Tenant-level sharding of disks with tenant-specific storage modules to enable policies per tenant in a distributed storage system

    公开(公告)号:US11354039B2

    公开(公告)日:2022-06-07

    申请号:US16879612

    申请日:2020-05-20

    Abstract: Embodiments include receiving an indication of a data storage module to be associated with a tenant of a distributed storage system, allocating a partition of a disk for data of the tenant, creating a first association between the data storage module and the disk partition, creating a second association between the data storage module and the tenant, and creating rules for the data storage module based on one or more policies configured for the tenant. Embodiments further include receiving an indication of a type of subscription model selected for the tenant, and selecting the disk partition to be allocated based, at least in part, on the subscription model selected for the tenant. More specific embodiments include generating a storage map indicating the first association between the data storage module and the disk partition and indicating the second association between the data storage module and the tenant.

    SYSTEMS AND METHODS FOR PROVIDING MANAGEMENT OF MACHINE LEARNING COMPONENTS

    公开(公告)号:US20210182729A1

    公开(公告)日:2021-06-17

    申请号:US16710499

    申请日:2019-12-11

    Abstract: A method can include receiving, at a workflow controller, a machine learning workflow, the machine learning workflow associated with a first task and a second task. The first task is training a machine learning model and the second task is deploying the model. The method can include segmenting, by the workflow controller, the machine learning workflow into a first sub-workflow associated with the first task and a second sub-workflow associated with the second task, assigning a first workflow agent to the first sub-workflow and assigning a second workflow agent to the second sub-workflow, selecting, by the first workflow agent and based on first resources needed to perform the first task, a first cluster for performing the first task and selecting, by the second workflow agent and based on second resources needed to perform the second task, a second cluster for performing the second task.

    Tenant-level sharding of disks with tenant-specific storage modules to enable policies per tenant in a distributed storage system

    公开(公告)号:US10671289B2

    公开(公告)日:2020-06-02

    申请号:US16268397

    申请日:2019-02-05

    Abstract: Embodiments include receiving an indication of a data storage module to be associated with a tenant of a distributed storage system, allocating a partition of a disk for data of the tenant, creating a first association between the data storage module and the disk partition, creating a second association between the data storage module and the tenant, and creating rules for the data storage module based on one or more policies configured for the tenant. Embodiments further include receiving an indication of a type of subscription model selected for the tenant, and selecting the disk partition to be allocated based, at least in part, on the subscription model selected for the tenant. More specific embodiments include generating a storage map indicating the first association between the data storage module and the disk partition and indicating the second association between the data storage module and the tenant.

    Smart storage recovery in a distributed storage system

    公开(公告)号:US09830240B2

    公开(公告)日:2017-11-28

    申请号:US14712762

    申请日:2015-05-14

    Abstract: Embodiments include obtaining at least one system metric of a distributed storage system, generating one or more recovery parameters based on the at least one system metric, identifying at least one policy associated with data stored in a storage node of a plurality of storage nodes in the distributed storage system, and generating a recovery plan for the data based on the one or more recovery parameters and the at least one policy. In more specific embodiments, the recovery plan includes a recovery order for recovering the data. Further embodiments include initiating a recovery process to copy replicas of the data from a second storage node to a new storage node, wherein the replicas of the data are copied according to the recovery order indicated in the recovery plan.

    Correctly identifying potential anomalies in a distributed storage system
    16.
    发明授权
    Correctly identifying potential anomalies in a distributed storage system 有权
    正确识别分布式存储系统中的潜在异常

    公开(公告)号:US09575828B2

    公开(公告)日:2017-02-21

    申请号:US14794676

    申请日:2015-07-08

    Abstract: A method for assisting evaluation of anomalies in a distributed storage system is disclosed. The method includes a step of monitoring at least one system metric of the distributed storage system. The method further includes steps of maintaining a listing of patterns of the monitored system metric comprising patterns which previously did not result in a failure within one or more nodes of the distributed storage system, and, based on the monitoring, identifying a pattern (i.e., a time series motif) of the monitored system metric as a potential anomaly in the distributed storage system. The method also includes steps of automatically (i.e. without user input) performing a similarity search to determine whether the identified pattern satisfies one or more predefined similarity criteria with at least one pattern of the listing, and, upon positive determination, excepting the identified pattern from being identified as the potential anomaly.

    Abstract translation: 公开了一种用于辅助评估分布式存储系统中的异常的方法。 该方法包括监视分布式存储系统的至少一个系统度量的步骤。 该方法还包括以下步骤:维护所监视的系统度量的模式的列表,其包括先前不会在分布式存储系统的一个或多个节点内导致故障的模式,并且基于该监视,识别模式(即, 监控系统度量的时间序列主题作为分布式存储系统中的潜在异常。 该方法还包括自动执行相似性搜索(即,不进行用户输入)以确定所识别的模式是否满足具有列表的至少一种模式的一个或多个预定义相似性标准的步骤,并且在正确定义之后,除了所识别的模式 被确定为潜在的异常。

    Using tiered storage and ISTIO to satisfy SLA in model serving and updates

    公开(公告)号:US10972364B2

    公开(公告)日:2021-04-06

    申请号:US16412604

    申请日:2019-05-15

    Abstract: Systems, methods, and computer-readable storage media are provided for storing machine learned models in a tiered storage. The model serving network evaluates where the models should be stored based on the model corresponding service level agreement. The model is generally stored at the lowest tiered storage device that is still capable of satisfying the model's service level agreement. In this way, the model serving network aims to store data that achieves the cheapest cost.

    Accessing composite data structures in tiered storage across network nodes

    公开(公告)号:US10797892B2

    公开(公告)日:2020-10-06

    申请号:US15907018

    申请日:2018-02-27

    Abstract: Aspects of the disclosed technology relate to ways to determine the optimal storage of data structures across different memory device is associated with physically disparate network nodes. In some aspects, a process of the technology can include steps for receiving a first retrieval request for a first object, searching a local PMEM device for the first object based on the first retrieval request, in response to a failure to find the first object on the local PMEM device, transmitting a second retrieval request to a remote node, wherein the second retrieval request is configured to cause the remote node to retrieve the first object from a remote PMEM device. Systems and machine-readable media are also provided.

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