Monthly Archives: December 2016

OpenStack

Is OpenStack Neutron ML2/OVS Production Ready for Large Scale Deployments?

One of my personal highlights of the recent Barcelona Summit was a session by Mirantis engineers Elena and Oleg titled “Is OpenStack Neutron Production Ready for Large Scale Deployments?”. In the session they outline a comprehensive control and data plane testing effort, run on two labs, one with 200 nodes and run of the mill hardware, and the other with 378 and top of the line hardware, all running the Mirantis distribution based off Mitaka with standard ML2/OVS, DVR, L2POP and VXLAN. In the session they show near line-rate speed for east/west and north/south routing with jumbo frames and VXLAN offload enabled. They were also able to spawn 24,500 VMs across 125 networks without errors and low CPU consumption.

Slides on SlideShare

https://twitter.com/assafmuller/status/795605316100587521

Turning our eyes to adoption, the OpenStack Foundation conducts a usage survey every 6 months. Looking at the April 2016 user survey, we can see that ML2 with Open vSwitch and Linux Bridge dwarf other solutions.

neutron-plugins-breakdown

https://www.openstack.org/assets/survey/April-2016-User-Survey-Report.pdf, slide 41, taking out “ML2” and “nova-network”.

Examining the openstack/neutron project via Stackalytics we see that ML2/OVS has a rich and robust community with 20 companies contributing over 5 patches in the Newton time frame. 779 people have contributed at least 1 patch to Neutron since its inception, 215 of which during the Newton timeframe. Some of the effort targeted the base Neutron platform, e.g. configuration options, database work, versioned objects, quotas or other in-tree ML2 drivers such as SRIOV. Looking at the contribution of everyone who has committed at least 5 patches in the Newton cycle, we are left with 50 authors, 42 of which contributed at least 1 patch to ML2/OVS.

neutron-community

Taken from http://stackalytics.com/?release=newton&module=neutron&metric=commits after filtering authors with less than 5 patches

Looking at the interactive version of the OpenStack user survey we can see that ML2/OVS is the most popular choice by an order of magnitude regardless of deployment size. And so to answer the question: “Is OpenStack Neutron ML2/OVS Production Ready for Large Scale Deployments?”. Yes, it is, of course it is. It has been for some time now.

Standard
OpenStack, Talks

Upstream Contribution – Give Up or Double Down?

Ever since I’ve been involved with OpenStack people have been complaining that upstream is hard. The number one complaint is that it takes forever to get your patches merged. I thought I’d take a look at some data and attempt to visualize it. I wrote some code that accepts an OpenStack project and a list of contributors and spits out a bunch of graphs. For example:

  • How long does it take to merge patches in a given project over time? Looking back, did governance changes affect anything?
  • Is there a correlation between the size of a patch and the length of time it takes to merge it? (Spoiler: The answer is… Kind of)
  • Looking at an author: Does the time to merge patches trend down over time?
  • Looking at the average length of time it takes to merge patches per author, how does it look like when we graph it as a function of the author’s number of patches? Reviews? Emails? Bug reports? Blueprints implemented?

The data suggestes answers for many of those questions and more.

Here’s the code for you to play with:

https://github.com/assafmuller/gerrit_time_to_merge

And some conclusions in the slides embedded below:

https://docs.google.com/presentation/d/17l720kXrAHJC9_gU81nuIGzJCosralsbtbHGUuXgaxk/edit?usp=sharing

Here’s a few resources about effective upstream contribution. It’s all content written by and for the Neutron community but it’s applicable to any OpenStack project.

Standard