公开(公告)号：US20160334998A1

公开(公告)日：2016-11-17

申请号：US14713851

申请日：2015-05-15

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Johnu George ,  Kai Zhang ,  Yathiraj B. Udupi ,  Debojyoti Dutta

IPC: G06F3/06 ,  G06F17/30

CPC classification number: G06F3/0608 ,  G06F3/0605 ,  G06F3/0631 ,  G06F3/0644 ,  G06F3/0665 ,  G06F3/067 ,  G06F17/30914 ,  H04L67/1097

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.

Abstract translation: 实施例包括接收与分布式存储系统的租户相关联的数据存储模块的指示，为租户的数据分配磁盘的分区，在数据存储模块和磁盘分区之间创建第一关联，创建一个 数据存储模块和租户之间的第二关联，以及基于为租户配置的一个或多个策略来创建数据存储模块的规则。 实施例还包括接收为租户选择的订阅模式的类型的指示，以及至少部分地基于为租户选择的订阅模型来选择要分配的磁盘分区。 更具体的实施例包括生成指示数据存储模块和磁盘分区之间的第一关联并指示数据存储模块和租户之间的第二关联的存储映射。

公开(公告)号：US11354039B2

公开(公告)日：2022-06-07

申请号：US16879612

申请日：2020-05-20

Applicant: Cisco Technology, Inc.

Inventor： Johnu George ,  Kai Zhang ,  Yathiraj B. Udupi ,  Debojyoti Dutta

IPC: G06F3/06 ,  H04L67/1097 ,  G06F16/84

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.

公开(公告)号：US10671289B2

公开(公告)日：2020-06-02

申请号：US16268397

申请日：2019-02-05

Applicant: Cisco Technology, Inc.

Inventor： Johnu George ,  Kai Zhang ,  Yathiraj B. Udupi ,  Debojyoti Dutta

IPC: G06F3/06 ,  H04L29/08 ,  G06F16/84

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.

公开(公告)号：US09830240B2

公开(公告)日：2017-11-28

申请号：US14712762

申请日：2015-05-14

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Johnu George ,  Marc Solanas Tarre ,  Kai Zhang ,  Yathiraj B. Udupi

IPC: G06F11/00 ,  G06F11/20 ,  G06F3/06

CPC classification number: G06F11/2094 ,  G06F3/0619 ,  G06F3/0647 ,  G06F3/067 ,  G06F11/00

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.

公开(公告)号：US09769084B2

公开(公告)日：2017-09-19

申请号：US14242131

申请日：2014-04-01

Applicant: Cisco Technology, Inc.

Inventor： Yathiraj B. Udupi ,  Debojyoti Dutta ,  Madhav V. Marathe ,  Raghunath O. Nambiar

IPC: G06F15/173 ,  H04L12/911 ,  G06F9/50

CPC classification number: H04L47/78 ,  G06F9/5044

Abstract: Systems and methods are described for allocating resources in a cloud computing environment. The method includes receiving a computing request, the request for use of at least one virtual machine and a portion of memory. In response to the request, a plurality of hosts is identified and a cost function is formulated using at least a portion of those hosts. Based on the cost function, at least one host that is capable of hosting the virtual machine and memory is selected.

公开(公告)号：US20170149687A1

公开(公告)日：2017-05-25

申请号：US14951110

申请日：2015-11-24

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Yathiraj B. Udupi ,  Xinyuan Huang ,  Aparupa Das Gupta ,  Marc Solanas Tarre ,  Rishabh Jain

IPC: H04L12/911 ,  H04L29/08

CPC classification number: H04L47/78 ,  H04L67/1002

Abstract: The present disclosure describes a method for cloud resource placement optimization. A resources monitor monitors state information associated with cloud resources and physical hosts in the federated cloud having a plurality of clouds managed by a plurality of cloud providers. A rebalance trigger triggers a rebalancing request to initiate cloud resource placement optimization based on one or more conditions. A cloud resource placement optimizer determines an optimized placement of cloud resources on physical hosts across the plurality of clouds in the federated cloud based on (1) costs including migration costs, (2) the state information, and (3) constraints, wherein each physical host is identified in the constraints-driven optimization solver by an identifier of a respective cloud provider and an identifier of the physical host. A migrations enforcer determines an ordered migration plan and transmits requests to place or migrate cloud resources according to the ordered migration plan.

