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Description
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Sailing Multi Cloud Traffic Management With Karmada - Zhonghu Xu, Huawei
Multi-Cloud is becoming a new trend in recent years. It is the superset of multiple public cloud, hybrid, on-premises, and edge. It not only provides higher availability and flexibility but also prevents vendor lock-in. Karmada is a kubernets management system, it is natively suitable for multi-cloud application management. In this topic, Zhonghu will show you what Karmada can do for Multi-Cloud applications communicating on flat network and different networks. There are already many solutions that can help acquire L3 network connectivity cross multi-cloud, like VPN peering, submariner, etc. So the biggest challenge is L7 traffic management, Zhonghu will elaborate on how Karmada perfectly integrates with Istio to provide flexible and rich L7 traffic management features: priority routing, failover to another cloud, and also brings natural security in east-west traffic.
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Next,
I
will
talk
about
commander
a
young
sensative
project
which
handles
multi-cloud
workload
of
situation
with
kubernetes
api.
It
is
natively
suitable
for
multi-cloud
application
management.
The
third
I
will
talk
about
multi-cloud
traffic
management.
This
may
be
the
most
tricky
point
user
can
face
when
adopting
multi-cluster
automatic
cloud.
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Let
me
do
a
quick
explanation
of
my
cloud.
My
is
when
an
individual
or
organization
uses
more
than
one
cloud
provider
for
the
itiness.
The
approach
enables
companies
to
better
support
their
business
technology
and
service
needs.
This
approach
enables
companions
to
better
support
their
business
technology
and
service
reliability
requirements
while
mitigating
over
reliance
on
a
single
cloud
provider
that
might
not
accomplish
all
tasks
effectively.
A
multi-cloud
strategy
can
encompass
private
public
and
hybrid
class
and
allows
business
to
seamlessly
manage
multi-profit
providers
and
virtual
infrastructure
performance,
while
achieving
great
efficiency.
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As
a
figure
indicates
89
of
the
respondents
report
having
a
multi-cloud
strategy,
it
is
still
the
de
facto
standard,
but
the
ways
in
which
organizations
arrive
at
multi-cloud
vary
depending
on
their
needs
and
mix
of
providers
to
them
most
are
taking
a
hybrid
approach.
You
can
see
at
the
figure
80
of
the
respondents
adopt
hybrid
hybrid
cloud.
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Second,
meeting
compliance
requirements
as
data
privacy
regulations
continue
to
ramp
up
the
need
for
companies
to
maintain
strict
levels
of
compliance
is
increasing.
Geographic
locations
need
to
be
accounted
for
in
order
to
satisfy
regulations
such
as
europe's
gdpr
aside
from
companies
integrating
its
own
on-premise
data
centers,
a
multi-cloud
strategy
is
typically
the
most
effective
and
efficient
approach.
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The
first
is
the
management
complexibility,
the
more
cloud
vendors
you
work
with
the
more
resources
your
item,
iit
team
has
to
manage.
Most
vendors
have
its
own
technology,
although
kubernetes
is
widely
used.
This
is
mitigated,
but
the
distribution
of
workloads
across
multi-cloud
is
still
a
challenge.
A
Second,
is
the
security
concerns?
The
responsibilities
of
protecting
data
in
the
cloud
is
shared
between
your
organization
and
cloud
providers.
Most
vendors
offer
built-in
cyber
security
tools
to
protect
customer
data.
Never
nevertheless,
working
with
multi-cloud
services
creates
additional
security
risks.
Such
you
need
a
multi-layered
security
approach
when
working
with
several
cloud
hosting
environment.
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It
is
simple
to
communicate
within
one
network.
However,
if
clouds
cross
clouds
they
could.
This
could
be
a
big
problem.
First,
we
need
to
figure
out
a
way
to
make
traffic
flow
across
cluster
across
cloud
like
a
single
network.
For
this,
the
most
reliable
way
is
the
direct
connect,
but
it
is
very
expensive.
