Hewlett Packard Enterprise Development LP

Rachit Gupta

Anand Andaneppa Ganjihal

Sagar Venkappa Nyamagouda

Smitha Jayaram

Ashutosh Kumar

Hiro Rameshlal Lalwani

Mohan Govindaraj

V V Satyanarayana Reddy N

Sherine Jacob

In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.

File system provisioning for workload

Examples described herein relate to migrating data in a system having multi-tiered physical storage. In an example, relative weights corresponding to a plurality of data access parameters may be determined based on a ranking associated with each of the plurality of data access parameters. Further, a priority metric corresponding to each of a plurality of candidate types may be determined based on the relative weights of the plurality of data access parameters. Furthermore, one or more candidate types may be selected from the plurality of candidate types based on the priority metric corresponding to each of the plurality of candidate types. Moreover, data containers corresponding to the selected one or more candidate types may be migrated from a first tier storage to a second tier storage.

Migration of data in a system having multi-tiered physical storage

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Bengaluru, India

Rachit Gupta