Dell Products Inc.

Ashutosh Singh

Benjamin Fauber

Michael David Shepherd

Landon Martin Chambers

An apparatus comprises a processing device configured to obtain first parameters characterizing an operating state of information technology (IT) resources of a data center and second parameters characterizing an operating state of cooling systems of the data center, to determine an overall operating state of the data center by aggregating the first and second parameters, to identify a power consumption profile based on the overall operating state, and to perform a joint training of first and second reinforcement learning agents based on the overall operating state and the power consumption profile. The processing device is also configured to generate first controls for the heterogeneous IT resources utilizing the first reinforcement learning agent and second controls for the cooling systems utilizing the second reinforcement learning agent, the first and second controls being configured to reduce power consumption while maintaining specified performance benchmarks for workloads executing in the data center.

Reducing power consumption of a data center utilizing reinforcement learning framework

A method may include receiving telemetry data associated with one or more information handling resources, receiving failure statistics associated with the one or more information handling resources, merging the telemetry data and the failure statistics to create training data, and implementing a gated recurrent unit to: (i) impute missing values from the training data and (ii) train a pattern recognition engine configured to predict a failure status of an information handling resource from operational data associated with the information handling resource.

Systems and methods for predicting information handling resource failures using deep recurrent neural network with a modified gated recurrent unit having missing data imputation

An apparatus comprises a processing device configured to obtain a time series dataset having missing values, the time series dataset comprising monitoring data associated with one or more assets. The processing device is also configured to generate, utilizing a machine learning algorithm, a reconstructed time series dataset having imputed values for the missing values in the obtained time series dataset, the machine learning algorithm comprising a generative network implementing inverse network parameter determination for network parameters of the generative network. The processing device is further configured to classify patterns in the obtained time series dataset utilizing the reconstructed time series dataset, to select remedial actions for controlling at least one of the one or more assets based at least in part on the classified patterns in the obtained time series dataset, and to initiate the selected remedial actions to control the at least one asset.

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Austin, TX, United States of America

Ashutosh Singh