SMART JOB SCHEDULING OF PIPELINES WITH BACKLOG INDICATOR

    公开(公告)号:US20250068463A1

    公开(公告)日:2025-02-27

    申请号:US18499115

    申请日:2023-10-31

    Applicant: Cohesity, Inc.

    Abstract: Techniques are described for configuring a data platform to schedule workloads using backlog indicators. For instance, processing circuitry of a data platform may obtain a generic backlog indicator for workloads to execute via the data platform. Each of the workloads may specify one or more storage system maintenance operations. Processing circuitry may obtain a custom backlog indicator for at least a subset of the workloads. A priority manager may calculate a single weighted backlog indicator value for each of the workloads by applying configurable weights to the generic backlog indicators and the custom backlog indicators. The data platform may schedule the workloads for execution on the data platform based on the single weighted backlog indicator value calculated for each workload. In some examples, the data platform processes the workloads according to the scheduling.

    Scaling virtualization resource units of applications

    公开(公告)号:US12164970B2

    公开(公告)日:2024-12-10

    申请号:US18228173

    申请日:2023-07-31

    Applicant: Cohesity, Inc.

    Abstract: A request to launch an application that is comprised of a plurality of layers is received. Each layer of the plurality of layers of the application is comprised of one or more corresponding virtualization resource units. The one or more corresponding virtualization resource units at each of the plurality of layers of the application is expressed as a resource ratio. It is determined that a surplus of resources is available for one or more applications. In response to determining that the surplus of resources is available for one or more applications, a priority associated with the application is determined. A version of the application is launched based on the determined priority associated with the application. The launched version of the application maintains the resource ratio.

    Restoring a database using a fully hydrated backup

    公开(公告)号:US12147305B2

    公开(公告)日:2024-11-19

    申请号:US18220099

    申请日:2023-07-10

    Applicant: Cohesity, Inc.

    Abstract: A request to restore a database to a particular point in time is received. It is determined that a closest preceding backup to the particular point in time is an incremental backup. One or more transaction log file segments needed to restore the database to the particular point in time are determined. An updated incremental backup is generated by applying the one or more determined transaction log file segments to the incremental backup. The updated incremental backup is restored to a primary system.

    EFFICIENT BACKUP AFTER A RESTORE OPERATION

    公开(公告)号:US20240370194A1

    公开(公告)日:2024-11-07

    申请号:US18771389

    申请日:2024-07-12

    Applicant: Cohesity, Inc.

    Abstract: A request to restore a specific backup instance is received. In response to the received request to restore the specific backup instance, a new reference backup instance based on the specific backup instance stored at the storage controlled by the backup system is created at a storage controlled by a backup system. Data associated with the specific backup instance is provided to a recipient system from the storage associated with a backup system. A constructive incremental backup snapshot of the recipient system is performed based on the new reference backup instance.

    Virtual machine boot data prediction

    公开(公告)号:US12106116B2

    公开(公告)日:2024-10-01

    申请号:US18220120

    申请日:2023-07-10

    Applicant: Cohesity, Inc.

    Inventor: Ayushi Jain Vedant

    Abstract: An indication that a virtual machine is starting is received. Requested data blocks associated with the virtual machine are identified. Based on identifiers of the requested data blocks, a trained learning model is used to predict one or more subsequent data blocks likely to be requested while the virtual machine is starting. The one or more subsequent data blocks are caused to be preloaded in a cache storage. It is determined that the one or more predicted subsequent data blocks are incorrect. It is determined that an end of a boot sequence associated with the virtual machine has been reached. In response to a determination that the end of the boot sequence associated with the virtual machine has been reached, the boot sequence associated with the virtual machine is used to update the trained learning model.

    DISTRIBUTED JOURNAL FOR HIGH PERFORMANCE CONTINUOUS DATA PROTECTION

    公开(公告)号:US20240111617A1

    公开(公告)日:2024-04-04

    申请号:US17957303

    申请日:2022-09-30

    Applicant: Cohesity, Inc.

    CPC classification number: G06F11/0787 G06F11/0727

    Abstract: A set of data changes to a storage associated with a source system is received. for recording the received set of changes, one or more data logs among a plurality of data logs stored in different nodes of a storage system is dynamically selected based at least in part on a dynamic analysis of metrics of the different nodes of the storage system. The data changes are logged in the one or more selected data logs. A reference to a portion of the one or more selected data logs associated with storing the data changes is recorded in a locator register log.

    EFFICIENTLY STORING DATA IN A CLOUD STORAGE
    9.
    发明公开

    公开(公告)号:US20240036751A1

    公开(公告)日:2024-02-01

    申请号:US18486861

    申请日:2023-10-13

    Applicant: Cohesity, Inc.

    CPC classification number: G06F3/0638 G06F3/0604 G06F3/067

    Abstract: A specification of content to be stored in a cloud storage is received at a client-side component. A first portion of the content is divided into a plurality of data chunks. One or more data chunks of the plurality of data chunks that are to be sent via a network to be stored in the cloud storage are identified. It is determined whether a batch size of the one or more identified data chunks does not meets a threshold size. One or more data chunks of a second portion of the content that are to be stored in the cloud storage are identified. It is determined that a size of a second batch of data chunks that includes the one or more identified data chunks of the first portion of the content and the one or more identified data chunks of the second portion of the content does not meet the threshold size. It is determined that a batch period is greater than or equal to a batch threshold period. The second batch of data chunks is written to a storage of a cloud server included in a data plane.

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