Sprint Communications Comapny L.p.

Shaun Michael Johnson

Brian John Washburn

Justin Alan Martin

Brian Elwood Hayward

Devin Kelley

R Charles Evans

Systems, methods, and computer-readable media are provided for, among other things, generating web server performance metrics from log file information and storing the metrics offline in a centralized database. A collector accesses log file information from a log file for a web server and generates web server performance metrics on a per web page basis. The web server metrics include a number of requests for a web page, error count for the web page, average response time for the web page, total number of bytes transferred for the web page, and a number of times a service level objection for the web page was exceeded. The web server metrics are stored in a centralized database such that the metrics are available for offline web server performance reporting and analysis.

Collection of web server performance metrics to a centralized database for reporting and analysis

A method is provided for garbage collection in a heap of an application server that uses automated garbage collection. The method comprises gathering information about a plurality of garbage collection events. The method further comprises identifying a correlation between garbage collection activity and a volume of applications transactions in the application server. The method further comprises, based on the correlation, forecasting at least one of a future utilization of heap memory and a future garbage collection activity based on a projected future transaction volume. The method further comprises tuning the heap based on the forecast.

Method and system for adaptive recovery of heap memory

A system provides network application transaction analysis. An analysis tool, when executed by a processor, measures an application transaction metric and calculates a historical system average and a historical application average associated with the application transaction metric based on server performance logs. The tool determines whether the application transaction metric differs from the historical system average by more than a first threshold amount during consecutive measurements. The tool determines whether the application transaction metric differs from the historical application average by more than a second threshold amount during consecutive measurements if the application transaction metric does not differ from the historical system average by more than the first threshold amount during consecutive measurements. The tool outputs an alarm to a user interface to enable a generation of an investigation if the application transaction metric differs from the historical application average by more than the second threshold amount during consecutive measurements.

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Kansas City, MO, United States of America

Shaun Michael Johnson