Dell Products Inc.

Nithish Kote

Parminder Singh Sethi

Thanuja C

Vinay Kumar Lokeshappa Malligere

Techniques for management of data security are disclosed. For example, a method comprises collecting data from one or more devices, and predicting security levels of respective portions of the data using one or more machine learning algorithms. In the method, security configurations for a subset of the respective portions of the data are implemented based, at least in part, on corresponding predicted security levels of the subset of the respective portions.

Prediction of security levels for incoming data

Techniques for failure prediction of controllers are disclosed. For example, a method comprises collecting data corresponding to operation of a plurality of controllers from one or more devices, and predicting, using one or more machine learning algorithms, at least one of degradation and failure of one or more controllers of the plurality of controllers based, at least in part, on the data corresponding to the operation of the plurality of controllers. Using the one or more machine learning algorithms, one or more corrective actions to prevent the at least one of the degradation and the failure of the one or more controllers are identified. Instructions comprising the one or more corrective actions are generated and transmitted to at least one user device.

Controller failure prediction and troubleshooting

A method comprises receiving telecommunications infrastructure data corresponding to a plurality of devices, determining at least one issue with at least one device of the devices based at least in part on the telecommunications infrastructure data, and identifying at least one remedial action to be performed to address the at least one issue. The identifying is performed using one or more machine learning techniques and the at least one remedial action comprises at least one of adding at least one additional device to the plurality of devices and reconfiguring the at least one device. One or more configuration templates are retrieved and inputted to at least one of the at least one additional device and the at least one device based at least in part on the at least one remedial action. A report including the at least one issue and the at least one remedial action is generated.

Telecommunications infrastructure device management using machine learning

Techniques are disclosed for intelligent infrastructure management in a radio access network. For example, a method obtains, from a plurality of baseband units of a radio access network, a plurality of data sets, wherein respective ones of the plurality of data sets correspond to operational statistics of respective ones of the plurality of baseband units. The method then generates a forecasted data set corresponding to one or more predicted operational statistics of each of the subset of baseband units, wherein the forecasted data set is generated using a first machine learning model. The method analyzes the forecasted data set to predict a future occurrence of a technical issue and to determine at least one corrective action for the predicted future occurrence of the technical issue, wherein the analysis is performed using a second machine learning model. The method then causes initiation of the at least one corrective action.

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Bengaluru, India

Nithish Kote