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From YouTube: Webinar: The Cybernetics of Observability and Monitoring
Description
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In this webinar, William Louth, Complexity Scientist and ‘Thaumaturgist’ at Instana, will answer this question (and more) through a cybernetic lens.
View this webinar and you will walk away with an understanding of:
* How Observability and Monitoring fit into the process of quality improvement in distributed systems
* The predicted evolution of Observability and Monitoring, with ever-increasing levels of complexity and rates of change
* How to effectively manage these systems in the future through control signals and automatic self-adaptive components
A
I
like
to
thank
everyone
for
joining
us
today,
welcome
to
today,
CN
CF,
webinar
titled,
the
cybernetics
of
observability
and
monitoring,
I'm
khalida
Campesina
member
technical
staff
at
VMware
and
also
cloud
native,
a
cloud
native
ambassador
of
me
moderating
today.
Sydnor.
We
would
like
to
welcome
our
presenter
today.
William
love.
He
is
a
complexity
scientist
at
in
stana.
A
So
before
we
get
started
for
Rio,
just
a
few
housekeeping
items
during
the
webinar,
you
are
now
able
to
you're
not
able
to
talk
as
an
attendee,
but
we
do
have
a
Q&A
box
at
the
bottom
of
the
screen.
You
can
access
that
feel
free
to
drop
your
questions
there
and
actually
like.
We
encourage
you
to
use
that
Q&A
as
a
pose
of
the
child,
because
much
easier
for
us
to
manage
aids
and
we'll
get
to
your
questions
at
the
end
of
the
presentation.
A
We
will
stop
around
quarter
to
11:00
Pacific
time,
and
so
we
are
clear
where
we
are.
This
is
an
official
webinar
of
the
CN
CF
and
as
such
its
subjects
to
the
CN
CF
code
of
conducts.
Please
do
not
add
anything
to
the
chat
or
questions
that
would
be
in
violation
of
that
code.
Basically,
please,
please
be
respectful
of
all
your
fellow
participants.
A
B
Thank
you
thanks
for
the
nice
introduction
and
good
a
good
afternoon
well
good
evening
in
terms
of
where
I
am
based
in
Europe
and
Holland,
and
it's
probably
a
good
morning
in
the
u.s.
yeah
has
already.
As
said
my
William
loud
time,
complexity.
Scientists
insist
on
it
just
a
little
bit
of
background
and
where
I'm
coming
from
for
the
last
20
years,
I
work
in
the
areas
of
observability,
controllability
and
operability.
So
observability.
B
At
least
were
in
a
guarantee
and
stan
is
I'm.
Looking
for
new
ways
of
building
observability
and
systems.
New
models
models
that,
like
you
to
consider
today,
would
probably
be
tracing
logging
and
metrics
and
I'm.
Looking
at
new
models
that
were
scaled
to
the
complexity
that
we
have
in
modern
applications,
I've
also
spent
many
years
building
controllability
into
applications,
quality
of
service
products
from
the
network
open
to
applications
and
reason,
resource
management.
B
This
is
something
I
will
touch
on
in
the
rest
of
the
talk.
The
other
area
is
so
signaling
is
very
high
level.
So
it's
looking
for
a
model
that
helps
with
effectiveness,
it's
operational
and
then
the
other
area
is
where,
if
you
look
at
logging,
we've
got
to
tracing
metering
is
another
thing
tracing
you
today
is
only
about
clock
time,
but
metering
is
about
any
type
of
resource.
So
that's
probably
the
big
difference
between
tracing
and
metering
is
that
you
can
have
multiple
measures
between
any
span
or
any
type
of
event.
B
And
then
I
think
the
future
will
be
around
simulation
and
and
there
with
all
of
this,
what
we're
doing
is
it's
very
detailed,
so
the
other
one
is
for
the
DevOps
group
and
the
the
inner
area
is
really
trying
to
give
people
more
Diagnostics
capabilities,
and
it's
really
about
reconstruction
explorative
of
the
reconstruction
itself.
It
is
focus
on
the
developer
and
it's
really
at
the
Machine
level
and
I
imagine
in
the
future.
B
It
will
really
be
consumed
mainly
by
machines,
because
I
don't
think
that
humans
can
work
at
that
level
anymore,
not
with
this
type
of
systems
of
our
building.
Ok,
so
to
bring
in
cybernetics
I
want
to
talk
about
why
we
need
to
look
at
a
cybernetic
review
of
observability
and
monitoring
and
that's
the
problem.
There
is
the
challenges.
We're
talking
about
is
complexity,
so
what's
complex
well,
I
mean
we
have
complicated
and
we
have
complex.
But
what
I
consider
as
we
look
at
complex
systems
and
concept,
complex
systems
are
really
dense
networks.
B
So
this
is
a
lot
of
interconnection
and
interactions
in
those
interconnections
consists
of
adaptive
ages.
Now
the
age
of
parts
of
people
chose
people
because
we've
had
age
of
technology
before
but
really
to
see
age,
and
you
could
probably
say
it's
a
micro
servers
or
a
library
and
welcome
to
you
know
cloud
is
an
example
of
adaptive
agent.
Where
you
purchase
a
you,
know,
purchase
a
service
and
the
service
is
able
to
scale
up
and
down,
depending
on
the
workload
so
and
in
doing
that,
it's
adaptive,
so
there
is
adaptive
nature
already
in
our
existence.
B
Today
we
are
working
at
complex
systems,
work
on
multiple
scales,
and
that
means
that
when
it
is
feedback
loops
between
things
and
our
boundaries
and
but
also
our
time
scales
and
space
scales,
and
that
sometime
is
feedback,
loop
alone
is
feeding,
is
signaling
toward
a
higher
level
feedback
systems.
