Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Sanjeev Kewal Verma

Muhammad Ali Siddiqui

Kendra Anne Yourtee

Gregory Suhyun Kang

Thomas Lowell Keller

Miles Childs Kaufmann

Arthur Carré

Alexander S Borst

Mark Christopher Veronda

Mathias G Ricken

Aaron T Olds

Charles Graham Haver Crissman

Josh M Goodspeed

David B Perry

Rick Duncan

Kyle Thomas Beck

John Michael Calandrino

Rishikesan Rangarajan

Kenneth Sean Gilmour

Srikant Meka

Techniques are described for managing program execution capacity used to execute software programs, such as for a group of computing nodes in use executing one or more programs for a user. Dynamic modifications to the program execution capacity of the group may include adding or removing computing nodes, such as based on automated determinations that previously specified criteria are currently satisfied. The described techniques further include tracking information about changes that occur in one or more capacity utilization metrics of interest in response to dynamic modifications to the program execution capacity of the group, and then using such learned responses to determine a customized capacity modification policy that is configured for the actual operation of the software program(s) for the user. In some situations, the techniques are used in conjunction with a fee-based program execution service that executes multiple programs on behalf of multiple users of the service.

Learned configuration of modification policies for program execution capacity

A method is provided for estimating past data by identifying a high frequency data set for a defined time period. A pattern is calculated for the high frequency data set and then the pattern is applied to a low frequency data set in a past time period to estimate a high frequency query point.

Data estimation

Systems and methods are provided for analyzing operating metrics of monitored metric sources. Aspects of the present disclosure may present for display information associated with the monitored metric source and the analysis of its operating metrics. Analysis comprises determination of reference values and tolerance levels which represent allowable deviations from the reference values. Input data includes a measurement of an operating parameter and a time stamp. Input data may be saved to a data store for using in future analysis of other input data. When input data is determined to be outside the tolerance level, notifications may be issued to alert administrators or systems of the anomaly.

Integration based anomaly detection service

Data estimation for storing correlated patterns of high frequency data sets

Examples of systems and methods are described for managing computing capacity by a provider of computing resources. The computing resources may include program execution capabilities, data storage or management capabilities, network bandwidth, etc. Multiple user programs can consume a single computing resource, and a single user program can consume multiple computing resources. Changes in usage and other environmental factors can require scaling of the computing resources to reduce or prevent a negative impact on performance. In some implementations, a fuzzy logic engine can be used to determine the appropriate adjustments to make to the computing resources associated with a program in order to keep a system metric within a desired operating range.

Reactive auto-scaling of capacity

Data estimation of historical data that identifies fine grained time period data and coarse grained time period data and applies correlated slopes to time periods

A method for processing an image such as a computer wallpaper identifies a characteristic color representative of the image. Image pixels with similar colors are separated into groups, and the average value of the R,G,B color components in each group is determined, after filtering out pixels with R,G,B values representing white, black, or grey. The group with the maximum difference between the highest average color component value and the lowest average color component value is identified as the characteristic color. Groups representing a number of pixels less than a certain percentage of all of the pixels are not considered. The characteristic color can be used in other displayed images at an intensity &#x3b1; determined by setting maximum and minimum values of &#x3b1;, with &#x3b1; being the lesser of &#x3b1;and &#x3b1;plus the average color span of all pixels in the image.

Automatic adjustment of computer interface colors using image processing

Methods, systems, and computer-storage media for customizing the appearance of a desktop background are provided. The desktop is the bottom surface drawn behind the application windows, gadgets, a task bar, and other user interface elements displayed on a computing screen. Embodiments of the present invention allow a user to customize the background by dragging an image representation from a desktop control panel onto a portion of the desktop. Once dropped onto the desktop, the image associated with the representation is used to generate the desktop background. When the desktop is extended to different monitors, the portion of desktop on each monitor may be customized separately.

Directly assigning desktop backgrounds

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Seattle, WA, United States of America

Sanjeev Kewal Verma