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From YouTube: Creating Chaos in the University with Linkerd and Chaos Mesh - J. B. C. Fajardo & S. A. M. Aguilar
Description
Don’t miss out! Join us at our upcoming event: KubeCon + CloudNativeCon North America 2021 in Los Angeles, CA from October 12-15. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
Creating Chaos in the University with Linkerd and Chaos Mesh - Jossie Bismarck Castrillo Fajardo & Sergio Arnaldo Méndez Aguilar, Universidad San Carlos de Guatemala
This talk presents a guide to create chaos engineering experiments using the technical approach and advantages of service meshes. It also shows how you use and interpret collected data from Linkerd during a chaos experiment and how Linkerd can complement Chaos Mesh for other types of chaos experiments. The talk is based on Jossie Castrillo thesis guided by professor Sergio Méndez both from San Carlos University of Guatemala. This talk also shows the challenges that a student has to develop and research about cloud native technologies in order to find valid use cases for Chaos Engineering that can be applied on real life situations. At the end, Jossie shows a short chaos experiment using Linkerd to generate faulty traffic and Chaos Mesh for additional chaos experiments, at the same time Sergio explains how to interpret the collected metrics and data from that experiment.
A
A
B
B
A
B
The
service
measures,
what
is
a
service
mesh,
so
a
service
mesh,
is
like
a
kind
of
let's
say,
future
door
ability
to
create
a
stable
network
in
your
services.
The
service
mesh
just
a
proxy
instance
in
your
deployments,
like
a
sidecar
kind
of
deployment
using
containers
in
order
to
evaluate
evaluate
the
traffic
and
decide
what
things
has
to
do,
and
that
kind
of
things
so
give
your
networking
more
reliable,
more
stable,
secure
and
everything
the
benefits
are
like.
Well,
you
can
check
the
state
of
your
services
in
the
network.
B
It's
really
scalable
and
implement
services
that
are
stable
in
general.
You
can
split
the
traffic
implement
some
security,
a
tls
layered,
and
that
kind
of
things
using
this
cycle
proxy
to
detect
the
traffic,
and
you
don't
have
to
modify
a
lot
of
times
your
application.
So
no
modifications
to
has
the
power
of
service
meshes.
A
Okay,
now,
what
is
chaos?
Engineering
in
an
easy
way,
is
making
experiments
on
a
system
to
improve
his
capacity
to
support
extreme
conditions
in
production.
The
objective
of
chaos
engineering
is
to
cause
failure
on
purpose,
of
course,
to
identify
where
and
under
what
condition.
Our
system
can
fail
and
improve
our
system
of
course.
A
A
Some
benefits
of
gas
engineering
well
identify
vulnerabilities
and
potential
failures,
improver
systems
and
have
a
proactive
system
design
here
are
some
examples
of
where
we
can
apply
some
case,
meaning
test.
First,
in
the
application
I
mean
at
a
software
level
in
networking
by
slowing
trapping
through
traffic
losing
packages,
dns
failures,
bots
killing
them
producing
some
fillers
in
the
bots,
the
cpu
run
memory
and
operating
systems,
as
some
examples,
of
course,
okay
yeah
for
casino
experiments.
We
define
a
group
of
steps
that
we
can
follow
to
create
a
this
experiment.
A
A
Yes,
define
this
architecture,
of
course,
define
a
healthy
state,
be
sure
of
what
what
kind
of
experiment
we
want
to
try.
We
can
try
a
lot
of
experiments,
of
course
define
a
hypothesis.
A
hypothesis
of
what
thing
could
happen,
identify
the
critical
points
of
the
experiments
to
implement
owners.
Understand
this
pretty
points
verify
what
could
go
wrong
do.
I
know
what
will
happen
if
it
fails
or
if
it
doesn't
set
a
smile
last
radius
for
the
beginning,
define
our
kpis
english
scales.
