Intelligent issue identifier for auto-detecting dynamic issue changes in a distributed network

A method comprising:

• detecting an application issue associated with an application running at a network node at a first timestamp;
• receiving a first set of data objects associated with the application issue occurring at the first timestamp;
• detecting the application issue associated with the application running at the network node at a second timestamp;
• receiving a second set of data objects associated with the application issue occurring at the second timestamp;
• determining one or more operation changes between a first set of the data objects and a second set of data objects;
• identifying, by a machine learning model and based on the one or more operation changes between the first set of the data objects and the second set of data objects, a cluster of issue patterns represents the one or more operation changes of the application which occurs between the first timestamp and the second timestamp, wherein each issue pattern in the cluster is associated with a unique strategic solution with a series of executable operations, wherein the machine learning model is trained based on a plurality of sets of previous data objects and corresponding issue patterns associated with the corresponding previous application issues, and wherein the plurality of sets of previous data objects comprises a plurality of operation changes associated with the previous application issues;
• processing, through a neural network, the issue pattern with application information to determine a series of executable operations associated with the application issue, wherein the series of the executable operations is indicative of a solution of the application issue and corresponds to the operation change of the operation status of the application;
• and
• deploying the series of the executable operations to the application running at the network node to solve the application issue to prevent a failure operation of the application.

A non-transitory computer-readable medium storing instructions that when executed by a processor cause the processor to:

• detect an application issue associated with an application running at a network node at a first timestamp;
• receive a first set of data objects associated with the application issue occurring at the first timestamp;
• detect the application issue associated with the application running at the network node at a second timestamp;
• receive a second set of data objects associated with the application issue occurring at the second timestamp;
• determine one or more operation changes between a first set of the data objects and a second set of data objects;
• identify, by a machine learning model and based on the one or more operation changes between the first set of the data objects and the second set of data objects, a cluster of issue patterns representing one or more operation changes of the application which occurs between the first timestamp and the second timestamp, wherein each issue pattern in the cluster is associated with a unique strategic solution with a series of executable operations, wherein the machine learning model is trained based on a plurality of sets of previous data objects and corresponding issue patterns associated with the corresponding previous application issues, and wherein the plurality of sets of previous data objects comprises a plurality of operation changes associated with the previous application issues;
• process, through a neural network, the issue pattern with application information to determine a series of executable operations associated with the application issue, wherein the series of the executable operations is indicative of a solution of the application issue and corresponds to the operation change of a operation status of the application;
• and
• deploy the series of the executable operations to the application running at the network node to solve the application issue to prevent a failure operation of the application.