IP Library › Granted Patent US 12,487,800
Granted Patent B2
US 12,487,800 · App. 18/340,817 · Granted Dec 2, 2025

Rebuilding container event logic from secondary and tertiary systems

Inventors: Nadiya Kochura (Bolton, MA); Jonathan D. Dunne (Dungarvan, IE); Fang Lu (Billerica, MA)
Assignee: International Business Machines Corporation
G06F8/433G06F8/35
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,487,800
App. No.
18/340,817
Granted
Dec 2, 2025
Kind
B2
Abstract

A method for tuning software container interdependent systems includes receiving, by a computer processor operating a prediction engine, data from one or more software containers. The data includes operations from one or more software applications in the software containers. The prediction engine identifies a plurality of dependencies in the operations. The prediction engine develops a model of interdependencies based on the identified dependencies. The prediction engine determines one or more failure candidates in the identified dependencies. The prediction engine generates a software architecture template based on the identification of failure candidates.

Claims (35)

1 . A computer implemented method for tuning software container interdependent systems, comprising:

receiving, by a computer processor operating a prediction engine, data from one or more software containers, wherein the data includes operations from one or more software applications in the software containers;

wherein the program instructions further comprise identifying, by the prediction engine, one or more of a plurality of dependencies that occur between different software containers, wherein a probability of dependency value is assigned to one or more of the identified plurality of dependencies that occur between different software containers, and identifying, by the prediction engine, a plurality of dependencies in the operations;

developing, by the prediction engine, a model of interdependencies based on the identified plurality of dependencies;

determining, by the prediction engine, one or more failure candidates in the identified plurality of dependencies;

performing, by the prediction engine, a statistical analysis of source code and the one or more software applications;

logging a plurality of operation associated events from the statistical analysis, wherein the identified one or more of plurality of dependencies are identified from the logged operation associated events; and

generating, by the prediction engine, a software architecture template operative to be used as a reference for successful deployment of a software based on the one or more failure candidates.

2 . The method of claim 1 , further comprising determining a probability of failure for one or more of the logged operations associated events, wherein the one or more failure candidates are determined based on the probability of failure.

3 . The method of claim 1 , further comprising identifying, by the prediction engine, common factors in the plurality of dependencies, wherein the software architecture template is generated based on the common factors identified.

4 . The method of claim 1 , further comprising identifying, by the prediction engine, anti-patterns in the operations, wherein the software architecture template is generated based on the anti-patterns identified.

5 . A computing device configured to tune software container interdependent systems, comprising:

a computer processor operating a prediction engine; and

a memory coupled to the computer processor, the memory storing instructions to cause the computer processor to perform acts comprising:

receiving, by the computer processor operating the prediction engine, data from one or more software containers, wherein the data includes operations from one or more software applications in the software containers;

wherein the program instructions further comprise identifying, by the prediction engine, one or more of a plurality of dependencies that occur between different software containers, wherein a probability of dependency value is assigned to one or more of the identified plurality of dependencies that occur between different software containers, and identifying, by the prediction engine, a plurality of dependencies in the operations;

developing, by the prediction engine, a model of interdependencies based on the identified plurality of dependencies;

determining, by the prediction engine, one or more failure candidates in the identified plurality of dependencies;

performing, by the prediction engine, a statistical analysis of source code and the one or more software applications;

logging a plurality of operation associated events from the statistical analysis, wherein the identified one or more of plurality of dependencies are identified from the logged operation associated events; and

generating, by the prediction engine, a software architecture template operative to be used as a reference for successful deployment of a software based on the one or more failure candidates.

6 . The computing device of claim 5 , wherein the instructions cause the processor to perform further acts comprising determining a probability of failure for one or more of the logged operations associated events, wherein the one or more failure candidates are determined based on the probability of failure.

7 . The computing device of claim 5 , wherein the instructions cause the processor to perform further acts comprising identifying by the prediction engine, common factors in the plurality of dependencies, wherein the software architecture template is generated based on the common factors identified.

8 . A computer program product for tuning software container interdependent systems, the computer program product comprising:

one or more non-transitory computer readable storage media, and program instructions collectively stored on the one or more non-transitory computer readable storage media, the program instructions comprising:

receiving, by a computer processor operating a prediction engine, data from one or more software containers, wherein the data includes operations from one or more software applications in the software containers;

wherein the program instructions further comprise identifying, by the prediction engine, one or more of a plurality of dependencies that occur between different software containers, wherein a probability of dependency value is assigned to one or more of the identified plurality of dependencies that occur between different software containers, and identifying, by the prediction engine, a plurality of dependencies in the operations;

developing, by the prediction engine, a model of interdependencies based on the identified plurality of dependencies;

determining, by the prediction engine, one or more failure candidates in the identified plurality of dependencies;

performing, by the prediction engine, a statistical analysis of source code and the one or more software applications;

logging a plurality of operation associated events from the statistical analysis, wherein the identified one or more of plurality of dependencies are identified from the logged operation associated events; and

generating, by the prediction engine, a software architecture template operative to be used as a reference for successful deployment of a software based on the one or more failure candidates.

9 . The computer program product of claim 8 , wherein the program instructions further comprise determining a probability of failure for one or more of the logged operations associated events, wherein the one or more failure candidates are determined based on the probability of failure.

10 . The computer program product of claim 8 , wherein the program instructions further comprise identifying, by the prediction engine, common factors in the plurality of dependencies, wherein the software architecture template is generated based on the common factors identified.

11 . The computer program product of claim 8 , wherein the program instructions further comprise identifying, by the prediction engine, anti-patterns in the operations, wherein the software architecture template is generated based on the anti-patterns identified.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 23, 2023
From: KOCHURA, NADIYA; DUNNE, JONATHAN D.; LU, FANG
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 064048/0929 →
Continuity (1)
Related Publication 20240427578A1 · Dec 26, 2024
References Cited (14)
US 9588815B1 · Mistry · 2017 [cited by applicant]
US 11314614B2 · Porras · 2022 [cited by applicant]
US 11561850B1 · Banerjee · 2023 [cited by applicant]
US 11586482B2 · Che · 2023 [cited by examiner]
US 20170109536A1 · Stopel · 2017 [cited by applicant]
US 20200012551A1 · Liang · 2020 [cited by examiner]
US 20210211408A1 · Porras · 2021 [cited by examiner]
US 20230036739A1 · Deppisch · 2023 [cited by examiner]
US 20240193032A1 · Tang · 2024 [cited by examiner]
WO 2017101252A1 · 2017 [cited by applicant]
Disclosed Anonymously, “Systematic Problem Determination and Security Inspection for Microservices,” IPCOM000263997D, IP.com, Oct. 29, 2020, 4 pages. [cited by applicant]
“Configure Logging Drivers”, downloaded Mar. 28, 2023 from https://docs.docker.com/config/containers/logging/configure, 4 pgs. [cited by applicant]
Benitez, L. “Life and Death of a Container”, downloaded Mar. 28, 2023 from, https://medium.com/devopsion/life-and-death-of-a-container-146dfc62f808, 12 pgs. [cited by applicant]
Montgomery, M. et al., “Graph-Driven Dependency Tracking of Container and Package Relationships”, Cisco Systems, Inc., IPCOM000253343D, IP.com, Mar. 23, 2018, 6 pages. [cited by applicant]