SECONDARY STORAGE OPERATION INSTRUCTION TAGS IN INFORMATION MANAGEMENT SYSTEMS

    公开(公告)号:US20180253239A1

    公开(公告)日:2018-09-06

    申请号:US15905103

    申请日:2018-02-26

    Abstract: According to certain aspects, a system can include a client computing device configured to: in response to user interaction, store an identifier associated with a first tag in association with a first file; and in response to instructions to perform a secondary copy operation, forward the first file, a second file, and the identifier associated with the first tag. The system may also include a secondary storage controller computer(s) configured to: based on a review of the identifier associated with the first tag, identify the first file as having been tagged with the first tag; electronically obtain rules associated with the first tag; perform on the first file at least a first secondary storage operation specified by the rules associated with the first tag; and perform on the second file at least a second secondary storage operation, wherein the first and second secondary storage operations are different.

    TRANSACTION LOG INDEX GENERATION IN AN ENTERPRISE BACKUP SYSTEM

    公开(公告)号:US20220283989A1

    公开(公告)日:2022-09-08

    申请号:US17576698

    申请日:2022-01-14

    Abstract: Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, operations performed as part of a backup process may be stored in transaction logs. These transaction logs may include information about a transaction performed between the client computing system and the network storage that hosts the backup of the client computing system. The transaction logs may be provided to a secondary storage system that can be used to form a backup index. The backup index may be used to facilitate accessing the data stored at the network storage. Advantageously, generating the transaction logs and separating the generation of the backup index from the backup process can reduce resource usage during performance of the backup and speed up the backup process while further reducing interaction with the storage manager.

    TRANSACTION LOG INDEX GENERATION IN AN ENTERPRISE BACKUP SYSTEM

    公开(公告)号:US20210034571A1

    公开(公告)日:2021-02-04

    申请号:US16526699

    申请日:2019-07-30

    Abstract: Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, operations performed as part of a backup process may be stored in transaction logs. These transaction logs may include information about a transaction performed between the client computing system and the network storage that hosts the backup of the client computing system. The transaction logs may be provided to a secondary storage system that can be used to form a backup index. The backup index may be used to facilitate accessing the data stored at the network storage. Advantageously, generating the transaction logs and separating the generation of the backup index from the backup process can reduce resource usage during performance of the backup and speed up the backup process while further reducing interaction with the storage manager.

    MACHINE-LEARNING BASED DATA OBJECT RETRIEVAL
    30.
    发明申请

    公开(公告)号:US20190250998A1

    公开(公告)日:2019-08-15

    申请号:US15897021

    申请日:2018-02-14

    CPC classification number: G06F11/1469 G06F11/1453 G06F11/1464 G06N20/00

    Abstract: An information management system is provided herein that uses machine learning to predict what data to recall from a secondary storage device and/or when to perform the recall. For example, a media agent in the information management system can generate and store context information when data recall requests are received from a client computing device, using the context information to train a recall machine learning model. The recall machine learning model may be trained such that the model predicts what data to recall and/or when to perform the recall. The media agent and/or the client computing device can then be configured to use the trained recall machine learning model to determine which data to recall and/or when to perform the recall.

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