So
it's
not
easy
to
to
drop
down
layers
and
still
understand
each
layer
and
without
looking
at
the
other
layers
of
bold
and
dynamic.
B
Where
were
multiplying
components,
you
know
thee,
the
number
of
components
is,
is
increasing
and
what
we've
had
is
we've
had
the
monoliths,
where
they
had
some
kind
of
libraries.
Do
you
know
components
were
embedded
within
them
and
then
we
have
micro
services
rippling
into
increasing
the
modularity
of
the
system.
And
then
you
know
the
natural
progression
is
probably
to
flows
and
functions.
We're
just
a
pattern
of
we
have
self-organizing
systems
or.
B
Or
organizing
books
and
there's
some
organization
within
there
now
the
connectivity,
which
is
important
to
the
to
the
complexities
of
the
important
aspect
of
it,
and
we
can
see
that
in
the
Insogna
this
is
actually
a
screen.
Sort
of
one
of
the
customers
have
been
stand
at
the
hats
and
has
I
haven't
put
the
whole
number
of
nulls
in
the
air
because
Plus
today,
but
behind
that
that
line
is
not
some
art
form
I
did
it's
actually
taken
from
one
of
our
customers
and
they're.
You
know,
as
you
can
see,
the
number
of
notes.
B
You
know
the
api's
are
large
the
number
of
dependencies
between
those
and
the
the
size
of
the
flows
flows
or
which
could
be
it's
considered
as
a
work
flow.
You
could
consider
as
an
endpoint
within
the
service,
and
you
can
see
that
it's
it's
getting
our
hand,
it
looks
complex
or
you
could
say
it
looks
complicated,
so
I
said
cloud
as
an
example
of
complexity.
We
have
self-service
the
large
network
access,
which
is
what
we
require
in
a
complex
system,
cooling
and
elasticity.
B
B
The
books
would
mount
up
over
time
and
then
there
will
be
another
large
release
and
then
someone
would
hopefully
force
those
books
and
introduce
less
less
number
of
bugs,
and
that
cycle
was
quite
big
and
the
the
height
here
represents
the
number
of
issues
that
can
come
in
and
how
long
they
stay
there.
Because
the
you
know,
if
you
have
an
issue
in
production
and
the
longer
stays,
then
you
know,
of
course,
then
the
greater
the
damage
it
is
to
the
system,
and
so
what
we've
had
is
this
shorter
we've
had
shorter
deployment
cycles.
B
The
problem
with
that,
of
course,
is
that
the
rate
of
change
and
the
growth
of
complexity
can
put
a
stress
stress
on
a
company
on
an
infrastructure,
and
there
are
two
likely
outcomes
you
either
have
adaptive
control
well,
I,
wouldn't
say
just
that.
That
was
a
useful
way,
because
you're
always
adapting
to
what
you
what
you're
sensing
within
the
environment
and
he'll
I
want
speed.
B
You
can
go
with
change
and
complexity
and
what
you
really
want
to
do
is
to
regulate
the
amount
of
change
and
they
meant
the
complexity
to
bring
it
on
into
your
system.
And
if
you
don't
able
to
do
that,
so
that's
the
kind
of
a
steering
mechanism
for
the
company
and
the
company
also
has
to
change
in
line
with
the
rate
of
change
that
is
happening
within
its
environment
and
also
within
its
own
infrastructure.
B
And
if
you're
not
able,
if
an
application
or
service
not
able
to
keep
up
with
her
or
company,
then
the
natural
thing
is
for
it
to
collapsed
on
the
distress
of
that
and
the
way
to
see
this
is
kind
of
like
I.
Don't
know
this.
Sometimes
it's
a
hit
and
miss
this
analogy.
But
if
you
remember
it
in
Terminator
2
at
the
end,
we
had
the
t1000
another
or
I
think
it
was
the
one
that
was
liquefied
liquefied.
B
You
can
see
that
trying
it
constantly
changing
shape
and
it
couldn't
stabilize
and
that's
what
happens
really
in
these
kind
of
environments,
where
rate
of
change
and
the
complexity
keeps
getting
our
hand
that
the
company
doesn't
know
what
or
the
or
infrastructure
is
not
able
to
stabilize
and
stability
is
really
a
sense
of
memory.
So
the
less-
and
this
is
a
challenge
in
Opera
operations
and
observability-
is
the
environment
constantly
changing?
How
do
you
create
a
model,
or
how
do
you
reason
about
something
that
that
reasoning
cannot
only
have
15
minutes
of
validity.
B
So
now
I'm
going
to
move
on
to
cybernetics.
So
that's
the
problem,
statement
and
cybernetics
is
kind
of
like
a
solution,
or
at
least
a
solution
to
how
we
can
use
observability
or
monitoring,
to
help
us
with
the
complexity
and
with
change
so
cybernetics.
That
standard
definition
is
as
the
study
of
control
and
communication
and
the
original
book
it
was
about
animals
and
the
machines
humans.
You
could
say
there
and
ready.
B
B
Of
communication
in
cybernetics,
we're
thinking
of
signaling
and
the
control
is
some
type
of
action
or
response
to
that,
and
you
can
see
a
little
bit
in
application
systems
we
have
near
today.
We've
had
a
reactive
manifesto,
reactive
systems
and
reactor
platforms,
which
is
a
response
to
to
how
to
handle
workload
and
how
to
handle
a
very,
very
ability
in
a
system.
B
So
classic
example
of
cybernetics
comes
down
to
always
the
feedback
system,
the
feedback,
loop
and
I'm,
taking
this
really
from
the
manufacturing
and
then
I'll
try
to
relate
a
little
bit
more
to
something
else.
