This is a discussion on The problem with automated provisioning (III of III) - Solaris Rss ; The third of my articles on the macro-level issues with (automated) provisioning, which build on the previous articles, specifically the comparison of Enterprise versus "web scale" deployments described in " The problem with automated provisioning (II of III) " and ...
The third of my articles on the macro-level issues with (automated) provisioning, which build on the previous articles, specifically the comparison of Enterprise versus "web scale" deployments described in "The problem with automated provisioning (II of III)" and the levels of complexity, in terms of automated provisioning, set up and configuration that is required.
As I've said before in this series of articles provisioning a thousand machines which all have the same OS, stack and code base, with updated configuration information is easier to set up than a thousand machines which use a mixture of four or five Operating Systems, which all have differing patch schedules, patch methods and code release schedules, with a diverse infrastructure and application software stack and multiple code bases. And to express this I've postulated the equation "(Automated) Provisioning Complexity = No. of Instances x Freq. of Change".
What I'd like to move the focus over to is that of runtime stability and the ability of a given system to support increasingly greater levels of complexity.
I find that it is important to recognise the place of observation and direct experience as well as theory and supposition (in research I find it's useful to identify patterns and then try to understand them).
Another trend that I have witnessed in regards to system complexity, including the requirement to provision a given system, is that the simpler and more succinct a given architectural layer, the more robust that layer is and more able to support layers above it which have higher levels of complexity.
Often Architectural layers are constrained in terms of there ability to support (and absorb) high numbers of differing components and high rates of change by the preceding layer in the stack. AKA the simpler the lowest levels of the stack the more stable they will be and thus more able to support diverse ecosystems with reasonable rates of change in the layers above them
The more complex the layer below the less stable it is likely to be (given the number of components and instances thereof and the rate of update which significantly drive up the level of complexity of the system).
This phenomenon is found in the differing compute environments I've been speaking about in these short articles, and again they affect the ability of a given system to be provisioned in any succinct and efficient manner.
More accurate Enterprise
Typically Enterprise IT ecosystems are woefully complex, due to a mixture of longevity (sweating those assets and risk aversion) and large numbers of functional systems (functional as in functional requirements) and non-functional components (i.e. heterogeneous infrastructure, with lots of exceptions, one off instances, etc.).
Subsequently they suffer from the issue that I've identifioed above, that is as lower levels are already compolex, they are constrained in the amount of complexity that can be supported at the level above, the accompanying diagram demonstrates the point.
More accurate Web Scale
Whilst Web Scale class systems often exhibit almost the opposite behaviour. Given they often use a radically simplified infrastructure architecture anyway (i.e. lots of similar and easily replaceable common and often 'commodity' components) in a 'platform' approach, there isn't often the high levels of heterogeneity that you see in a typical Enterprise IT ecosystem (homogeneous). And this approach is often found in the application and logical layers above the infrastructure, i.e. high levels of commonality of software environment, used as an application platform to support a variety of functionality, services, code and code bases.
Subsequently, because of the simple nature of low level layers of the architecture they are much more robust and capable of withstanding change (because introducing change into a complex ecosystem often leads to something, somewhere breaking, even with exceptional planning). This stability and robustness ensures that the overall architecture is better equipped to cope with change and with the frequency of change, and that layers of high levels of complexity can be supported.
And so that concludes my articles on provisioning, and the problems with it, for the time being, although I might edit them a little, or at least revisit them, when I have more time.