Agile Operations: A Formula for Just-In-Time Provisioning

  One of the ways in which traditional architectures and deployment models is actually superior (yes, I said superior) to cloud computing is in provisioning.  Before you label me a cloud heretic, let me explain. In traditional deployment models capacity is generally allocated based on anticipated peaks in demand. Because the time to acquire, deploy, and integrate hardware into the network and application infrastructure this process is planned for and well-understood, and the resources required are in place before they are needed. In cloud computing, the benefit is that the time required to acquire those resources is contracted to virtually nothing, making capacity planning much more difficult. The goal is just-in-time provisioning – resources are not provisioned until you are sure you’re going to need them because part of the value proposition of cloud and highly virtualized infrastructure is that you don’t pay for resources until you need them. But it’s very hard to provision just-in-time and sometimes the result will end up being almost-but-not-quite-in-time. Here’s a cute [whale | squirrel | furry animal] to look at until service is restored.

While fans of Twitter’s fail whale are loyal and everyone will likely agree its inception and subsequent use bought Twitter more than a bit of patience with its often times unreliable service, not everyone will be as lucky or have customers as understanding as Twitter. We’d all really rather prefer not to see the Fail Whale, regardless of how endearing he (she? it?) might be.

But we also don’t want to overprovision and potentially end up spending more money than we need to. So how can these two needs be balanced?