So
there
you
have
in
a
manufacturing
system,
you
have
an
assembly
line.
You
have
some
kind
of
input
coming
into
a
controller
and
controller
has
both
the
input,
which
is
some
kind
of
supply
system,
and
then
those
goals
given
to
the
controller
to
regulate
that
flow.
B
The
flow
passes
along
into
some
kind
of
you
know
we
have
activities,
are
being
performed
in
the
process
that
outputs
we
have
caught
hell.
When
it
comes
out
to
the
to
the
process.
We
have
an
output
that
is
measured
by
a
sensor
and
have
feedback.
You
know,
that's
done,
those
measurements
are
fed
back
into
the
controller,
and
this
is
where
I
can
see
is
like
you
can
regulate
the
amount
of
FLOTUS
going
through
a
system.
B
The
output
incur
is
a
linear
to
do
to
the
input
or
is
there
some
degradation
ever
relation
when
you
start
exceed
a
certain
rate
of
flow
would
in
yellow
in
the
system,
and
that
could
see
that
you're
slowing
down
and
then
you
have
to
take
a
corrective
action
and
that's
typically,
what
you're
looking
at
is
a
feedback
system.
That's
cybernetics.
B
If
you
were
to
manage,
maybe
like
if
you
were
thinking
like
quality
of
service,
where
you
had
something
to
have
to
begin
a
task,
you
would
have
a
resource
pool
which
you
could
see
that
as
worker
threads
or
something
like
that
and
when
you
begin
an
action
so
in.
If
you
look
in
the
middle
of
this
chart
here,
you
have
a
begin
and
that
takes
a
read
reserve,
something
from
the
pool.
B
Of
course
you
take
it
from
the
pool
itself
and
that's
the
minus
sign
there,
and
if
it's
not
there,
it
doesn't
genuinely
do
some
kind
of
blocking
and
then
after
you've
reserved
that
that
means
like
you've,
got
your
talking.
Think
of
a
restaurant,
you
reserve
a
table,
you
get
your
reservation
and
off
you
go.
Do
your
work
so
and
then
you
put
that
into
your
spare
tool,
and
this
could
be
like
I've
reserved
two
seats
and
then
later
maybe
I
have
to
ring
up,
and
someone
says
oh
or
maybe
I
make
a
change.
B
Then
I
need
another
additional
two
seats.
Well,
I
need
to
remember
what
I
already
and
then
so
you
have
this
reserve
pool,
which
is
what
you've
taken
from
the
resource
pool
and
then
when,
of
course,
when
I
go
and
have
my
dinner
with
colleagues,
see
you
and
then
you're
completely.
Do
you
release
that?
B
And
that
goes
back
into
the
control
system
and
that
really
allows
other
people
to
come
to
the
table
so
control
there
is,
you
know
how
much
capacity
you
put
into
the
into
the
resource
pool
and
then
at
the
duration
of
how
long
someone
is
in
a
brassiere
state
and
then
till
they
get
into
the
early
to
the
release.
So
this
is
where
you
know
you
can
find.
B
Is
a
I
can
share
some
links
in
Indian
style
website
later
on
on
this,
for
the
information
on
this,
so
cybernetics
really
comes
down
to
feedback
flow
systems,
control
about
it,
controlling
the
feedback
and
the
flow,
and
then
the
communication,
which
is
the
sensory
feedback.
The
signals
are
going
through
the
system,
if
you
think
of
DevOps
DevOps,
is
very
similar.
B
It's
about
feedback
on
the
changes
that
you're
making
and
how
how
good
those
changes
are
in
production,
the
workflow
itself,
how
much
flowing
or
how
much
should
the
rate
of
change
that
you
allow
going
into
the
system-
and
that's
is
where
you
put
in
some
controls.
Some
people
call
them
error,
budget
sort
of
people,
look
at
the
risk
levels
or
so
to
put
it
more
formal
and
then,
of
course,
you're
communicating
and
communicating
between
human
to
human
or
animal
to
animal,
and
then
is
communication
between
man
and
machine
and
even
within
the
process.
A
B
Resources
in
terms
of
resource
modeling
and
the
policies
that
we
have
with
that
now,
of
course,
this
all
sounds
like
you're
looking
for
something
you
know
when
you
think
about
this:
we're.
What
we're
really
trying
to
do
in
all
DevOps
is
always
have
some
intelligence,
its
intelligence
yeah,
and
what
does
that
mean?
Intelligence?
Is
action
appropriate
to
the
context?
B
Oh
well,
those
are
the
forms
of
intelligence
in
terms
of
reasoning,
but
when
we
think
of
DevOps,
which
is
a
very
action-oriented,
is
that
we're
looking
to
make
an
appropriate
action
to
the
context
we're
in
and
this
is
it
then
the
next
problem
is
what
is
that
context
because
context
is
the
environment
to
the
system,
but
this
is
something
that
is
changing,
and
this
is
where
observability
comes
into
it
so
context.
Context
context
is
the
circumstances
that
form
the
setting
for
an
event.
Sorry
that's
going
off
here.
B
B
You
know
look
for
filter
film
and
to
explore
help,
explain
that
what
we
should
be
doing,
what
we
should
be
trying
to
construct
the
what
is
the
context
work,
we're
trying
to
reconstruct
or
simulate
and
film.
Of
course
you
have
these
kind
of
things
here
we
have,
you
have
a
setting
a
scene,
an
act
and
their
sequence
there,
and
then
you
have
something.
You
know
those
actors
within
the
scene
and
they
make
it
behave.
B
You
know
they
have
some
behavioral
changes
there
and,
of
course,
changes
within
the
environment
happen
so,
and
these
are
all
happening
at
with
lower
level
of
events,
and
so
this
is
what
I
mean
by
context.
