Hypothetical configuration analysis
An embodiment for dynamically tuning system configuration settings across multiple systems using hypothetical configuration analysis. The embodiment may gather input data for a target system, the input data including configuration settings data, a series of configuration setting parameters, and telemetry data. The embodiment may generate, from the input data, a machine learning model configured to process network data and the configuration settings data from the target system. The embodiment may determine dependencies between a given parameter from the series of configuration setting parameters and a given resource from a series of resources using the generated machine learning model. The embodiment may further predict, using the generated machine learning model, and based on the determined dependencies, performance outcomes under a tuneable range of the series of configuration setting parameters. The embodiment may generalize the generated machine learning model across a plurality of secondary systems.
1 . A computer-based method for dynamically tuning system configuration settings across multiple systems using hypothetical configuration analysis, the method comprising:
gathering input data for a target system, the input data including configuration settings data, a series of configuration setting parameters, and telemetry data;
leveraging analysis of variance (ANOVA) techniques to determine if the telemetry data may be drawn from a same population;
generating, from the input data, a machine learning model configured to process network data and the configuration settings data from the target system;
determining dependencies between a given parameter from the series of configuration setting parameters and a given resource from a series of resources using the generated machine learning model;
predicting, using the generated machine learning model, and based on the determined dependencies, performance outcomes under a tuneable range of the series of configuration setting parameters; and
generalizing the generated machine learning model across a plurality of secondary systems.
2 . The computer-based method of claim 1 , further comprising:
performing entity analysis on the gathered input data to determine a proportion of setting types.
3 . The computer-based method of claim 1 , further comprising:
storing the chat history, using a chatbot backend server, within a storage component.
4 . The computer-based method of claim 1 , further comprising:
performing statistical computations on the gathered input data to determine preliminary dependencies between the configuration setting parameters on the telemetry data.
5 . The computer-based method of claim 1 , wherein determining dependencies between a given parameter from the series of configuration setting parameters and a given resource from a series of resources using the generated machine learning model further comprises:
calculating a score reflecting the dependency between the given parameter and the given resource; and
normalizing the calculated score.
6 . The computer-based method of claim 1 , further comprising:
outputting the predicted performance outcomes to a user via a user interface.
7 . A computer system, the computer system comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:
gathering input data for a target system, the input data including configuration settings data, a series of configuration setting parameters, and telemetry data;
leveraging analysis of variance (ANOVA) techniques to determine if the telemetry data may be drawn from a same population;
generating, from the input data, a machine learning model configured to process network data and the configuration settings data from the target system;
determining dependencies between a given parameter from the series of configuration setting parameters and a given resource from a series of resources using the generated machine learning model;
predicting, using the generated machine learning model, and based on the determined dependencies, performance outcomes under a tuneable range of the series of configuration setting parameters; and
generalizing the machine learning model across a plurality of secondary systems.
8 . The computer system of claim 7 , performing entity analysis on the gathered input data to determine a proportion of setting types.
9 . The computer system of claim 7 , further comprising:
storing the chat history, using a chatbot backend server, within a storage component.
10 . The computer system of claim 7 , further comprising:
performing statistical computations on the gathered input data to determine preliminary dependencies between the configuration setting parameters on the telemetry data.
11 . The computer system of claim 7 , wherein determining dependencies between a given parameter from the series of configuration setting parameters and a given resource from a series of resources using the generated machine learning model further comprises:
calculating a score reflecting the dependency between the given parameter and the given resource; and
normalizing the calculated score.
12 . The computer system of claim 7 , further comprising:
outputting the predicted performance outcomes to a user via a user interface.
13 . A computer program product, the computer program product comprising:
one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, the program instructions executable by a processor capable of performing a method, the method comprising:
gathering input data for a target system, the input data including configuration settings data, a series of configuration setting parameters, and telemetry data;
leveraging analysis of variance (ANOVA) techniques to determine if the telemetry data may be drawn from a same population;
generating, from the input data, a machine learning model configured to process network data and the configuration settings data from the target system;
determining dependencies between a given parameter from the series of configuration setting parameters and a given resource from a series of resources using the generated machine learning model;
predicting, using the generated machine learning model, and based on the determined dependencies, performance outcomes under a tuneable range of the series of configuration setting parameters; and
generalizing the machine learning model across a plurality of secondary systems.
14 . The computer program product of claim 13 , performing entity analysis on the gathered input data to determine a proportion of setting types.
15 . The computer program product of claim 13 , further comprising:
storing the chat history, using a chatbot backend server, within a storage component.
16 . The computer program product of claim 13 , further comprising:
performing statistical computations on the gathered input data to determine preliminary dependencies between the configuration setting parameters on the telemetry data.
17 . The computer program product of claim 16 , wherein determining dependencies between a given parameter from the series of configuration setting parameters and a given resource from a series of resources using the generated machine learning model further comprises:
calculating a score reflecting the dependency between the given parameter and the given resource; and
normalizing the calculated score.