Emc IP Holding Company LLC

Honeywell International Inc.

Jitendra Singh

Rahul Deo Vishwakarma

Manu Taranath

Rajesh V Poojary

Greg Bernhardt

Arvind S Shetty

Shelesh Chopra

Bing Liu

Lu Chen

Arvind R Shetty

Embodiments described herein relate to a method for probabilistically forecasting the state of hardware components. The method may include obtaining data items corresponding to a hardware component and performing an analysis of the hardware component. The analysis may include making a variety of probability predictions as to whether a label from among a set of possible labels is likely to be the correct label. The set of probabilities from the aforementioned analysis are then analyzed to determine which predicted label has the tightest range, and the prediction with the tightest range for a certain label is displayed to a user in a ranked fashion that includes a quantity of such probability prediction ranges. Such a display may allow an administrator to take action as to which hardware components should be replaced and in what order.

System and method for probabilistically forecasting health of hardware in a large-scale system

One embodiment provides a computer implemented method of estimating replication completion time. The method includes creating a historical dataset of prior replication data; determining a set of replication parameters to consider; inputting the historical dataset and the set of replication parameters to a replication completion time estimator module; generating a replication completion time prediction based on the historical dataset and the set of replication parameters; and generating a confidence prediction corresponding to the replication completion time prediction.

System and method for approximating replication completion time

Embodiments are directed to a method and system of forecasting a disk drive survival period in a data storage network, by obtaining operating system data and manufacturer data for the disk drive to create a dataset, screening the dataset to identify a number of features to be selected for model creation, wherein the data set includes censored data and non-censored data, and performing, in an analytics engine, semi-parametric survival analysis on the data set using transfer learning on the model to provide a time-based failure prediction of the disk drive. A graphical user interface provides to a user the failure prediction in one of text form or graphical form.

System and method for survival forecasting of disk drives using semi-parametric transfer learning

Methods, apparatus, and processor-readable storage media for estimating replication completion time using machine learning techniques are provided herein. An example computer-implemented method includes obtaining, from one or more data deduplication storage systems, data related to one or more historical replication operations; generating at least one curve fitting function by processing at least a portion of the obtained data using one or more machine learning techniques; generating an estimate for completion time of at least one unexecuted replication operation associated with at least one of the one or more data deduplication storage systems by processing input data from the at least one data deduplication storage system using the at least one curve fitting function; and performing one or more automated actions based at least in part on the generated estimate.

Estimating replication completion time using machine learning techniques

Devices, methods, and systems for distributed rule based automated fault detection are described herein. One system includes a data extractor engine configured to: extract configuration data relating to an environment based on a number of defined rules, and receive monitored data relating to the environment, an AFD engine configured to evaluate the monitored data in view of the configuration data to determine a state of the environment, and a fault generation engine to determine whether the state of the environment is outside a range defined by the number of defined rules.

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Bengaluru, India

Jitendra Singh