“The Canonical Distribution of Openstack”

ubuntu-16-04-lts

For more informations about Openstack we can view this link:

https://www.openstack.org/

UBUNTU OPENSTACK AUTOPILOT

The best way to build an Iaas infrastructure for our lab with Ubuntu, it is using as platform the following suite of elements composed by OpenStack Cloud System, Landscape, Maas and Juju, let’s see how to install this entire environment on our lab. But before to proceed with that it’s a good starting point knowing something about this new project:

https://www.ubuntu.com/download/cloud/autopilot

diagram-cloud-openstack-autopilot2

In this new talk we’ll show you how install all the elements on our virtual nodes with all their components. For have more details about Conjure-up we suggest you to see this video:

https://www.ubuntu.com/download/cloud/conjure-up

or that one for Autopilot

The lab where we’ll build our infrastructure is based on an IBM System x3650 M4 as physical host, with VMware ESX as Hypervisor and it’ll be composed by the following requirements:

A minimum of 6 machines with these roles:

  • 1 vm for the Maas server (HDD=150GB, 8GB RAM);
  • 1 vm for the Juju controller (HDD=100GB, 8GB RAM);
  • 1 vm for the Landscape Autopilot (HDD=100GB, 8GB RAM);
  • 4 vm for the cloud (6 or more if you want HA) with (HDD=400GB x 2, 16GB RAM, CPU=2Core):
    • At least 1 vm must have 2 NICs or in HA 3 vm must have 2 NICs;
    • At least 4 vm must have 2 disks;

Remember we’ve to to set the vnet on vSwitch in VMware ESX in promiscuos mode, otherwise they won’t allow network traffic to the containers. Now lets begin with this first guide to prepare our environment with Maas, then next the guides for this lab.

the topics will be covered by:


“cya to the next 1…. Njoy !”
bye dakj

Disclaimer: All the tutorials included on this site are performed in a lab environment to simulate a real world production scenario. As everything is done to provide the most accurate steps to date, we take no responsibility if you implement any of these steps in a production environment.

“We learn from our mistakes”

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