Microsoft Technology Licensing, LLC

Prasanna Kumar Jayapal

Mark J Gilbert

Adam C DePue

David C James

Michael Joseph Healy

David James Armour

Lorenzo Rizzi

Ashvin Sanghvi

Brett D A Flegg

Shafqat U Khan

Leyu Feng

Bippen Bisht

Paul A Mestemaker

Nedialko K Paskov

Brandon M Waterloo

Content can be replicated automatically to facilitate distribution of the content to one or more devices. Determinations can be made automatically regarding where to replicate data, how to replicate data, and when to replicate data based on a variety of collected data with respect to devices, content servers, content sources, and content. More particularly, one or more content servers can be identified as targets for content replication and a protocol can be determined to replicate content from at least one source to the one or more content servers identified. Subsequently, replication can be initiated to the one or more content servers with the corresponding protocol at a determined time. Further, similar mechanisms can be employed to enable automatic determination of how and when to distribute or replicate content from one or more content servers to one or more target devices.

Automatic content replication

Feedback data can be received from client agents running on client computer systems. The feedback data can include data on the symptoms and conditions for rules that relate computer system symptoms to computer system conditions. The feedback data can be analyzed to produce correlation information that quantifies a degree of correlation between the symptom(s) and condition(s) related by the rule(s). It may be determined whether analysis of the feedback data warrants including one of the rules in correlation mappings to be applied to monitored computer systems. If so, then that rule can be included in the mappings. Information representing the correlation mappings may be transmitted to the monitored computer system(s). Additionally, information representing the correlation mappings may be transmitted to the monitored computer system(s), and types of feedback data collected from the client agents can be altered on the basis of the correlation information.

Feedback-based symptom and condition correlation

Configuration data and performance data can be collected from computing machines running a target program. Periods of stress for the computing machines can be identified using the performance data, and a set of the computing machines can be grouped under a stress profile using the performance data. One or more configuration points can be identified on the set of machines, and an effect of each of the configuration point(s) on performance of the set of machines can be inferred using the configuration data and the performance data. The inferred effect(s) of the configuration point(s) can be used to determine a baseline set of configuration settings.

Inferring effects of configuration on performance

Aspects of the subject matter described herein relate to model based application management. In aspects, models are defined that include structural and behavioral characteristics, rules, relations, and so forth between entities of an application. An instruction regarding a management function is received. The models are used to generate actions that when executed manage the application in accordance with the instruction.

Model based distributed application management

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Bothell, WA, United States of America

Prasanna Kumar Jayapal