Adaptable pages, widgets and features based on real time application performance

A computer program product comprising:

• one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
• program instructions to present a user interface to a user on a computer system, the user interface including at least one widget that is activated to execute a task requiring an allocation of resources of a server to perform a function;
• program instructions to receive performance data associated with tasks performed in response to the at least one activated widget in the user interface;
• program instructions to run a machine learning model for predicting, based on said received performance data, a future potential performance degradation issue associated with the user interface;
• program instructions to, in response to predicting a future performance issue associated with the user interface, responsively modify or disable the activated widget in the user interface, and
• program instructions to detect an occurrence of a performance degradation issue in the computer system, and responsive to detecting an occurrence of a performance degradation issue in the computer system, evaluate data comprising current executing tasks that are involved or affected by the detected performance degradation and a performance weight of a current executing task, the performance weight of the current executing task computed as a sum of one or more performance weights of one or more activated widgets corresponding to the current executing task, and said program instructions further cause the computer system to:
• receive, as input to the machine learning model, said evaluated data;
• and
• train, using said evaluated data and activities associated with a usage of widgets activated in the user interface by different users performing a variety of tasks, to learn which usage of activated widgets successfully accomplish each task and which usage of activated widgets result in a performance degradation issue.

A system comprising at least one processor comprising hardware, the at least one processor configured to:

• present a user interface to a user on a computer system, the user interface including at least one widget that is activated to execute a task requiring an allocation of resources of a server to perform a function;
• receive performance data associated with tasks performed in response to the at least one widget activated in the user interface;
• run a machine learning model for predicting, based on said received performance data, a future potential performance degradation issue associated with the user interface;
• and
• in response to predicting a future performance issue associated with the user interface, responsively modifying or disabling the activated widget in the user interface;
• and
• responsive to detecting an occurrence of a performance degradation issue in the computer system, evaluating data comprising current executing tasks that are involved or affected by the detected performance degradation and a performance weight of a current executing task, the performance weight of the current executing task computed as a sum of one or more performance weights of one or more activated widgets corresponding to the current executing task, and said at least one processor further configured to:
• receive, as input to the machine learning model, said evaluated data;
• train, using said evaluated data and activities associated with a usage of widgets activated in the user interface by different users performing a variety of tasks, to learn which usage of activated widgets successfully accomplish each task and which usage of activated widgets result in a performance degradation issue.