Context
is
really
the
reconstruction
of
an
an
event.
We
name
that
environment,
at
least
enough
that
it
can
make
sense
to
us
of
work.
What
the
system
looked
like
at
that
time
in
terms
of
the
nodes,
a
topology,
what
flows
were
happening
and
what
it
was
the
health
of
the
system
itself.
B
So
that's
what
we're
trying
to
do?
We're
always
observability
is
reconstruction,
and
so,
but
the
definition
of
observability,
which
is,
has
changed.
You
know
in
the
last
few
years
the
the
original
definition
was
the
inference
of
internal
states
of
a
system
from
the
knowledge
of
its
external
outputs.
So
the
state
there,
what
we're
talking
about,
would
probably
be
more
health
state,
the
quality
of
what
he
was
doing
was
it
operating
because
observability
was
really
around
measure
and
measure
all
10.
B
You
understand
the
state
of
the
system
and
States
tend
to
be
conditions
and
it's
a
very
limited
number
of
conditions,
at
least
when
we
think
about
modeling,
because
if
we
didn't
think
about
the
system
had
a
million
states,
it
wouldn't
really
be
a
very
useful
model.
So,
generally,
what
we
always
do?
We
try
to
bring
group
them
into
categories
of
states
where
they
represent.
This
is
a
good
state.
You
know
healthy
and
okay,
state
degraded
state
defective
state.
So
we
take
these
kind
of
higher
levels
of
categorization
of
a
state
and
that's
all
observer
abilities.
B
You
should
be
trying
to
do
so.
Observability
is
in
and
how
we
do.
That
is
of
course,
coming
back
from
cybernetics
is
you're,
always
looking
for
signals
within
your
environment.
So
an
observer
is
observing
other
systems,
and
this
doesn't
mean
that
the
observer
is
a
scientist
system.
It
could
be
ones
microservice,
looking
at
another
service
or
agent,
look
at
another
agent,
but
they're
observing
each
other's
behavior
and
those
insane
behaviors
are
signals
and
from
those
signals
we
infer
state,
but
the
best
way
to
see
this.
B
If
you
think
about
animals-
because
you
know
animals
also
have
signaling
and
animals
like-
will
make
aggressive
moves
like
they
will
move
their
hands
up
and
show
to
a
girl
and
Jenny
we've
got
signals.
The
signal
of
you
know
aggression
and,
and
and
if
that
happens
frequently,
you
know
for
a
release
consecutively.
We
probably
in
fear
a
state
of
that
this
animal
is
what
other
men,
or
you
know,
or
crazy
or
angry,
and
this
is
the
state
we
infer
from
it.
B
The
modern
way
is
really
to
think
about
sensors
and
we're
really
moving
into
a
multi
sensor
environment.
We
have
centers
who
are
making
collections.
These
are
generally
being
passed
on
to
agents
because
we
can't
always
get
to
the
back-end
systems
that
we
have,
and
sometimes
when
we're
writing
to
multiple
back-end
systems,
so
just
generally
ages
are
placed
in
there
and
they're
really
channels
for
pushing
those
collections
off
into
another
system,
and
this
other
system
is
I'm,
really
acting
like
a
fusion.
It's
taking
all
the
sensory
data,
I'm
fusing
it
into
to
make
sense
of
it.
B
So
it's
thinking
metrics
as
taking
traces
as
taken
logs.
It's
taken,
the
apology,
discovered
a
configuration
and
it
reconstructs
a
model
which
is
diffusion
like
a
topology
of
system
and
the
apology
structurally
could
be.
You
know
the
nodes,
the
holster
or
living
in,
and
the
apology
could
also
be
the
call
graph
and
there
and
what
it's
focused
on
of
course,
then,
once
it's
got
a
model
is
focus
on
change,
but
change
is
not
really
what
we
want.
We
know
change
is
always
happening
so
that
way.
D
is
not
a
youth
very
useful
thing.
B
To
be
constantly
monitoring.
I
mean
you
can
monitor
the
rate
of
change,
but
it's
not
going
to
tell
you
much
unless
you
have
a
means
of
classifying
those
changes
and
then
you
look
for
signals
for
those
and
then
to
derive
a
status
because
at
the
end
of
the
day,
what
we're
always
trying
to
do
is
what
the
chairman,
the
status
or
the
state
of
one
or
more
or
our
systems
and
I
could
break
it
down
into
subsystems.
Just
come
on.
B
Okay,
so
that's
all
we
and
I
think
we're
also
seeing
within
the
observability
spaces
were
moving
from,
and
this
is
where
recollection
was
probably
more
of
the
legacy
view.
If
we
go
back
to
the
legacy
where
we
had
these
tables
and
what
we
did
there
is
we
had
certain
capabilities,
so
we
seriously
do
the
recollection
is
what
happened
at
this
point
in
time,
and
so
generally
you
look
bird
tag
you
or
look
for
a
time
and
a
window
and
he
search
for
it,
and
then
you
identify
something
but
the
problem
there
is.
B
What
should
you
be
searching
for-
and
this
is
this
might
have
not
being
so
much
of
a
problem
before
when
you
were
working
with
a
moment
that
changed
and
changed
until
you
know
it
didn't
change
unless
every
six
months
or
so
so
you
could
build
up
some
knowledge,
but
when
everything
is,
is
changing
rapidly
and
this
very
little
memory
in
there.
What
you
really
want
to
do
is
the
system
to
focus
on
recognition,
recognition
of
similarities,
or
you
know,
a
recognition
that
something
is
even
different
and
then
signify.
B
You
know,
of
course,
once
you
can
recognize
something,
then
you
can
give
significance
to
where
and
that
can
be.
