Previous Topic: Monitor WebSphere Application Server

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Scenario 1—General Tuning

This scenario assumes that you are not aware of specific problems areas but suspect or have been made aware of performance degradation.

In this scenario, you run CA Mainframe Application Tuner in non-targeted mode without WAS transaction filters. No detailed reports are produced.

  1. Submit a monitor for each occurrence of the associated J2EE Servant Regions (not the Control Region), unless you already know the Servant Region.

    When specifying monitoring criteria for WebSphere, leave the Application URL Filters fields blank, as shown following.

    CA MAT ------------- WebSphere Application Server Criteria --------------- COMMAND ===> Expert Mode ==> N Y - Collect PSW samples N - Disable PSW sampling Specify Application URL Filters: => => => => Specify System Class Filters: => => => Specify values. Press END to return to Monitoring Criteria

    There can be 1 to n occurrences of this Servant address space per WAS Server.

  2. Analyze the monitor data sets to view the results.

    Select the WebSphere Transactions option to view the transactions detected by CA Mainframe Application Tuner, as shown following.

    CA MAT ------------ WebSphere Transactions ----------------- Row 1 to 9 of 9 COMMAND ===> SCROLL ===> HALF Primary commands: GARbage, WAS (Environment info), SQL Profile: PLANTS1 Mode: URL Line commands: N - URL Name Total Avg Max Min LC Transaction URL Count Resp Resp Resp ________________________________ ______ ________ ________ ________ ------------------------------------------------------------------------------ __ /burn/goReflect 5 80658.00 86386.00 76104.00 __ /burn/goWait 8 14607.25 28045.00 943.00 __ /burn/go 5 8141.00 11519.00 5632.00 __ /PlantsByWebSphere/servlet/Shopp 17 2967.65 16547.00 83.00 __ /burn/goCache 5 1584.60 2825.00 679.00 __ /burn/goVector 5 512.80 1415.00 3.00 __ /PlantsByWebSphere/servlet/Image 56 284.36 2157.00 24.00 __ /burn/gogobble 8 27.63 74.00 5.00 __ /PlantsByWebSphere/shopping.jsp 1 8.00 8.00 8.00

  3. Follow up with a subsequent targeted monitor based on the data reported. See scenario 2.