Machine-learning model to predict probability of success of an operator in a paas cloud enviornment

Patent metadata

Field	Value
Publication number	US-2022156631-A1
Application number	US-202016950228-A
Country	US
Kind code	A1
Filing date	Nov 17, 2020
Priority date	Nov 17, 2020
Publication date	May 19, 2022
Grant date	—

Abstract

Systems and methods are provided that integrate a machine-learning model, and more specifically, utilizing a platform as a service (PaaS) cloud to predict probability of success for an operator in an environment. An embodiment comprises a system having: a processor that executes computer executable components stored in memory, trained machine-learning model that predicts probability of success for deployment of an operator in an environment with a namespace of a platform as a service (PaaS) cloud, and a deployment component that receives a first operator and a first namespace and employs the trained machine-learning model to predict success of deployment of the first operator in a first environment.

First claim

What is claimed is:

1 . A system, comprising:
a processor that executes computer executable components stored in memory; a machine-learning model that predicts probability of success for deployment of an operator in an environment with a namespace of a platform as a service (PaaS) cloud; and a deployment component that receives a first operator and a first namespace and employs the machine-learning model to predict success of deployment of the first operator in a first environment.

2 . The system of claim 1 , further comprising a training component that trains the machine-learning model with capabilities of the first operator and a set of configurations that apply to the first namespace.

3 . The system of claim 2 , wherein the training component employs as input a set of configurations that apply to the first namespace where the first operator is to be deployed.

4 . The system of claim 2 , wherein the training component employs an output associated with deployment of the first operator to other namespaces in the PaaS cloud.

5 . The system of claim 1 , wherein the trained machine-learning model learns from prior histories of operator deployments in a configured environment to predict success of a second operator deployment.

6 . The system of claim 1 , wherein the trained machine-learning model employs similarity learning to predict the success of deployment of the first operator in the first environment.

7 . The system of claim 6 , wherein the similarity learning is utilized to provide instructions regarding troubleshooting deployment of the first operator deployment in the first environment.

8 . The system of claim 6 , wherein the similarity learning is used to generate a flag regarding security issues associated with the first environment.

9 . The system of claim 1 , wherein information regarding a plurality of environments is utilized to provide operational functionality information for performing similarity learning analysis between the first environment and a second environment.

10 . A computer-implemented method that employs a processor and memory comprising:
predicting, using a machine-learning model, probability of success for deployment of an operator in an environment with a namespace of a platform as a service (PaaS) cloud; and receiving, using a deployment component, a first operator and a first namespace and employing the machine-learning model to predict success of deployment of the first operator in a first environment.

11 . The computer-implemented method of claim 10 , further comprising training, using a training component, the machine-learning model with capabilities of the first operator and a set of configurations that apply to the first namespace.

12 . The computer-implemented method of claim 11 , further comprising employing as input configurations that apply to the first namespace where the first operator is to be deployed.

13 . The computer-implemented method of claim 11 , further comprising employing an output associated with deployment of the first operator to other namespaces in the PaaS cloud.

14 . The computer-implemented method of claim 10 , further comprising learning from prior histories of operator deployments in a configured environment to predict success of a second operator deployment.

15 . The computer-implemented method of claim 10 , further comprising employing similarity learning to predict the success of deployment of the first operator in the first environment.

16 . The computer-implemented method of claim 15 , further comprising utilizing the similarity learning to provide instructions regarding troubleshooting deployment of the first operator deployment in the first environment.

17 . The computer-implemented method of claim 16 , further comprising utilizing the similarity learning to generate a flag regarding security issues associated with the first environment.

18 . The method of claim 10 , further comprising utilizing information regarding a plurality of environments to provide operational functionality information for performing similarity learning analysis between the first environment and a second environment.

19 . 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:
predict, using a machine-learning model, probability of success for deployment of an operator in an environment with a namespace of a platform as a service (PaaS) cloud; and receive, using a deployment component, a first operator and a first namespace and employ the machine-learning model to predict success of deployment of the first operator in a first environment.

20 . The computer program product of claim 19 , the program instructions executable by the processor to further cause the processor to train the machine-learning model with capabilities of the first operator and a set of configurations that apply to the first namespace.

Assignees

• IBM

Inventors

• Kanso Ali
• Hwang Jinho
• Bulut Muhammed Fatih
• Nadgowda Shripad
• Lin Chen

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06N3/02

  Neural networks · CPC title

• G06F8/60 Primary

  Software deployment · CPC title

• G06N7/005

  Physics · mapped topic