Zhuo Zhang, 4625810 SE 41St Pl, Sammamish, WA 98029

8499066, Jul 30, 2013

Dec 6, 2010

12/961285

Zhuo Zhang - Mercer Island WA, US
Swaminathan Subramanian - Sunnyvale CA, US
Muhammad Ali Siddiqui - Snoqualmie WA, US

Amazon Technologies, Inc. - Reno NV

G06F 15/177
G06F 15/173
G06F 3/00

709223, 709200, 709203, 709224, 709226, 710 15

Techniques are described for performing automated predictions of program execution capacity or other capacity of computing-related hardware resources that will be used to execute software programs in the future, such as for a group of computing nodes that execute one or more programs for a user. The predictions that are performed may in at least some situations be based on historical data regarding corresponding prior actual usage of execution-related capacity (e. g. , for one or more prior years), and may include long-term predictions for particular future time periods that are multiple months or years into the future. In addition, the predictions of the execution-related capacity for particular future time periods may be used in various manners, including to manage execution-related capacity at or before those future time periods, such as to prepare sufficient execution-related capacity to be available at those future time periods.

8346921, Jan 1, 2013

Nov 19, 2010

12/950630

Joshua M. Goodspeed - Seattle WA, US
Zhuo Zhang - Mercer Island WA, US
Swaminathan Subramanian - Sunnyvale CA, US
Muhammad Ali Siddiqui - Snoqualmie WA, US

Amazon Technologies, Inc. - Reno NV

G06F 15/173

709224, 709200, 709203, 709223

Techniques are described for managing program execution capacity or other capacity of computing-related hardware resources used to execute software programs, such as for a group of computing nodes that is in use executing one or more programs for a user. Dynamic modifications to the program execution capacity of the group may include adding or removing computing nodes, such as in response to automated determinations that previously specified triggers are currently satisfied, and may be automatically governed at particular times based on automatically generated predictions of program execution capacity that will be used at those times by the group, such as to verify that requested dynamic execution capacity modifications at a time are within the predicted execution capacity values for that time. 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.

2022032, Oct 13, 2022

Jan 10, 2022

17/647500

- Bozeman MT, US
Istvan Cseri - Seattle WA, US
Tyler Jones - Redwood City CA, US
Daniel E. Sotolongo - Seattle WA, US
Zhuo Zhang - Kirkland WA, US

G06F 16/2455
G06F 16/2453

A streaming ingest platform can improve latency and expense issues related to uploading data into a cloud data system. The streaming ingest platform can organize the data to be ingested into per-table chunks and per-account blobs. This data may be committed and may be made available for query processing before it is ingested into the target source tables. This significantly improves latency issues. The streaming ingest platform can also accommodate uploading data from various sources with different processing and communication capabilities, such as Internet of Things (IOT) devices.

2023007, Mar 9, 2023

Oct 25, 2022

18/049325

- Bozeman MT, US
Istvan Cseri - Seattle WA, US
Tyler Jones - Redwood City CA, US
Daniel E. Sotolongo - Seattle WA, US
Zhuo Zhang - Kirkland WA, US

G06F 16/2455
G06F 16/2453
G06F 16/25
G06F 16/22

A streaming ingest platform can improve latency and expense issues related to uploading data into a cloud data system. The streaming ingest platform can organize the data to be ingested into per-table chunks and per-account blobs. This data may be committed and may be made available for query processing before it is ingested into the target source tables. This significantly improves latency issues. The streaming ingest platform can also accommodate uploading data from various sources with different processing and communication capabilities, such as Internet of Things (IOT) devices.