Amazon Technologies, Inc.

Sumit Lohia

Chiranjeeb Buragohain

John Walter Heitmann

Blake Meike

Christopher Reddy Kommareddy

Liang Chen

Alex Maclinovsky

Geoffrey Scott Pare

Thomas William Whitcomb

Zachary S Musgrave

Kenneth L Hamer

William J Alford

Evan Michael McLain

Geoffry Scott Pare

Brian Gregory Long

Justin Thomas Pfifer

Tom Whitcomb

John W Heitmann

Techniques are described for managing program execution capacity, such as for a group of computing nodes that are provided for executing one or more programs for a user. In some situations, dynamic program execution capacity modifications for a computing node group that is in use may be performed periodically or otherwise in a recurrent manner, such as to aggregate multiple modifications that are requested or otherwise determined to be made during a period of time, and with the aggregation of multiple determined modifications being able to be performed in various manners. Modifications may be requested or otherwise determined in various manners, including based on dynamic instructions specified by the user, and on satisfaction of triggers that are previously defined by 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.

Dynamically modifying program execution capacity

Software is deployed to, and executed at, one or more computing devices in a computing system based on current conditions in the computing system and the capabilities of the different computing devices to handle the software. A request to run a software process calls a manager which determines an optimal place to run the software process. The manager can consider factors such as response time, user demands, bandwidth, processor utilization, storage utilization, security considerations, compatibility considerations and cost. Once a computing device is selected to run the software process, the manager facilitates movement of code and/or data to the computing device.

Elastic application framework for deploying software

System and method for agentless computing system configuration management in networked environments. A configuration management service may be implemented as a service on a network with a standard network interface. A client may communicate with the service to specify a configuration for a target system, for example through a browser interface. The specified configuration may be stored by the service. The service may generate a package according to the specified configuration. The package may be delivered to the target system via the network. The package may then install the configuration, for example, one or more software, data, or other digital components, on the target systems in accordance with the specified configuration. The clients may request that the service verify and/or update the installed configuration on the target system. The service may, in response, generate an update package for the installed configuration. Target systems may include computer systems and virtual machines.

System and method for configuration management service

Techniques are described for managing program execution capacity, such as for a group of computing nodes that are provided for executing one or more programs for a user. In some situations, dynamic program execution capacity modifications for a computing node group that is in use may be performed periodically or otherwise in a recurrent manner, such as to aggregate multiple modifications that are requested or otherwise determined to be made during a period of time. In addition, various operations may be performed to attribute causality information or other responsibility for particular program execution capacity modifications that are performed, including by attributing a single event as causing one capacity modification, and a combination of multiple events as possible causes for another capacity modification. The techniques may in some situations be used in conjunction with a fee-based program execution service that executes multiple programs on behalf of multiple users of the service.

Attributing causality to program execution capacity modifications

A computing resource monitoring service receives metrics data from customer applications and resources provided by a computing resource service provider. The metrics data is stored in observation journals within an observation journal logical data container. A map reduce processor is configured to obtain a current index file, the index file comprising a mapping of the metrics data stored within a metric store and a manifest comprising a listing of all processed observation journals. The map reduce processor may process the unprocessed observation journals to separate, sort and aggregate metrics data stored therein. This process generates index artifacts that may be merged with the index file, in an append-only process, to generate a new index file. An index pointer may be modified such that the map reduce processor refers to a different version of the index file in order to support roll back of metrics data.

Scale-out of data that supports roll back

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Seattle, WA, United States of America

Sumit Lohia