge without centralizing the data. In addition, AnyLog manages and monitors all the distributed edge resources as if they are a single machine.
Hyperledger Fabric serves as a shared metadata layer allowing all the nodes of the network to process data as a single unified machine. The shared metadata is organized on Hyperledger Fabric in the form of policies that determine how nodes operate. Some examples: 1) Very similar to an optimizer in a DBMS that determines, per query, which indexes to use, nodes register policies describing the data that they store, such that when a query is issued, the policies determine the nodes that participate in the query process. 2) When a new schema is created, it is registered as a policy on the blockchain such that all the nodes processing the same type of data will use the same schema. 3) Messages are signed by the sender’s private key. To process a message, the processing node will use the sender’s policy to retrieve the sender’s public key and authenticate the sender. Using permissions policies, the processing node validates the sender’s permissions.
If you're interested in how blockchain is being used in telecom, join our Telecom Special Interest Group: https://wiki.hyperledger.org/display/TCSIG/
Sign up to the group's list to get notified of upcoming events and feel free to introduce yourself: https://lists.hyperledger.org/g/telecom-sig
----
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License
Creative Commons Attribution license (reuse allowed)
A
C
So
hi
everyone
thanks
for
having
me
here
at
this
for
the
meeting
and
great
great
to
be
with
you
and
I've
I'm
sharing
my
my
email
here.
So
if
somebody
is
interested
in
more
information
or
try,
the
the
approach
that
I'll
be
presenting
feel
free
to
reach
out
to
me
afterwards.
What
any
log
is
doing
is
we
are
transforming
The
Edge
to
Virtual
Cloud.
So
we
know
that
the
edge
is
a
very
complicated
environment
and
I'll
talk
a
little
bit
about
that.
C
What
any
look
does
and
I'll
and
I'll
try
to
be
very
detailed
to
show
you
the
the
details
of
the
story.
What
any
load
does
it
makes
The
Edge
an
environment
which
is
very
simple
and
easy
to
interact
with
and
at
high
level
what
that
means
that
all
the
distributed
data
becomes
accessible
and
available
for
a
single
point,
so
companies
or
users
could
interact
with
their
Edge.
C
Servers,
Raspberry,
Pi,
switches,
gateways
and
sensors
is
if
all
those
distributed
resources
are
a
single
machine
which
makes
it
very
much
like
a
cloud
you
log
into
some
to
some
web
page
and
from
there
you
could
manage
all
your
data
and
all
your
resources.
The
same
thing
is
achievable
using
this
approach
that
I'll
be
showing
and
explaining.
C
C
C
What
companies
are
forced
to
do
in
this
scenario
is
to
centralize
the
data
if
we
continue
in
this
Circle.
The
next
thing
to
mention
is
that
the
resources
that
resources
are
very
limited.
It's
not
the
same
servers
and
the
same
setup
which
you've
got
you've
got
at
the
cloud.
You've
got
different
Raspberry
Pi's
switches
which
are
not
uniform,
connectivity,
sometimes
lost
so
Edge
resources
are
lacking
or
lagging
behind
what
the
cloud
is
able
to
offer
and
then
a
very
common
problem.
C
You
you
put
some
process
on
some
Edge
nodes
and
this
process
could
see
local
data,
but
in
many
cases
to
get
to
the
right
decision
or
do
the
process
right.
You
need
to
see
data
which
is
not
locally
on
your
node.
So
what
do
you
do?
Then?
This
becomes
complicated.
Now
you
have
to
transfer
the
data
between
nodes
and
write
some
complex
protocols.
This
is
not
something
which
is
simple
and
easy
to
do.
C
Companies
try
to
minimize
their
work
at
the
age
and
and
yeah
and
centralize
the
data
and
for
the
things
that
they've
got
to
resolve
at
the
edge.
You
know
if
you
have
to
shut
up
shut
some
valve,
you
cannot
bring
the
data
to
the
cloud
first,
you
have
to
figure
out
how
to
do
it
at
the
edge
and
it's
in
this
scenario
becomes
a
very
complicated
process.
C
So
that's
that's
the
state
of
the
edge
and
what
I'll
show
you
is
a
way
using
the
blockchain
using
hyperledger
fabric,
a
way
to
overcome
all
those
issues.
But
before
I
talk
on
the
solution,
let
me
just
paint
what
companies
are
doing
today,
how
the
typical
architecture
looks
like
so
you've
got
Edge
you've
got
Edge
devices
or
applications
that
generate
data
at
the
edge
and
what
what
what
companies
are
doing.
