SELECTING OPTIMAL DISK TYPES FOR DISASTER RECOVERY IN THE CLOUD

    公开(公告)号:US20220035715A1

    公开(公告)日:2022-02-03

    申请号:US16983988

    申请日:2020-08-03

    Abstract: Embodiments for providing automated selection of optimal disk types for virtualized storage by defining a minimum number of backup samples, selecting, if the minimum number of backup samples is not met for a backup operation, a solid state drive (SSD) for a virtual machine (VM) storage for a disaster recovery operation, otherwise selecting a hard disk drive (HDD) for the VM storage. The method further defines a cold HDD threshold (CHT) value and a minimal percentage of backups (PPT) value, and obtains a cold backup count based on the CHT value. It compares a ratio of the cold backup count to an amount of backups (AB) for the disaster recovery operation to the defined PPT value, and if the ratio is greater than the PPT value, it selects the SSD rather than HDD for the VM storage.

    DATACENTER IoT-TRIGGERED PREEMPTIVE MEASURES USING MACHINE LEARNING

    公开(公告)号:US20210349776A1

    公开(公告)日:2021-11-11

    申请号:US17382065

    申请日:2021-07-21

    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.

    AUTOMATIC IO STREAM TIMING DETERMINATION IN LIVE VM IMAGES

    公开(公告)号:US20210248046A1

    公开(公告)日:2021-08-12

    申请号:US16784074

    申请日:2020-02-06

    Abstract: One example method includes capturing IOs, adding the IOs to a journal, adding undo data to the journal for one or more locations, and using the IOs and the undo data to determine when, during a timespan defined by the journal, a backup could have been taken. This determination may involve the use of undo data which indicates what the content of a particular location was prior to the first IO directed to that location during the timespan defined by the journal.

    MERKLE SUPER TREE FOR SYNCHRONIZING DATA BUCKETS OF UNLIMITED SIZE IN OBJECT STORAGE SYSTEMS

    公开(公告)号:US20210232595A1

    公开(公告)日:2021-07-29

    申请号:US16803918

    申请日:2020-02-27

    Abstract: Embodiments extend using sparse Merkle trees for smart synchronization of S3 buckets by overcoming fixed size limitations through creating another Merkle tree when the fixed size limit of the first tree is exceeded, and creating yet another tree when the second tree is filled up, and so on as needed. The method maintains a super Merkle tree of trees, in which each tree can be synchronized separately by keeping a strict division to trees according to generation number. The generation is passed from a source site to a target site during replication operations. Syncing between two data sites is efficient as the super Merkle tree of the source is synced with the super Merkle tree of the target using the hashes on the nodes, as in normal Merkle tree sync operations.

    MERKLE TREE FOREST FOR SYNCHRONIZING DATA BUCKETS OF UNLIMITED SIZE IN OBJECT STORAGE SYSTEMS

    公开(公告)号:US20210232594A1

    公开(公告)日:2021-07-29

    申请号:US16803902

    申请日:2020-02-27

    Abstract: Embodiments extend using sparse Merkle trees for smart synchronization of S3 buckets by overcoming fixed size limitations through creating another Merkle tree when the fixed size limit of the first tree is exceeded, and creating yet another tree when the second tree is filled up, and so on as needed. The method maintains a list of trees, in which each tree can be synchronized separately by keeping a strict division to trees according to generation number. The generation is passed from a source site to a target site during replication operations. The tagging of the generation number also makes it easy and efficient to remove an older version of an element or deal with deleted elements. This allows efficient syncing between two data object buckets without a size limitation on number of elements in a bucket.

    INDEXING SPLITTER FOR ANY PIT REPLICATION

    公开(公告)号:US20210064576A1

    公开(公告)日:2021-03-04

    申请号:US16557791

    申请日:2019-08-30

    Abstract: A method, apparatus, and system for transmitting file system metadata from an indexing splitter running in a VM to a source side RPA is disclosed. The operations comprise: capturing one or more file system events in a production virtual machine (VM) at an indexing splitter; transmitting file system metadata representing the captured file system events from the indexing splitter to a data splitter, the data splitter being an agent running on a host system hosting the VM; transmitting the file system metadata inside one or more special input/output (I/O) commands associated with a predetermined tag from the data splitter to a source side replication protection appliance (RPA) alongside regular storage system I/O command data; identifying the special I/O commands at the source side RPA based on the predetermined tag; and recovering the file system metadata from the special I/O commands at the source side RPA.

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