Date: Thu, 28 Mar 2024 17:03:01 +0000 (UTC)
Message-ID: <1436374730.809.1711645381814@c4e992b7d277>
Subject: Exported From Confluence
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The planning of edge cloud in deployment perspective focuses on =
achieving high availability, partitioning, eventual consistency, load balan=
cing and replication.
1st Architecture: Centralised Control Plane Sce=
nario
[1]
In the first stage of deployment, we focus on the "easiest and fastest" =
deployment mechanism, with satisfying=
the MVP (minimum viable product) reference architecture. This will be=
the reference point to try and investigate different deployment mechanisms=
.
=
[1]
In the second stage, we try to modify the Centralised Control Plane=
edge cloud by adding Large/ Medium Edges in between the centralised DC and=
Small Edge. A deployment of thi=
s type will enjoy simplified management of compute resources across regions=
. From deployment aspects, we can investigate high availability=
, load balancing and replication. These test are described in details below=
.
2nd Architecture: Distributed Control plane Sce=
nario
The Centralised Contro=
l Plane Scenario comes at the co=
st of losing the ability to manage instances in an edge or far edge cloudle=
ts during a network partition between the Regional data center and Edge. Th=
is can lose partitioning of the cloud, reduce its availability, and highly =
loaded datacenter.
A deployment of this type will benefit from greater autonomy in the eve=
nt of a network partition between the edge site and the main datacenter. Th=
is means that Distributed Control plane to handle complex tasks s=
uch as traffic engineering, enable load balancing, highly partitioned and a=
vailable cloud.
[1]
Case tests for each architecture
For each architecture, we test and investigate different optio=
ns of cloud setup and deployment
Case 1
=3D=3D Kubernetes on OpenStack =3D=3D
Experiment with various ways of deploying OpenStack and configuring its =
services. We experiment by bringing in containers and Kubernetes on top of =
Openstack if users have the freedom to choose the best cloud environment to=
run any given application, or part of an application, while still enjoying=
scalability, control, and security.
Case 2
=3D=3D Openstack on Kubernetes =3D=3D
So in this case we experiment self-healing infrastructure, check if we c=
an prove that OpenStack will be more resilient to the failure of core servi=
ces and individual compute nodes, than previous deployment mechanism. We ch=
eck also performance based on resource efficiencies that come with a container-based infrastructure. <=
/span>
In this case, we experiment in particular Rolling Update that Kuber=
netes offers to Openstack. So we recheck performance again with Rolling Upg=
rade mechanism and see if we can ensure continuous delivery wi=
th zero downtime=
p>
During Testing of these cases, we also:
o Investigate load balancer to reduce lat=
ency using:
-
- on openstack using HAProxy: https://en.wikipedia.o=
rg/wiki/HAProxy
on kubernetes clusters using clusters federation:
To be more specific, I will be working on istio, canary de=
ployment, kubernetes auto scaling, service discovery, high a=
vailability setup in kubernetes, deployment of mysql on kubernetes wit=
h helm, and spinnaker continuous delivery platform deployment .
- Investigate providing high availability and replication between multipl=
e instances of database engine by clusters federation using kubernetes, dep=
loying Galera cluster for database, such as Keepalive or HAProxy, and virtu=
al IP using VRRP
=E2=80=A2 Investigate different d=
atabase engines such as RDBMSs (availability and consistency), and dynamo d=
b (availability and portability).
=E2=80=A2 Test cloud performance =
and benchmark using OpenStack Rally and Yardstick. Test includes:
o Latency and packet delay
o Scaling performance
o How different deployments affect the OS performance
o Performance of basic cloud operations
o Availability Robustness and security
=E2=80=A2 Investigate the issue with containers caused by little flexibi=
lity in operating systems. Running containers on different operating system=
s such as Windows.
o What is the performance drop caused by =
running a minimal linux core and deploying the containers on top of that?
o Compare this with Windows Containers introduced in Windows 10 and Window=
s server 2016.
On extra time we also:
Investigate different containers. Compare characteristics such as =
tradeoff between performance and security
o Linux containers (LXC): Linux admi=
ns can use Linux containers to create a layer of separation between the ope=
rating system kernel and the application layer.
o Docker containers: a lightweight, standalone, executable package of=
software that includes everything needed to run an application: code, runt=
ime, system tools, system libraries and settings.
o Kata containers: lightweight Virtual Machines (VMs) that feel and p=
erform like containers, but provide the workload isolation and security adv=
antages of VMs.
=E2=80=A2 Investigate high a=
vailability resource manager such as Linux Pacemaker. Check: https://docs.openstack.org/h=
a-guide
=E2=80=A2 Experiment OpenStack's Live-migration feature
o Preconditions
o Constraints
o Capabilities
o Evaluate the necessity of this when using Kubernetes.
o Encrypted live migration ( https://docs.openstack=
.org/security-guide/instance-management/security-services-for-instances.htm=
l#trusted-images/
Reference=
s
[1]: https://wiki.openstack.org/wiki/Edge_Computing_Group/Edge_Reference_Arc=
hitectures
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