First,
you
have
to
collect
the
data
if
some
some
nodes
at
the
edge
that
would
host
the
data
in
here.
C
C
This
is
too
much
data,
so
maybe
I'll
just
take
summaries
or
part
of
the
data,
so
you
build
those
Solutions
and
from
there
the
data
goes
upstream
and
there
are
more
layers
which
I'm
skipping,
but
but
but
eventually
the
data
would
reach
out
to
the
cloud
where
you
host
the
data
in
let's
call
it
domain
databases,
databases
that
are
built
to
satisfy
the
structures
and
to
create
the
environment
where
you
could
connect
your
applications
and
and
and
from
there
you
could
service
the
data
to
the
applications.
So
obviously
this
is
a
complicated
setup.
C
It
takes
months
to
build
those
things
by
the
way
it's
over.
70
of
each
project
fails,
and
this
is
just
part
of
the
story
right
on
top
of
it
like
we
said
there
are
things
that
you've
got
to
do
at
the
edge
or
you
need
to
do
at
the
edge.
So
now
you
have
to
push
applications
to
the
edge
which
makes
the
story
more
complicated
and
again
we
could
go
on
and
on
and
talk
about
this
architecture,
but
there
is
no.
C
There
is
no
other
way,
that's
what
companies
are
building
and
obviously
this
is
Project
based
it's
our
to
build
management
scale,
those
setups
and
those
architectures.
You
end
up
with
multiple
software
Stacks
in
the
different
tiers
data
is
siled
and
obviously
very
big
impact
on
the
cost.
But
that's
that's
the
the
why
companies
deal
with
Edge
data
today
and
let
me
show
you
kind
of
a
different
approach,
so
so
the
first
thing
that
I
would
talk
about
it's
in
the
context
of
hyperledger
hyperledger
fabric.
C
We
use
it
first
as
a
way
to
deal
with
the
metadata
and
and
I'll
talk
about
a
process
and
then
you'll
see
how
it
fits
into
the
bigger
picture
so
the
way
that
it
works,
we've
got
hyperledger
and
we've
got
nodes
that
host
data
and
we'll
talk
about
those
nodes.
And
what
happens
here
is
the
nodes
host
the
data
hyperledger
posts,
the
metadata,
the
information
about
the
data,
and
this
could
be.
We
call
the
information
that
we
write
to
the
blockchain.
C
So
again,
the
data
lives
on
the
on
those
nodes.
Those
are
the
blue
circles
on
the
left
to
the
to
the
blockchain,
and
the
metadata
is
on
the
blockchain
and
on
in
that
context,
users,
applications
nodes,
could
create
policies
and
to
create
a
policy
or
to
save
the
policy
or
to
add
the
policy
to
the
method
data.
All
that
you
need
is
to
create
the
policy,
which
is
a
Json
structure
that
lists.
What's?
What's
the
policies
about,
and
then
you
give
the
policy
to
one
of
the
nodes
in
the
network.
C
It
doesn't
matter
which
one
this
policy
would
add
the
information
into
the
blockchain.
So,
from
the
application
point
of
view,
the
the
process
is
very
simple:
it's
just
a
rest
request
where
the
policy
is
being
added
to
a
node,
the
node
would
take
it.
It
has
an
API
to
hyperledger,
would
put
it
on
the
blockchain.
C
But
what's
interesting
is
that
if
something
is
added
to
the
blockchain,
all
the
other
nodes
that
participate
here
have
a
process
which
would
which
continuously
synchronize
their
metadata
their
knowledge
with,
what's
being
added
to
the
blockchain
and
by
the
way
they
could
also
list
the
things
that
they
care
about.
So
they
don't
need
to
bring
everything,
but
everything
that
they
care
about
would
be
available
to
all
those
nodes
that
participate
in
this
process
of
hosting
the
data.
C
At
the
same
time,
if
a
node
or
a
user
or
application
needs
information
which
is
of
the
metadata,
you
could
just
reach
out
to
any
node,
because
all
the
nodes
are
in
this
model
are
synchronized
and
ask
for
whatever
he
wants,
and
it
would
get
it
back
what's
what's
we
will
focus
in
this
talk
is
about
data
distribution
policies,
so
data
distribution
policies
are
when
a
node
post
data.
It
would
register
on
the
blockchain
a
policy
that
would
say
I'm
hosting
this
type
of
data.
