Amazon Technologies, Inc.

Richard Alan Hamman

Matthew James Eddey

Anton André Eicher

Andries Petrus Johannes Dippenaar

Gavin Alexander Bramhill

Marcin Piotr Kowalski

Mathew Daniel

Duncan Matthew Clough

Gideon Jan-Wessel Redelinghuys

Almero Gouws

Gideon Klompje

Pavel Tcholakov

Roland Paterson-Jones

Kevin Pakiry

Gareth Lennox

Fernand Sieber

Thomas George Mathew

Graeme Kruger

Bhavani Morarjee

Tarek Khaled Ismail Eltalawy

Sara Mohamed Ali

Paul Maree

Roland Alexander Paterson-Jones

Izak Van Der Merwe

Methods and apparatus for client-directed placement of remotely configured service instances are described. One or more placement target options are selected for a client of a network-accessible service based on criteria such as service characteristics of the placement targets. The selected options, including a particular placement target that includes instance hosts configurable from remote control servers, are indicated programmatically to the client. A determination is made that a service instance is to be configured at the particular placement target on behalf of the client. A remote control server is configured to issue administrative commands to an instance host at the particular placement target to configure the service instance.

Client-directed placement of remotely-configured service instances

Disclosed are various embodiments for replicating and maintaining aggregated descriptive data for cloud services. In one embodiment, updates to descriptive data that describes a resource of a customer provided by a cloud service are received by an aggregated descriptive data service from a backend service. An aggregated descriptive data store is then updated by the aggregated descriptive data service based at least in part on the received updates.

Replicating and managing aggregated descriptive data for cloud services

Various embodiments for testing and verifying cloud services using computational graphs. In one embodiment, a computational graph is generated that represents corresponding actions performed by a plurality of agents and data associated with the corresponding actions. The computational graph is generated based at least in part on data describing a plurality of calls to an application programming interface (API) or a static analysis of the API. A plurality of test cases are generated for the API by analyzing the computational graph.

Testing and verifying cloud services using a computational graph

An orchestration request indicating input data types and output data types of a collection of functions is received at a network-accessible service. At the service, one or more I/O operations that are to be performed at a data store to execute a dataflow corresponding to the request are inferred based on analysis of the input data types and output data types. A template representing the dataflow is stored, comprising respective nodes representing the functions and the I/O operations, as well as an indication of the sequencing of execution of the functions and I/O operations.

Orchestrating dataflows with inferred data store interactions

A technology is described for determining a launch workflow for launching multiple computing instances on a physical host in a computing service environment using a predicted launch time. An example method may include receiving a launch plan to launch a plurality of computing instances on a physical host within a computing service environment. A first launch workflow and a second launch workflow may then be determined for the launch plan where the first launch workflow and the second launch workflow may specify different sequences of operations performed to launch the computing instance on the physical host. Predicted launch times for the first launch workflow and the second launch workflow may be obtained and the predicted launch times may be compared to determine a launch workflow used in executing the launch plan.

Computing instance launch workflow

A technology is described for managing computing capacity within a computing service environment. An example method may include determining a state of a computing service environment having a group of physical hosts that execute computing instances and determining a predicted demand for computing instance launches on the group of physical hosts. A co-location of the computing instance launches on the group of physical hosts may then be determined by applying the predicted demand for computing instance launches to the state of the computing service environment. A predicted launch time for the computing instance launches may then be determined using the co-location of the computing instance launches as an input feature. An additional physical host may then be added to the group of physical hosts to decrease the predicted launch time.

Computing service capacity management

A technology is described for predicting a launch time for a computing instance. An example method may include receiving a request for a predicted launch time to launch a computing instance on a physical host within a computing service environment. Data associated with launch features of a computing instance may then be obtained, where the launch features may be determined to have an impact on a launch time of the computing instance on a physical host within a computing service environment. The launch features of the computing instance may then be input to a machine learning model that outputs the predicted launch time for launching the computing instance within the computing service environment.

Computing instance launch time

A technology is described for providing a recommended launch configuration for a computing instance based on a predicted launch time. An example method may include receiving a launch plan to launch a computing instance on a physical host within a computing service environment, where the launch plan includes a launch configuration. Upon receiving the launch plan, a predicted launch time may be determined for the computing instance based on the launch configuration. The launch configuration may then be analyzed to identify changes to the launch configuration that may result in an improved predicted launch time as compared to the predicted launch time. A recommended change may then be provided for the launch configuration as a result of a determination that the change to the launch configuration results in the improved predicted launch time.

Recommended launch configuration

Systems and methods for providing on-demand diagnostics in a virtual environment are disclosed. In one embodiment, a method comprises storing a list of approved diagnostic tests. A diagnostic service can be running in the multi-tenant environment. The diagnostic service can receive a request to run a selected diagnostic test from the list of approved diagnostic tests on one or more hardware resources in the virtual environment. In response to the request, the selected diagnostic test can be transmitted to the one or more hardware resources.

On-demand diagnostics in a virtual environment

Technology is described for determining computing instance placement. A request to launch a computing instance in a computing service environment may be received. Estimated launch times for the computing instance to launch on individual physical hosts in a group of physical hosts may be identified. A physical host in the group of physical hosts that can provide placement of the computing instance may be selected based in part on the estimated launch times for the computing instance and additional factors related to placement of the computing instance.

Computing instance placement using estimated launch times

Technology is described for monitoring computing instance launch times. A launch plan for launching a computing instance in a computing service environment may be identified. A predicted launch time for launching the computing instance on a selected physical host in the computing service environment may be determined using a launch time prediction model. An actual launch time for launching the computing instance in the computing service environment may be identified. The predicted launch time that is determined using the launch time prediction model may be compared with the actual launch time for launching the computing instance in the computing service environment. The actual launch time may be determined as not being within a defined threshold of the predicted launch time. An alert may be generated to indicate that the actual launch time is not within the defined threshold of the predicted launch time.

Automated management of computing instance launch times

Technology is described for reducing computing instance launch times. A computing instance that is expected to be launched in a computing service environment during a defined time period may be identified. A machine image associated with the computing instance may be determined to be cached in the computing service environment using a launch time prediction model to reduce a launch time for launching the computing instance as compared to not caching the machine image. At least one physical host in the computing service environment that is available to cache the machine image may be selected to lower the launch time of the computing instance as predicted by the launch time prediction model. The machine image may be stored in the physical host in order to minimize the launch time for launching the computing instance in the computing service environment, using the processor.

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Western Cape, South Africa

Richard Alan Hamman