Amazon Technologies, Inc.

Infinera Corporation

Sachin Jain

Tate Andrew Certain

Matthew Scott Garman

Fiorenzo Cattaneo

James Robert Hamilton

Danny Wei

Roland Paterson-Jones

David Nolan Sunderland

Swaminathan Sivasubramanian

Bradley E Marshall

Nicholas J Maniscalco

Roland Alexander Paterson-Jones

Ashok Kunjidhapatham

Sharfuddin Syed

Mohit Misra

Abhinava Shivakumar Sadasivarao

Tjandra Trisno

Anthony Jorgenson

Mana Palai

Lu Biao

Biju Mathew

Balaji Gopalakrishnan

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 telemetry manager receives, from a network server, global data collection information about network components in an optical network device. The global data collection information includes identifiers for network nodes in the network components from which telemetry data are to be collected, and reporting frequency and encoding format for sending collected telemetry data to the network server. The telemetry manager identifies, from the global data collection information, local data collection information specified for a network component, and sends this information to a telemetry agent in the network component. The telemetry manager receives telemetry data generated by a network node of the network component, where the data is provided according to instructions in the local data collection information. The telemetry manager converts the telemetry data from its native format to an encoding format specified by the global data collection information, and sends the encoded telemetry data to the network server.

Streaming telemetry for optical network devices

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

Usage of shared resources can be managed by enabling users to obtain different types of guarantees at different times for various types and/or levels of resource capacity. A user can select to have an amount or rate of capacity dedicated to that user. A user can also select reserved capacity for at least a portion of the requests, tasks, or program execution for that user, where the user has priority to that capacity but other users can utilize the excess capacity during other periods. Users can alternatively specify to use the excess capacity or other variable, non-guaranteed capacity. The capacity can be for any appropriate functional aspect of a resource, such as computational capacity, throughput, latency, bandwidth, and storage. Users can submit bids for various types and combinations of excess capacity, and winning bids can receive dedicated use of the excess capacity for at least a period of time.

Managing operational throughput for shared resources

Virtual data stores may be sparsely provisioned by virtual data storage services in a manner that controls risk of implementation resource shortages. Relationships between requested data storage space size, data storage server capacity, allocated data storage space size and/or allocated data storage space utilization may be tracked on a per data store, per customer, per data storage server, and/or a per virtual data storage service basis. For each such basis, a set of constraints may be specified to control the relationships. The set of constraints may be enforced during implementation resource allocation, and by migration of data storage space portions to different implementation resources as part of a sparse provisioning load balancing. Sparse provisioning details may be made explicit to virtual data storage service customers to varying degrees including explicit, aggregate on a per customer basis, and aggregate on a per virtual data storage service basis.

Virtual data storage service with sparse provisioning

Techniques, including systems and methods, for capturing data sets include performing a client-side two-phase commit to ensure one or more data consistency conditions. A logical volume may represent a data set that is distributed among a plurality of physical storage devices. One or more client devices are instructed to block at least acknowledgment of write operations. When the one or more client devices have blocked at least acknowledgment of write operations, one or more servers in communication with the physical storage devices are instructed to capture corresponding portions of the data set. When the servers have been instructed to capture corresponding portions of the data set, the client devices are instructed to resume at least acknowledgment of write operations.

Techniques for capturing data sets

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Sammamish, WA, United States of America

Sachin Jain