Amazon Technologies, Inc.

Feng Wang

Calvin Yue-Ren Kuo

Chen Yang

James A Gosling

Kadirvelu Rathnavelu

Kyunghwan Choi

Yen-Lin Chen

Robert Battle

Nicolas Webb

Ankit Masrani

Georgie Mathews

Simon Poile

Prateek Yadav

Shekar Chandra Bommas

Victor Gails

Shirley Zheng

A technology is described for distributed querying of computing hubs for device-generated data. In one example, computing hubs may provide a storage service to devices included in a local device network, and the devices may send device-generated data to the computing hubs to be stored by the storage service. A distributed query may be initiated by identifying the computing hubs that have the device-generated data stored on the computing hubs and sending a message containing query instructions to the computing hubs. The query instructions, when executed on the computing hubs, retrieve the device-generated data from the storage service included on the computing hubs, and the computing hubs may send the device-generated data to a service provider environment.

Distributed querying of computing hubs

Export of data from an edge device to a provider network may be managed. An edge device may receive different data streams from different client devices in a client network. According to an export configuration received at the edge device, one of the data streams may be selected. A next portion of data in the data stream may be identified and the identified portion may be sent to a data stream destination in a remote network by the edge device.

Managed data export to a remote network from edge devices

Systems and methods are described for implementing a framework for testing on-demand code execution that is configured to be executed on coordinated devices. The testing framework allows the execution of tasks in a network based on-demand system. The resulting generation of an I/O file for accessing local resources can be intercepted and either processed via a handler process or transmitted to the local resource.

Testing of executable code for local device coordinator

A client network implements data locality-aware MapReduce using IoT devices. An IoT service of a provider network receives configuration settings from a user to implement map nodes and reduce nodes at the client network. Each map node is configured to receive its own locality-based partition of data from one or more data sources (e.g., sensors) that are within a map function data locality (e.g., a particular location of the network). Each map node then processes the locality-based partition of the data, generates results, and sends the results to a reduce node. The reduce node may be located at a remote provider network. A coordinator node may also be deployed to one or more devices of the client network. If a map node or a compute node becomes overloaded, the coordinator node may redirect some data to a map node or a reduce node of a remote provider network.

Data locality-aware MapReduce using IoT devices

Growing community of inventors

Seattle, WA, United States of America

Feng Wang