If you are administering an Intranet web service (internal to a
company, publicly inaccessible), you can tell what connection speed
most of your users have, the number of possible users, and therefore
the maximum request rate. You can be sure that the service will not
gain a sudden popularity that will drive the demand rate up
exponentially. Since there are a known number of users in your
company, you know the expected limit. You can optimize the Intranet
web service for high-speed connections, but don't
forget that some users might connect to the Intranet with a slower
dial-up connection. Also, you probably know at what hours your users
will use the service (unless your company has branches all over the
world, which requires 24-hour server availability) and can optimize
service during those hours.
If you are administering an Internet web service, your knowledge of
your audience is very limited. Depending on your target audience, it
can be possible to learn about usage patterns and obtain some
numerical estimates of the possible demands. You can either attempt
to do the research by yourself or hire professionals to do this work
for you. There are companies who release various survey reports
available for purchase.
Once your service is running in the ideal way, know what to expect by
keeping up with the server statistics. This will allow you to
identify possible growth trends. Certainly, most web services cannot
stand the so-called Slashdot Effect, which
happens when some very popular news service (Slashdot, for instance)
releases an exotic report on your service and suddenly all readers of
this news service are trying to hit your site. The effect can be a
double-edged sword: on one side you gain free advertising, but on the
other side your server may not be able to withstand the suddenly
increased load. If that's the case, most clients may
not succeed in getting through.
Just as with the Intranet server, it is possible that your users are
all located in a given time zone (e.g., for a particular
country-specific service), in which case you know that hardly any
users will be hitting your service in the early morning. The peak
will probably occur during late evening and early night hours, and
you can optimize your service during these times.