Any point in time replication to the cloud

    公开(公告)号:US11669545B2

    公开(公告)日:2023-06-06

    申请号:US17107393

    申请日:2020-11-30

    CPC classification number: G06F16/27 G06F16/215

    Abstract: Systems, apparatus, and methods for any point in time replication to the cloud. Data is replicated by replicating data to a remote storage or a data bucket in the cloud. At the same time, a metadata stream is generated and stored. The metadata stream establishes a relationship between the data and offsets of the data in the production volume. This allows continuous replication without having to maintain a replica volume. The replica volume can be generated during a rehydration operation that uses the metadata stream to construct the production volume from the cloud data.

    TRUE ZERO RTO: PLANNED FAILOVER
    108.
    发明申请

    公开(公告)号:US20210406139A1

    公开(公告)日:2021-12-30

    申请号:US16910694

    申请日:2020-06-24

    Abstract: One example method includes performing, as part a planned failover procedure, operations that include connecting a replica OS disk to a replica VM, powering up the replica VM, booting an OS of the replica VM, disconnecting a source VM from a network, and connecting replica data disks to the replica VM. IOs issued by an application at the source VM continue to be processed by the source VM while the replica OS disk is connected, the replica VM is powered up, and the OS of the replica VM is booted.

    Datacenter IoT-triggered preemptive measures using machine learning

    公开(公告)号:US11106528B2

    公开(公告)日:2021-08-31

    申请号:US16156832

    申请日:2018-10-10

    Abstract: One example method includes performing a machine learning process that involves performing an assessment of a state of a computing system, and the assessment includes analyzing information generated by an IoT edge sensor in response to a sensed physical condition in the computing system, and identifying an entity in the computing system potentially impacted by an event associated with the physical condition. The example method further includes identifying a preemptive recovery action and associating the preemptive recovery action with an entity, and the preemptive recovery action, when performed, reduces or eliminates an impact of the event on the entity, determining a cost associated with implementation of the preemptive recovery action, evaluating the cost associated with the preemptive recovery actions and identifying the preemptive recovery action with the lowest associated cost, implementing the preemptive recovery action with the lowest associated cost, and repeating part of the machine learning process.

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