公开(公告)号：US20240054019A1

公开(公告)日：2024-02-15

申请号：US18046970

申请日：2022-10-17

Applicant: NetApp, Inc.

Inventor： Tal Ohayon ,  Idan Schwartz

IPC: G06F9/50 ,  G06F9/48

CPC classification number: G06F9/5033 ,  G06F9/4812

Abstract: A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a correlation component that, based on a first context defining a first spare resource instance to be utilized in a networked system, identifies a second spare resource instance, and an evaluation component that generates an availability score defining prediction of availability of the first spare resource instance based on a second context defining the second spare resource instance, wherein the evaluation component further deploys the first spare resource instance, based on the availability score, in the networked system.

公开(公告)号：US20220129322A1

公开(公告)日：2022-04-28

申请号：US17206871

申请日：2021-03-19

Applicant: NetApp, Inc.

Inventor： Idan Schwartz ,  Ohad Muchnik ,  Jonathan Cohen ,  Kevin McGrath ,  Amiram Shachar

IPC: G06F9/50 ,  G06F9/48

Abstract: Systems, methods, and machine-readable media for predicting interruptions to the use of spare cloud resources and rebalancing based on those predictions are disclosed. A computing platform collects data for customers over time. The computing platform runs a machine learning algorithm on the historical data to generate a prediction classifier. The prediction classifier relates to a time window for prediction into the future, on the order of minutes or hours. The prediction classifier is run on monitored data from ongoing activity with a cloud provider to generate a risk score. Each risk score may identify an amount of risk that a spare cloud resource related to new resource metrics data will be interrupted within the future time frame corresponding to that prediction classifier. If predicted to be interrupted, the customer may be assisted in rebalancing to other resources. As a result, interruptions can be predicted hours into the future.

公开(公告)号：US11915053B2

公开(公告)日：2024-02-27

申请号：US17206871

申请日：2021-03-19

Applicant: NetApp, Inc.

Inventor： Idan Schwartz ,  Ohad Muchnik ,  Jonathan Cohen ,  Kevin McGrath ,  Amiram Shachar

IPC: G06F9/50 ,  G06F9/48

CPC classification number: G06F9/5038 ,  G06F9/4887 ,  G06F9/505 ,  G06F9/5072 ,  G06F2209/503

Abstract: Systems, methods, and machine-readable media for predicting interruptions to the use of spare cloud resources and rebalancing based on those predictions are disclosed. A computing platform collects data for customers over time. The computing platform runs a machine learning algorithm on the historical data to generate a prediction classifier. The prediction classifier relates to a time window for prediction into the future, on the order of minutes or hours. The prediction classifier is run on monitored data from ongoing activity with a cloud provider to generate a risk score. Each risk score may identify an amount of risk that a spare cloud resource related to new resource metrics data will be interrupted within the future time frame corresponding to that prediction classifier. If predicted to be interrupted, the customer may be assisted in rebalancing to other resources. As a result, interruptions can be predicted hours into the future.

公开(公告)号：US20240007492A1

公开(公告)日：2024-01-04

申请号：US18344664

申请日：2023-06-29

Applicant: NetApp, Inc.

Inventor： Yun Shen ,  Azzedine Benameur ,  Alex Xeong-Hoon Ough ,  Idan Schwartz

IPC: H04L9/40 ,  H04L41/16

CPC classification number: H04L63/1425 ,  H04L41/16

Abstract: Systems and methods for identifying anomalous activities in a cloud computing environment are provided. According to one embodiment, a customer's infrastructure may be fortified by leveraging deep learning technology (e.g., an encoder-decoder machine-learning (ML) model) to predict events in the cloud environment. During a training phase, the ML model may be trained to make a prediction regarding a next event based on a predetermined or configurable length of a sequence of contextual events. For example, historical events (e.g., cloud application programming interface (API) events logged to a cloud activity trace) observed within the customer's cloud infrastructure over the course of a particular date range may be split into appropriate event/context pairs and fed to the ML model. Subsequently, during a run-time anomaly detection phase, the ML model may be used to predict a next event based on a sequence of immediately preceding events to facilitate identification of anomalous activity.