Amazon Technologies, Inc.

Joseph E Fitzgerald

Eric Jason Brandwine

Marvin Michael Theimer

Benjamin Warren Mercier

Jonathan A Jenkins

Hannah Rebecca Lewbel

Techniques are described for facilitating use of software components by software applications in a configurable manner. In some situations, the software components are fee-based components that are made available by providers of the components for use by others in exchange for fees defined by the components providers, and in at least some situations, the software components may have various associated restrictions or other non-price conditions related to their use. The described techniques facilitate use of such software components by software applications in a configured manner. Furthermore, in at least some situation, the execution of such software applications is managed by an application deployment system that controls and tracks the execution of the software application on one or more computing nodes, including to manage the execution of any software components that are part of the software application.

Managing use of software components

The deployment of content and computing resources for implementing a distributed software application can be optimized based upon customer location. The volume and geographic origin of incoming requests for a distributed software application are determined. Based upon the volume and geographic origin of the incoming requests, content and/or one or more instances of the distributed software application may be deployed to a geographic region generating a significant volume of requests for the distributed software application. Content and/or instances of a distributed software application might also be speculatively deployed to a geographic region in an attempt to optimize the performance, cost, or other attribute of a distributed software application.

Optimized deployment based upon customer locality

A server identifies a user device that is missing, communicates with the user device and causes the user device to disable access to content stored on the user device, and to generate a message that includes information facilitating return of the user device to an owner of the user device.

Facilitating return of a missing user device to a device owner

Update preferences might be utilized to specify that an update to an application should not be applied until the demand for the application falls below a certain threshold. Demand for the application is monitored. The update to the application is applied when the actual demand for the application falls below the specified threshold. The threshold might be set such that updates are deployed during the off-peak periods of demand encountered during a regular demand cycle, such as a diurnal, monthly, or yearly cycle.

Deploying application updates based on deployment plan

A test environment is created for optimizing the execution of a programmable execution service ('PES') application. The test environment is created in one embodiment by replicating a production network and one or more production virtual machine instances executing the PES application. Once the test environment has been created, the test environment is utilized to identify optimized values for one or more application parameters consumed by the PES application. The optimized values may be selected to optimize the output of a fitness function that is based upon one or more direct and/or indirect performance metrics associated with the PES application. Once the optimized values for the application parameters have been identified, the generated values are applied to production virtual machine instances executing the PES application.

Optimizing the execution of an application executing on a programmable execution service

Technologies are described herein for capacity availability aware auto scaling. Capacity event auto scaling rules can be defined that specify how computing resources are to be scaled during a capacity event. The capacity event auto scaling rules can be defined to allow utilization of the computing resources to increase during a capacity event. A probability that capacity will be available for providing computing resources during a capacity event can also be computed. Standard auto scaling rules utilized by an auto scaling component can then be modified based upon the computed probability. Other types of actions might also be taken based upon the computed probability, such as reserving instances of computing resources.

Capacity availability aware auto scaling

Deploying updates to an application during periods of off-peak demand

Virtualization technologies can be adapted to allow a single physical computing machine to be shared among multiple virtual networks by providing one or more virtual machines simulated in software by the single physical computing machine, with each virtual machine acting as a distinct logical computing system. Virtual network instances with overlapping network addresses can be supported on the same computing system, allowing users to specify the virtual network in greater detail. Techniques are described for programmatically provisioning virtual networks. Each virtual network can have its own virtual address space, allowing multiple networks to have overlapping address spaces. The virtual IP addresses or other addresses for one or more components of a virtual network could share the same address but still operate without interfering with each other.

Programatically provisioning virtual networks

Instances of computing resources might need to be de-scaled that have become unnecessary following a deployment of an update to an application. Instances might also need to be de-scaled as a result of decreased demand for the application. If de-scaling of instances of computing resources is required, the percentage of a paid-for time period for each instance is determined. Instances that have utilized the greatest percentage of their paid-for time period may then be de-scaled.

Descaling computing resources

A test environment is created for use in selecting updates for deployment to a programmable execution service ('PES') application. The test environment is created in one embodiment by replicating a production network and one or more production virtual machine instances executing the PES application. Once the test environment has been created, the test environment is utilized to test and select updates for deployment to the PES application. The updates may be selected by deploying the updates to the test environment and using the test environment to determine whether the deployed updates are compatible with the PES application, permit the PES application to continue to operate performantly, and/or permit the PES application to meet one or more business performance metrics. Once the updates have been selected, the updates may be applied to production virtual machine instances executing the PES application.

Selecting updates for deployment to a programmable execution service application

Update preferences are specified that define factors for use in determining how and when updates to an application are to be deployed. The update preferences may include economic factors, temporal factors, operational factors, and other types of factors. The update preferences are utilized to create a deployment plan that specifies how and when the updates are to be applied to the application in view of the specified factors. The deployment plan is utilized to deploy the updates to the application.

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Seattle, WA, United States of America

Joseph E Fitzgerald