C
Now
how
this
comes
into
play,
you'll,
see
in
the
next
slides,
but
what's
important
now
to
to
remember,
is
that
we've
got
a
shared
metadata
layer
which
makes
all
the
metadata
available
to
all
the
nodes
and
if
a
node
or
a
user
adds
something
to
the
metadata,
this
metadata
becomes
available
to
all
the
nodes
that
that
participate
here
and
we'll
see
how
how
this
functionality
is
being
leveraged
in
up.
You
know
managing
the
data
at
the
edge
so
from
here.
Let's
just
forget
it
we'll
get
back
to
the
blockchain
part
and
how
it's
being
played.
C
Let's,
let's
move,
let's
switch
gears
and
think
about
the
data
for
for
a
few
minutes
and
then
we'll
see
how
all
those
things
come
together.
So
in
that
sense,
we've
got
things
that
generate
data
could
be
you
know,
a
smart
home
got
a
lot
of
sensors
lots
of
things
that
that
generate
data.
What
we
will
do
here
will
take
all
the
data
and
connect
it
to
a
node
that
hosts
any
logo
need.
Okay,
so
data
from
whatever
is
generating
data
here
could
be
the
refrigerator
it
could
be
a
thermostat
whatever
it
is.
C
It
would
go
to
a
node
that
could
sit
at
the
home.
This
node
would
get
the
data,
and
there
is
here
part
of
the
technology
which
we're
gonna.
I
will
not
talk
about
it
in
this
presentation,
but
if
somebody
is
interested
again
we'll
be
happy
to
to
share
more
information
offline,
but
the
note
gets
the
data
and,
what's
important
to
understand
is
that
he
knows
in
an
automated
way
how
to
manage
the
data.
So
you
don't
need
to
send
an
engineer
there.
C
All
you
have
to
do
is
take
the
data
from
your
Edge
devices,
bring
it
to
a
node
at
the
edge.
This
node
would
look
at
the
data
would
know
how
to
host
it
like
a
locally
and
store
it.
Luckily,
all
of
that
is
done
with
full
automation,
but
then
there
are
other
use
cases
right.
If
you
think
about
a
car
generating
a
lot
of
data
from
a
lot
of
sensors,
so
again
the
same
story,
we
could
host
the
the
analog
node,
the
trunk
of
the
car.
C
This
node
would
would
host
the
data,
who
does
the
data
locally
and
again,
what's
important
to
remember.
This
is
completely
automated.
No
Engineers,
no
domain
knowledge.
If
you're
a
member
of
one
of
the
previous
slides,
it
was
all
about
domain
knowledge
that
needs
to
be
brought
to
the
edge
nodes
here,
just
place
any
log
connected
devices
and
push
the
data,
and
then
there
so
many
use
cases
right.
C
So,
with
all
those
use
cases
just
bring
the
data
to
nodes
and
by
the
way
we
don't
say
where
those
nodes
needs
to
live,
it's
really
up
to
to
the
user
could
be
on
the
production
floor.
Could
be
you
know
at
the
local
data
center
and
it
could
be
on
the
device
itself,
whatever
the
use
case
is,
and
the
claim
is
now
and
I'll
show
you
how
everything
comes
together.
But
the
claim
is
now
is
interesting
and
it
says
that
there
is
nothing
else
that
needs
to
be
done
from
here
on.
C
The
data
is
available
to
the
applications
and
what
I'll
show
you
and
maybe
from
from
the
previous
slide,
you
could
start
to
figure
it
out,
but
if
somebody
needs
the
data,
let's
say:
I'm
driving
a
car
and
I
want
to
find
when
I
live
near
San
Francisco
I
want
to
find
charging
stations
available
in
in
San
Francisco
that
have
a
line
which
is
smaller
than
five
minutes
of
weight.
I
could
issue
the
query
and
get
the
result
from
The
Edge
node,
rather
than
from
from
a
centralized
database.
C
In
this
model,
there
is
a
place
for
a
centralized
database.
I'll
I'll
talk
about
it,
but
but
in
that
context
that
we're
currently
talking
you
get
the
data
from
the
edge
in
real
time
without
intermediaries
very
efficient.
In
the
same
way,
let's
say
a
user
or
a
process
wants
to
deal
with
Energy
share
it
issues,
a
query:
it
sees
exactly
what
it
would
it
would.
C
It
would
get
exactly
what
it
would
get
if
we
were
to
transfer
first,
the
data
to
some
centralized
database,
but
here
it
comes
from
the
edge
and
again
everything
here
is
done
without
Engineers.
