MAU: Monthly active users. The number of users who have accessed the service in the last month.
Engagement: How many times on average an active user performs a particular transaction.
Primary resource: The one system-level resource that is the main limiting factor for the service.
Capacity limit: The point at which performance starts to degrade rapidly or become unpredictable.
Core driver: A factor that strongly drives demand for a primary resource.
Time series: A sequence of data points measured at equally spaced time intervals. For example, data from monitoring systems.
How much are you using now
Identify the limiting resources for each service. Your monitoring system is likely already collecting resource use data for CPU, RAM, storage and bandwidth. Typically it collects this data at a higher frequency than required for capacity planning. A summarization or statistical sample may be sufficient for planning purposes and will generally simplify calculations. Combining this data with the data from the inventory system will show how much spare capacity you currently have.
Tracking everything in the inventory database and using a limited set of standard hardware configurations also makes it easy to specify how much space, power, cooling and other data center resources are used per device. With all of that data entered into the inventory system, you can automatically generate the data-center utilization rate.
The monitoring system directly provides data on current usage and current capacity. It can also supply the normal growth rate for the preceding years. Look for any noticeable step changes in usage, and see if these correspond to a particular event, such as the roll-out of a new product or a special marketing drive. If the offset due to that event persists for the rest of the year, calculate the change and subtract it from subsequent data to avoid including this event-driven change in the normal growth calculation. Plot the data from as many years as possible on a graph, to determine if the normal growth rate is linear or follows some other trend.
The second step is estimating additional growth due to marketing and business events, such as new product launches or new features. For example, the marketing department may be planning a major campaign in May that it predicts will increase the customer base by 20 to 25 percent. Or perhaps a new product is scheduled to launch in August that relies on three existing services and is expected to increase the load on each of those by 10 percent at launch, increasing to 30 percent by the end of the year. Use the data from any changes detected in the first step to validate the assumptions about expected growth.
Sign up for Computerworld eNewsletters.