You
know
it's
significant
or
insignificant,
and
then
what
we
there
is
is
suggestiveness.
We
want
the
tooling
or
the
monitoring
solutions
to
be
more
suggestive
of
what
we
should
look
at
rather
than
those
searching
or
exploring.
B
We
just
simply
I,
don't
think
operations
that
they
have
simply
get
the
bandwidth
to
go
around
exploring
and
it
probably
would
have
been
a
useful
tool
previously
when
we
didn't
have
such
systems
that
we
have
today,
but
with
the
change
with
the
growth
in
the
complexity
with
the
number
of
entities.
I,
don't
think
that's
scalable
anymore,
so
you
chura
t
we
have
to
move
to
more
recognition
and
suggestive
solutions
like
inspections
or
advice
tools
for
a
human
to
scale,
as
opposed
to
a
machine.
B
So
why
do
we
observe
so
we
contend
to
be
thinkable
observability?
Why
do
we
observe
well
in
the
do-more?
The
generalist
term
is
to
monitor
signals.
I
mean
we
always
here.
I'll
come
back
to
what
signals
are
later,
but
of
course
everybody's
is
is
taking
measurements
and
hoping
from
in
there.
You
know
that
we
know
the
measurements
have
a
lot
of
noise
and
then
we're
looking
for
a
signal,
but
no
one
really
knows
what
a
signal
is,
but
generally
that's
what
we're
looking
for
we're
looking
to
monitor
signals
and
in
the
environment.
B
So
this
is
why
we
observe
why
do
we
monitor,
of
course
is
why
would
we
just
keep
watching
for
signals,
because
we
want
to
control
status?
Our
infrastructure
is
something
that
we're
responsible
for
so
and
the
way
we
can
manage,
that
is
by
looking
at
the
states
of
the
system
so
where,
when
we
look,
the
control
States
were
really
trying
to
manage
the
service
quality
to
manage
the
service
that
were
delivering.
B
B
Observability
is
basically
a
subordinate
or
servant
to
control
ability
and
control
ability
is
really
where
we're
all
going
and
control
ability
can
manifest.
In
that
there's,
a
machine
taking
over
control
and
self
regulating
itself
or
control
ability
could
be
just
humans,
responding
to
what
they
see
within
their
observer
ability
or
monitoring
space.
So.
B
So
that's
where
controllability
comes
into
it
now
I'm
going
to
touch
a
little
bit,
because
when
we
come
to
cybernetics,
you
know,
cybernetics
is
really
about
control,
but
is
about
control
of
other
systems,
and
you
can
also
have
second-order
cybernetics,
which
is
control
over
the
controller.
So
when
you,
when
you
think
about
controllability,
you
can
also
apply
it
to
monitoring,
because
you
have
to
observe,
observe
observation
is
an
action
and
observation.
It
is
an
action
that
needs
can
be
regulated.
Then.
A
B
Have
basically
some
kind
of
control
t
battle,
so
monitoring
which
some
people
would
like
to
imagine
monitoring
is
the
old
traditional
where
you
were
your
pin
your
server
and
you
say:
are
you
up
or
down
and
I
think
that
was
used?
Those
who
used
to
usefulness
that
up,
because
we
would
focus
really
on
the
state
of
the
system,
but
it
really
didn't
give
us
an
accurate
assessment
of
it.
That
was
unfortunate,
but
I
think
monitoring
still
is
here
today.
B
It
just
me
did
I
think
we
were
associated
when
we
think
about
monitor
we
always
associated
with
a
technology
or
tooling.
It's
really
a
process.
Monitoring
is
the
process
that
manages
our
hell.
We
observe
our
environment,
so
that's
what
I
wanted
to
talk
about
is
because
there's
always
a
bit
of
this
observability
versus
monitoring,
and
you
know
observer
abilities
even
greatly.
The
monitoring
are
bigger
than
monitoring
I.
B
It
increases
overhead
and
storage,
cost
increases
overhead
and
cognitive
workload
for
both
the
machine
as
well
as
human
and,
let
you
know
you
wanted
to
direct
attention
and
you
want
to
integrate
multiple
senses
and
observability
today
tends
to
be
like
slice
down
a
silo
like
here's
tracing
here's
logging,
here's
there's
something
else
in
here
is
like
events
or
so,
and
so
you
monitoring
is
really
diffusion.
It's
integrating
the
senses
and
it's
reconstructing
many
memories,
because
observability
is
really
a
collection
system.
It
might
allow
you
to
explore
it
mile.
B
So
monitoring
is
about
identifying
patterns
that
observer
believes
not
is
focused
on
if
the
observability
is
about
collecting
them
and
assigning
significance
to
them,
aiding
in
the
reasoning
of
the
human
and
the
Machine
and
guiding
or
actually
in
there.
And
of
course
we
want
feedback
on
the
actions
that
we
take
to
help
to
help
regulate
our
observability
and
also
what
we've
done
in
terms
of
control
itself.
So
action
can
also
manifest
in
actually
towards
observability
or
action
to
the
context
of
the
environment
that
we're
doing
here.
B
Today
they
are
signaling
or
you
know,
regulations
policy
back
into
observability
and
controllability
is
also
feeding
into
monitoring
and
monitoring
is
also
they're.
Helping
to
regulate.
Also
the
controllability
there-
and
this
is
saying
with
controllability
is
fitting
into
management
management
is
really
is,
is
management
view
is
how
much
change
do
we
want
to
take
care
of
I
mean?
How
can
we
do
that
change,
which
is
constant
to
controllability?
How
can
we
monitor
that
change
or
the
success
of
that
and
then
he'll?
