I recently wrote a blog for GigaOm’s Earth2Tech site on “Green Code.” The idea is that the quality of code matters. Two coders, writing code for the same application, can have a tremendous difference in efficiency. And that can translate to big differences in power consumption and resource costs — particularly in a virtualized or on-demand environment.

Over here on the Coradiant blog, I can speculate a bit more specifically about what this means. One of the interesting things you can do with user experience is to measure the total processing involved in a page or a user visit.

Because much of the delay on the Internet comes from network performance, two applications with significantly different host efficiency might seem as fast as one another to an end user, so you can’t really measure this just by trying two sites.

But the precision of Real User Monitoring technologies makes even millisecond differences in host processing time clear. And while web operators usually look at average (or percentile) host time, one of the more unusual ways to measure host time is to sum it. This effectively shows you the “total thinking done” for a user’s session.

This can be the start of some pretty fascinating math. Once you know host time per session, you can see how many host-seconds your infrastructure devotes to a visitor. This can show you things like whether a certain class of users is consuming more than its fair share of “heavy” searches.

(Incidentally, on the Coradiant.com site, this often reveals blog spammers from China posting comments about their various vitamins, and more questionable offerings.)

But you can also tie this host time back to IT costs.

I’m teaching a course on data center growth as part of Interop’s Data Center Summit in New York next week (more on this in a later post.) In preparing for that session, I spent a lot of time looking at the cost models behind on-demand hosting, managed servers, collocation, and global CDNs. And it made me realize there are good ways to model IT costs that vary widely according to each business.

Let’s look at combining these two metrics — host time and IT costs — to better understand the business impact of IT.
If you have a good model for IT costs (such as collocation, power, cooling, and storage) and you divide your monthly IT costs by the sum of host time for the month, you know your IT-cost-per-host-second. You don’t want to include bandwidth costs, which aren’t related to the host time.

If you then multiply host-seconds for each user session by that IT cost, you can calculate how much each user session costs you.

This is an excellent basis for evaluating change across releases. It will reflect increased costs in hosting (such as the introduction an application accelerator,) reductions in delay (such as a drop in host time from the AFE’s application acceleration functions reducing the load on servers,) and even changes in pages per session.

You can actually report average IT cost per user session.

As a result, you’ll now know the actual impact of that deployment: Did the reduction in IT-cost-per-host-second outweigh the investment in the AFE? How many weeks did it take to pay the cost back? Is the additional site navigation costing us more?

Of course, there are many other benefits to reducing host time, from user satisfaction to increased capacity to reduced SLA refunds. But this idea of IT-cost-per-host-second is a nice, concrete way to think about what code changes or other modifications to your operations do to your business.

Now back to the fascinating sessions at Web 2.0.