DYNAMIC WORKLOAD TUNING
    4.
    发明申请

    公开(公告)号:US20220171657A1

    公开(公告)日:2022-06-02

    申请号:US17108301

    申请日:2020-12-01

    Abstract: Techniques are provided for dynamic workload tuning of a data pipeline that includes a plurality of stages, each associated with a respective storage element, a storage element monitor, and a resource manager. In one embodiment, the techniques involve the storage element monitor determining a utilization of a storage element associated with a first stage of the plurality of stages, comparing the utilization of the storage element to a first threshold, generating a signal based on the comparison of the storage element to the first threshold, output the signal; and the resource manager receiving the signal, determining that the signal indicates an increase or decrease of resources for the first stage, and adjusting compute resources for the first stage based on the signal in order to effect a change in the utilization of the storage element.

    INCREASED IN-LINE DEDUPLICATION EFFICIENCY
    5.
    发明申请
    INCREASED IN-LINE DEDUPLICATION EFFICIENCY 有权
    提高在线重复效率

    公开(公告)号:US20140181465A1

    公开(公告)日:2014-06-26

    申请号:US14190492

    申请日:2014-02-26

    CPC classification number: G06F3/0641 G06F3/0604 G06F3/0683 G06F17/30159

    Abstract: Exemplary embodiments for increased in-line deduplication efficiency in a computing environment are provided. Embodiments include incrementing the size of data samples from fixed size data chunks for each nth iteration for reaching a full size of an object requested for in-line deduplication, calculating in nth iterations hash values on data samples from fixed size data chunks extracted from the object, and matching in a nth hash index table the calculated nth iteration hash values for the data samples from the fixed size data chunks with a corresponding hash value of existing objects in storage, wherein the nth hash index table is built for each nth iteration of the data samples belonging to the fixed data chunks.

    Abstract translation: 提供了用于在计算环境中提高在线重复数据删除效率的示例性实施例。 实施例包括从每固定大小的数据块中增加数据样本的大小,以达到用于进行在线重复数据消除所请求的对象的全部大小的第n次迭代的全尺寸的数据样本的大小;在从对象中提取的固定大小数据块的数据样本上进行第n次迭代计算散列值 ,并且在第n个散列索引表中匹配来自具有存储中的现有对象的相应哈希值的固定大小数据块的数据样本的计算的第n个迭代散列值,其中第n个散列索引表是针对 属于固定数据块的数据样本。

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