Integrated development environment for development and continuous delivery of cloud-based applications

Patent metadata

Field	Value
Publication number	US-12170597-B2
Application number	US-202217961014-A
Country	US
Kind code	B2
Filing date	Oct 6, 2022
Priority date	Oct 8, 2021
Publication date	Dec 17, 2024
Grant date	Dec 17, 2024

Abstract

A system implements a cloud-based digital platform allows developers to build new applications/services and then deploy to cloud platforms among continuous deployment, A/B test, blue/green deployment, and canary deployment. The system configures a service mesh on top of a cluster of computers. The system initializes a new service via templates that include common libraries, security scan pipeline, monitoring as code pipeline, and code coverage management for internal policy compliances, as well automated cloud resources request and provisioning. One or more proxy services, that extract data from the data sources using filters, can be executed. The system may use machine learning based models that are trained using the data extracted by the proxy service. The system allows automatic provisioning, computation orchestration, storage requests, and artificial intelligence insight feedback, as well as automated self-services to navigate complex systems and reduce on-boarding times of the platform.

First claim

What is claimed is:

1. A computer-implemented method for maintaining services on a cloud platform, the method comprising:
configuring a service mesh, said service mesh comprising a plurality of microservices on a cloud platform, the plurality of microservices receiving data from a respective set of one of more data sources (collectively, “plurality of data sources”);
initializing a new service for the service mesh based on a machine learning based model, the initializing comprising, initializing parameters of the machine learning based model;
executing one or more proxy services on the cloud platform, each proxy service configured to:
extract feature data from the plurality of data sources using one or more filters, each filter being specific to said plurality of microservices and configured to extract a respective subset of data from the respective set of one or more of data sources for the respective one of the plurality microservices;
provide the extracted feature data for training the machine learning based model; and
train the machine learning based model based on the extracted feature data;

configuring the new service based on the trained machine learning based model, wherein the new service generates derived data based on execution of the trained machine learning based model; and
extending the service mesh by including the configured new service in the service mesh such that the configured new service is one of the plurality of microservices and including the derived data in a data source of the plurality of data sources.

2. The computer-implemented method of claim 1 , wherein the machine learning based model is configured to predict a score indicating a measure of expected load on a system associated with the service mesh.

3. The computer-implemented method of claim 2 , wherein the feature data comprises a feature representing a measure of interactions with a service.

4. The computer-implemented method of claim 2 , wherein the feature data comprises a feature representing a measure of interactions with a group of services.

5. The computer-implemented method of claim 2 , wherein the feature data comprises a first feature representing a first measure of interactions with a service and a second feature representing a second measure of interactions with a group of services.

6. The computer-implemented method of claim 2 , further comprising:
sending an alert responsive to predicting a change in load on the system exceeding a threshold value.

7. The computer-implemented method of claim 2 , further comprising:
sending instructions to the cloud platform to reconfigure computing resources associated with the system responsive to predicting a change in load on the system exceeding a threshold value.

8. The computer-implemented method of claim 2 , further comprising:
sending instructions to the cloud platform to increase computing resources associated with the system responsive to predicting an increase in load on the system exceeding a threshold value.

9. The computer-implemented method of claim 2 , further comprising:
sending instructions to the cloud platform to decrease computing resources associated with the system responsive to predicting a decrease in load on the system exceeding a threshold value.

10. The computer-implemented method of claim 1 , wherein a service from the plurality of microservices of the service mesh acts as a data source from the plurality of data sources.

11. A non-transitory computer readable storage medium storing instructions that when executed by a computer processor cause the computer processor to perform steps comprising:
configuring a service mesh, said service mesh comprising a plurality of microservices on a cloud platform, the plurality of microservices receiving data from a respective set of one or more data sources (collectively, a “plurality of data sources”);
initializing a new service for the service mesh based on a machine learning based model, the initializing comprising, initializing parameters of the machine learning based model;
executing one or more proxy services on the cloud platform, each proxy service configured to:
extract feature data from the plurality of data sources using one or more filters, each filter being specific to said plurality of microservices and configured to extract a respective subset of data from the respective set of one or more plurality of data sources for the respective one of the plurality of microservices;
provide the extracted feature data for training the machine learning based model; and
train the machine learning based model based on the extracted feature data;

configuring the new service based on the trained machine learning based model, wherein the new service generates derived data based on execution of the trained machine learning based model; and
extending the service mesh by including the configured new service in the service mesh such that the configured new service is one of the plurality of microservices and including the derived data in a data source of the plurality of data sources.

12. The non-transitory computer readable storage medium of claim 11 , wherein the machine learning based model is configured to predict a score indicating a measure of expected load on a system associated with the service mesh.

13. The non-transitory computer readable storage medium of claim 12 , wherein the feature data comprises a feature representing a measure of interactions with a service.

14. The non-transitory computer readable storage medium of claim 12 , wherein the feature data comprises a feature representing a measure of interactions with a group of services.

15. The non-transitory computer readable storage medium of claim 12 , wherein the feature data comprises a first feature representing a first measure of interactions with a service and a second feature representing a second measure of interactions with a group of services.

16. The non-transitory computer readable storage medium of claim 12 , further comprising:
sending an alert responsive to predicting a change in load on the system exceeding a threshold value.

17. The non-transitory computer readable storage medium of claim 12 , further comprising:
sending instructions to the cloud platform to reconfigure computing resources associated with the system responsive to predicting a change in load on the system exceeding a threshold value.

18. The non-transitory computer readable storage medium of claim 12 , further comprising:
sending instructions to the cloud platform to increase computing resources associated with the system responsive to predicting an increase in load on the system exceeding a threshold value.

19. The non-transitory computer readable storage medium of claim 12 , further comprising:
sending instructions to the cloud platform to decrease computing resources associated with the system responsive to predicting a decrease in load on the system exceeding a threshold value.

20. A computer system comprising:
a computer processor; and
a non-transitory computer readable storage medium storing instructions that when executed by a computer processor cause the computer processor to perform steps comprising:
configuring a service mesh, said service mesh comprising a plurality of microservices on a cloud platform, the plurality of microservices receiving data from a respective set of one or more data sources (collectively, a “plurality of data sources”);
initializing a new service for the service mesh based on a machine learning based model, th

Assignees

• Humana Inc

Inventors

• Ho Tom
• Wang Guan
• Nakka Subramanyam

Classifications

• G06F8/35

  model driven · CPC title

• H04L63/145

  the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms · CPC title

• H04L67/1004

  Server selection for load balancing · CPC title

• G06F9/547

  Remote procedure calls [RPC]; Web services · CPC title

• H04L47/781

  Centralised allocation of resources · CPC title