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Factors that are central to the task of configuring the AOM are also called performance drivers. Performance drivers for AOM include concurrent users and average think time. Other important factors such as hardware resources will set limits on overall capacity or capacity per server.
These factors are critical in initially configuring your AOMs, particularly when specifying values for the AOM component parameters MaxTasks, MaxMTServers, and MinMTServers, which are discussed in Tuning AOM Components for CPU and Memory Utilization.
The number of concurrent users is the total number of user sessions supported at any one time. It also includes sessions supporting anonymous browser users. For planning and tuning purposes, you must consider concurrent users (and total users) at multiple levels:
The maximum number of concurrent users per Siebel Server—assuming, for example, that a particular Siebel Server machine is dedicated to running AOM components—depends on the average think time, on your hardware resources, and on the nature of your Siebel applications deployment.
In terms of configuration, the maximum number of concurrent users for the AOM is limited by the value of the MaxTasks parameter. The effective maximum is also limited by the number of multithreaded processes for this AOM and by your hardware resources.
Depending on the average think time and other factors, each multithreaded process (process within the AOM) typically supports a maximum of about 100 concurrent users. Configure enough multithreaded processes (using the MaxMTServers parameter) to support the maximum number of concurrent users required for your peak loads.
NOTE: Some complex or specialized Object Manager components support fewer concurrent users. For example, Object Managers for Siebel eCommunications (part of Siebel Industry Applications) and Siebel Configurator typically support about 25 concurrent users. For more information about the Siebel Configurator Object Manager, see Tuning Siebel Configurator for Performance.
The think time is the average elapsed time between operations performed by users in a Siebel application. Think time includes the time required by users to conduct customer interactions, enter data into the application, and work in other applications.
Determine the average think time based on the usage patterns typical of your user base. After the application has been configured, perform a clickstream analysis for your key processes, and try to capture the time between the user actions (operations) that are represented by the clicks. Also use the
Consider the average time between each operation (such as clicking New) and each overall transaction (such as performing all steps for creating a new contact). Mouse clicks do not equate to operations if they do not send a request to the Siebel application infrastructure. Calculate the overall average think time based on all of these factors.
The ratio of 100 (100 tasks per process), based on a 30-second think time, is assumed in the formula for setting the MaxMTServers parameter. This formula is presented in Tuning AOM Components for CPU and Memory Utilization.
The ratio of 100 is based on having approximately three users running operations at the exact same time (100/30 = approximately 3.3). It is generally observed that each multithreaded process can handle about three operations at the same time with minimal performance degradation.
With longer think times, one multithreaded process may support more than 100 concurrent tasks; with shorter think times, fewer tasks. For example, if the think time is 15 seconds between user operations, then about 50 tasks per process could be supported (15 * 3.3 = approximately 50, or 50/15 = approximately 3.3.
Which Siebel applications and other modules you are using, how you have configured your Siebel applications, how you have deployed your applications, and other such factors also affect AOM performance and how many concurrent users you can support. Some of these factors include:
Hardware resources for each Siebel Server machine, particularly CPU and memory, are a factor in how many concurrent users can be supported for each AOM component. For example, a four-way machine has twice the resources of a two-way machine and can potentially support twice as many concurrent users. Key hardware resources for AOM performance include:
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