US 11,888,605 B2
Methods and systems for making effective use of system resources
Andrey Gusev, San Francisco, CA (US); Ronald Yang, Cupertino, CA (US); Scott Hansma, San Francisco, CA (US); Jesse Collins, Oakland, CA (US); and Alan Arbizu, Foster City, CA (US)
Assigned to Salesforce, Inc., San Francisco, CA (US)
Filed by Salesforce, Inc., San Francisco, CA (US)
Filed on Sep. 28, 2022, as Appl. No. 17/936,301.
Application 17/936,301 is a continuation of application No. 17/070,836, filed on Oct. 14, 2020, granted, now 11,496,555.
Application 17/070,836 is a continuation of application No. 16/259,964, filed on Jan. 28, 2019, granted, now 10,810,514, issued on Oct. 20, 2020.
Application 16/259,964 is a continuation of application No. 14/703,682, filed on May 4, 2015, granted, now 10,192,169, issued on Jan. 29, 2019.
Application 14/703,682 is a continuation of application No. 13/276,531, filed on Oct. 19, 2011, granted, now 9,026,624, issued on May 5, 2015.
Claims priority of provisional application 61/421,989, filed on Dec. 10, 2010.
Prior Publication US 2023/0016877 A1, Jan. 19, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. H04L 47/125 (2022.01); H04L 67/1008 (2022.01); H04L 49/90 (2022.01); G06N 20/00 (2019.01); G06F 16/2455 (2019.01); H04L 69/329 (2022.01); H04L 67/1014 (2022.01); H04L 67/1001 (2022.01); G06F 9/50 (2006.01); H04L 43/0876 (2022.01); H04L 67/10 (2022.01)
CPC H04L 67/1008 (2013.01) [G06F 9/5011 (2013.01); G06F 16/2455 (2019.01); G06N 20/00 (2019.01); H04L 43/0876 (2013.01); H04L 47/125 (2013.01); H04L 49/90 (2013.01); H04L 67/10 (2013.01); H04L 67/1001 (2022.05); H04L 67/1014 (2013.01); H04L 69/329 (2013.01); G06F 2209/5019 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, with one or more hardware processing devices, a plurality of database queries to be performed using resources associated with a database, each database query having an associated group of features;
analyzing, utilizing machine learning techniques, the group of features for each database query to collect query execution data observations about the plurality of database queries;
creating, utilizing machine learning techniques, a machine learning model based on the query execution data observations, the machine learning model comprising a function configured to predict a resource allocation of a subsequent database query;
predicting, with the machine learning model, a particular resource allocation for a particular subsequent query; and
providing, with the one or more hardware processing devices, resources to service the particular subsequent database query according to the particular resource allocation.