So
what
what's
happening
here
under
the
hoods
is
when
a
query
is
being
issued?
We
identify
the
location
of
the
needed
of
the
data
at
the
edge,
so
we
could
have
millions
of
nodes
in
this
model.
When
a
queries
is
issued,
we
identify
whether
relevant
data
resides
and
I'll.
Show
you
more
details
of
that.
Then
it's
all
a
peer-to-peer
play.
C
We
connect
the
the
consumer,
the
application
with
the
nodes
that
host
the
data.
It's
all
peer-to-peer,
it's
all
done
in
an
automated
way
and
it's
in
real
time
and
in
a
very
secure
way.
So
again,
I
want
I.
Don't
have
time
to
talk
about
the
security,
but
it's
a
more
secure
model
than
the
cloud
which
is
also
an
interesting
topic
to
discuss
so
from
here.
C
Let
me
show
you
how
what's
going
on
internally
and
give
you
a
little
bit
more
details
and
then
I'll
show
you
a
quick,
a
quick
demo
of
of
how
it
works,
so
we
get
a
feel
how
it
looks
like
so
so.
The
first
thing
is:
let's
take
the
nodes
and
deploy
it
wherever
data
is
being
generated
and
again
we
don't
care
where
the
nodes
live,
but
each
one
of
those
nodes
is
connected
to
devices
plcs
sensors.
C
Whatever
assist,
then
there
is
another
layer
which
we
mentioned
before,
which
is
you
know
the
blockchain
layer
which
holds
the
metadata
and
between
those
nodes
and
the
and
the
shared
metadata
layer.
This
is
this
continuous
synchronization
process,
where
policies
are
being
added
and
immediately
all
those
policies
are
available
to
all
the
nodes
in
the
network.
So
you
could
think
about
all
those
nodes,
although
they
are
distributed,
they're
working
against
the
shared
metadata
layer,
which
makes
them
operate
like
one
unified
machine
which
is
fully
synchronized.
B
C
And
being
pushed
into
those
nodes
at
the
edge
and
like
we
said
before,
that's
the
only
thing
that
needs
to
be
done
from
here
on
the
data
is
available
and
the
way
that
it
works.
An
application
needs
data.
It
takes
its
query
and
hand
it
hands
the
query
to
one
of
the
nodes
in
the
network.
It
doesn't
matter
which
one
this
node
may
not
have
any
relevant
data
right,
but
it
just
bring
the
query
to
a
node
in
the
network.
C
This
node
looks
at
the
query
at
one
hand,
and
it
has
the
shared
metadata
layer
right.
So
it
knows,
and
if
you
remember,
we
circled
the
distribution
policies,
so
it
understands
where
the
relevant
data
resides.
So
it
could
do
a
lookup
to
the
blockchain
where
it
says
you
know
it,
which
are
the
nodes
that
host
the
data
of
table
X
or
in
the
case
of
Machinery
I,
want
to
look
at
machines
since
owner.
Are
you
know
a
particular
company
or
Machines
of
version?
Why
or
nodes
in
a
region
or
whatever?
C
C
Those
notes
are,
are
those
notes
would
process
the
data
locally
and
return
a
result
to
a
unified
result
to
the
application?
Sort
of
application
sends
a
query
and
it
sees
exactly
what
it
would
see
if
it
would
work
against
a
synchronized
database
and
I
could
also
argue.
I
won't
go
into
it,
but
if
somebody's
interested
again,
we
could
take
it
offline
or
reach
out
to
me.
This
is
more.
This
process
is
more
efficient
than
running
against
a
centralized
database.
C
This
is
really
we're
talking
here
about
real
time
or
near
real
time,
and
obviously
the
data
remains
at
the
edge.
What
also
is
important
to
say
is
that
the
cloud
or
centralized
databases
always
have
a
role
in
the
sense
that
there
is
some
data
that
has
to
be
in
the
cloud,
for
you
know,
other
reasons
that
what
we've
discussed
here
or
in
addition
or
whatever,
whatever
the
reason
is
so
that
the
cloud
is,
is
like
a
client
to
this
thing.
It's
like
an
application
where
it
could
query
the
data.
C
C
So
you
can
think
about
if
it's
extending
you
know
the
cloud
to
the
edge
or
creating
a
virtual
virtual
Cloud
over
page
and
before
I
show
you
the
the
demo
here
is
just
you
know,
a
summary
slide.
