Systems and methods for routing data payloads through a plurality of microservices using machine learning

Patent metadata

Field	Value
Publication number	US-11893426-B2
Application number	US-202217651729-A
Country	US
Kind code	B2
Filing date	Feb 18, 2022
Priority date	Feb 18, 2022
Publication date	Feb 6, 2024
Grant date	Feb 6, 2024

Abstract

Methods and systems for routing data payloads through a plurality of microservices are disclosed. The method may comprise: receiving a data payload and first data; predicting, by a trained machine learning model based on the first data, a path through a plurality of microservices associated with the data payload; generating a modified data payload by modifying, via the orchestrator service, the data payload to include: a first header, wherein the first header comprises a first microservice destination address associated with a first microservice of the plurality of microservices and a second header nested within the first header, wherein the second header comprises a second microservice destination address associated with a second microservice of the plurality of microservices; forwarding the modified data payload to the first microservice based on the first header for processing; and forwarding the modified data payload to the second microservice based on the second header for processing.

First claim

What is claimed is:

1. A computer-implemented method for routing data payloads through a plurality of microservices, the method comprising:
receiving, by one or more processors via an orchestrator service, a data payload and first data that includes information regarding the data payload;
determining, by one or more processors via the orchestrator service, that one or more microservices of a plurality of microservices are required to process the data payload based on the information regarding the data payload and one or more tasks associated with the data payload that are performed by the one or more microservices of the plurality of microservices;
predicting, by a trained machine learning model executed by the one or more processors and based on the first data, a path through the one or more microservices associated with the data payload, wherein the trained machine learning model is trained based on: (i) second data that includes one or more prior data payloads and prior feedback data as test data, and (ii) third data that includes one or more prior microservices corresponding to the one or more prior data payloads, to learn associations between the test data and the corresponding prior feedback data for each of the one or more prior data payloads, such that the trained machine learning model is configured to determine a path through the one or more microservices associated with the data payload in response to input of the first data and the data payload;
generating a modified data payload by modifying, by the one or more processors via the orchestrator service, the data payload to include:
a first header, wherein the first header comprises a first microservice destination address associated with a first microservice of the one or more microservices; and
a second header nested within the first header, wherein the second header comprises a second microservice destination address associated with a second microservice of the one or more microservices;

forwarding, by the one or more processors via the orchestrator service, the modified data payload to the first microservice based on the first header, such that the modified data payload is processed by the first microservice; and
forwarding, by the one or more processors via the first microservice, the modified data payload output by the first microservice to the second microservice based on the second header, such that the modified data payload is processed by the second microservice.

2. The method of claim 1 , further comprising:
forwarding, by the one or more processors, the second processed modified data payload output by the second microservice to one of:
a database;
an output stream; or
an outbound API.

3. The method of claim 1 , wherein the processing by the first microservice comprises one or more of:
filtering data in the modified data payload;
inserting additional data into the modified data payload; or
generating a message associated with the modified data payload.

4. The method of claim 1 , wherein the modified data payload is encrypted.

5. The method of claim 4 , wherein the modified data payload is encrypted using one or more of: DES; Triple DES; AES; RSA; Twofish; Blowfish; Threefish; ElGamal; IDEA; RC6; Elliptic Curve Cryptography; or Diffie-Hellman.

6. The method of claim 1 , wherein the prior feedback data comprises one or more feedback scores for each of the one or more prior data payloads.

7. The method of claim 1 , further comprising:
receiving, by the one or more processors, a second data payload; and
generating a second modified data payload by modifying, by the one or more processors via the orchestrator service, the second data payload to include:
a second data payload header, wherein the second data payload header comprises the first microservice destination address associated with the first microservice of the plurality of microservices; and
an additional data payload header nested within the second data payload header, wherein the additional data payload header comprises a third microservice destination address associated with a third microservice.

8. The method of claim 1 , wherein the orchestrator service is one of a plurality of orchestrator services, the method further including:
selecting the orchestrator service of the plurality of orchestrator services based on the data payload.

9. The method of claim 1 , wherein the modified data payload further includes a third header nested within the second header, wherein the third header comprises a third microservice destination address associated with a third microservice.

10. The method of claim 9 , further comprising forwarding, by the one or more processors, the modified data payload to the third microservice based on the third header for processing by the third microservice.

11. A system for routing data through a plurality of micro services, the system comprising:
at least one memory storing instructions; and
at least one processor executing the instructions to perform a process including:
receiving, via an orchestrator service, a data payload;
receiving, via the orchestrator service, first data that includes information regarding the data payload;
predicting, by a trained machine learning model executed by the at least one processor and based on the first data, a path through the plurality of microservices associated with the data payload, wherein the trained machine learning model is trained based on: (i) second data that includes one or more prior data payloads and prior feedback data as test data, and (ii) third data that includes one or more prior micro services corresponding to the one or more prior data payloads, to learn associations between the test data and the corresponding prior feedback data for each of the one or more prior data payloads, such that the trained machine learning model is configured to determine a path through the one or more micro services associated with the data payload in response to input of the first data and the data payload;
generating a modified data payload by modifying, via the orchestrator service, the data payload to include:
a first header, wherein the first header comprises a first micro service destination address associated with a first micro service of the plurality of microservices; and
a second header nested within the first header, wherein the second header comprises a second microservice destination address associated with a second microservice of the plurality of microservices, to generate a modified data payload;
forwarding, via the orchestrator service, the modified data payload to the first microservice based on the first header, such that the modified data payload is processed by the first micro service; and

forwarding, via the first micro service, the modified data payload output by the first micro service to the second microservice based on the second header, such that the modified data payload is processed by the second microservice.

12. The system of claim 11 , wherein the process further includes:
forwarding, by the at least one processor, the modified data payload output by the second microservice to one of:
a database;
an output stream; or
an outbound API.

13. The system of claim 11 , wherein the processing by the first microservice comprises one or more of:
filtering data in the modified data payload;
inserting additional data into the modified data payload; or
generating a message associated with the modified data payload.

14. The system of claim 11 , wherein the modified data payload is encrypted using one or more of: DES; Triple DES; AES; RSA; Twofish; Blowfish; Threefish; ElGamal; IDEA; RC

Assignees

• Capital One Services Llc

Inventors

• Moreau-Arnott Elijah
• Lee Jonathan
• Randall Garrett

Classifications

• G06F9/54 Primary

  Interprogram communication · CPC title

• H04L9/0838

  Key agreement, i.e. key establishment technique in which a shared key is derived by parties as a function of information contributed by, or associated with, each of these (network architectures or network communication protocols for key exchange in a packet data network H04L63/061) · CPC title

• G06F9/5038 Primary

  considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration (scheduling strategies G06F9/4881 and subgroups) · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title