UrbanCode Deploy with vSphere and Chef

In a previous post (Cooking up deployments on stacked clouds) I looked at using UrbanCode Deploy with OpenStack and Chef. I showed one way of creating a stack from a Heat template, injecting the UCD agent and Chef  into one of the newly provisioned servers, cooking a Chef recipe and deploying an application on it. This time round I look at how to do almost the same thing with VMware vSphere.

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Old dog, new trick: RTC and UrbanCode Deploy

Calling Rational Team Concert an old dog isn’t fair  – it’s only just over 5 years old –  and I mean it in the nicest possible way in the context of the Jazz Jumpstart team being “old”, and the Rational Emerging Technologies Team, well, “emerging”.

Anywho, as is my wont, I was going through what could be in the next CLM release, noticed the bit about Rational Team Concert Build and IBM UrbanCode Deploy and decided to try out this “emerging” feature with Rational Team Concert 4.0.5 RC1.

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Managing Android platform source with RTC

My previous post on populating and testing a large Jazz repository could be tested with JMeter was primarily focused on the Work Item capability in RTC. I also needed to populate the repository with lots of SCM data and rather than take the “dumb” (ie. random) path as I had done with the Work Items, I wanted to use a more “realistic” set of artifacts.

I didn’t have to look very far: the Android platform which has apparently become the leading smart phone platform (canalys.com) and 300+ million Android-based smartphone activations (prweb.com). There are already a bunch of posts on Jim’s blog and Jazz.net on how to use RTC for Android app development, so I turned my attention to the Android Platform source, from the AOSP, which proves to be a different kettle of fish when it comes to SCM.

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(J)Metering a Jazz server – Part II

Continuing my journey on the JMeter road, I found Jmeter has a number of other features that help to make Test Plans more generic and useful.

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(J)Metering a Jazz server – Part I

Tox wrote (is writing) a series on monitoring Jazz performance. Timely indeed as I’ve been working out just how a CLM server performs under load : a single-tier server holding about 3 million Work Items, with 300+ project areas, 3000 registered users (upto 500 concurrent with 20% of them raising ~5 work items an hour ), 50+ GB of SCM content (120 components, 300 streams).

There’s enough excellent stuff on CLM performance on Jazz.net (articles 790, 720 , 641, 814) but what I wanted was to see what happened with the specific repository size described above.

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