Where,
on
on
the
left
side,
you
see
all
the
reasons
why
the
cloud
is
attractive,
why
companies
are
using
the
cloud
or
you
know,
centralized
database.
In
that
sense,
you
know
you
manage
the
data
from
a
single
point
right
and
that
you
could
do
it
in
the
cloud
at
the
edge.
C
You
have
those
distributed
nodes
that
you
did
need
to
deal
with
each
one
individually.
Well,
this
approach
from
the
user
point
of
view.
From
the
application
point
of
view,
all
the
data
Academy
manage
and
be
available
from
a
single
from
a
single
point,
as
if
it
is
centralized
a
vote,
it
remains
at
the
edge
if
you
have
a
unified
view
of
the
data,
even
if
the
data
remains
at
the
edge
using
an
analog.
C
High
availability
is
supported
in
supported
in
the
in
the
in
the
cloud,
but
it's
missing
at
the
age
we
provide
High
availability.
We
want
again
I,
don't
have
time
to
talk
about
that
here,
but
this
is
highly
available.
It
is
very
secure
and
obviously
the
the
only
reason
to
push
processes
to
the
edge
is
real
time
right.
You
need
the
real
Time
That's,
the
only
not
reason,
but
the
only
advantage
of
of
of
of
of
the
edge
over
the
cloud
well
since
you're
at
the
edge
with
any
log.
C
Look
here
are
all
the
reasons
why
you
move
all
your
data
from
The
Edge
to
the
cloud,
but
we
could
bring
all
this
functionality
to
the
edge
and
make
the
edge
appear
to
you
is
if
it
is
a
centralized
database
or
or
a
cloud
and
from
here.
Let
me
show
you
a
quick,
quick
demo,
so
this
is
the
live
demo.
This
is
not
a
mock-up
or
anything
like
that.
So,
if
you
see
those
circles,
those
are
are
analog
nodes
that
are
deployed
all
over
the
world.
C
The
network
is
this
collection
of
node,
practically
speaking,
you
know.
The
network
is
a
little
bit
vague
term.
Practically
speaking,
we
take
rafana
connected
to
one
of
the
nodes
in
the
network.
It's
completely
decentralized,
so
it
doesn't
matter
which
node
so
pick.
A
node
I
think
I
picked
this
second
one
from
the
left
connected
grafana
to
it
and
from
here
we
could
interact
with
and
see
all
the
data
as
if
it
lives
on
the
Node
that
we're
connected
to
and
I
could
move
from.
C
One
data
set
to
another-
and
this
is
all
real
time,
so
this
would
move
as
as
we
speak,
grafana
issues
the
queries
as
if
it
is
working
against
a
centralized
database,
but
there
is
no
centralized
database.
The
data
is
distributed
all
over
the
world
here
on
those
nodes,
and
you
know
users
could
interact.
I
could
have
thousands
and
thousands
of
data
sets,
and
there
is
no
problem,
no
scaling
issues.
You
could
just
interact
with
the
data
as
if
it
lives
on
the
Node
that
you're
connected
to
and
to
make
it
more
tangible.
C
Let's
move
to
this
to
this
GUI.
This
is
just
a
rest.
Gui
a
rest.
It's
like
Postman,
if
you're
familiar
or
curl
so
I
could
send
requests
to
nodes
in
the
network
and
I
selected.
One
note
so
this
is
the
node
that
I'm
connected
to.
So
it's
one
node
from
those
nodes
from
you
know
those
nodes
and
I'll
send,
let's
maybe
send
the
query
to
the
snow.
C
So
so
here
is
an
example
of
the
query
and
I'm
sending
the
query
to
the
network:
I'm
not
telling
the
query
where
it
needs
to
go
to
and
I'm
hitting,
send
and
I'm
getting
the
data
back
as
if
I
was
working
against
a
centralized
database.
You
can
see
here
the
data,
but
there
is
no
centralized
database
right.
What
I've
got
is
just
those
nodes
at
the
edge
and,
if
I
scroll,
all
the
way
to
the
bottom.
There
is
a
summary
section
here
and
you
could
see
the
data
came
back
from
six
nodes.
C
This
node
looked
at
the
query
and
looked
at
the
Shared
metadata
and
said:
I
don't
have
the
answer,
but
I
know
who
does
and
it
shipped
the
query
to
those
nodes
to
six
other
nodes
at
the
age
that
have
the
data
got
the
result,
aggregated
the
result
and
returned
the
result
into
my
spring
as
if
it
is
you
know
one
physical
database,
although
there
is
no
physical
database,
it's
all
it's
all
distributed
all
over
those
Edge
nodes.