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A
Next,
let's
take
a
look
at
the
challenge
of
managing
multi
container
clusters.
Firstly,
there
are
too
many
clusters
if
users
want
to
manage
their
own
clusters
on
multi-cloud,
you
need
to
create
and
manage
the
life
cycle
of
the
cluster
and
even
some
repetitive
setup
and
the
fragmented
api
endpoints
of
different
vendors.
A
Even
if
you
use
cloud
vendor
managed
coordinated
services
like
gk,
uks
or
huawei
cce,
which
have
all
passed
the
since
cf
conformance
test,
and
they
provide
the
consistent
api
as
upstream
kinetics.
The
cluster
is
totally
hosted.
Users
are
free
of
cluster
life
management,
but
there
are
still
other
challenges
listed
in
the
figure.
First,
it
is
the
workload
fragment.
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We
can
use
story
class
too.
We
can
override
this
story
class
storage
class
according
to
different
cloud
provider.
The
following
diagram
shows
how
commander
resources
are
involved
when
propagating
resources
to
member
cluster.
First,
it
is
the
policy
controller
and
camera
scheduler
to
bind
the
resources
to
different
cluster
workers
and
then,
if
binded,
the
worker
will
create
the
resources
in
member
clusters.
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Okay,
let's
talk
about
the
propagation
policy
in
dtl
provocation
policy
includes
two
important
spec.
The
first
one
is
the
resource
selector,
which
is
used
to
match
resources
that
the
propagation
policy
will
apply
to
here.
It
is
very
flexible.
We
can
select
one
resource
by
name
or
by
or
even
by
label.
Selector.
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This
example
is
to
do
some
customization
of
different
different
cluster
deployment.
We
can
look
at
here.
We
have
three
clusters:
cluster
one
and
class
two
located
in
dc1,
which
is
an
on-premise
environment.
Cluster
is
a
hosted
public
cloud,
kubernetes
cluster
to
save
image,
downloading
bandwidth
and
latency.
A
We
may
override
the
immense
registry
for
different
cloud
and
here
for
the
deployments
in
cluster
1
and
class
2.
We
want
to
download
image
from
the
from
a
local
registry
and
for
cloud
for
class
3.
We
want
to
download
the
image
from
the
registry
managed
by
cloud
provider.
We
can
use
the
example
here
to
override
them.
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Second,
it
is
east
west
traffic
across
class
for
inner
service
communication.
Application
can
be
deployed
in
any
class
in
any
cloud.
Even
one
application
can
be
deployed
across
clouds.
Sometimes
they
need
to
communicate
to
each
other.
Then
how
can
we
do
statistical
discovery?
How
to
build
connect
across
cloud?
How
to
make
sure
the
traffic
is
secure.
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Kubernetes
is
only
corporate
compatible
of
resolving
local
class
service
for
cluster
four
service
spans
across
different
clusters.
It
is
resolvable,
but
remote
service
instance
is
hard
to
access,
because
grenade
starts
service
load-basing
via
rptvos,
which
only
redirects
requests
to
local
cluster
service
enterprise.
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The
third
is
integrated
with
mature
services.
Service.
Mesh
solution
like
issue
issue,
is
the
most
popular
service
mesh
project
able
to
support
application
level
management,
especially
on
traffic
management.
Their
trust
and
observability,
with
the
help
of
easter
commander,
can
seamlessly
make
easter
eggs
traffic
flow
between
multi-cloud.
As
native
as
single
class.
With
this
skill
layer,
7
drives
like
hddb
grpc
could
be
well
controlled.
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Eq
extends
a
new
kind
of
xcs
nds
to
facilitate
the
asset.
Resolve
nds
is
used,
is
used
by
dns
proxy
to
fetch
dns
name
tables
from
issue
control
plan,
and
then
it
appeals,
dns
lookup
table
to
serve
dns
resolve
from
local
application.
The
name
table
contains
all
the
services
across
across
clusters
for
cognitive
service.