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公开(公告)号：US11151012B2
公开(公告)日：2021-10-19
申请号：US16752425
申请日：2020-01-24
Applicant: NetApp, Inc.
Inventor： Jason Sprague , Nir Nossenson , Sibel Kadioglu , Ravi Kesarwani , Omri Kessel
Abstract: An example system and method to provide a dashboard for users to analyze and review their hyper-scaler usage and spending and offer optimizations to predict optimal use of reserved and unreserved instances on various hyper-scaler platforms. While hyper-scaler platforms offer flexibility for users to scale their use on a platform, there is a potential risk of rapid cost overruns in large enterprise organizations that may be difficult to control and predict. In some examples, the system can determine an optimal number of reserved instances using past usage data and/or prediction data from a user may be used by the system to make forward predictions about reserving an optimal number of instances and minimizing hyper-scaler resource use.
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公开(公告)号：US11740798B2
公开(公告)日：2023-08-29
申请号：US17650917
申请日：2022-02-14
Applicant: NETAPP, INC.
Inventor： Nir Nossenson , Kai Niebergall , Francisco Jose Assis Rosa , John Jason Sprague , Omri Kessel
IPC: G06F3/06
CPC classification number: G06F3/0613 , G06F3/0604 , G06F3/067 , G06F3/0635 , G06F3/0644 , G06F3/0659
Abstract: Methods and systems for a networked storage system are provided. One method includes predicting an IOPS limit for a plurality of storage pools based on a maximum allowed latency of each storage pool, the maximum allowed latency determined from a relationship between the retrieved latency and a total number of IOPS from a resource data structure; identifying a storage pool whose utilization has reached a threshold value, the utilization based on a total number of IOPS directed towards the storage pool and a predicted IOPS limit; detecting a bully workload based on a numerical value determined from a total number of IOPS issued by the bully workload for the storage pool and a rising step function; and implementing a corrective action to reduce an impact of the bully workload on a victim workload.
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公开(公告)号：US20230393751A1
公开(公告)日：2023-12-07
申请号：US18218252
申请日：2023-07-05
Applicant: NETAPP, INC.
Inventor： Nir Nossenson , Kai Niebergall , Francisco Jose Assis Rosa , John Jason Sprague , Omri Kessel
IPC: G06F3/06
CPC classification number: G06F3/0613 , G06F3/0604 , G06F3/0659 , G06F3/0635 , G06F3/0644 , G06F3/067
Abstract: Methods and systems for a networked storage system are provided. One method includes predicting an IOPS limit for a plurality of storage pools based on a maximum allowed latency of each storage pool, the maximum allowed latency determined from a relationship between the retrieved latency and a total number of IOPS from a resource data structure; identifying a storage pool whose utilization has reached a threshold value, the utilization based on a total number of IOPS directed towards the storage pool and a predicted IOPS limit; detecting a bully workload based on a numerical value determined from a total number of IOPS issued by the bully workload for the storage pool and a rising step function; and implementing a corrective action to reduce an impact of the bully workload on a victim workload.
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公开(公告)号：US20210232479A1
公开(公告)日：2021-07-29
申请号：US16752425
申请日：2020-01-24
Applicant: NetApp, Inc.
Inventor： Jason Sprague , Nir Nossenson , Sibel Kadioglu , Ravi Kesarwani , Omri Kessel
Abstract: An example system and method to provide a dashboard for users to analyze and review their hyper-scaler usage and spending and offer optimizations to predict optimal use of reserved and unreserved instances on various hyper-scaler platforms. While hyper-scaler platforms offer flexibility for users to scale their use on a platform, there is a potential risk of rapid cost overruns in large enterprise organizations that may be difficult to control and predict. In some examples, the system can determine an optimal number of reserved instances using past usage data and/or prediction data from a user may be used by the system to make forward predictions about reserving an optimal number of instances and minimizing hyper-scaler resource use.
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