B
Do
we
see
that
change
is
happening
and
that's
probably
the
way
to
see
switching
zig-zagging
from
the
strategic
and
tactical
and
is
a
maturity
track
in
that?
So
maturity
is
really
going
from
observer
ability
to
monitoring,
as
in
at
least
in
my
definition
of
monitoring,
which
is
the
Morton
version
and
not
in
terms
of
product
specific,
like
pinion
and
there's.
B
A
the
maturity
is
from
the
top
to
a
modern
observer
ability
to
monitoring,
to
increase
controllability
in
the
system,
both
machine
and
man,
and
and
scaling
that
and
then,
of
course,
to
management,
which
is
always
about
the
policies
of
that
change.
So
how
am
I
gue
so
observability
is
looking
monitoring
a
seen.
Controllability
is
acting
and
management
is
regulating.
That's
the
way
I
like
to
see
it.
B
So
just
another
way
of
seeing
observability
monitoring
is
observability
is
given
a
sensory
data,
and
monitoring
is
the
semantics
that
we
attach
to
a
sensory
data
and
the
sniff
against
we
derived
from
that.
So
you
can
see
that
the
century
data
as
it
moves
up
from
the
bottom
to
the
top
left
hand
corner,
is
really
getting
smaller
and
that's
where
we
increase.
It
begins
because
we're
really
loving
to
all
to
get
greater
significance
by
crunching
down
that
data
and
turning
it
into
information
as
well.
B
Now
some
people
call
it
data,
but
this
amount
there,
okay,
effective
monitoring
then
depends
on
connecting
contextualizing
and
what
I
mean
connecting
already
mean
I'm
rebuilding
we
connecting
Association
of
we
have
made
a
change
that
changes
rippling
into
the
environment.
We
then
connect
that
back
to
the
control
that
we
want
to
have
on
that,
and
then
the
context
is
build
and
rebuild
in
our
environment
of
what
it
looks
like.
So
an
observer
ability
to
is
always
rebuilding
context.
B
A
B
I
mean
in
the
very
general
senses,
one
thing
is
contained
within
another,
which
is
the
isolation
and
then
the
the
flows
that
cross
over
those
boundaries,
and
so
what
we
want
to
do
is
keep
speeding
up
that
change.
Connecting
that
change
back
to
the
controller
that
changed
it
does
happen,
our
effects
within
the
environment
and
then
relating
that
rebuilding
and
context
and
new
context
that
represents
that
new
state,
so
we're
in
the
hole
straight
now.
This
is
where
we
get
on
to
cognition
and
I'm.
B
B
Is
the
kind
of
it's
I've
changed
the
color
here
to
purple
because
I
see
this
is
where
we're
heading.
This
is
the
future,
and
this
is
really
what
we're
all
looking
for
and
like
when
you
buy
a
product
as
his
AI
ops
or
something
that
says
intelligent
this
and
you're
really
looking
for
something
is
cognition
within
what
we're
collecting
in
what
monitoring.
B
So
what
is
cognition
and
and
that's
the
process
of
acquiring
knowledge,
understanding
through
taught
experience
and
the
senses,
so
experience
could
be
of
the
reconstruction
of
episodic
memory
or
even
the
memory
itself
living
it.
And,
of
course
we
have
to
do
to
create
an
experience.
We
need
a
sense.
We
need
a
sense
of
the
environment
we
in
and
then
we
have
to
think
through
that
we
have
we
creating
representations
from
the
experience
and
from
the
senses,
and
it's
not
sequential,
of
course,
because
thought
itself
impacts
it
experience
and
sense.
B
It's
there,
because
there
there's
regulation
there.
So
you,
the
more
attentive
you
are
to
something
the
more
you
see
the
more
the
thought
you
can
consider
and
that
to
give
the
thought
that
you
give
to
it
and
then
then
the
experience
heightens.
So
it's
the
circular
processes
between
those
systems.
Now
my
interest
is
that
what
I,
what
I
I
see?
B
At
the
moment,
oh
there's
some
rate
limiting
so
I'm
gonna
have
to
slow
you
down
for
a
bit.
There
are
some
handshaking
going
on,
so
we've
gone
from
or
PC,
which
is
you
know
the
remote
procedure
calls
to
what
I
could
consider
conversations
conversations
between
services
and-
and
you
see
this
with
the
reactive
manifests,
where
they're
trying
to
signal
when
they're
available
to
or
whether
they
want
something
else
to
be
recreated
and
consumed
by
them.
B
So
it's
not
so
much
know
about
the
workflow
that
we're
looking
at,
but
also
we're
looking
at
other
services.
So
if
you
think
more
concrete,
maybe
it's
a
bit
too
high
in
the
sky
with
that
one.
If
you
think
about
you,
have
a
circuit
breaker,
a
second
breaker,
it's
kind
of
cognition
is
that
if,
when
something
breaks,
so
if
you
try
to
access
the
database
and
a
few
times
and
fails,
then
you
put
it
into
you
kind
of
you.
B
And
what
you
see
then
is
a
kind
of
a
sense
that
like
what
we
would
have
in
a
workplace
wherever
were
if
a
manager
was
walking
over
to
someone's
desk
and
before
when
we
had
these
old
paper
cabinets
and
people
with
trays
where
people
would
put
the
work
and
a
tray.
If
you
came
over
to
a
person
he
seen
their
work
in
the
tray
was
was
quite
high.
He
wouldn't
drop
the
new
work
and
it
would
probably
look
for
another
service
to
do
that.
B
And
that's
really
what
we're
doing
we're
seeing
services
take
on
this
new
semantics
and
it's
actually
quite
challenging
for
observability
tools,
because
we
can't
know
distinguish
between.
So
let's
say
you
service
talks
to
another
service
and
the
first
few
times
it's
time
so
door
fails
and
then
it
doesn't
have
a
retry
policy.
