Data management in a public cloud network

Patent metadata

Field	Value
Publication number	US-12452127-B2
Application number	US-202418426663-A
Country	US
Kind code	B2
Filing date	Jan 30, 2024
Priority date	Jan 30, 2024
Publication date	Oct 21, 2025
Grant date	Oct 21, 2025

Abstract

A computer-implemented method is disclosed for predicting, based on a previous usage of a cloud-based computing resource by a number of users of the cloud-based computing resource, a future usage of the cloud-based computing resource. The method includes predicting, based on the predicted future usage of the cloud-based computing resource, an anomaly event at the cloud-based computing resource. The method also includes implementing a first anomaly mitigation action, based on the prediction of the anomaly event at the cloud-based computing resource and re-evaluating a status of the anomaly event at the cloud-based computing resource after the implementation of the first anomaly mitigation action. The method further includes implementing a second anomaly mitigation action at the cloud-based computing resource, based on the re-evaluation of the status of the anomaly event.

First claim

What is claimed is:

1. A computer implemented method comprising:
predicting, based on a previous usage of a cloud-based computing resource by a plurality of users of the cloud-based computing resource, a future usage of the cloud-based computing resource;
predicting, based on the predicted future usage of the cloud-based computing resource, an anomaly event at the cloud-based computing resource;
implementing a first anomaly mitigation action, based on the prediction of the anomaly event at the cloud-based computing resource by:
identifying one or more top contributing users from the plurality of users of the cloud computing resource usage and throttling the one or more top contributing users contributing user's access based on the first anomaly mitigation action;

re-evaluating a status of the anomaly event at the cloud-based computing resource after the implementation of the first anomaly mitigation action; and
implementing a second anomaly mitigation action at the cloud-based computing resource, based on the re-evaluation of the status of the anomaly event by:
placing the received incoming data request of the plurality of data requests from the one or more top contributing users in a queue; and
evaluating the received incoming data request of plurality of incoming data requests for the cloud-based computing resource and determining an emitting speed quota and the received incoming data request speed based on the evaluating; and
dynamically adjusting an emitting speed of a queue data request based on the determined emitting speed quota and the received incoming data request speed.

2. The method of claim 1 , wherein the top contributing user is determined from the plurality of users that is responsible for a highest usage of the cloud-based computing resource.

3. The method of claim 2 , wherein the determining of the top contributing user from the plurality of users comprises identifying the top contributing user based on a machine learning algorithm.

4. The method of claim 3 , further comprising throttling of an access of the top contributing user to the cloud-based computing resource.

5. The method of claim 1 , wherein the predicting the anomaly event at the cloud-based computing resource comprises predicting the anomaly event based on a regression analysis of a plurality of database usage metrics related to the cloud-based computing resource.

6. The method of claim 1 , wherein the cloud-based computing resource comprises a database (DB) central processing unit (CPU) server.

7. A non-transitory machine-readable storage medium that provides instructions that, if executed by a processor, are configurable to cause said processor to perform operations comprising:
predicting, based on a previous usage of a cloud-based computing resource by a plurality of users of the cloud-based computing resource, a future usage of the cloud-based computing resource;
predicting, based on the predicted future usage of the cloud-based computing resource, an anomaly event at the cloud-based computing resource;
implementing a first anomaly mitigation action, based on the prediction of the anomaly event at the cloud-based computing resource by:
identifying one or more top contributing users from the plurality of users of the cloud computing resource usage and throttling the one or more top contributing users contributing user's access based on the first anomaly mitigation action;

re-evaluating a status of the anomaly event at the cloud-based computing resource after the implementation of the first anomaly mitigation action; and
implementing a second anomaly mitigation action at the cloud-based computing resource, based on the re-evaluation of the status of the anomaly event by:
placing the received incoming data request of the plurality of data requests from the one or more top contributing users in a queue;
evaluating the received incoming data request of plurality of incoming data requests for the cloud-based computing resource and determining an emitting speed quota and the received incoming data request speed based on the evaluating; and
dynamically adjusting an emitting speed of a queue data request based on the determined emitting speed quota and the received incoming data request speed.

8. The non-transitory machine-readable storage medium of claim 7 , wherein the top contributing user is determined from the plurality of users that is responsible for a highest usage of the cloud-based computing resource.

9. The non-transitory machine-readable storage medium of claim 8 , wherein the determining of the top contributing user from the plurality of users comprises identifying the top contributing user based on a machine learning algorithm.

10. The non-transitory machine-readable storage medium of claim 9 , further comprising throttling of an access of the top contributing user to the cloud-based computing resource.

11. The non-transitory machine-readable storage medium of claim 7 , wherein the predicting the anomaly event at the cloud-based computing resource comprises predicting the anomaly event based on a regression analysis of a plurality of database usage metrics related to the cloud-based computing resource.

12. The non-transitory machine-readable storage medium of claim 7 , wherein the cloud-based computing resource comprises a database (DB) central processing unit (CPU) server.

13. A system comprising:
a processor;
a cloud-based computing resource digitally connected with the processor;
a non-transitory machine-readable storage medium that provides instructions that, if executed by the processor, are configurable to cause the system to perform operations comprising:
predicting, based on a previous usage of the cloud-based computing resource by a plurality of users of the cloud-based computing resource, a future usage of the cloud-based computing resource;
predicting, based on the predicted future usage of the cloud-based computing resource, an anomaly event at the cloud-based computing resource;
implementing a first anomaly mitigation action, based on the prediction of the anomaly event at the cloud-based computing resource by:
identifying one or more top contributing users from the plurality of users of the cloud computing resource usage and throttling the one or more top contributing users contributing user's access based on the first anomaly mitigation action;

re-evaluating a status of the anomaly event at the cloud-based computing resource after the implementation of the first anomaly mitigation action; and
implementing a second anomaly mitigation action at the cloud-based computing resource, based on the re-evaluation of the status of the anomaly event by:
placing the received incoming data request of the plurality of data requests from the one or more top contributing users in a queue; and
evaluating the received incoming data request of plurality of incoming data requests for the cloud-based computing resource and determining an emitting speed quota and the received incoming data request speed based on the evaluating; and
dynamically adjusting an emitting speed of a queue data request based on the determined emitting speed quota and the received incoming data request speed.

14. The system of claim 13 , wherein the top contributing user is determined from the plurality of users that is responsible for a highest usage of the cloud-based computing resource.

15. The system of claim 14 , wherein the determining of the top contributing user from the plurality of users comprises identifying the top contributing user based on a machine learning algorithm.

16. The system of cl

Assignees

• Salesforce Inc

Inventors

• Zong Bo
• Wong Tony
• Yao Ran
• Wong Kin Lun
• Cho Daeki

Classifications

• H04L67/535 Primary

  Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34) · CPC title

• H04L41/16

  using machine learning or artificial intelligence · CPC title

• H04L41/069 Primary

  using logs of notifications; Post-processing of notifications · CPC title