-
公开(公告)号：US11188413B1
公开(公告)日：2021-11-30
申请号：US17011372
申请日：2020-09-03
Applicant: NETAPP, INC.
Inventor： Kausik Ghatak
Abstract: Methods and systems for networked systems are provided. A reinforcement learning (RL) agent is deployed during runtime of a networked system having at least a first component and a second component. The RL agent detects a first degradation signal in response to an error associated with the first component and a second degradation signal from the second component, the second degradation signal generated in response to the error. The RL agent identifies from a learned data structure an action for fixing degradation, at both the first component and the second component; and continues to update the learned data structure, upon successful and unsuccessful attempts to fix degradation associated with the first component and the second component.
-
公开(公告)号：US11531583B2
公开(公告)日：2022-12-20
申请号：US17525161
申请日：2021-11-12
Applicant: NETAPP, INC.
Inventor： Kausik Ghatak
Abstract: Methods and systems for networked systems are provided. A reinforcement learning (RL) agent is deployed during runtime of a networked system having at least a first component and a second component. The RL agent detects a first degradation signal in response to an error associated with the first component and a second degradation signal from the second component, the second degradation signal generated in response to the error. The RL agent identifies from a learned data structure an action for fixing degradation, at both the first component and the second component; and continues to update the learned data structure, upon successful and unsuccessful attempts to fix degradation associated with the first component and the second component.
-
公开(公告)号：US20220075683A1
公开(公告)日：2022-03-10
申请号：US17525161
申请日：2021-11-12
Applicant: NETAPP, INC.
Inventor： Kausik Ghatak
Abstract: Methods and systems for networked systems are provided. A reinforcement learning (RL) agent is deployed during runtime of a networked system having at least a first component and a second component. The RL agent detects a first degradation signal in response to an error associated with the first component and a second degradation signal from the second component, the second degradation signal generated in response to the error. The RL agent identifies from a learned data structure an action for fixing degradation, at both the first component and the second component; and continues to update the learned data structure, upon successful and unsuccessful attempts to fix degradation associated with the first component and the second component.
-
公开(公告)号：US20200322432A1
公开(公告)日：2020-10-08
申请号：US16376875
申请日：2019-04-05
Applicant: NETAPP, INC.
Inventor： Kausik Ghatak , Sandeep Vasanth Kamath , Manoj
IPC: H04L29/08 , G06N20/00 , H04L12/911
Abstract: Methods and systems for a networked storage system are provided. One method includes utilizing a training dataset for prioritizing a plurality of events of a networked storage system using a plurality of resources. Each event is associated with a plurality of parameters that impact a severity level determination for each event; and each event is provided an initial priority score based on a time when each event is selected for resolution. The plurality of parameters may include an event source. The method further includes using the training dataset to identify a weight of each parameter by executing an iterative prediction algorithm; determining a priority score of a new event based on the weight of each parameter; updating the training dataset using the priority score of the new event; and adjusting a resource impacted by the new event, based on the priority score.
-
-
-
Search Results
4
records
(took 0.016 seconds)