And
then,
if
succeeds
now
that
service,
where
this,
the
calmer
service
will
report
back
to
its
client,
that
has
succeeded
and
we
wouldn't
be
aware
to
it.
B
It
succeeded
in
what
it
needed
to
do,
because
it
eventually
got
there
and
even
though
the
other
service
failed
in
terms
of
the
conversation
with
the
calling
services
succeeded
eventually,
and
so
how
do
we
do
that?
Observability,
because
how
does
a
trace
to
a
to
focus
on
trace,
know
when
well?
Okay,
we
are
were
were
allowing
failure,
but
so
I
can
ignore
these
traces,
because
the
final
trace
succeeded.
So
we
we
can't
seem
to
capture
dot
today
with
distributed
tracing.
B
So
we
have
to
do
something
different,
so
I
see
what
we're
doing
we
service
connection
cognition
is
how
services
process
and
store
information
about
our
services
is,
is
looking
at
how
services
interact
with
each
other
in
a
more
dynamic
and
adaptable
way
in
sensing,
the
other
service.
So
if
you
sense
that
the
time
or
the
other
service
is
slowing
down
the
response
time,
you
might
change
your
own
behavior
in
terms
of
how
quickly
you
call
it
now
the
way
they
would
do.
That,
of
course,
is
signalling
and
there
is
kind
of
a
signaling
already
there.
B
Today's,
like
HTTP
error
codes,
have
that
where
they
can
say,
I
drop,
do
or
I'm
overloaded.
So
we
do
have
signals
already
embedded
in
systems
yeah.
We
just
need
to
formalize
and
create
a
universal
as
signaling
glossary
that
works
across
whatever
protocol
a
has
so
signals
are
involved
to
convey
meaning
and
influence
to
receivers.
B
So
the
signal
in
itself
is
to
tell
something
information
and,
of
course,
when
someone
receives
a
signal,
they
are
meant
to
change
the
behavior
and
that's
why
people
send
an
error
code
back
or
a
HP
code,
because
they
say
redirect
this
or
I'm
dropping
this
or
I'm
delaying
you,
or
this
is
to
try
to
change
the
behavior
of
the
caller,
so
Sanders
obtain
effects
and
receive
is
obtained.
Yeah.
Thank
you.
B
Obtain
two
minutes
ago
receives
two
receivers
obtain
information,
so
the
cycle
very
similar
that
the
feedback
systems
you
were
signals
emitted
is
transmitted
over
some
kind
of
mediums
is
received
on
it
by
receiver.
The
receiver
records
it
reasons
about
it.
Response
it
already
already
feels
like
cybernetics
and
feeds
back
feels
like
what
we
do.
We're
observability
so
signal
is
very
different
than
a
message
from
a
login
that
the
signal
just
the
signal
itself,
has
a
meaning.
There
is
no
difference
between.
B
If
you
don't
have
to
unpack
the
message
and
look
inside-
and
this
is
where
logging
fails
is
that
logging
is
not
a
signal.
It's
a
message
and
inside
that
we
hope
to
derive
a
message,
but
reading
what
we
should
be
looking
at
in
the
future
is
more
focus
on
just
sending
the
signal,
the
meaning
directly
and,
of
course,
how
do
we
do
that
in
the
environment
is
what
we
can
look
for
a
solution
like
Sigma
Sigma
G,
which
is
a
mechanism
of
coordination
and
through
an
environment
between
agents
and
actions?
B
Typically,
you
have
an
agent
or
micro
service.
It
creates
actions,
these
leave
signals
or
signs,
and
within
the
environment
and
medium
and
the
kind
of
classic
example
is
ants
going
around
when
they
find
foods.
They
put
a
pheromone
down
on
the
surface
of
a
you
know
like
a
grid
and
then
a
patch
and
then
another
and
picks
it
up
and
follows
along.
So
this
is
where
basically,
the
history
of
previous
actions
affect
or
stimulate
or
dampen
the
effects
of
future
actions.
B
So
the
effect
stimulates-
and
it
could
be
both
negative
or
positive-
the
in
action
and
produces
more
effects
within
the
environment
and
that's
what
we
probably
need
a
signal
and
what
I'm
working
on
in
Stannah
is
on
signifying
and
whereas,
where
I
try
to
create
a
universal
as
signal
in
language
and
mechanism
for
inference
of
state,
around
services,
resources
and
context.
This
context
that
the
environments
were
existing
in
the
resources
or
schedules
like
Cuba
nativism
and
services,
which
is
the
systems
micro
services
that
we
have,
and
this
is
the
final
slide
just
up
there.
B
So
just
a
recap:
when
we
look
at
what
I
think
where
we're
moving
to
is
it's
not
going
to
scale
with
metrics
traces
and
logs?
We
have
millions
of
metrics,
we
have
even
more
on
traces
and
logs
and
people
just
don't
know
how
to
give
significance
to
them
and
class
by
them,
and
it's
never
going
to
happen
on
the
backend.
So
if
the
metric
is
not
already
as
a
signal-
and
it
has
significance
at
its
name-
it's
more
than
likely
not
going
to
be
useful
to
management.
B
So
what
we
need
to
do
is
bring
in
a
newer
form
of
observability
technology,
around
signals
that
can
translate
into
states
that
we
all
agree
like
status
page.
You
know
we
have
these
various
the
states
and
that's,
and
that
is
how
we
drill
down
into
metrics
and
traces.
We
look
for
States
when
stage
change
does
something
that's
interesting.
We
look
at
the
signals
that
generated
from
the
signals.
We
determine
the
time
windows
which
are
useful
for
looking
at
metrics
traces
and
locks.
