Microsoft Technology Licensing, LLC

Mahlon David Fields

Eric J Horvitz

Mehmet Iyigun

Cenk Ergan

Yevgeniy Bak

Michael Richard Fortin

Alexander Kirshenbaum

Neel K Jain

Bradley Michael Waters

Barry M Nolte

Richard Wurdack

Steven M Carroll

Embodiments of the invention provide techniques for managing cache metadata providing a mapping between addresses on a storage medium (e.g., disk storage) and corresponding addresses on a cache device at which data items are stored. In some embodiments, cache metadata may be stored in a hierarchical data structure comprising a plurality of hierarchy levels. When a reboot of the computer is initiated, only a subset of the plurality of hierarchy levels may be loaded to memory, thereby expediting the process of restoring the cache metadata and thus startup operations. Startup may be further expedited by using cache metadata to perform operations associated with reboot.

Managing cache data and metadata

Techniques and systems are provided that work to minimize the energy usage of computing devices by building and using models that predict the future work required of one or more components of a computing system, based on observations, and using such forecasts in a decision analysis that weighs the costs and benefits of transitioning components to a lower power and performance state. Predictive models can be generated by machine learning methods from libraries of data collected about the future performance requirements on components, given current and recent observations. The models may be used to predict in an ongoing manner the future performance requirements of a computing device from cues. In various aspects, models that predict performance requirements that take into consideration the latency preferences and tolerances of users are used in cost-benefit analyses that guide powering decisions.

Reducing power consumption of computing devices by forecasting computing performance needs

Systems and methods are provided that determine the actual amount of time a processor consumes in executing a code portion. The actual execution time of a code portion may be accurately determined by taking into consideration context switches and/or overhead time corresponding to the code portion. Determining the actual execution time of a code portion may include recording context switches and time values that occur during the execution of the code portion. This information along with overhead measurements may be used to generate the actual execution time of a code portion, as will be described in more detail below. For example, the switched-out intervals resulting from the context switches and the overhead time associated with the time measurements may be subtracted from the elapsed time to produce the actual execution time of a code portion.

Determining an actual amount of time a processor consumes in executing a portion of code

Performing atomic operations on data entities having an arbitrary size is disclosed. Version data is associated with a data entity. The version data is saved to a first attribute. The data entity is then accessed. The saved version data is compared to the current version data. If the two are equal, the data entity is valid.

Atomic operations on data structures

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Kirkland, WA, United States of America

Mahlon David Fields