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LifeCycle Application Management Blog

General discussion of technology and application support issues related to all phases of application development and management.

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WMIWebWalkMonitoringIISEnd-user experience 13 Jul 2009
Jerry Champlin
Front End-User Interface and Web Services by Jerry Champlin Comment (0)
This is the fifth entry in a 10-Series Blog posts every week in regards to Software-Plus-Services: Application Instrumentation, Stress Testing and Production Management.    

Some of the most valuable business relevant metrics reside in the front end components of an application served by IIS or other web server components. As with the other layers of the application stack, the standard metrics available through WMI provide a good starting point. However, these metrics do not give visibility into the end-user experience.

The recommended best practice is to combine this information with synthetic user transaction monitoring via a user transaction simulation service such as WebWalkTM. This layer of monitoring allows for proactive tracking of the end-user experience. Alert thresholds should be set for metrics such as content/functionality validation, transaction execution times, and rates of change for transaction execution times. This component of external monitoring should be replicated behind the load balancer within the application environment to provide visibility into the behavior originating from individual servers or virtual machines. Combining external synthetic transaction monitoring with internal synthetic transaction monitoring enables the operations/delivery organization or outsourced provider to be proactive in managing the application.  

In this tier, the value of the data appearing in access logs should also be monitored. This can be achieved by customizing the logging parameters to expose the user or client associated with each request and the amount of time it took to return the request to the browser. Frequency based log analysis can then be employed to capture access frequency and average/max/min response time metrics associated with the requests on an aggregate basis. This data is highly valuable for isolating application response time issues. Additionally, the raw data can be used as part of the overall data set to identify resource utilization by an individual user or client.

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