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    Efficient utilization of storage resources on data recovery sites using machine learning
    31.
    发明授权
    Efficient utilization of storage resources on data recovery sites using machine learning 有权

    公开(公告)号：US11269541B2

    公开(公告)日：2022-03-08

    申请号：US16662603

    申请日：2019-10-24

    Applicant: EMC IP Holding Company LLC

    Inventor： Amihai Savir ,  Avitan Gefen ,  Kfir Wolfson ,  Itay Azaria

    IPC: G06F3/06 ,  G06F11/14 ,  G06N20/00 ,  G06F17/18

    Abstract: Embodiments for dynamically allocating journal space for Do streams across multiple applications. A shared Do stream process has a dynamic block allocation component that provides a certain amount of buffering of a data flush for an application, using space that would normally be allocated for, but unused by, other applications, thus preventing the need for one or more of the applications to move to fast-forward mode when possible. Certain machine learning techniques are used in order to predict the required Do stream for each application according to past experience with the application, and this prediction is used to intelligently allocate Do Streams between the different applications.

    RESCUE PACKAGE FOR UNCONTROLLABLE SPLITTERS
    35.
    发明申请
    RESCUE PACKAGE FOR UNCONTROLLABLE SPLITTERS 有权

    公开(公告)号：US20210271574A1

    公开(公告)日：2021-09-02

    申请号：US16802795

    申请日：2020-02-27

    Applicant: EMC IP Holding Company LLC

    Inventor： Jehuda Shemer ,  Saar Cohen ,  Kfir Wolfson ,  Itay Azaria

    IPC: G06F11/14 ,  G06F16/27

    Abstract: A data protection system configured to replicate data may generate rescue packages that allow the system to recover when communication between a splitter or source of the production data being replicated and an appliance that stores the replicated data is disrupted. The rescue package is stored on a datastore and is then retrieved by the data protection system or another splitter. After processing the rescue package, which may contain IOs that the data protection is unaware of due to the communication disruption, replication may resume normally.

    Data reduction techniques for a multi-sensor internet of things environment
    37.
    发明授权
    Data reduction techniques for a multi-sensor internet of things environment 有权

    公开(公告)号：US10931546B2

    公开(公告)日：2021-02-23

    申请号：US16024757

    申请日：2018-06-30

    Applicant: EMC IP Holding Company LLC

    Inventor： Jehuda Shemer ,  Kfir Wolfson ,  Assaf Natanzon

    IPC: H04L12/26 ,  H04L12/24 ,  H04Q9/02 ,  H04L29/08

    Abstract: Data reduction techniques are provided for a multi-sensor IoT environment. An exemplary method comprises: dynamically determining, by a device within a distributed network comprised of a plurality of sensors, an amount of sensor data to be collected by and/or transmitted by a sensor within the distributed network based on at least one predefined spatial-based rule and/or at least one predefined temporal-based rule; and processing the sensor data based on the dynamically determined amount of sensor data. A percentage of the plurality of sensors within the distributed network that collect and/or transmit the sensor data can optionally be specified. One or more sensors optionally collect the sensor data at a default resolution and a predefined spatial-based rule and/or a predefined temporal-based rule specifies a predefined trigger for at least one sensor to collect and/or transmit the sensor data at a higher resolution.

    AUTOMATIC SNAPSHOT AND JOURNAL RETENTION SYSTEMS WITH LARGE DATA FLUSHES USING MACHINE LEARNING
    39.
    发明申请
    AUTOMATIC SNAPSHOT AND JOURNAL RETENTION SYSTEMS WITH LARGE DATA FLUSHES USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20200334199A1

    公开(公告)日：2020-10-22

    申请号：US16455429

    申请日：2019-06-27

    Applicant: EMC IP Holding Company LLC

    Inventor： Kfir Wolfson ,  Itay Azaria ,  Amihai Savir ,  Avitan Gefen

    IPC: G06F16/11 ,  G06F11/14 ,  G06F3/06 ,  G06N20/00

    Abstract: Predicting large data flushes by collecting usage data for system assets, analyzing the data using machine learning on each asset and the whole system to determine usage trends, predicting a next large data flush using a time-series model, and determining if a size of the predicted next flush size is too large relative to journal storage space in order to advance fast forward mode. Further, protecting history information by pausing distribution of data from journal volumes to replica volumes, taking storage-level snapshots of the replica and the journal volumes, storing a snapshot timestamp for each of the storage-level snapshots in a a snapshot database prior to advancing the fast forward mode or un-pausing distribution.

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