Granted Patent B2

US 12,626,182 · App. 17/223,445 · Granted May 12, 2026

Inventors: Priyajith Chembakassery (Bangalore, IN); Dinup Padmanabha Pillai (Vignana Nagar, IN)

Assignee: International Business Machines Corporation

G06N20/00G06F18/211G06F18/214H04L67/51

Abstract

Claims (42)

1 . A computer-implemented method, the method comprising:

receiving, by a processor, microservice data regarding two or more microservices, wherein said microservice data includes service dependency data and microservice use data;

determining a utilization context associated with each the two or more microservices, wherein the utilization context includes a time period of utilization of each of the two or more microservices, resource needs associated with the utilization of each of the two or more microservices, a contextual background associated with the utilization of each of the two or more microservices, and a pattern of performance associated with the utilization of each of the two or more microservices;

analyzing, using an AI model, the microservice data and utilization context to determine features of the two or more microservices relating to utilization contexts of the two or more microservices;

combining a subset of the two or more microservices as a microservice bundle based on the features and a classification of the AI model, wherein the microservice bundle includes two or more microservices configured to share microservice resources, comprising:

responsive to a roll out of a change for a first microservice, determining a second microservice that will become nonoperational based on the roll out of change for the first microservice and an impact of the roll out of change on shared resources; and

unbundling the first microservice and the second microservice, wherein the first microservice and second microservice have independent resources; and allocating resources to the microservice bundle based on the combined subset of the two or more microservices.

2 . The method of claim 1 , wherein the classification of the two or more microservices as a bundle includes a time component.

3 . The method of claim 1 , wherein the AI model is trained using data regarding traffic patterns between the two or more microservices.

4 . The method of claim 1 , wherein the AI model is trained using data regarding component interactions of microservices.

5 . The method of claim 1 , wherein the AI model is trained using data regarding resource utilization of microservices.

6 . The method of claim 1 , wherein the AI model is trained using data regarding criticality of microservices.

7 . The method of claim 1 , wherein the AI model is trained using data regarding a roll out of a change in a microservice.

8 . A system comprising:

a memory; and

a processor in communication with the memory, the processor being configured to perform operations comprising:

receiving microservice data regarding two or more microservices, wherein said microservice data includes service dependency data and microservice use data;

determining a utilization context associated with each the two or more microservices, wherein the utilization context includes a time period of utilization of each of the two or more microservices, resource needs associated with the utilization of each of the two or more microservices, a contextual background associated with the utilization of each of the two or more microservices, and a pattern of performance associated with the utilization of each of the two or more microservices;

analyzing, using an AI model, the microservice data and utilization context to determine features of the two or more microservices relating to utilization contexts of the two or more microservices;

combining a subset of the two or more microservices as a microservice bundle based on the features and a classification of the AI model, wherein the microservice bundle includes two or more microservices configured to share microservice resources, comprising:

responsive to a roll out of a change for a first microservice, determining a second microservice that will become nonoperational based on the roll out of change for the first microservice and an impact of the roll out of change on shared resources; and

unbundling the first microservice and the second microservice, wherein the first microservice and second microservice have independent resources; and

allocating resources to the microservice bundle based on the combined subset of the two or more microservices.

9 . The system of claim 8 , wherein the classification of the two or more microservices as a bundle includes a time component.

10 . The system of claim 8 , wherein the AI model is trained using data regarding traffic patterns between the two or more microservices.

11 . The system of claim 8 , wherein the AI model is trained using data regarding component interactions of microservices.

12 . The system of claim 8 , wherein the AI model is trained using data regarding resource utilization of microservices.

13 . The system of claim 8 , wherein the AI model is trained using data regarding criticality of microservices.

14 . The system of claim 8 , wherein the AI model is trained using data regarding a roll out of a change in a microservice.

15 . A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations, the operations comprising:

receiving microservice data regarding two or more microservices, wherein said microservice data includes service dependency data and microservice use data;

determining a utilization context associated with each the two or more microservices, wherein the utilization context includes a time period of utilization of each of the two or more microservices, resource needs associated with the utilization of each of the two or more microservices, a contextual background associated with the utilization of each of the two or more microservices, and a pattern of performance associated with the utilization of each of the two or more microservices;

analyzing, using an AI model, the microservice data and utilization context to determine features of the two or more microservices relating to utilization contexts of the two or more microservices;

combining a subset of the two or more microservices as a microservice bundle based on the features and a classification of the AI model, wherein the microservice bundle includes two or more microservices configured to share microservice resources, comprising:

responsive to a roll out of a change for a first microservice, determining a second microservice that will become nonoperational based on the roll out of change for the first microservice and an impact of the roll out of change on shared resources; and

unbundling the first microservice and the second microservice, wherein the first microservice and second microservice have independent resources; and

allocating resources to the microservice bundle based on the combined subset of the two or more microservices.

16 . The computer program product of claim 15 , wherein the classification of the two or more microservices as a bundle includes a time component.

17 . The computer program product of claim 15 , wherein the AI model is trained using data regarding traffic patterns between the two or more microservices.

18 . The computer program product of claim 15 , wherein the AI model is trained using data regarding component interactions of microservices.

19 . The computer program product of claim 15 , wherein the AI model is trained using data regarding resource utilization of microservices.

20 . The computer program product of claim 15 , wherein the AI model is trained using data regarding criticality of microservices.

Assignments (1)

Continuity (1)

References Cited (11)