公开(公告)号：US09575828B2

公开(公告)日：2017-02-21

申请号：US14794676

申请日：2015-07-08

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Amit Agarwal ,  Debojyoti Dutta ,  Johnu George ,  Manoj Sharma ,  Kai Zhang ,  Marc Solanas Tarre ,  Aparupa Das Gupta ,  Yathiraj B. Udupi ,  Seth Mason

IPC: G06F11/00 ,  G06F11/07 ,  H04L29/08

CPC classification number: G06F11/079 ,  G06F11/0709 ,  G06F11/0727 ,  G06F11/0751 ,  G06F11/0787 ,  G06F11/0793 ,  H04L67/1034 ,  H04L67/1097

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。

公开(公告)号：US20160350146A1

公开(公告)日：2016-12-01

申请号：US14726336

申请日：2015-05-29

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Yathiraj B. Udupi ,  Debojyoti Dutta ,  Madhav V. Marathe ,  Raghunath O. Nambiar

IPC: G06F9/455 ,  H04L12/911 ,  H04L12/14

CPC classification number: G06F9/45558 ,  G06F9/50 ,  G06F2009/45595 ,  H04L41/0893 ,  H04L45/123 ,  H04L45/127 ,  H04L45/586

Abstract: The present disclosure describes, among other things, a method for optimizing task scheduling in an optimally placed virtualized cluster using network cost optimizations. The method comprises computing a first network cost matrix for a plurality of available physical nodes, determining a first solution to a first optimization problem of virtual machine placement onto the plurality of available physical nodes based on the first network cost matrix, wherein the first solution comprises one or more optimally placed virtual machines, computing a second network cost matrix for allocating one or more tasks to one or more possible optimally placed virtual machines of the first solution, and determining a second solution to a second optimization problem of task allocation onto one or more possible optimally placed virtual machines of the first solution based on the second network cost matrix.

Abstract translation: 本公开尤其描述了一种使用网络成本优化在优化的虚拟化集群中优化任务调度的方法。 该方法包括计算多个可用物理节点的第一网络成本矩阵，基于第一网络成本矩阵来确定虚拟机放置到多个可用物理节点上的第一优化问题的第一解决方案，其中第一解决方案包括 一个或多个优化放置的虚拟机，计算用于将一个或多个任务分配给所述第一解决方案的一个或多个可能的最佳放置的虚拟机的第二网络成本矩阵，以及确定任务分配到一个或多个任务上的第二优化问题的第二解决方案 基于第二网络成本矩阵的第一解决方案的更可能的最佳放置的虚拟机。

公开(公告)号：US20160142336A1

公开(公告)日：2016-05-19

申请号：US14541524

申请日：2014-11-14

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Yathiraj B. Udupi ,  Debojyoti Dutta ,  Lewis W. Tucker

IPC: H04L12/923 ,  H04L12/743 ,  H04L29/08

CPC classification number: H04L45/7453 ,  G06F9/5077 ,  H04L41/0813 ,  H04L41/0853 ,  H04L47/762 ,  H04L67/10 ,  H04L67/32 ,  H04L67/327 ,  H04L69/24

Abstract: The present disclosure describes a distributed, advertisement-based, solution for scheduling virtual resources in cloud infrastructures such as the OpenStack. The scheduling algorithm distributes the scheduling requirements and host state feasibility checks to the individual hosts in the datacenter, which can periodically send a summarized advertisement to the scheduler controller listing the number of instances of different type(s) of virtual resources that a particular host can support. The scheduler controller, thus no longer has to compute and maintain individual host states, and the scheduling problem is reduced to selecting the feasible advertisements that satisfy a given request. The solution can be extended to a scenario of multiple scheduler controllers using the same distributed, advertisement-based, approach.

公开(公告)号：US20210397982A1

公开(公告)日：2021-12-23

申请号：US17464530

申请日：2021-09-01

Applicant: Cisco Technology, Inc.

Inventor： Aparupa Das Gupta ,  Rahul Ramakrishna ,  Yathiraj B. Udupi ,  Debojyoti Dutta ,  Manoj Sharma

IPC: G06N5/02 ,  G06F11/30

Abstract: A method for ranking detected anomalies is disclosed. The method includes generating a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule. The method further includes ranking nodes of the graph based on the edge weights. The method further includes ranking detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies.