Amazon Technologies, Inc.

Other

Peter Nicholas Desantis

Matthew Scott Garman

Roland Alexander Paterson-Jones

Luis Felipe Cabrera

Tate Andrew Certain

Atle Normann Jorgensen

Eric Jason Brandwine

Michael David Marr

Marvin Michael Theimer

James Alfred Gordon Greenfield

Allan Henry Vermeulen

Samuel James McKelvie

Hilliard Bruce Siegel

Jagwinder Singh Brar

Ethan Zane Evans

Matthew David Klein

Jacob Adam Gabrielson

Ian Paul Nowland

Richendra Khanna

Edward L Wallace

Justin Mason

Andrew R Jassy

Charles Louis Ward

Léon Thrane

David R Richardson

Nafea Bshara

Marcin Piotr Kowalski

Quinton Robin Hoole

Ian Roger Searle

Matthew James Eddey

Abhinav Agrawal

Marc J Brooker

Adam David Gray

Cornelle Christiaan Pretorius Janse Van Rensburg

Jacobus Johannes Nicolaas Van Der Merwe

James Robert Hamilton

Jonathan A Jenkins

Matthew Shawn Wilson

Michael Phillip Czamara

Brett Richard Taylor

Georgy Machulsky

Christopher Richard Jacques De Kadt

David Everard Brown

Andrew Hayden

Matthew T Corddry

Leon Thrane

Erik James Fuller

Charles D Ward

Stephen M Roegge

J Andrew McIntyre

Leah Shalev

Allan H Vermeullen

Omer Ilany

Techniques are described for managing execution of programs. In some situations, program execution is managed for multiple users using excess program execution capacity of one or more computing systems. In some such situations, excess or otherwise unused program execution capacity may be made available to execute programs on a temporary basis, such that the programs executing using the excess program execution capacity may be terminated at any time if other preferred use for the excess program execution capacity arises. The excess program execution capacity may in some situations be provided in conjunction with other dedicated program execution capacity that is allocated to particular users, such as to use unused dedicated capacity of some users as excess capacity for other users. In some situations, the techniques are used in conjunction with a fee-based program execution service that executes multiple programs on behalf of multiple users of the service.

Managing use of program execution capacity

Methods and apparatuses for improving network packet transmission performance in terms of latency with reduced packet retransmission times and fewer packet drops in congested networks are provided. Packet-switched networks can experience long delays while waiting for out-of-order packets or re-transmissions of lost packets. In addition, network faults such as transmission path failures can result in excessive delay while attempting to find a new route over which to transmit packets. To improve packet transmission performance, application data may be encapsulated into first network packets by a first transport protocol having an interface exposed to the application, in the first network packets may be encapsulated into second network packets according to a second transport protocol. The second transport protocol can enable the second network packets of a same packet flow to be transmitted across multiple paths over the network.

Ethernet traffic over scalable reliable datagram protocol

Virtual resources may be provisioned in a manner that is aware of, and respects, underlying implementation resource boundaries. A customer of the virtual resource provider may specify that particular virtual resources are to be implemented with implementation resources that are dedicated to the customer. Dedicating an implementation resource to a particular customer of a virtual resource provider may establish one or more information barriers between the particular customer and other customers of the virtual resource provider. Implementation resources may require transition procedures, including custom transition procedures, to enter and exit dedicated implementation resource pools. Costs corresponding to active and inactive implementation resources in a dedicated pools associated with a particular customer may be accounted for, and presented to, the customer in a variety of ways including explicit, adjusted per customer and adjusted per type of virtual resource and/or implementation resource.

Virtual provisioning with implementation resource boundary awareness

Generally described, systems and methods are provided for monitoring and detecting causes of failures of network paths. The system collects performance information from a plurality of nodes and links in a network, aggregates the collected performance information across paths in the network, processes the aggregated performance information for detecting failures on the paths, analyzes each of the detected failures to determine at least one root cause, and initiates a remedial workflow for the at least one root cause determined. In some aspects, processing the aggregated information may include performing a statistical regression analysis or otherwise solving a set of equations for the performance indications on each of a plurality of paths. In another aspect, the system may also include an interface which makes available for display one or more of the network topology, the collected and aggregated performance information, and indications of the detected failures in the topology.

Monitoring and detecting causes of failures of network paths

Techniques are described for managing access of executing programs to non-local block data storage. In some situations, a block data storage service uses multiple server storage systems to reliably store copies of network-accessible block data storage volumes that may be used by programs executing on other physical computing systems, and snapshot copies of some volumes may also be stored (e.g., on remote archival storage systems). A group of multiple server block data storage systems that store block data volumes may in some situations be co-located at a data center, and programs that use volumes stored there may execute on other computing systems at that data center, while the archival storage systems may be located outside the data center. The snapshot copies of volumes may be used in various ways, including to allow users to obtain their own copies of other users' volumes (e.g., for a fee).

Providing executing programs with access to stored block data of others

An overlay network analysis method obtains data including but not limited to client packet traffic data on an overlay network, and performs one or more analyses based on the obtained data to generate and output topological and/or performance information for the overlay network and/or the network substrate on which the overlay network is implemented. Client traffic data collected for specific client resource instances may be analyzed to generate performance metrics for the overlay network between the instances. Aggregated client traffic data for specific clients may also be analyzed to generate mappings of the clients' private network implementations on the overlay network, as well as performance metrics for the clients' private networks on the overlay network. In addition, client traffic data from multiple clients may be aggregated and analyzed to generate mappings and performance metrics for the overlay network as a whole.

Network traffic mapping and performance analysis

Implementation resources are operated in a manner furthering a particular purpose while excluding use of the implementation resources for other purposes. At least some of the implementation resources have capacity that is usable to implement multiple other resources. The capacity of the implementation resources is allocated in a manner that satisfies one or more conditions on the capacity of the implementation resources that is used. Generally, the capacity is allocated in a manner that reduces the likelihood that resources initiated close in time will fail together should underlying implementation resources fail. The implementation resources may be hardware devices that implement virtual computer systems.

Resource allocation to reduce correlated failures

Methods and apparatus for profile-guided preloading for virtualized resources are described. A block-level storage volume whose contents are to be populated via data transfers from a repository service is programmatically attached to a compute instance. An indication of data transfers from the repository to a block storage service implementing the volume is obtained, corresponding to a particular phase of program execution at the compute instance. A storage profile is generated, based at least in part on the indication of data transfers. The storage profile is subsequently used to pre-load data from the repository service on behalf of other compute instances.

Profile-guided data preloading for virtualized resources

Techniques are described for managing access of executing programs to non-local block data storage. In some situations, a block data storage service uses multiple server storage systems to reliably store network-accessible block data storage volumes that may be used by programs executing on other physical computing systems. A group of multiple server block data storage systems that store block data volumes may in some situations be co-located at a data center, and programs that use volumes stored there may execute on other physical computing systems at that data center. If a program using a volume becomes unavailable, another program (e.g., another copy of the same program) may in some situations obtain access to and continue to use the same volume, such as in an automatic manner in some such situations.

Managing access of multiple executing programs to non-local block data storage

Providing access to stored computing snapshots

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Seattle, WA, United States of America

Peter Nicholas Desantis