Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

The Invention Science Fund I LLC

Avid Technology Limited

The Boeing Company

Microsoft Corporation

Air Products and Chemicals, Inc.

James Robert Hamilton

Michael David Marr

Michael J Manos

Burton Jordan Smith

Stefano Stefani

Tate Andrew Certain

Matthew T Corddry

Lowell L Wood, Jr

Roderick A Hyde

Clarence T Tegreene

Edward K Y Jung

Jordin T Kare

Nathan P Myhrvold

Robert W Lord

Charles Whitmer

William Henry Mangione-Smith

Craig J Mundie

Howard Lee Davidson

Robert Victor Welland

Richard F Rashid

Arne M Josefsberg

Luis Felipe Cabrera

Pradeep Vincent

Danny Wei

Sachin Jain

Fiorenzo Cattaneo

Timothy Andrew Rath

Wei Xiao

Vladimir Sadovsky

Matthew Scott Garman

Alan Michael Judge

Kiran-Kumar Muniswamy-Reddy

Justin Oliver Pietsch

Yijun Lu

Miguel Mascarenhas Filipe

Bjorn Patrick Swift

Swaminathan Sivasubramanian

Jakub Kulesza

Stuart Henry Seelye Marshall

Rande Anthony Blackman

Osvaldo Patricio Morales

Eric Jason Brandwine

Peter George Ross

Jonathan A Jenkins

Matthew David Klein

Michael Phillip Czamara

Grant Alexander Macdonald McAlister

Nathan Bartholomew Thomas

Chiranjeeb Buragohain

Marvin Michael Theimer

Darin Lee Frink

Chris Demetrios Karkanias

Eric C Peters

Brett Richard Taylor

Ruston Panabaker

Oren Rosenbloom

Katherine H Cornog

Sompong Paul Olarig

William Jefferson Westerinen

Christian L Belady

Michael Richard Fortin

Nachiketh Rao Potlapally

Sean Michael James

Hubert Van Hoof

Tanmoy Dutta

John Mark Miller

Girish Chander

David Thomas Gauthier

David Earl Heckerman

Michael Jack Sinclair

Colin Laird Lazier

Alastair Wolman

Sharad Agarwal

Bryan James Donlan

Ming Zhang

Kestutis Patiejunas

Feng Zhao

Balan Sethu Raman

Peter Nicholas Desantis

John David Dunagan

Surendra Verma

Brock Robert Gardner

Robert P Fitzgerald

Craig R Frink

Roland Alexander Paterson-Jones

Avi R Geiger

Yadhu Nandh Gopalan

John William Eichelberg

Anders Bertil Vinberg

Joel Mathew Soderberg

John D Mehr

Nuria M Oliver

Emre Mehmet Kiciman

Sean P Nolan

Mark Christopher Seigle

Eric Lee Nicks

Navjot Virk

James Charles Kleewein

Jeffrey D Kurtze

David G Campbell

A system that implements a scalable data storage service may maintain tables in a non-relational data store on behalf of clients. The system may provide a Web services interface through which service requests are received, and an API usable to request that a table be created, deleted, or described; that an item be stored, retrieved, deleted, or its attributes modified; or that a table be queried (or scanned) with filtered items and/or their attributes returned. An asynchronous workflow may be invoked to create or delete a table. Items stored in tables may be partitioned and indexed using a simple or composite primary key. The system may not impose pre-defined limits on table size, and may employ a flexible schema. The service may provide a best-effort or committed throughput model. The system may automatically scale and/or re-partition tables in response to detecting workload changes, node failures, or other conditions or anomalies.

System and method for implementing a scalable data storage service

A system and a method for operating an aircraft during a climb phase of flight include a control unit configured to receive data regarding one or both of a current flight or one or more previous flights of the aircraft from one or more sensors of the aircraft. The control unit is further configured to determine efficient climb phase parameters for the aircraft based on the data. The aircraft is operated during the climb phase of one or both of the current flight or one or more future flights according to the efficient climb phase parameters.

System and method for operating an aircraft during a climb phase of flight

Customers of a shared-resource environment can provision resources in a fine-grained manner that meets specific performance requirements. A customer can provision a data volume with a committed rate of Input/Output Operations Per Second (IOPS) and pay only for that commitment (plus any overage), and the amount of storage requested. The customer will then at any time be able to complete at least the committed rate of IOPS. If the customer generates submissions at a rate that exceeds the committed rate, the resource can still process at the higher rate when the system is not under pressure. Even under pressure, the system will deliver at least the committed rate. Multiple customers can be provisioned on the same resource, and more than one customer can have a committed rate on that resource. Customers without committed or guaranteed rates can utilize the uncommitted portion, or committed portions that are not being used.

