Amazon Technologies, Inc.

Matthew Scott Garman

Tate Andrew Certain

Peter Nicholas Desantis

Atle Normann Jorgensen

Roland Alexander Paterson-Jones

Sachin Jain

Fiorenzo Cattaneo

James Robert Hamilton

Roland Paterson-Jones

Danny Wei

David Nolan Sunderland

Swaminathan Sivasubramanian

Bradley E Marshall

James Alfred Gordon Greenfield

Nicholas J Maniscalco

Abhinav Agrawal

Andrew R Jassy

Eric Jason Brandwine

Christopher Richard Jacques De Kadt

Kevin Ross O'Neill

Mark Joseph Cavage

David Everard Brown

David John Ward, Jr

Charles Louis Ward

Cornelle Christiaan Pretorius Janse Van Rensburg

Peter N DeSantis

Marc John Brooker

Nathan Bartholomew Thomas

Gang Li

David Nunnerley

Michael Phillip Czamara

John William Eichelberg

Derek Solomon Pai

Joshua Marc Burgin

Marc J Brooker

Rajesh Viswanathan

Peter N De Santis

Joby Sky Lafky

Stephen Alden Elliott

William Trotter Shelton

Charles D Ward

Terrance Douglas Hanold

Asem Rustum

Lawrence Wheeless

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

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

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

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

Managing access of multiple executing programs to nonlocal block data storage

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

Managing committed processing rates for shared resources

A set of virtualized computing services may include multiple types of virtualized data store differentiated by characteristics such as latency, throughput, durability and cost. A sequence of captures of a data set from one data store to another may be scheduled to achieve a variety of virtualized computing service user and provider goals such as lowering a probability of data loss, lowering costs, and computing resource load leveling. Data set captures may be scheduled according to policies specifying fixed and flexible schedules and conditions including flexible scheduling windows, target capture frequencies, probability of loss targets and/or cost targets. Capture lifetimes may also be managed with capture retention policies, which may specify fixed and flexible lifetimes and conditions including cost targets. Such data set capture policies may be specified with a Web-based administrative interface to a control plane of the virtualized computing services.

Data set capture management with forecasting

Providing access to stored computing snapshots

Systems and methods permit customers to purchase resource instances on a spot purchasing mode basis, which permits a customer to specify a maximum price that the customer is willing to pay for one or more resource instances (e.g., virtual machine instances running on physical servers). Further, the resource instances purchased under the spot purchasing mode can be guaranteed to run for at least a specified period of time, even if the current variable spot cost value is exceeds the original bid price. Such instances run for defined blocks of time which may be selectable by the customer, thereby providing the customer some level of assurance that the instances will not terminate prematurely due to spot cost value fluctuations. Instances are assigned into a spot block instance pool from other types of pool based on the estimated excess capacity of such other types of pools. The estimate of excess capacity is based on statistical analysis of the usage of the instances.

Interruptible resource instances for block time periods

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Seattle, WA, United States of America

Matthew Scott Garman