Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Kendra Anne Yourtee

Muhammad Ali Siddiqui

Sanjeev Kewal Verma

Thomas Lowell Keller

Miles Childs Kaufmann

Amos Dylan Vance

Alexander S Borst

Gregory Suhyun Kang

Peter Varnum Commons

Arthur Carré

Ivan Eduardo Gonzalez

Mathias G Ricken

Aaron T Olds

Josh M Goodspeed

Brian Michael Wahlert

Angel Monterrubio

Lloyd Gene Giberson

John Chad Parry

Eugene Polonsky

Abhishek Dhasmana

Nelson Sampaio Araujo, Jr

Nelson S Araujo

Rishikesan Rangarajan

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

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

The techniques described herein provide software testing that may concurrently process a user request using a live version of software and a shadow request, which is based on the user request, using a shadow version of software (e.g., trial or test version, etc.). The live version of software, unlike the shadow version, is user-facing and transmits data back to the users while the shadow request does not output to the users. An allocation module may vary allocation of the shadow requests to enable a ramp up of allocations (or possibly ramp down) of the shadow version of software. The allocation module may use allocation rules to dynamically initiate the shadow request based on various factors such as load balancing, user attributes, and/or other rules or logic. Thus, not all user requests may be issued as shadow requests.

Software testing using shadow requests

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

This disclosure is directed in part to testing of different versions of software or software components (software versions) and analyzing results of use (e.g., user interaction) of the different software versions. The techniques described herein provide software testing that varies the allocation to enable a ramp up of allocations to/from another software version. The allocation module may use allocation rules to assign requests to each software version based on various factors such as load balancing, user attributes, past user assignment, and/or other rules or logic. An analysis of the different software versions may include an analysis of system performance resulting from operation of each software version. An analysis may determine attributes of each user and then allocate the user to a software version based on at least some of the determined attributes.

Software testing analysis and control

Technologies are described herein for incorporating streaming and/or sampling in real-time log analysis. Representative samples of log data are extracted from the log files on a number of monitored hosts and streamed in real-time to log processors for processing. The log processors accumulate and process the representative samples of log data, and track a data completeness value representing an indication of a proportion of total log data represented by the representative samples received. The representative samples of log data are merged and collated. Estimated metrics are calculated from the merged and collated representative samples and the data completeness, and the estimated metrics are published to consumers in near real-time.

Streaming and sampling in real-time log analysis

An administrative authority for virtual machines can send one or more delegated policy settings to a virtual machine manager. The virtual machine manager can in turn send management instructions that include the one or more policy settings to one or more virtual machine hosts. As such, a user's request for a virtual machine at a virtual machine host can be granted or denied based on the delegated policy settings. The policy settings can be updated periodically, and can include additional information about starting, stopping, expiring, saving, or even deleting virtual machines by particular users, as well as users accessing from particular locations. In addition, an agent operating at the virtual machine host can monitor and report virtual machine activity, to ensure unauthorized virtual machines are quickly stopped and reviewed until authorized.

Managing virtual machines with system-wide policies

The embodiments contemplate a system and method for a restriction of virtual machines for a group of one or more users. A predefined policy may include a restriction related to the group, as well as a measure of enforcement to initiate in the event of a violation of the restriction. The measure of enforcement may include a warning message or denial of resources. The restriction may be resource-based, time-based, machine-based, or a combination. The restriction may be a group-level restriction, in which the entire group actions are monitored, or a user-level restriction, in which the user's actions are monitored. In the event a user is assigned to more than one group, the policies of each group are assigned a priority level. The priority level dictates the restriction as well as the measure of enforcement.

Virtual machine self-service restrictions

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Seattle, WA, United States of America

Kendra Anne Yourtee