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Chapter 1. Overview of NFV
Network Functions Virtualization (NFV) is a software solution that virtualizes a network function, such as a network switch, on general purpose, cloud-based infrastructure. NFV allows the Communication Service Provider to move away from traditional or proprietary hardware.
For a high-level overview of NFV concepts, see the Network Functions Virtualization Product Guide.
OVS-DPDK and SR-IOV configuration depends on your hardware and topology. This guide provides examples for CPU assignments, memory allocation, and NIC configurations that might vary from your topology and use case.
Use Red Hat OpenStack Platform director to isolate specific network types, for example, external, project, internal API, and so on. You can deploy a network on a single network interface, or distributed over a multiple-host network interface. With Open vSwitch you can create bonds by assigning multiple interfaces to a single bridge. Configure network isolation in a Red Hat OpenStack Platform installation with template files. If you do not provide template files, the service networks deploy on the provisioning network. There are two types of template configuration files:
-
network-environment.yaml- this file contains network details, such as subnets and IP address ranges, for the overcloud nodes. This file also contains the different settings that override the default parameter values for various scenarios. -
Host network templates, for example,
compute.yamlandcontroller.yaml- define the network interface configuration for the overcloud nodes. The values of the network details are provided by thenetwork-environment.yamlfile.
These heat template files are located at /usr/share/openstack-tripleo-heat-templates/ on the undercloud node.
The Hardware requirements and Software requirements sections provide more details on how to plan and configure the heat template files for NFV using the Red Hat OpenStack Platform director.
You can edit YAML files to configure NFV. For an introduction to the YAML file format, see: YAML in a Nutshell.
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