The AMD uses a URL auto-learning algorithm to choose and report the most popular URLs observer in monitored traffic.
The AMD uses a pool of auto discovered URLs. It consists of members and candidates. The auto-learning algorithm aggregates the loads of all URLs for all monitored servers. The server IP address, port or any other attribute of the server is not used by the auto-learning algorithm. If a URL becomes the member of the pool, it is reported for all servers, regardless the activity on individual servers.
URLs are inserted into the list of candidates and the list of members and moved between the lists according to the configuration parameters set in the Advanced setting section of the URL Auto-learning screens in the RUM Console.
The AMD removes the URLs from the member list every specified interval, as controlled by the Cleanup interval property.
Use the Percentage of new URLs property to control the portion of the members pool to be freed at the beginning of each interval and reserved for new highly active URLs. A candidate URL becomes a pool member if it is more popular than the portion of members defined by the Page loads threshold property.
If you want to report URLs from all servers in the farm, regardless of the individual host name, you can deselect Use host name to exclude host names from the URL auto-learning algorithm.
Enable the Slow page weight property to ensure that slow operation URLs with high volume loads are included in the auto-learning algorithm.
Thx for your reply, I have used above but I added an extra url with an regular expression that the customer really needs. This works fine.The new url is added to the auto learned url's
If I hit the button described above, why should it matter that this url is used for auto learning or not?
It means that we are enforcing that the URL should meet the criteria of auto-learning algorithm before reporting.
Note: A large number of URLs defined by using regular expressions can have an adverse effect on the performance of the AMD, because resolving regular expressions is processor-intensive.