IP Library › Granted Patent US 12,452,272
Granted Patent B1
US 12,452,272 · App. 18/161,709 · Granted Oct 21, 2025

Reducing resource consumption spikes in an anomaly detection framework

Inventors: Sathish Kumar Subramanian (Cupertino, CA); Nandish Nanjappa (Fremont, CA); Yijou Chen (Cupertino, CA)
Assignee: Fortinet, Inc.
H04L63/1425G06F9/455G06F9/545G06F16/9024G06F16/9038G06F16/9535G06F16/9537G06F21/57H04L43/045H04L43/06H04L63/10H04L67/306H04L67/535G06F16/2456
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Quick Facts
Patent No.
US 12,452,272
App. No.
18/161,709
Filed
Jan 30, 2023
Granted
Oct 21, 2025
Kind
B1
Art Unit
2458
USPC
709/224
Abstract

Reducing resource consumption spikes in an anomaly detection framework, including: gathering information describing historical job execution associated with a plurality of customers; assigning, based on the information describing historical job execution, each of the plurality of customers to a particular grouping of a plurality of groupings, wherein each of the plurality of groupings corresponds to a particular time offset within a time interval; and scheduling, for each grouping of the plurality of groupings, at a time offset of a corresponding grouping, execution of one or more jobs associated with one or more customers of the corresponding grouping.

Claims (32)

1. A method of reducing resource consumption spikes in an anomaly detection framework, the method comprising:

gathering, with a computing system having at least a hardware processor and a memory system coupled with the hardware processor, information describing historical job execution associated with a plurality of customers;

assigning, with the computing system and based on the information describing historical job execution, each of the plurality of customers to a particular grouping of a plurality of groupings, wherein each of the plurality of groupings corresponds to a particular time offset within a time interval;

scheduling, with the computing system and for each grouping of the plurality of groupings, at a time offset of a corresponding grouping, execution of one or more jobs associated with one or more customers of the corresponding grouping; and

executing, with the computing system, the one or more jobs associated with one or more customers of the corresponding grouping.

2. The method of claim 1 , wherein the information describing historical job execution describes, for a particular customer, one or more runtime metrics for execution of jobs for the particular customer.

3. The method of claim 1 , wherein the time interval comprises a time interval for batch processing of data in an anomaly detection framework.

4. The method of claim 1 , wherein executing the one or more jobs associated with one or more customers of the corresponding grouping further comprises executing, at each time offset in the time interval, one or more scheduled jobs.

5. The method of claim 4 , wherein executing the one or more jobs associated with one or more customers of the corresponding grouping further comprises executing the one or more scheduled jobs is performed at each of a plurality of occurrences of the time interval.

6. The method of claim 1 , wherein assigning each of the plurality of customers to the particular grouping of the plurality of groupings comprises:

calculating, for each of the plurality of customers, a corresponding runtime metric; and

identifying, for each of the plurality of customers, the particular grouping based on the corresponding runtime metric.

7. The method of claim 2 , wherein the runtime metric for a particular customer comprises an average job runtime for the particular customer.

8. The method of claim 2 , wherein the runtime metric for a particular customer comprises a probabilistic distribution of job runtimes for the particular customer.

9. The method of claim 1 , wherein identifying the particular grouping is further based on the particular time offset corresponding to the particular grouping.

10. The method of claim 1 , wherein assigning each of the plurality of customers to the particular grouping of the plurality of groupings comprises assigning each of the plurality of customers to the particular grouping of the plurality of groupings based on a corresponding data warehouse assignment for each of the plurality of customers.

11. A non-transitory computer program product for reducing resource consumption spikes in an anomaly detection framework, the computer program product disposed on a non-transitory computer readable medium, the non-transitory computer program product including computer program instructions configurable to carry out the steps of cause a computer system to:

gather information describing historical job execution associated with a plurality of customers;

assign, based on the information describing historical job execution, each of the plurality of customers to a particular grouping of a plurality of groupings, wherein each of the plurality of groupings corresponds to a particular time offset within a time interval;

schedule, for each grouping of the plurality of groupings, at a time offset of a corresponding grouping, execution of one or more jobs associated with one or more customers of the corresponding grouping; and

execute, with the computing system, the one or more jobs associated with one or more customers of the corresponding grouping.

12. The non-transitory computer program product of claim 11 , wherein the information describing historical job execution describes, for a particular customer, one or more runtime metrics for execution of jobs for the particular customer.

13. The non-transitory computer program product of claim 11 , wherein the time interval comprises a time interval for batch processing of data in an anomaly detection framework.

14. The non-transitory computer program product of claim 11 , wherein execution of the one or more jobs associated with one or more customers of the corresponding grouping further comprises executing, at each time offset in the time interval, one or more scheduled jobs.

15. The non-transitory computer program product of claim 14 , wherein execution of the one or more jobs associated with one or more customers of the corresponding grouping further comprises executing the one or more scheduled jobs is performed at each of a plurality of occurrences of the time interval.

16. The non-transitory computer program product of claim 11 , wherein assigning each of the plurality of customers to the particular grouping of the plurality of groupings comprises:

calculating, for each of the plurality of customers, a corresponding runtime metric; and

identifying, for each of the plurality of customers, the particular grouping based on the corresponding runtime metric.

17. The non-transitory computer program product of claim 12 , wherein the runtime metric for a particular customer comprises an average job runtime for the particular customer.

18. The non-transitory computer program product of claim 12 , wherein the runtime metric for a particular customer comprises a probabilistic distribution of job runtimes for the particular customer.

19. The non-transitory computer program product of claim 11 , wherein identifying the particular grouping is further based on the particular time offset corresponding to the particular grouping.

20. The non-transitory computer program product of claim 11 , wherein assigning each of the plurality of customers to the particular grouping of the plurality of groupings comprises assigning each of the plurality of customers to the particular grouping of the plurality of groupings based on a corresponding data warehouse assignment for each of the plurality of customers.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 7, 2024
From: LACEWORK, INC.
To: FORTINET, INC.
Reel/Frame 069301/0327 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 30, 2023
From: SUBRAMANIAN, SATHISH KUMAR; NANJAPPA, NANDISH; CHEN, YIJOU
To: LACEWORK, INC.
Reel/Frame 062536/0989 →
Continuity (6)
Continuation In Part 17504311 · Oct 18, 2021
Continuation 16665961 · Oct 28, 2019
Continuation 16134794 · Sep 18, 2018
Provisional Application 63351532 · Jun 13, 2022
Provisional Application 62650971 · Mar 30, 2018
Provisional Application 62590986 · Nov 27, 2017
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