B
A
A
Very
good,
and
now
we
have
some
time
for
Q&A
again,
please
drop
your
questions
on
the
Q&A
box.
It's
at
the
very
bottom
there
of
the
screen
and
I
have
a
question
here
for
you
from
Christian
Hyde
and
Rick
I
apologize.
If
that
was
mispronounced,
the
question
is:
in
the
beginning,
you
talked
about
metering
being
an
evolution
of
tracing.
Can
you
tell
us
more
about
what
I.
B
Mean
yeah
yeah,
okay.
So
when
I
worked,
my
previous
company,
which
is
auto
ethics
of
James
Park
I,
focus
on
creating
a
matrix
for
the
machine,
the
matrix
world
and
what
I
did
there
was
and
how
I
distinguish
that
between
tracing
is
that
simms,
which
is
what
the
name
is
like
seems
as
in
the
game,
but
it
has
an
S
Zed
in
a
it
seems
trying
to
curry
construct.
B
Behavior
reconstructed
hell
code
execute.
So
if
you
take
it
the
fundamental
level,
what
code
is
in
terms
of
you
know
how
it
runs
and
most
systems
is
there's
threads
running.
They
execute
or
sometimes
noted
a
go
routines.
They
they
execute,
calls
which
are
stack
pop
ups
and
you
know
pushing
and
popping
out
stack
frames
and
then,
of
course,
this
is
how
we
create
scope
of
execution.
A
B
Of
course,
you
could
also
speed
it
up
if
you
wanted,
but
a
mimicked
behavior
and
the
probably
the
best
way
to
see
that
is
like,
if
you
think
about
a
mime
artists
on
the
street
where
he
is
cleaning
the
windows.
We
know
he's
not
cleaning
a
window
when
he,
but
we
know
from
his
movements
that
he's
mimicking
the
cleaning
of
the
windows.
B
So
what
I
did
here
mirroring
is
basically
mimicking
that
is
so
mirroring
that
in
terms
of
tracing
Harrow
differs
tracing
today
is
not
really
being
designed
to
allow
the
reconstruction
or
the
replay
of
a
machine
like
it
had
happened
in
the
real
world.
So
Sims
said:
let's
create
those
event
stream
them
in
and
have
enough
information
in
it
that
we
can
actually
reconstruct
the
flow
of
it
and
to
another
product
or
another
monitoring
tool.
A
B
Would
be
like
what
we
could
do
it
actually
I
think
the
best
analogy
and
I
should
have
said
this.
Was
you
drink
so
today,
then
we
do
an
action.
We
hope
they.
We
use
the
same
mechanism
or
same
system
in
play
when
we're
looking
at
something
something
in
the
real
world.
But
when
we
dream
we
are
rooms
are
very
real
and
vivid.
We
experience
it,
but
we
don't
have
action,
we
don't
go,
our
control
system
is
turned
off
and
that's
what
seems
was
trying
to
do
it
says.
B
A
B
Well,
they
can
always
LinkedIn
or
my
email
addresses
William
dots
loud
at
in
standard
comm.
William
know
that
in
Seneca
aut'ority
can
just
have
a
check.
Oh
I
also
have
a
Twitter
account,
also
Latics
a
UT
o/l
80
ICS,
so
there's
also
there's
also
a
Twitter
account
automatics
and
auto
epics
is
actually
a
name
for
flow.
It's
it's
from
a
book.
The.
A
A
B
Well,
it's
donna
is
definitely
covering
all
the
boxes
in
terms
of
the
logging
day.
Ups,
the
basic
of
observability
and
the
reason
I
have
three
giant
and
Stan
and
came
back
in
is
to
bring
in
the
signifying
technology.
There
will
be
announcement
coming
up
short
in
that,
but
in
Stanley
has
pretty
much
covered.
The
whole
observability
Facebook,
of
course,
with
the
growing
complexity
is,
and
we
have
to
come
up
with
new
management
models,
and
this
is
the
feedback
system
so
and
Stan
is
branching
out
into
other
areas
and
looking
at
how
do
we
see
feedback?
B
How
do
we
better
support,
DevOps
and
not
just
Pro
and
diagnostics?
It's
great!
It's
an
it's
got
great
diagnostic
capability
and
it's
also
able
to
discover
and
automatically
and
dynamically
the
context
you
know
reconstruct
your
topology
reconstruct
those
call
graphs
and
that's
wonderful.
So
that's
basically
a
great
foundation
for
me.
It's
not
just
taking
traces.
It's
also
got
the
environment
context
for
what
it
would
miss
a
day,
even
though
it
has
it
to
some
degree
with
incidents
in
the
product.
B
Still,
you
know
working
with
observability
tools
and
that
will
there
will
be
a
moment
where
they're
they're
gonna
feel
that
these
things
are
not
working
for
them
and-
and
I
recently
did
a
talk
where
someone
said
that
yeah
they,
the
manager
in
their
company
had
said
this
is
more
the
business
process
level.
He
said,
I,
don't
want
anybody
producing
a
metric
that
I
cannot
associate
in
action
with
and
I.
B
Think
we
haven't
came
to
that
realization
in
me
in
the
space
of
observer
ability
and
monitor
we're
in
this
kind
of
big
data,
Hadoop
phase,
where
everybody
just
give
me
more
data
and
is
grabbing.
It
thinks
if
I
keep
collecting
more
that's
going
to
be
useful
for
and
we're
gonna
get
as
as
these
systems
evolve
and
in
this
greater
adoption
of
micro
service
platform,
we're
going
to
see
these
kind
of
problems
come
up
and
there's
going
to
be
in
need
for
language
that
can
help
man,
a
machine
and
I
hope
signified,
will
do
that.