Automated mainframe database maintenance

Patent metadata

Field	Value
Publication number	US-2023273908-A1
Application number	US-202217680558-A
Country	US
Kind code	A1
Filing date	Feb 25, 2022
Priority date	Feb 25, 2022
Publication date	Aug 31, 2023
Grant date	—

Abstract

Systems and methods for automated mainframe database maintenance are provided. In implementations, a method includes obtaining, by a computing device, real-time performance metrics of a mainframe database; automatically generating, by the computing device, a predicted maintenance task as an output of a trained database maintenance task classification machine learning (ML) model based on an input of the real-time performance metrics; automatically generating, by the computing device, a time to execute the predicted maintenance task as an output of a trained database maintenance triggering ML model based on an input of the predicted maintenance task and the real-time performance metrics; automatically generating, by the computing device, maintenance task instructions for the mainframe database based on the predicted maintenance task, the time to execute the predicted maintenance task, and a maintenance profile of the mainframe database; and automatically initiating, by the computing device, the execution of the maintenance task instructions.

First claim

What is claimed is:

1 . A method, comprising:
obtaining, by a computing device, real-time performance metrics of a mainframe database; automatically generating, by the computing device, a predicted maintenance task as an output of a trained database maintenance task classification machine learning (ML) model based on an input of the real-time performance metrics; automatically generating, by the computing device, a time to execute the predicted maintenance task as an output of a trained database maintenance triggering ML model based on an input of the predicted maintenance task and the real-time performance metrics; automatically generating, by the computing device, maintenance task instructions for the mainframe database based on the predicted maintenance task, the time to execute the predicted maintenance task, and a maintenance profile of the mainframe database; and automatically initiating, by the computing device, the execution of the maintenance task instructions by the mainframe database.

2 . The method of claim 1 , wherein:
the database maintenance task classification ML model is trained by correlating historic performance related metrics of a time series dataset with a historic maintenance data profile of the time series dataset utilizing a first classification machine learning technique; and the database maintenance triggering ML model is trained with the historic performance related metrics of the time series dataset and the historic maintenance data profile of the time series dataset utilizing a first regression machine learning technique.

3 . The method of claim 2 , wherein, the historic performance related metrics include the following three sets of time-series historical variables: variables related to a mainframe performance, variables related to an organizational database aspect, and variables related to access plans.

4 . The method of claim 3 , wherein:
the variables related to a mainframe performance include: average transaction response time, central processing unit (CPU) usage, and number of transactions considered in a predefined time period; the variables related to an organizational database aspect include: table space availability, index space availability, and table fragmentation rate; and the variables related to access plans include: table size, table type, number of indexes, types of indexes, and number of columns.

5 . The method of claim 1 , further comprising:
automatically generating, by the computing device, an indication of an anomalous behavior as an output of a trained database monitor anomaly detection ML model based on an input of the real-time performance metrics, wherein the database monitor anomaly detection ML model is trained with the historic performance related metrics of the time series dataset utilizing a second regression machine learning technique; and automatically sending, by the computing device, an alert indicating the anomalous behavior to a remote client device via a network connection.

6 . The method of claim 1 , further comprising;
automatically generating, by the computing device, a probability value of an imminent failure as an output of a trained database maintenance failure profile ML model based on an input of the real-time performance metrics, wherein the database maintenance failure profile ML model is trained by correlating the historic performance related metrics of the time series dataset with a historic failure data profile of the time series dataset utilizing a second classification ML technique; automatically determining, by the computing device, an imminent failure in response to determining that the probability value is greater than a predetermined failure threshold value; automatically generating, by the computing device, a suggested recovery profile as an output of a trained database maintenance failure profile ML model based on an input of the imminent failure, wherein the database maintenance failure profile ML model is trained by correlating the historic performance related metrics of the time series dataset with a historic recovery profile of the time series data; and automatically sending, by the computing device, the suggested recovery profile to a remote client device of a user via a network connection.

7 . The method of claim 1 , further comprising performing the training of the database maintenance task classification ML model and the database maintenance triggering ML model.

8 . The method of claim 1 , further comprising automatically generating, by the computing device, the time series dataset based on catalog data from a mainframe catalog of the mainframe database.

9 . The method of claim 1 , wherein the maintenance task instructions are executed without a requirement to restart the mainframe database.

10 . A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
obtain real-time performance metrics of a mainframe database; automatically generate a predicted maintenance task as an output of a trained database maintenance task classification machine learning (ML) model based on an input of the real-time performance metrics; automatically generate a time to execute the predicted maintenance task as an output of a trained database maintenance triggering ML model based on an input of the predicted maintenance task and the real-time performance metrics; automatically generate maintenance task instructions for the mainframe database based on the predicted maintenance task, the time to execute the predicted maintenance task, and a maintenance profile of the mainframe database; and automatically initiate the execution of the maintenance task instructions by the mainframe database.

11 . The computer program product of claim 10 , wherein:
the database maintenance task classification ML model is trained by correlating historic performance related metrics of a time series dataset with a historic maintenance data profile of the time series dataset utilizing a first classification machine learning technique; and the database maintenance triggering ML model is trained with the historic performance related metrics of the time series dataset and the historic maintenance data profile of the time series dataset utilizing a first regression machine learning technique.

12 . The computer program product of claim 11 , wherein the historic performance related metrics include the following three sets of time-series historical variables: variables related to a mainframe performance, variables related to an organizational database aspect, and variables related to access plans.

13 . The computer program product of claim 12 , wherein:
the variables related to a mainframe performance include are selected from one or more of the group consisting of: average transaction response time, central processing unit (CPU) usage, and number of transactions considered in a predefined time period; the variables related to an organizational database aspect are selected from one or more of the group consisting of: table space availability, index space availability, and table fragmentation rate; and the variables related to access plans are selected from one or more of the group consisting of: table size, table type, number of indexes, types of indexes, and number of columns.

14 . The computer program product of claim 10 , wherein the program instructions are further executable to:
automatically generate an indication of an anomalous behavior as an output of a trained database mo

Assignees

• Kyndryl Inc

Inventors

• Souza Vaz Marcelo De
• Eugenio Fernandes Marcos
• Bianchi Thiago
• Weingart Adriana Melges Quintanilha

Classifications

• G06F16/217 Primary

  Database tuning (G06F16/2282 takes precedence; database performance monitoring G06F11/3409) · CPC title

• G06F2201/80

  Database-specific techniques · CPC title

• G06F11/3409

  for performance assessment · CPC title

• G06F11/004

  Error avoidance (G06F11/07 and subgroups take precedence) · CPC title

• G06N3/08

  Learning methods · CPC title