So
the
data
came
back
from
six
nodes
and
you
could
see
here.
C
The
time
is
listed
as
zero
and
the
reason
is
we
don't
print
fraction
of
seconds.
So
we
took
less
than
a
second
to
do
that,
and
this
is
live
data
which
is
being
ingested
right
now
and
again.
Everything
that
I'm,
showing
here
is
done
without
a
single
line
of
code.
It's
just
deploy
any
log
on
edge
nodes.
Configure
any
log,
push
your
data
and
that's
it
now
you
could
run
queries.
You
don't
need
domain
knowledge.
You
don't
need
to
send
engineer.
There's
you
you
just
need
to
host
any
log
at
your
at
your
age.
C
It.
Let
me
show
you:
maybe
this
is
a
cool
example
by
the
way,
those
buttons
here
you
could
configure
it
to
things,
frequently
used,
queries
and
commands
that
you
use
so
in
the
same
way,
we
could
monitor
the
resources.
I'll
show
you
a
little
bit
of
that,
but
maybe
before
let
me
show
you
here
is
a
different
query,
but
this
time,
instead
of
me
running
the
query,
I
could
let
you
run
the
query.
So,
if
I
go
here
to
code,
you
could
run
it
very
curl
or
this
QR
code.
C
So
if
you'll
take
your
cell
phone
right
now
and
put
it
against
this
QR
code,
you
could
try
here.
I
did
and
you
could
see
the
data
came
back
to
my
cell
phone
and
if
you
tried
it
and
you
scroll
all
the
way
to
the
bottom,
you'll
see
also
that
the
data
came
back
from
multiple
nodes
at
the
edge
right
and
the.
C
The
idea
is
that
if
you
tried
it,
it
should
work,
and
unless
you
have
some
security
block
and
and
the
the
thing
to
say
here
is
that
we
didn't
send
Engineers
to
your
cell
phone
right
very
easy
to
integrate.
It
into
your
applications,
I
mean
every
thing
that
I
do
here,
you
could
just
place
is
a
rest
request
to
the
node.
C
Maybe
one
more
thing
that
I'll
show
you
on
the
data
side
is
videos
and
images,
so
those
are
managed
in
the
same
way
keep
the
videos
distributed,
obviously
think
about
the
a
a
a
store
with
many
many
branches,
you
know
could
be
thousands
of
cameras.
Obviously
you
don't
want
to
to
centralize.
All
of
that,
and
probably
you
could,
even
if
you
want
to
you,
can't
and
even
if
you'll
bring
the
data
to
the
some
centralized
location.
What
are
you
going
to
do
there?
So
here
is
an
example.
C
So
this
is
a
query
on
the
AI
part
that
looks
at
video,
and
you
could
see
here.
The
query
asks
to
rank
order
by
the
number
of
people
that
are
visiting
the
store,
so
you
could
see
zero
one
going
all
the
way
up
to
five
people
at
some
points,
but
then,
if
I
want
to
see
the
videos,
why
centralize
it?
Let's
just
bring
the
videos
that
I
care
about.
C
Let's
say
that
I
want
to
see
those
videos
and
then
I
can
just
bring
just
the
things
that
I
care
about
to
my
application
to
to
where
I
want
to
look
at
it
versus.
Let's
take
the
videos
from
all
the
stores,
all
the
time
and
centralize
it,
which
is
very
unefficient.
We
all
understand
the
limitations
of
that
and
then
the
next
thing
that
I'll
show
you
is
that
that
and
again,
I
could
go
with
a
lot
of
details,
but
at
a
high
level
the
same
setup
not
only
manage
the
data.
C
The
data
is
obviously
the
harder
thing.
The
bigger
problem,
but
the
same
setup
manage
all
the
resources
so
again
from
a
single
point,
in
the
same
way
that
I
interacted
with
data
I
get
I
could
I
could
monitor
all
my
resources,
so
you
could
see.
I
could
bring
to
to
a
point
very
much
like
at
the
cloud.
I
could
sit
and
through
a
single
point,
have
alerts
so
here
I
think
his
this
space
is
under
50
percent.
C
It
Flex
it
as
red
or
if
data
is
not
being
ingested
or
whatever
your
use
case
is.
You
could
monitor
all
your
Edge
as
if
it
is
running
through
a
single
point,
and
is
it
all
your
age
is
a
single
machine.
