Skip to main content
  1. Blog
  2. Article

Canonical
on 11 March 2015

NFV and SDN on OpenStack for network operators


Network Functions Virtualisation (NFV) and Software-Defined Networking (SDN) are two of the hottest infrastructure technologies around, particularly for telecoms and network operators wanting to map their services in a more efficient, scalable, and cost-effective way.

One of the biggest areas of interest for operators as they look to integrate those technologies is to have a quick and easy way to deploy and map them onto cloud infrastructure, particularly OpenStack. This whitepaper gives an overview of the ever-changing infrastructure landscape for network operators, and the challenges they face when implementing NFV and SDN technologies.

The eBook also presents a reference architecture for integrating NFV and SDN technologies onto Ubuntu OpenStack clouds, allowing for maximum flexibility in configuration, management, and scaling.

Download eBook

Related posts


Gabriel Aguiar Noury
16 June 2026

A look into Ubuntu Core 26: Building a local AI inference appliance in a virtual machine

Internet of Things Article

Welcome to this blog series which explores innovative uses of Ubuntu Core. Throughout this series, Canonical’s Engineers will show what you can build with this Core 26 release, highlighting the features and tools available to you.  In this first blog, Farshid Tavakolizadeh, Engineer Manager for Canonical’s Industrial team, will show you h ...


Pedro Lazzarotto
12 June 2026

A decade of Ubuntu on IBM Z and IBM LinuxONE

Partners Article

This year we celebrate a decade of Ubuntu Server support on the s390x architecture: marking a long-standing collaboration between Canonical and IBM that began at LinuxCon 2015. The first release happened on April 21, 2016, bringing Ubuntu 16.04 LTS (Xenial Xerus) to IBM Z and IBM LinuxONE platforms.  A first for Ubuntu on IBM That ...


Pedro Lazzarotto
11 June 2026

AI at the edge: simplifying infrastructure with Cisco and Canonical

AI Article

Legacy infrastructure was not designed for the requirements of the AI era. While large-scale model training remains centralized in data centers, test-time inference is rapidly shifting to the edge to reduce latency and bandwidth consumption. This shift creates a new frontier for enterprise AI, but deploying at the edge introduces signific ...