International Business Machines Corporation

Richard P Oubre, Jr

Matthew G Borlick

Lokesh M Gupta

Micah Robison

James E Olson

Usman Ahmed

Karl Allen Nielsen

Todd Charles Sorenson

Richard Harry Hopkins

David Arvin Verburg

Feng Gao

Brian Robert Bailey

Richard H Hopkins

A machine learning module is trained by receiving inputs comprising attributes of a computing environment, where the attributes affect a likelihood of failure in the computing environment. In response to an event occurring in the computing environment, a risk score that indicates a predicted likelihood of failure in the computing environment is generated via forward propagation through a plurality of layers of the machine learning module. A margin of error is calculated based on comparing the generated risk score to an expected risk score, where the expected risk score indicates an expected likelihood of failure in the computing environment corresponding to the event. An adjustment is made of weights of links that interconnect nodes of the plurality of layers via back propagation to reduce the margin of error, to improve the predicted likelihood of failure in the computing environment.

Perform preemptive identification and reduction of risk of failure in computational systems by training a machine learning module

Input on a plurality of attributes of a computing environment is provided to a machine learning module to produce an output value that comprises a risk score that indicates a likelihood of a potential malfunctioning occurring within the computing environment. A determination is made as to whether the risk score exceeds a predetermined threshold. In response to determining that the risk score exceeds a predetermined threshold, an indication is transmitted to indicate that potential malfunctioning is likely to occur within the computing environment. A modification is made to the computing environment to prevent the potential malfunctioning from occurring.

Using a machine learning module to perform preemptive identification and reduction of risk of failure in computational systems

A system determines that a given drive in an array is due for replacement. The system ranks available replacement drives based on their predicted rates of failure. The system replaces the given drive with a replacement drive that has the lowest predicted rate of failure.

Storage array drive recovery

A determination is made as to whether a plurality of storage volumes controlled by a processor complex are secondary storage volumes that are in an asynchronous copy relationship with a plurality of primary storage volumes. A storage device timeout value for a storage device that stores the plurality of storage volumes is changed from a predetermined low value to a predetermined high value, wherein the predetermined high value is indicative of a greater duration of time than the predetermined low value, in response to determining that each of the plurality of storage volumes controlled by the processor complex and stored in the storage device are secondary storage volumes that are in the asynchronous copy relationship with the plurality of primary storage volumes.

Determining adjustments of storage device timeout values based on synchronous or asynchronous remote copy state

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San Jose, CA, United States of America

Richard P Oubre, Jr