So
from
a
single
point
you
could
monitor
everything
which
makes
the
edge
the
distributed.
Edge,
manageable
and
accessible.
Very
much
like
like
the
club.
C
So
if
you
talk
you
talk
about
if
you're
a
tackle
company,
you're,
obviously
looking
for
way
to
leverage
the
5G,
your
private,
Network,
offering
and
so
on
and-
and
here
is
the
way-
I
mean
everything
that
I.
What
I
showed
you
here
was
leveraging
the
network
as
the
thing
that
service
the
data
which
is
I,
think
very
powerful
in
the
context
of
Telco
that
wants
to
to
make
their
services
available.
C
So
if
we
think
about
the
model
today
about
the
role
of
the
Telco,
we
see
the
edge
as
a
place
that
generate
data
and
the
Telco
plays
mostly
on
the
purple
side,
where
they
service
pipes
to
transfer
the
data
from
The
Edge
to
the
cloud,
and
if
you
think
about
what's
what's
there
so
at
the
edge,
you
would
find
more
proprietary
software
like
we
said
there
are
no
standards
there.
Each
company
builds
its
own
solution
and
figures
out
what
to
do,
and
then
the
networking
is
the
thing
that
Bridges
would
create
the
pack.
C
C
C
So
so
it's
a
kind
of
an
easy,
you
know,
add
home
for
a
Telco
for
a
networking
proposition,
but
now
the
Telco
is
also
playing
not
only
as
the
connector
or
a
pipe
between
the
The
Edge
and
the
cloud.
But
it
also
is
someone
that
brings
a
platform
and
when
we
talk
about
the
edge
data
or
data
that
remains
at
the
edge,
it's
the
network
that
connects
the
connect,
the
the
data
with
applications.
C
So
the
Telco,
the
networking,
become
the
thing
that
service,
the
data,
which
is
obviously
a
very
interesting
role,
and
the
model
here
is
instead
of
taking
data
and
copying
data
from
The
Edge
to
the
cloud
and
make
the
cloud
providers
the
ones
that
serve
with
the
data.
This
model
would
always
stay,
but
what
about
the
model?
Next
to
it,
where
some
data
remains
in
place
and
service
by
the
network
by
the
Telco
companies
to
the
applications?
And
you
saw
the
benefits,
you
saw
the
value,
both
the
users.
C
B
B
C
Yeah,
so
so
the
requirements
from
the
network
itself
is
is
pretty
low.
There
is
no
restrictions,
I
mean
it's
all
based
on
peer-to-peer,
so
there
are
solutions
where,
where
messages
could
cross
across
Network
offerings,
if
that's
what
you're
saying
and
a
company
could
also
deploy,
you
know
an
overlay
network.
There
are
a
lot
of
of
ways
to
approach
it,
so
we
we
don't.
We
don't
deal
with
the
network
itself
and
that's
the
the
opportunity
I
think
for
the
Telco
to
say
you
know
we
could
bridge
between.
C
C
C
You
could
transfer
data
to
the
cloud
you
don't
need
to,
but
you
may
have
business
or
technical
reasons
because
of
something
that
is
not
related
to
what
I
discussed
here.
That
says
you
know
this
is
great,
but
some
data
needs
to
to
go
to
the
cloud.
What
we're
saying,
even
in
that
case,
even
if
you
lower
the
amount
of
data
that
goes
to
the
cloud,
you
could
have
big
advantages.
So
let's
say
you
used
to
move
a
hundred
percent.
Let's
say
80
of
your
data.
C
You
move
to
the
cloud
you
could
move,
Now,
50
or
30,
or
in
some
cases
like
you
say
you
could
just
rely
at
the
edge
huge
cost
saving.
Obviously
the
data
at
the
edge
is
serviced
in
real
time.
So
that's
a
good,
a
big
Advantage
we
talked
so
there
is
cost.
There
is
real
time
there
is
ownership
issues.
You
could
keep
everything
inside
your
firewalls.
There
is
a
lot
of
value
in
what
we're
doing,
but
nevertheless,
in
many
cases.
A
C
C
What
we
could
do
is
put
an
overlay
Network
on
top
of
it,
and
so
it
would
be
even
more
efficient
and
more
predictable
and
so
on,
but
I
tried,
but
but
for
sure
this
is
way
more
efficient
than
let's
take
all
the
so
so
look
what
companies
are
doing
today
because
of
all
the
problems
that
we've
discussed
at
the
beginning.
