Cloud computing-based adaptive storage layering system and method

    公开(公告)号:US12130831B2

    公开(公告)日:2024-10-29

    申请号:US17615551

    申请日:2021-01-29

    摘要: The disclosure provides a cloud computing-based adaptive storage layering system and method. The system includes a data node management module, a metadata management module, an adaptive storage layering module, and a pre-aggregated query routing module. According to predefined rules, node capacity is expanded and shrunk, collected queried hit models and scanned file paths are aggregated and sorted, and layering loading and pre-loading are performed on files. Based on the disclosure, an efficient On-Line Analytical Processing (OLAP) query execution engine may be constructed, to deal with complex OLAP queries of various reporting system. Therefore, the execution efficiency of cloud OLAP engines can be significantly enhanced.

    On-demand backups for management components in software-defined data centers

    公开(公告)号:US12093133B2

    公开(公告)日:2024-09-17

    申请号:US18134053

    申请日:2023-04-13

    申请人: VMWARE, INC.

    IPC分类号: G06F16/14 G06F11/14

    CPC分类号: G06F11/1448 G06F11/1458

    摘要: System and method for backing up management components of a software-defined data center (SDDC) managed by a cloud-based service uses backup rules for the SDDC, which are used to configure a backup manager agent in the SDDC. The backup rules are then used by the backup manager agent to determine whether at least one of system logs generated by the management components in the SDDC, which are monitored by the backup manager agent, satisfies the backup rules to initiate a backup operation for at least one of the management components of the SDDC.

    Learning-based storage reduction in an overlay network

    公开(公告)号:US12038884B2

    公开(公告)日:2024-07-16

    申请号:US18214899

    申请日:2023-06-27

    发明人: Indrajit Banerjee

    摘要: An overlay network is augmented to provide more efficient data storage by processing a dataset of high dimension into an equivalent dataset of lower dimension, wherein the data reduction reduces the amount of actual physical data but not necessarily its informational value. Data to be processed (dimensionally-reduced) is received by an ingestion layer and supplied to a learning-based storage reduction application that implements the data reduction technique. The application applies a data reduction algorithm and stores the resulting dimensionally-reduced data sets in the native data storage or third party cloud. To recover the original higher-dimensional data, an associated reverse algorithm is implemented. In general, the application coverts an N dimensional data set to a K dimensional data set, where K