Amazon Technologies, Inc.

Christopher David Byskal

Geoffrey Scott Pare

Bruce Erwin Brown

Brian James Schuster

Hok Peng Leung

Preston Alan Tamkin

Alexis Levasseur

Joseph Richard Thompson

Aleksey A Polesskiy

Justin Miles

Alexander Lambertus Eusman

Jonathan Paul Thompson

Christopher Paul Dury

Natalia Rose Cavalho

Richard Franklin Porter

Zane Grey Reynolds

Natalia Rose Carvalho

New application sessions can be placed on reclaimable resource capacity that can be provided at lower cost than dedicated capacity, but with risk that the capacity might be reclaimed before completion of the session. A number of instance types can be determined that are capable of hosting a new session. Risk scores can be calculated for the instance types, as well as the cost of instances of each type. An instance type can be selected for the session using the risk scores and cost information, where the instance type will have relatively low risk with a relatively low cost for the instance. Once an instance type is selected, a resource pool can be selected from which to provide the resource instance of the selected type. The instance can then be allocated and caused to host the session or perform related functionality.

Risk assessment for placement of hosted sessions

A game-hosting service of a service provider network is configured to place game sessions on fleets of virtual machine (VM) instances using a value-based approach. In order to place a game session on a fleet, the game-hosting service may determine one or more attributes of the game session request, such as player attributes and/or game attributes. The game-hosting service may also determine available fleets of VM instances allocated to a subscriber associated with the game session request, which may be located across disparate geographical regions. A value(s) may be determined based on the attributes of the game session request, and the value(s) may be used to select a fleet of the available fleets for hosting the game session.

Value-based game session placements

A game-streaming service of a service provider network is configured to assign virtual machine (VM) instances to game streaming requests using a value-based approach. In order to assign a VM instance to a game streaming request for streaming a game to a client device of a player associated with the game streaming request, the game-streaming service may determine one or more attributes of the game streaming request, such as player attributes and/or game attributes. The game-streaming service may also determine available VM instances allocated to a subscriber associated with the game streaming request, which may be located across disparate geographical regions. A value(s) may be determined based on the attributes of the game streaming request, and the value(s) may be used to select one of the available VM instances for streaming the game as part of a corresponding game session.

Value-based streaming instance assignment

Technologies are disclosed herein for generating a stand-alone client application for communicating with a network service based on a proprietary communication protocol. A service model description may be parsed to identify resources associated with the network service. Thereafter, executable program code may be generated describing the resources as a set of basic commands and pathnames. The program code may be packaged as the stand-alone client application without inclusion of program code for communication via the proprietary communication protocol.

Generating client applications from service model descriptions

Techniques for content testing during image production are described herein. Information associated with a content item may be loaded onto a first virtual machine instance and a second virtual machine instance. On the second virtual machine instance, production of a virtual machine image including the information associated with the content item may be initiated. On the first virtual machine instance, a first instance of the content item may be executed. At least part of a time during which the first instance of the content item is executed may occur during the production of the virtual machine image. Upon completion of production of the virtual machine image and approval of its use, the virtual machine image may be replicated onto other content item instances, and the first virtual machine instance may be terminated or repurposed.

Content testing during image production

Live migration can be performed for a process executing on an allocated resource for an application session. During a session it might be determined that it is necessary, or would at least be advantageous, to migrate the process to different resource capacity. New capacity such as a new computing instance can be identified, and synchronized with a current instance to reflect current state information. This can include obtaining state data from a serialized file or transaction log, among other such options. The client devices on the session can be caused to connect or direct state data to the new instance, and when ready the new instance can be responsible for the process, such as by taking over as the application server for the session. Once the client devices are utilizing the new instance, the process can be terminated on the old instance and the instance destroyed or reallocated.

Live migration for hosted sessions

The present disclosure provides for matching users into user groups based on user attributes. A neural network is trained using training data including user data of users in an observed grouping and various outcomes or optimization parameters associated with the observed grouping. The trained neural network is used to process a set of queried players determine a match quality for the queried players with respect to certain outcomes or optimization parameters based at least in part on the user data associated with the users. The match quality is a measure of how optimized a group containing the queried users is with respect to the optimization parameters and can be compared to a threshold. The queried users may be matched into a group if the match quality meets or exceeds the threshold.

Matching players for networked gaming applications

Techniques for concurrent execution of multiple content item processes, such as video game processes, on a single virtual machine instance are described herein. In some examples, developers may provide an indication of a desired content item process quantity count, which is a quantity of content item processes for concurrent execution of content item sessions on each of one or more virtual machine instances. Also, in some examples, the content item process quantity count may be adjustable such that it may be changed, for example in response to various conditions or events. Also, in some cases, collected performance metric information may be used to make intelligent decisions regarding scaling of content item processes. In some examples, when a content item session stops executing on a healthy content item process, the content item process may remain active and may be reused by executing one or more subsequent content item sessions.

Virtual machine with multiple content processes

Techniques for content deployment, scaling and telemetry are described herein. Once a content item is made accessible for deployment, one or more instances of the content item may be installed as an image on a virtual machine instance. Additional instances of the content item may then be launched on the same virtual machine instance and/or by copying the image onto any number of additional virtual machine instances. One or more instances of a content item may be configurable to execute with multiple simultaneously assigned users sessions. Information associated with content item instances may be collected, such as a number of occupied user sessions, a number of unoccupied user sessions, content item instance duration, memory usage, and the like. This information may, in some cases, be used to match and assign authorized users to appropriate content item instances.

Content deployment, scaling, and telemetry

Techniques for wait time based scaling of content item instances are described herein. In some examples, a customer of a computing service provider may select a wait time threshold for joining one or more of the customer's content items that are hosted by the computing service provider. When a joining user logs-in and requests to join a content item, a calculation may be made regarding an estimated wait time for the user to join one or more existing content item instances. The estimated wait time may then be compared to the wait time threshold. A determination may then be made regarding whether or not to launch a new content item instance in response to the user's join request. In some examples, this determination may be based, at least in part, on the comparison of the estimated wait time to the wait time threshold.

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Issaquah, WA, United States of America

Christopher David Byskal