Skip to main content

Availability

Your product has little or no value if its functionality isn't available to users. Bugs, crashes, and network outages are examples of what might make your product's functionality unavailable at times.

Product managers therefore typically attach an availability constraint (nonfunctional requirement) to each functional requirement of the product. If one of the functions of the product is to generate reports, for example, a product manager should specify how likely it should be at any particular time that a user will be able to use this functionality.

The question with nonfunctional requirements is always the metric - how you measure them. How do you measure availability? Here are some options:
  • mean-time-between-failures (MTBF) - the average amount of time elapsed between failures to deliver the functionality.
  • failure rate - the frequency that the product fails to deliver the functionality. Failure rate is the reciprocal of MTBF, and often is expressed in terms of failures per hour.
  • uptime - percentage of the time that the functionality is available.
  • downtime - percentage of the time that the functionality is not available.
  • mean-time-between-system-abort (MTBSA) - the average amount of time elapsed between complete "reboots" of the system.
  • mean-time-between-critical-failure (MTBCF) - distinguishes between critical and noncritical failures.
  • maintenance free operating period (MFOP) - the average amount of time that the functionality is available without any special intervention or system maintenance.
Of course, a prospective customer will always want 100% uptime, but such availability is typically not practical to achieve. If you base a contract on 100% uptime, you will almost certainly be in violation of your contract at some point.

UPDATE: Scott Sehlhorst adds a number of important observations in this entry's comments. One thing he notes is that I neglected to mention MTTR:
  • mean-time-to-repair (MTTR) - the average amount of time it takes to repair the system after its functionality becomes unavailable. For hardware products, it usually refers to the time to replace a module or part. For software products, it can refer to the amount of time it takes to reboot or restart the system.
Also, some people use "availability" to refer strictly to uptime, and consider all of these parameters to be "reliability" metrics.

Comments

Roger L. Cauvin said…
Sounds like you have some good experience with the various metrics, Scott. I did leave out MTTR, I am going to update the entry to include it.

I also do think it's important to provide context to these metrics. You can use a product in many different circumstances; the availability requirements should specify the possible circumstances to the extent practicable.

As I've stated before, just finding - or even exploring - the right combination of metrics is arguably more important than assigning all of the exact numbers and exhaustive circumstances. (When there's a contract involved, these other factors are obviously still very important.)
Mon May 08, 09:09:00 PM 2006
Post a Comment

Popular posts from this blog

Why Spreadsheets Suck for Prioritizing

The Goal As a company executive, you want confidence that your product team (which includes all the people, from all departments, responsible for product success) has a sound basis for deciding which items are on the product roadmap. You also want confidence the team is prioritizing the items in a smart way. What Should We Prioritize? The items the team prioritizes could be features, user stories, epics, market problems, themes, or experiments. Melissa Perri  makes an excellent case for a " problem roadmap ", and, in general, I recommend focusing on the latter types of items. However, the topic of what types of items you should prioritize - and in what situations - is interesting and important but beyond the scope of this blog entry. A Sad but Familiar Story If there is significant controversy about priorities, then almost inevitably, a product manager or other member of the team decides to put together The Spreadsheet. I've done it. Some of the mos...
32 comments
Read more

5 Ways Companies Make Product Decisions

In the last blog entry, we reviewed the  four problems that companies face, or are trying to overcome, as they make product decisions .  Now we'll look at the ways that most companies make their product decisions. Companies that develop, market, and sell products and solutions make strategic and ongoing tactical decisions.  They decide what features to include in their products, what messages they will use to communicate the value of their products, what marketing tactics they will use, what prospective customers they will target, and many day-to-day choices. Whether or not these decisions are deliberate or ad hoc, most companies use some combination of the following ways of making product decisions. (A downloadable "map" that summarizes the product decision landscape is included at the end of this article.) Customer Wants Product decisions based on feature requests, focus groups, and what prospects and customers say they want. Companies are selling products to ...
12 comments
Read more

Is Customer Development Pseudoscience?

The “Science” of Lean Startup Lean startup practitioners embrace the scientific method, seeking the "truth" about what business model and strategy will lead to product success. We do so by: Formulating hypotheses Crafting and running experiments to test them Learning from the experiments Iteratively feeding our learnings back into revised hypotheses Sounds pretty scientific, at least in spirit, doesn't it? Yet this process actually neglects a key ingredient in the scientists' mode of operation. To identify what’s missing, let’s examine “customer development”. Customer Development Steve Blank is one of the pioneers of the lean startup movement. He introduced into the lean startup lexicon the term “customer development”. Customer development consists of sessions and interactions with customers to test hypotheses. For example, a product manager might interview a prospect, asking if she agrees with the product manager’s hypotheses about the problems she faces or the ...
9 comments
Read more