Committed processing rates for shared resources

Commitments against various resources can be dynamically adjusted for customers in a shared-resource environment. A customer can provision a data volume with a committed rate of Input/Output Operations Per Second (IOPS) and pay only for that commitment (plus any overage), for example, as well as the amount of storage requested. The customer can subsequently adjust the committed rate of IOPS by submitting an appropriate request, or the rate can be adjusted automatically based on any of a number of criteria. Data volumes for the customer can be migrated, split, or combined in order to provide the adjusted rate. The interaction of the customer with the data volume does not need to change, independent of adjustments in rate or changes in the data volume, other than the rate at which requests are processed.

Managing committed request rates for shared resources

A monitoring system and method are configured to detect a status of a door for a component on a vehicle. The monitoring system includes a sound sensor positioned within an auditory range of the door. The sound sensor detects sound energy generated in relation to a closed position of the door and an open position of the door. The sound sensor outputs a sound signal indicative of the sound energy. A monitoring control unit is in communication with the sound sensor. The monitoring control unit receives the sound signal and analyzes the sound signal to determine the status of the door.

Monitoring systems and methods for vehicles

A system for providing resources to customers includes racks, sub-rack level secure containment units in the racks, and a provisioning control system. The sub-rack level secure containment units each enclose system resources. The racks hold two or more of the sub-rack level secure containment units. For each customer, the provisioning control system may provision a set of sub-rack level secure containment units to the customer. The provisioning control system may monitor, and create a record of, events of physical access to the system resources in each customer's set of sub-rack level secure containment units.

Providing system resources with secure containment units

Methods and apparatus for token-based admission control for replicated writes are disclosed. Data objects are divided into partitions, and corresponding to each partition, at least a master replica and a slave replica are stored. A determination as to whether to accept a write request directed to the partition is made based at least in part on one or more of (a) available throughput capacity at the master replica, and (b) an indication, obtained using a token-based protocol, of available throughput capacity at the slave replica. If the write request is accepted, one or more data modification operations are initiated.

Token-based admission control for replicated writes

A system for performing computing operations in a data center includes one or more sets of computer systems, one or more primary power systems, and a reserve power system. The primary power systems include at least one power distribution unit that supplies power to at least one of the sets of computer systems. The reserve power system automatically supplies power to at least one of the sets of computer systems if a condition is met (such as a failure of the primary power system).

Reserve power system for data center

Customers of shared resources in a multi-tenant environment can have token buckets allocated that have an associated depth and fill rate, with each token enabling the customer to obtain an amount of work from a shared resource. A resource management system can monitor one or more system or output metrics, and can adjust a global fill rate based at least in part upon values of the monitored metrics. Such an approach can provide a fair distribution of work among the customers, while ensuring that the metrics stay within acceptable ranges and there are no drastic changes in performance levels of the system. The fill rate can update dynamically with changes in the monitored parameters, such that the system can float near an equilibrium point. Commitments for specific minimum service levels also can be met.

Adaptive throttling for shared resources

Managing committed processing rates for shared resources

Methods and apparatus for client-selectable power source options for network-accessible service units are described. A programmatic interface is implemented to enable clients of a service to select, from among a plurality of power source categories including a renewable category and a non-renewable category, a power source category to be used for a service unit. Based on inputs received via the interface, the respective amounts of power to be obtained from renewable and non-renewable categories during a time period may be estimated. A verification operation comparing the estimated amounts to the amount of power that is actually obtained from the different sources may be performed.

Client-selectable power source options for network-accessible service units

A pricing policy to be applied to token population changes at a token bucket used for admission control during burst-mode operations at a work target is determined. Over a time period, changes to the token population of that bucket are recorded. An amount to be charged to a client is determined, based on the recorded changes in token population and an associated pricing amount indicated in the policy.

Token-based policies burst-mode operations

A set of techniques is described for monitoring and analyzing crashes and other malfunctions in a multi-tenant computing environment (e.g. cloud computing environment). The computing environment may host many applications that are executed on different computing resource combinations. The combinations may include varying types and versions of hardware or software resources. A monitoring service is deployed to gather statistical data about the failures occurring in the computing environment. The statistical data is then analyzed to identify abnormally high failure patterns. The failure patterns may be associated with particular computing resource combinations being used to execute particular types of applications. Based on these failure patterns, suggestions can be issued to a user to execute the application using a different computing resource combination. Alternatively, the failure patterns may be used to modify or update the various resources in order to correct the potential malfunctions caused by the resource.

Monitoring and analysis of operating states in a computing environment

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Seattle, WA, United States of America

James Robert Hamilton