Microsoft Technology Licensing, LLC

Other

Prashant Ratanchandani

Jason Carl Hendrickson

Dongmei Zhang

Hang Li

Mehmet Iyigun

Glenn Frederick Evans

Yevgeniy Bak

Bin Zhao

Yingnong Dang

Leonardo Esteban Blanco

Kenneth S Reneris

Greg D Schechter

Shi Han

Mark Yalovsky

Rolf Arnold Ebeling

Gregory David Swedberg

Jeffrey Che Fuller

Stuart Sechrest

Cong Chen

Yau Ning Chin

Richard Gains Russell

Song Ge

Peter Seraphim Ponomarev

Sameer Avinash Nene

Shawn M Woods

Ryan M Hurst

Christina L Rhodes

Nathan Teeuwen

Shuo-Hsien Hsiao

Robert Whittemore Dreyfoos

Ishfaq Mohammad Ismail

Feng Liang

Brian D Wentz

Pavan Kumar Josyula Venkata

John Corey Morgan

Jonathan M Au

Hemchand Alla

Chao Bian

Xiangpeng Zhao

Systems and methods for monitoring operational performance of at least one application containing no explicit instrumentation are described. Data relating to the operational performance of the application can be obtained and provided to a telemetry server for analysis without the use of diagnostic instrumentation within the application. Generic and targeted performance data can be obtained using responsiveness components within a computing operating system.

Performance monitoring for applications without explicit instrumentation

Two different process management views can be displayed, and a user can request to switch between the two views. The user can select a process in either view and have the selected process terminated. One view is a simplified view that identifies processes and whether they are non-responsive. The other view is an expanded view that identifies processes and the amount of various system resources used by each of those processes. Various additional information can be displayed in the expanded view, such as identifiers of various windows, tabs, and/or services associated with each of the processes.

Process management views

Execution traces are collected from multiple execution instances that exhibit performance issues such as slow execution. Call stacks are extracted from the execution traces, and the call stacks are mined to identify frequently occurring function call patterns. The call patterns are then clustered, and used to identify groups of execution instances whose performance issues may be caused by common problematic program execution patterns.

Analyzing software performance issues

A description language and a tool for identifying, analyzing and addressing performance issues in event traces. With this language, a behavior descriptor may be defined to describe a simple behavior within a performance trace, by specifying values for a relatively small number of attributes. Even a relatively unsophisticated user can define behavior descriptors, but complex behaviors may be defined based on interactions between multiple behavior descriptors. A trace analysis tool may use a library of behavior descriptors to identify performance issues in performance traces by matching behavior descriptors to the performance trace. This analysis may be used in any number of ways, including reporting to a user detected performance issues reflected in a set of performance traces, filtering a set of performance traces to remove those performance traces corresponding to known performance issues or prioritizing efforts to resolve performance issues.

Description language for identifying performance issues in event traces

Described is an adaptive scheduler associated with a desktop window manager that dynamically controls the rate at which graphics frames are composed. Values corresponding to performance when composing a frame are measured, and the frame composition rate is adjusted as necessary based on the values. The measured data is sampled to provide smooth adjustments. The sampled data is evaluated as to whether the current frame rate is too slow, too fast, or acceptable. If too slow, the frame rate may increased relative to the refresh rate, while if too fast, the frame rate is decreased relative to the refresh rate. In one implementation, the frame rate is too fast if a count of missed frames achieves a missed threshold value, or if a count of late frames achieves a late threshold value. The frame rate is too slow if a count of early frames exceeds an early threshold value.

Adaptive scheduling to maintain smooth frame rate

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Sammamish, WA, United States of America

Prashant Ratanchandani