Emc IP Holding Company LLC

Dell Products Inc.

Davidson Devasigamony

Alik Saring

Ramya Ramachandran

Jai Kumar

Sean Soltysik

Deepak Kamath Neerebailur

The technology described herein is directed towards intelligent storage frontend port re-balancing/allocation in a datacenter's port groups having data maintained in masking view data. Artificial intelligence/machine learning (AI/ML) models accurately monitor the storage array frontend port utilization metrics to perform dynamic port rebalancing between overutilized and underutilized storage ports. The input to the models can further include historic seasonality and/or historical trending utilization data to more effectively predict the future growth of frontend port utilization based on model forecasting. The models can also be used to determine more optimal frontend port placement with respect to new storage provisioning requests. The models can be part of cloud-based monitoring tools, or can be coupled to a storage array to intelligently and dynamically perform the storage frontend port rebalancing operations.

Frontend storage port rebalancing and allocation using machine learning techniques

An apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to obtain input data representing information characterizing a storage capacity associated with a file system of a given storage system. The at least one processing device is also configured to predict a change to the storage capacity of the file system of the given storage system based on at least a portion of the obtained input data. The at least one processing device is further configured to cause the storage capacity of the file system of the given storage system to change based on a result of the prediction.

Automatic management of file system capacity using predictive analytics for a storage system

Methods, apparatus, and processor-readable storage media for automatically remediating storage device issues using machine learning techniques are provided herein. An example computer-implemented method includes obtaining performance-related information pertaining to one or more storage devices; automatically determining one or more workflows to be executed in connection with remediating at least one issue attributed to at least a portion of the one or more storage devices, wherein determining the one or more workflows comprises applying one or more machine learning techniques to the obtained performance-related information; and performing one or more automated actions based at least in part on the one or more workflows.

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Boxborough, MA, United States of America

Davidson Devasigamony