A
B
Well,
right
now
the
technology
are,
we
are
going
to
use
lingard
in
some
parts,
just
for
the
faulty
traffic
and
linker.
This
is
a
service
mesh
for
kubernetes.
It's
really
easier
to
install
and
implement
in
the
way
to
get
some
more
surfability
implement
security.
Debugging
your
services
in
the
network.
A
B
Let's
go
to
the
demonstration
part
well
talking
about
how
this
demonstration
is
built.
We
have
the
architecture
a
little
bit
about
the
things
that
we
are
doing
on
this.
We
have
a
kubernetes
cluster
installed.
Installing
gpa
you
can.
We
have
two
nodes
with
two
cpus
of
with
four
gigabytes
of
ram.
We
have
two
main
applications:
a
client
that
sends
traffic
to
apache
deployment
on
kubernetes.
B
In
this
way,
linkerd
is
going
to
send
the
traffic
to
apache
the
50
and
the
other
50
to
the
error,
injector
deployment
that
returns,
faulty
traffic
and
you're
splitting
this
traffic
50
and
50
50
good
50
baht.
That's
the
first
part
using
a
linkard
for
this
kind
of
of
pretty
basic
chaos.
Experiment
using
service
meshes
just
for
faulty
traffic
and
see
what
happens.
B
In
the
other
side,
we
are
going
to
use
chaos
mesh,
trying
to
kill
pots
failure
pots
in
some
way
and
see
what
happens
using
the
same
experiment,
putting
all
the
things
together
down.
We
are
going
to
do
a
walkthrough
over
the
different
dashboards
of
linkardi
and
killsmesh,
and
explain
a
little
bit
the
code
of
these
experiments
using
link
or
the
ikea's
mesh
in
general.
A
A
A
B
Yeah
in
this
part
now:
well
we
have
the
linker
the
dashboard
it's
showing
how
the
apache
and
the
client
are
in
these
connections
and
in
traffic
and
that
things
well,
if
we
apply
the
the
traffic
split,
we
are
going
to
see
how
the
traffic
is
failuring
and
that
kind
of
things
and
the
relation
between
the
faulty
traffic,
the
success
rate
and
that
kind
of
things
in
this
dashboard.
So
maybe
we
can
move
to
that
part
to
the
traffic
splitting
faulty
injection
of
the
traffic.
B
B
B
I
guess
basically
the
this
part
of
the
traffic
split.
Maybe
we
can
come
back
to
the
main
to
the
main
dashboard.
Okay.
So,
let's
go
to
the
client
part
in
the
client
part
is
going
to
show
this
relation
between
these
deployments,
how
the
client
is
sending
traffic
to
the
error
injector
on
the
apache.
Basically,
the
client
sends
the
traffic
to
the
apache
service
on
the
kubernetes
and
it's
going
to
the
traffic
to
the
other
service
that
connects
to
the
deployment
to
the
error
injector.
B
Yeah
linker
d
provides
you
with
some
dashboard
that
shows
the
requests
per
second
of
of
your
of
your
current
service
that
is
running
right
now
is
only
be
affected
by
the
faulty
traffic
of
of
linkerd,
using
this
faulty
traffic
feature
using
traffic
splitting
and
that's
the
general
metrics
that
is
showing
this
dashboard.
The
success
rate,
the
records
rate
and
the
latest
thing
that
are
basically
the
golden
metrics
for
this
kind
of
dashboards
and
information
that
link
are
these
shows,
so
that's
the
way
that
we
use
service
mesh
to
generate
faulty
traffic.
A
A
A
A
Okay,
some
lessons
that
we'll
learn
with
all
of
this
process.
First,
how
to
implement
step-by-step
calc
experiments,
implement
cal
experiments
to
improve
our
system
is
not
as
difficult
as
it
sounds
and
understand
how
a
student
thesis
can
help
up
and
source
projects
and
the
industry
to
grow
up
and
solve
some
real
life.