[cpwatson]

From my experience Little's law is typically used to quantify the number of users in the system, a measure of concurrency. For the purpose of our tools we leverage the calculation to provide insight on "offered load" or the time spent w/in the service for a given interval. We do have a challenge in that many of our downstream dependencies are called concurrently. At the current time this prevents us from easily decomposing the demand in a service cleanly among it's dependencies. Some of this has to do with our transaction tracing framework and the granularity at which we require call behavior to be easily time-ordered. We believe we can solve this overtime with an improved framework. In the case of Mogul we leverage the demand calculation to understand who is the largest contributor, pointing us in the direction of possible optimization. If we are using the utility to triage an issue we typically find that an increase in the demand or offered load within the problematic dependency tends to easily correlate with the demand of the calling service. I think we are just at the beginning of leveraging this data in a more effective manner, and getting away from having eyeballs look at a dashboard is definitely a goal.