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Best Practices for Converting Siebel ARM Files

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Review the following information as recommendations of best practice when converting Siebel ARM files.

• Make sure the Siebel ARM feature has flushed data to the Siebel ARM file before converting the file. The Siebel ARM feature creates empty Siebel ARM files before data is flushed to the file. For details on this process, see SARM Memory Size Limit (Alias SARMMaxMemory).
• Change the value of the SARM Memory Size Limit (Alias SARMMaxMemory) to a lower setting if the Siebel ARM files remain empty on a consistent basis. For details on this process, see SARM Memory Size Limit (Alias SARMMaxMemory).
• Make sure the Siebel ARM file name and path name, as necessary, are correct when referencing the Siebel ARM files in the commands.
• Concatenate Siebel ARM files to increase the amount of performance data for a given process. For example, as the Siebel ARM feature saves five Siebel ARM binary files for each process, concatenate these files to view performance data for multiple requests for this process. (For details on the number of files saved, see SARM Data File Size Limit (Alias SARMMaxFileSize).)

  TIP:  Use a third-party utility to concatenate Siebel ARM files on Windows. Use the command cat list_of_files filename.sarm to concatenate Siebel ARM files on UNIX.

  NOTE:  Only concatenate Siebel ARM files of the same process.

• Gather performance analysis data on your Siebel application before customizing the application. These baseline measurements provide a good reference when monitoring the performance of your Siebel application after any customizations.
• Run a user session trace analysis if there are performance problems for an individual user during a particular session. The user trace session trace data identifies each request the user made and identifies which request required the longest time when compared to a base line.
• Use the performance aggregation data to diagnose performance at a given point in time or for a certain process. Reviewing the data by group can diagnose the area that is performing poorly. After reviewing a high-level view of the performance data, extrapolate a more detailed review by running the comma-separated value analysis. For details on running this analysis, see Running Siebel ARM Data CSV Conversion.
• Compile performance aggregation data over a period of time to determine a trend analysis.

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