Oracle International Corporation

Amazon Technologies, Inc.

Tyler Ogren Vrooman

Greg Edvenson

Graham Schwinn

Matthew Charles King

Sajesh Kumar Saran

Kumud Nepal

James Merton Tidman

Wei Ting Chen

Brad Nathaniel Arnold

Jack G Song

Nicholas Michael Vallidis

Todd Nelson Schoepflin

Jared Justin Huntington

Example embodiments facilitate prioritizing the recycling of computing resources, e.g., server-side computing systems and accompanying resources (e.g., non-volatile memory, accompanying firmware, data, etc.) leased by customers in a cloud-based computing environment, whereby computing resources (e.g., non-volatile memory) to be forensically analyzed/inspected, sanitized, and/or updated are prioritized for recycling based on estimates of when the computing resources are most likely to require recycling, e.g., via background sanitizing and updating. Computing resources that are likely to be recycled first are given priority over computing resources that are more likely to be recycled later. By prioritizing the recycling of computing resources according to embodiments discussed herein, other cloud-based computing resources that are used to implement computing resource recycling can be efficiently allocated and preserved.

Predictive recycling of computer systems in a cloud environment

The present memory restoration system enables a collection of computing systems to prepare inactive rewritable memory for reserve and future replacement of other memory while the other memory is active and available for access by a user of the computing system. The preparation of the reserved memory part is performed off-line in a manner that is isolated from the current user of the active memory part. Preparation of memory includes erasure of data, reconfiguration, etc. The memory restoration system allows for simple exchange of the reserved memory part, once the active memory part is returned. The previously active memory may be concurrently recycled for future reuse in this same manner to become a reserved memory. This enables the computing collection infrastructure to 'swap' to what was previously the inactive memory part when a user vacates a server, speeding up the server wipe process.

Concurrent memory recycling for collection of servers

Example embodiments employ a selective memory swapping system for selectively placing non-volatile memory devices of a computer system offline, e.g., for background updating, and online, for use by a computer system, whereby the background updating process includes a mechanism for performing forensics analysis and updating of offline memory devices while an alternate memory device is usable by a user of the first computer system.

Memory device forensics and preparation

Example embodiments facilitate prioritizing the recycling of computing resources, e.g., server-side computing systems and accompanying resources (e.g., non-volatile memory, accompanying firmware, data, etc.) leased by customers in a cloud-based computing environment, whereby computing resources (e.g., non-volatile memory) to be forensically analyzed/inspected, sanitized ,and/or updated are prioritized for recycling based on estimates of when the computing resources are most likely to require recycling, e.g., via background sanitizing and updating. Computing resources that are likely to be recycled first are given priority over computing resources that are more likely to be recycled later. By prioritizing the recycling of computing resources according to embodiments discussed herein, other cloud-based computing resources that are used to implement computing resource recycling can be efficiently allocated and preserved.

Prioritized ranking for memory device preparation

Accelerated non-volatile memory device inspection and forensics

Configurable memory device connected to a microprocessor

Prioritized non-active memory device update

This disclosure describes techniques for synchronizing independent data streams. In some instances, a computing device couples to multiple independent sensors, such as cameras, and applies accurate timestamp information to the individual frames of sensor data from the independent sensors. After aligning these data streams by applying these accurate timestamps, the computing device may, in some instances, encode and transmit these timestamped data streams to one or more entities for further processing. In one example, a first camera (e.g., a depth camera configured to generate a depth map) may capture images of an environment, as may a second camera (e.g., an Red-Green-Blue (RGB) camera configured to generate color images). The resulting images may be temporally aligned with one another via the timestamping, and the resulting aligned images from both the depth sensor and the RGB camera may be used to create a three-dimensional (3D) model of the environment.

Synchronizing data streams

This disclosure describes techniques for automatically identifying endpoint devices based on differing power loads drawn by the endpoint devices. In some instances, the endpoint devices are distributed about one or more facilities, such as item handling facilities, and may comprise an array of sensors for tracking inventory items within, into, and out of these facilities. These sensors may include cameras, weight sensors, RFID readers, microphones, and the like. Further, multiple ones of these sensors may couple to a power injector (e.g., a power-over-Ethernet (POE) injector) that provides both data and power to each sensor over a cable. The power injector may in turn couple to a network switch and/or a controller, which may identify the endpoint devices based on their modulated power loads.

Automatic discovery of endpoint devices

Growing community of inventors

Seattle, WA, United States of America

Tyler Ogren Vrooman