Samsung Electronics Co., Ltd.

Qinling Zheng

Changho Choi

Zhan Ping

Vikas Sinha

Nima Elyasi

Ehsan M Najafabadi

Yasser Zaghloul

A method for predicting a time-to-failure of a target storage device may include training a machine learning scheme with a time-series dataset, and applying the telemetry data from the target storage device to the machine learning scheme which may output a time-window based time-to-failure prediction. A method for training a machine learning scheme for predicting a time-to-failure of a storage device may include applying a data quality improvement framework to a time-series dataset of operational and failure data from multiple storage devices, and training the scheme with the pre-processed dataset. A method for training a machine learning scheme for predicting a time-to-failure of a storage device may include training the scheme with a first portion of a time-series dataset of operational and failure data from multiple storage devices, testing the machine learning scheme with a second portion of the time-series dataset, and evaluating the machine learning scheme.

Systems and methods for predicting storage device failure using machine learning

A system and method for active disturbance rejection based thermal control is configured to receive, at a first active disturbance rejection thermal control (ADRC) controller, a first temperature measurement from a first thermal zone. The ADRC controller generates a first output control signal for controlling a first cooling element, wherein the first output control signal is generated according a first estimated temperature and a first estimated disturbance calculated by a first extended state observer (ESO) of the first ADRC controller.

Active disturbance rejection based thermal control

Provided are systems, methods, and apparatuses for latency-aware edge computing to optimize network traffic. A method can include: determining network parameters associated with a network architecture, the network architecture comprising a data center and an edge data center; determining, using the network parameters, a first programmatically expected latency associated with the data center and a second programmatically expected latency associated with the edge data center; and determining, based at least in part on a difference between the first programmatically expected latency or the second programmatically expected latency, a distribution of a workload to be routed between the data center and the edge data center.

Systems and methods for latency-aware edge computing

In a method for training a machine learning model, the method includes: segmenting, by a processor, a dataset from a database into one or more datasets based on time period windows; assigning, by the processor, one or more weighted values to the one or more datasets according to the time period windows of the one or more datasets; generating, by the processor, a training dataset from the one or more datasets according to the one or more weighted values; and training, by the processor, the machine learning model using the training dataset.

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San Jose, CA, United States of America

Qinling Zheng