公开(公告)号：US20230281470A1

公开(公告)日：2023-09-07

申请号：US17653541

申请日：2022-03-04

Applicant: NetApp, Inc.

Inventor： Andy Anheng Hwang ,  Timothy K. Emami ,  Sotirios Efstathios Maneas

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: Systems/techniques that facilitate machine learning classification of object store workloads are provided. In various embodiments, a system can access a resource utilization descriptor associated with an object store. In various aspects, the system can generate, via execution of a machine learning model (e.g., a deep learning neural network, a random forest model), a classification label based on the resource utilization descriptor. In various instances, the system can perform one or more electronic actions based on the classification label. In various cases, the classification label can indicate/identify a computing fault of the object store, can indicate/identify resources of the object store that are being underutilized and/or overutilized, and/or can indicate whether a workload corresponding to the resource utilization descriptor could be properly transplanted to a different object store. Accordingly, the one or more electronic actions can include generating warnings/recommendations regarding such computing fault, such underutilized/overutilized resources, and/or such transplantation.