公开(公告)号：US20210124510A1

公开(公告)日：2021-04-29

申请号：US16662185

申请日：2019-10-24

Applicant: EMC IP HOLDING COMPANY LLC

Inventor： Paulo Ferreira ,  Adriana Prado ,  Pablo Da Silva ,  Ron Arnan ,  Hagay Dagan ,  Adnan Sahin

IPC: G06F3/06 ,  G06N20/00 ,  G06N5/04

Abstract: Telemetry data gathered from active deployed SAN nodes is used to create a machine learning model that predicts storage system performance, e.g. in terms of response time. The telemetry data may be filtered to remove outlier values and less relevant information before creating the training dataset. Engineered features may be created that include types of data that are not present in the telemetry data. For example, data types from the telemetry data may be combined to create engineered features that are more relevant than the individual data types. The engineered features are included in the training dataset. The machine learning model may be used to test possible configurations for a planned SAN node based on expected workload and performance requirements. Outputted data may include satisfactory configurations for a planned storage system.

公开(公告)号：US11347414B2

公开(公告)日：2022-05-31

申请号：US16662185

申请日：2019-10-24

Applicant: EMC IP HOLDING COMPANY LLC

Inventor： Paulo Ferreira ,  Adriana Prado ,  Pablo Da Silva ,  Ron Arnan ,  Hagay Dagan ,  Adnan Sahin

IPC: G06F3/06 ,  G06N5/04 ,  G06N20/00

Abstract: Telemetry data gathered from active deployed SAN nodes is used to create a machine learning model that predicts storage system performance, e.g. in terms of response time. The telemetry data may be filtered to remove outlier values and less relevant information before creating the training dataset. Engineered features may be created that include types of data that are not present in the telemetry data. For example, data types from the telemetry data may be combined to create engineered features that are more relevant than the individual data types. The engineered features are included in the training dataset. The machine learning model may be used to test possible configurations for a planned SAN node based on expected workload and performance requirements. Outputted data may include satisfactory configurations for a planned storage system.

公开(公告)号：US09940024B1

公开(公告)日：2018-04-10

申请号：US15358408

申请日：2016-11-22

Applicant: EMC IP Holding Company LLC

Inventor： Dan Aharoni ,  Hui Wang ,  Gil Ratsaby ,  Hagay Dagan ,  Amnon Naamad

IPC: G06F12/00 ,  G06F13/00 ,  G06F13/28 ,  G06F3/06

CPC classification number: G06F3/0604 ,  G06F3/061 ,  G06F3/0649 ,  G06F3/0665 ,  G06F3/0683 ,  G06F3/0685 ,  G06F3/0689 ,  G06F11/30 ,  G06F11/3409 ,  G06F2201/87

Abstract: Determining cumulative workload skew is described. Measurements for one or more logical devices are determined. The set of measurements include, for each of N storage tiers, a workload measurement identifying workload directed to the single tier, and a capacity measurement identifying an amount of data stored in the single tier. N points may be determined using the measurements. Each point corresponds to a different storage tier and has a first coordinate identifying a cumulative percentage of data portions stored in the storage tier and all other tiers having a higher performance ranking than the one storage tier, and a second coordinate denoting an aggregated percentage of total workload directed to the foregoing cumulative percentage of data portions. A curve representing a cumulative workload skew may be determined using these N points and a point of origin.

公开(公告)号：US11275766B2

公开(公告)日：2022-03-15

申请号：US16901428

申请日：2020-06-15

Applicant: EMC IP HOLDING COMPANY LLC

Inventor： Gil Ratsaby ,  Ron Arnan ,  Hagay Dagan

IPC: G06F16/28 ,  G06F16/16 ,  G06F16/22

Abstract: A complex object generator is implemented, for example, as an integrated development environment. The complex object generator includes a hierarchical object relationship data structure describing classes of objects, relationships between the classes of objects, and metrics associated with the classes of objects. The hierarchical object relationship data structure is parsed by parser to create a hierarchy of Java classes. A user interface uses the hierarchy of Java classes to constrain selection of objects and metrics during creation of the complex object. As input is received relative to selected objects and metrics, the complex object is incrementally built. By constraining object and metric selection using the hierarchy of Java classes, the complex object is guaranteed to be valid when built, thus reducing or eliminating the number of errors associated with building complex objects.

公开(公告)号：US20210390121A1

公开(公告)日：2021-12-16

申请号：US16901428

申请日：2020-06-15

Applicant: EMC IP HOLDING COMPANY LLC

Inventor： Gil Ratsaby ,  Ron Arnan ,  Hagay Dagan

IPC: G06F16/28 ,  G06F16/22 ,  G06F16/16

Abstract: A complex object generator is implemented, for example, as an integrated development environment. The complex object generator includes a hierarchical object relationship data structure describing classes of objects, relationships between the classes of objects, and metrics associated with the classes of objects. The hierarchical object relationship data structure is parsed by parser to create a hierarchy of Java classes. A user interface uses the hierarchy of Java classes to constrain selection of objects and metrics during creation of the complex object. As input is received relative to selected objects and metrics, the complex object is incrementally built. By constraining object and metric selection using the hierarchy of Java classes, the complex object is guaranteed to be valid when built, thus reducing or eliminating the number of errors associated with building complex objects.