Abstract: In general, techniques are described for providing network device modeling in preconfigured network modeling environments. A device comprising a memory and a processor may be configured to perform the techniques. The processor may interface with a network device within the preconfigured network environment to iteratively adapt pre-defined configuration objects of the network device. The processor may conduct, for each iteration of the adaptation of the pre-defined configuration objects, a simulation to collect a simulation dataset representative of an operating state of the network device. The processor may generate, based on the operational data, a model representative of the network device that predicts, responsive to configuration parameters for the network device, an operating state of the network device. The memory may store the model.

Abstract: In general, techniques are described for automated network device model creation using randomized test beds. A device comprising a processor may be configured to perform the techniques. The processor may generate, based on simulation configuration files, configuration objects for performing a plurality of simulation iterations with respect to the network device operating within a test environment. Each of the simulation iterations may be configured to randomly assign parameters within the test environment. The driver may conduct, based on the configuration objects, each of the simulation iterations within the test environment to collect simulation datasets representative of operating states of the network device. The analytics module may perform machine learning with respect to each of the simulation datasets to generate a model that predicts, responsive to configuration parameters, an operating state of the network device when configured with the configuration parameters for the network device.

Abstract: In general, techniques are described for providing diversity in simulation datasets during modeling. A device comprising a memory and a processor may be configured to perform the techniques. The memory may store simulation configuration files for conducting simulations of the network device within a test environment. The processor may conduct, based on the simulation configuration files, each of the simulations with respect to the network device to collect corresponding simulation datasets indicative of an operating state of the network device. The processor may determine a level of similarity between the simulation datasets, and select, responsive to a comparison of the level of similarity to a diversity threshold, a subset of the simulation datasets. The processor may generate, based on the selected subset of the simulation datasets, a model representative of the network device that predicts, responsive to configuration parameters for the network device, an operating state of the network device.

Abstract: In general, techniques are described for providing diversity in simulation datasets during modeling. A device comprising a memory and a processor may be configured to perform the techniques. The memory may store simulation configuration files for conducting simulations of the network device within a test environment. The processor may conduct, based on the simulation configuration files, each of the simulations with respect to the network device to collect corresponding simulation datasets indicative of an operating state of the network device. The processor may determine a level of similarity between the simulation datasets, and select, responsive to a comparison of the level of similarity to a diversity threshold, a subset of the simulation datasets. The processor may generate, based on the selected subset of the simulation datasets, a model representative of the network device that predicts, responsive to configuration parameters for the network device, an operating state of the network device.

Abstract: In general, techniques are described for providing diversity in simulation datasets during modeling. A device comprising a memory and a processor may be configured to perform the techniques. The memory may store simulation configuration files for conducting simulations of the network device within a test environment. The processor may conduct, based on the simulation configuration files, each of the simulations with respect to the network device to collect corresponding simulation datasets indicative of an operating state of the network device. The processor may determine a level of similarity between the simulation datasets, and select, responsive to a comparison of the level of similarity to a diversity threshold, a subset of the simulation datasets. The processor may generate, based on the selected subset of the simulation datasets, a model representative of the network device that predicts, responsive to configuration parameters for the network device, an operating state of the network device.

Abstract: In general, techniques are described for providing network device modeling in preconfigured network modeling environments. A device comprising a memory and a processor may be configured to perform the techniques. The processor may interface with a network device within the preconfigured network environment to iteratively adapt pre-defined configuration objects of the network device. The processor may conduct, for each iteration of the adaptation of the pre-defined configuration objects, a simulation to collect a simulation dataset representative of an operating state of the network device. The processor may generate, based on the operational data, a model representative of the network device that predicts, responsive to configuration parameters for the network device, an operating state of the network device. The memory may store the model.

Abstract: In general, techniques are described for automated network device model creation using randomized test beds. A device comprising a processor may be configured to perform the techniques. The processor may generate, based on simulation configuration files, configuration objects for performing a plurality of simulation iterations with respect to the network device operating within a test environment. Each of the simulation iterations may be configured to randomly assign parameters within the test environment. The driver may conduct, based on the configuration objects, each of the simulation iterations within the test environment to collect simulation datasets representative of operating states of the network device. The analytics module may perform machine learning with respect to each of the simulation datasets to generate a model that predicts, responsive to configuration parameters, an operating state of the network device when configured with the configuration parameters for the network device.

Abstract: In general, techniques are described for providing diversity in simulation datasets during modeling. A device comprising a memory and a processor may be configured to perform the techniques. The memory may store simulation configuration files for conducting simulations of the network device within a test environment. The processor may conduct, based on the simulation configuration files, each of the simulations with respect to the network device to collect corresponding simulation datasets indicative of an operating state of the network device. The processor may determine a level of similarity between the simulation datasets, and select, responsive to a comparison of the level of similarity to a diversity threshold, a subset of the simulation datasets. The processor may generate, based on the selected subset of the simulation datasets, a model representative of the network device that predicts, responsive to configuration parameters for the network device, an operating state of the network device.