They
say
we
don't
have
much
Choice,
even
even
if
it's
it's
great
to
leave
data
at
the
edge.
There
is
no
solution
for
it.
C
C
We
understand
it
doesn't
make
sense,
but
companies
are
doing
it
with
iot
data
with
Edge
data,
because
there
is
no
other
way
and
what
I
showed
you
now
is
that
there
is
another
way
you
could
keep
all
your
data
out,
some
of
your
data
at
the
edge
and
make
it
available
and
accessible
without
efforts,
it's
all
Plug
and
Play.
So
you
know
you
go
to
a
deployment,
you
know
deploy
analog
and
then
you
could
do
what
I've
done
without
Engineers
without
any
effort.
C
This
is
something
that
could
change
the
way
companies
interact
with
data
and
brings
an
opportunity
which
wasn't
available
to
makes
it
available,
but
but
on
the
on
the
time
thing
for
sure
this
is
more
efficient
than
let's
take
all
the
data,
build
those
huge
databases
at
the
cloud
and
then
interact
with
the
data.
This
means
that
what
we're
saying
you
could
keep
data
attention,
run,
queries
and
get
the
result
very
efficiently
and
what's
transferred
on
the
network
is
just
the
query
one
side
and
the
results
back,
which
is
way
more
efficient.
C
So
so,
okay,
so
I'm
not
sure
what
you
mean
by
replica.
So
so
what
I
showed
you
is
that
in
this
model
you
take
your
data
and
you
push
it
into
nodes
at
the
age.
Each
one
of
those
nodes
has
different
data.
You've
got
the
data
that
you
know
if
in
the
industrial
floor,
so
all
the
Machinery
that
is
in
this
floor
would
be
connected
to
a
node
pushed
to
this
node,
and
then
you
could
have
another
industrial
or
industrial
plant
which
is
in
a
different
location.
C
So
you'll
have
a
different
node
there
and
you
could
have
a
node
that
is
in
charging
station
each
one
of
those
nodes
have
different
data
right,
but
nevertheless
you
issue
a
query.
It
would
go
to
the
nodes
that
have
the
relevant
data
so
that
the
the
the
process
knows
how
to
identify
where
at
the
edge
the
relevant
data
resides
and
bring
it
from
there
very
much
like
very
much
like
Google
right,
like
you're
doing
a
Google
search.
It
would
send
you
to
the
websites
that
are
relevant
for
your
search
here.
It's
a
SQL
query.
C
It
would
send
you
to
the
nodes
that
have
the
relevant
data.
There
is
maybe
another
question
that
is
kind
of
implied
on
high
availability,
so
nodes
have
high
availability
in
the
sense
that
you
could
deploy
it
such
that
there
are
replicas.
So
obviously
the
data
of
a
particular
node
is
not
replicated
on
all
the
nodes
right,
but
you
could
say
instead
of
one
node
that
holds
the
data.
I
want
to
have
two
or
three
nodes
that
would
create
replica
of
each
other.
So
you've
got
islands
of
replicas
such
that.
C
C
C
C
So
when
application
connects
to
the
network,
what
it
says
it's
something
like
that:
it
sees
a
list
of
databases,
for
example
like
San
Andreas
and
for
lights
and
Battery.
It
sees
the
list
of
tables
okay
and
then
for
a
particular
table.
It
could
say,
give
me
the
list
of
columns
right
and
then
it
could
issue
a
query
in
exactly
the
same
way
of
that
it
would
work
against
a
centralized
database.
C
It
doesn't
care
where
the
data
is,
but
as
as,
let's
say,
administrators,
some
someone
that
cares
or
wants
to
know
I
could
look
at
it
from
a
slightly
different
view.
So
again
you
see
this
table
that
likes
and
this
database
slides
and
Android
and
all
its
tables,
battery,
inverter
and
so
on,
and
if
you
go
all
the
way
to
the
right,
you
could
see
the
physical
nodes
that
host
the
data.
C
C
Right
that
is
correct.
Okay,
that
is
correct,
but
what's
also
interesting.
There
is
that
you
know
this,
sometimes
in
con
with
the
conventional
approach.
This
is
the
hard
decision
you
know
when
we
go
back
to
the
project,
you
need
to
figure
out
how
much
data
is
there?
What
what,
how
much,
what
what
compute
power
I
need
to
set
there,
where
this
model
is
much
easier
in
the
sense
that
it
scales
horizontally?