Data management in a public cloud network

Patent metadata

Field	Value
Publication number	US-12549498-B2
Application number	US-202418616536-A
Country	US
Kind code	B2
Filing date	Mar 26, 2024
Priority date	Jan 30, 2024
Publication date	Feb 10, 2026
Grant date	Feb 10, 2026

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, a future usage of the cloud-based computing resource and then predicting, based on the predicted future usage, an anomaly event at the computing resource. The method also includes identifying a top contributing user that is responsible for the anomaly event and throttling an access of the top contributing user to the computing resource. The method further includes evaluating a speed of data requests received at the computing resource from the top contributing user after the throttling, and a utilization level of the computing resource. The method also includes dynamically adjusting the speed of data requests received at the computing resource, based on the evaluation of the utilization level of the computing resource, to maintain the utilization level of the computing resource within a predetermined target range.

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; identifying a top contributing user from the plurality of users that is responsible for the anomaly event at the cloud-based computing resource; throttling an access of the top contributing user to the cloud-based computing resource; evaluating a speed of data requests received at the cloud-based computing resource from the top contributing user after the throttling, and a utilization level of the cloud-based computing resource; and dynamically adjusting the speed of data requests received at the cloud-based computing resource, based on the evaluation of the utilization level of the cloud-based computing resource, to maintain the utilization level of the cloud-based computing resource within a predetermined target range, wherein the dynamically adjusting the speed of data requests comprises dynamically adjusting the speed of data requests based on a regression analysis of the utilization level of the cloud-based computing resource and the speed of data requests, wherein the regression analysis is based on a regularized learning aggregated over a plurality of locally liner zones of relationship between the utilization level of the cloud-based computing resource and the speed of data requests, wherein the regression analysis is further based on an estimated utilization rate of the cloud-based computing resource, an estimated value of the speed of data requests, and a baseline utilization level of the cloud-based computing resource, the estimated utilization rate of the cloud-based computing resource and the estimated value of the speed of data requests being dynamically computed based on near-past historical data, wherein the regularized learning comprises a step-wise enumeration for a causative combination of the estimated utilization rate of the cloud-based computing resource and the baseline utilization level of the cloud-based computing resource, the causative combination causing to maintain the utilization level of the cloud-based computing resource within the predetermined target range, wherein the predetermined target range comprises 60% to 70% of a maximum utilization level of the cloud-based computing resource.

2 . The method of claim 1 , wherein the anomaly event comprises a deviation from an expected pattern or a normal operational parameter related to a security or performance aspect of the cloud-based computing resource.

3 . The method of claim 2 , wherein the deviation from the expected pattern comprises an overuse of the cloud-based computing resource by at least one of the plurality of users.

4 . 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; identifying a top contributing user from the plurality of users that is responsible for the anomaly event at the cloud-based computing resource; throttling an access of the top contributing user to the cloud-based computing resource; evaluating a speed of data requests received at the cloud-based computing resource from the top contributing user after the throttling, and a utilization level of the cloud-based computing resource; and dynamically adjusting the speed of data requests received at the cloud-based computing resource, based on the evaluation of the utilization level of the cloud-based computing resource, to maintain the utilization level of the cloud-based computing resource within a predetermined target range, wherein the dynamically adjusting the speed of data requests comprises dynamically adjusting the speed of data requests based on a regression analysis of the utilization level of the cloud-based computing resource and the speed of data requests,
wherein the regression analysis is based on a regularized learning aggregated over a plurality of locally liner zones of relationship between the utilization level of the cloud-based computing resource and the speed of data requests,
wherein the regression analysis is further based on an estimated utilization rate of the cloud-based computing resource, an estimated value of the speed of data requests, and a baseline utilization level of the cloud-based computing resource, the estimated utilization rate of the cloud-based computing resource and the estimated value of the speed of data requests being dynamically computed based on near-past historical data,
wherein the regularized learning comprises a step-wise enumeration for a causative combination of the estimated utilization rate of the cloud-based computing resource and the baseline utilization level of the cloud-based computing resource, the causative combination causing to maintain the utilization level of the cloud-based computing resource within the predetermined target range,
wherein the predetermined target range comprises 60% to 70% of a maximum utilization level of the cloud-based computing resource.

5 . The non-transitory machine-readable storage medium of claim 4 , wherein the anomaly event comprises a deviation from an expected pattern or a normal operational parameter related to a security or performance aspect of the cloud-based computing resource.

6 . The non-transitory machine-readable storage medium of claim 5 , wherein the deviation from the expected pattern comprises an overuse of the cloud-based computing resource by at least one of the plurality of users.

7 . 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 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;
identifying a top contributing user from the plurality of users that is responsible for the anomaly event at the cloud-based computing resource;
throttling an access of the top contributing user to the cloud-based computing resource;
evaluating a speed of data requests received at the cloud-based computing resource from the top contributing user after the throttling, and a utilization level of the cloud-based computing resource; and
dynamically adjusting the speed of data requests received at the cloud-based computing resource, based on the evaluation of the utilization level of the cloud-based computing resource, to maintain the utilization level of the cloud-based computing resource within a predetermined target range, wherein the dynamically adjusting the speed of data requests comprises dynamically adjusting the speed of data requests based on a regression analysis of the utilization level of the cloud-based computing resource and the speed of data requests,
wherein the regression analysis is based on a regularized learning aggrega

Assignees

• Salesforce Inc

Inventors

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

Classifications

• H04L47/12

  Avoiding congestion; Recovering from congestion · CPC title

• H04L43/0876

  Network utilisation, e.g. volume of load or congestion level · CPC title

• H04L67/10

  in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) · CPC title

• H04L47/22

  Traffic shaping · CPC title

• H04L41/5096

  wherein the managed service relates to distributed or central networked applications · CPC title