公开(公告)号：US11709704B2

公开(公告)日：2023-07-25

申请号：US17408259

申请日：2021-08-20

Applicant: Cisco Technology, Inc.

Inventor： Komei Shimamura ,  Xinyuan Huang ,  Amit Kumar Saha ,  Debojyoti Dutta

IPC: G06F9/48 ,  G06F9/50

CPC classification number: G06F9/4881 ,  G06F9/5038 ,  G06F9/5066 ,  G06F9/5088

Abstract: In one embodiment, a method for FPGA accelerated serverless computing comprises receiving, from a user, a definition of a serverless computing task comprising one or more functions to be executed. A task scheduler performs an initial placement of the serverless computing task to a first host determined to be a first optimal host for executing the serverless computing task. The task scheduler determines a supplemental placement of a first function to a second host determined to be a second optimal host for accelerating execution of the first function, wherein the first function is not able to accelerated by one or more FPGAs in the first host. The serverless computing task is executed on the first host and the second host according to the initial placement and the supplemental placement.

公开(公告)号：US20210312324A1

公开(公告)日：2021-10-07

申请号：US16842334

申请日：2020-04-07

Applicant: Cisco Technology, Inc.

Inventor： Xinyuan Huang ,  Debojyoti Dutta ,  Elvira Dzhuraeva

IPC: G06N20/00 ,  G06K9/62

Abstract: The present disclosure is directed to system and methods for providing machine learning tools such as Kubeflow and other similar ML platforms with human-in-the-loop capabilities for optimizing the resulting machine models. In one aspect, a machine learning integration tool includes memory having computer-readable instructions stored therein and one or more processors configured to execute the computer-readable instructions to execute a workflow associated with a machine learning process; determine, during execution of the machine learning process, that non-automated feedback is required; generate a virtual input unit for receiving the non-automated feedback; modify raw data used for the machine learning process with the non-automated feedback to yield updated data; and complete the machine learning process using the updated data.

公开(公告)号：US20200279187A1

公开(公告)日：2020-09-03

申请号：US16288563

申请日：2019-02-28

Applicant: Cisco Technology, Inc.

Inventor： Xinyuan Huang ,  Debojyoti Dutta

IPC: G06N20/00 ,  G06F11/34

Abstract: Joint hyper-parameter optimizations and infrastructure configurations for deploying a machine learning model can be generated based upon each other and output as a recommendation. A model hyper-parameter optimization may tune model hyper-parameters based on an initial set of hyper-parameters and resource configurations. The resource configurations may then be adjusted or generated based on the tuned model hyper-parameters. Further model hyper-parameter optimizations and resource configuration adjustments can be performed sequentially in a loop until a threshold performance for training the model based on the model hyper-parameters or a threshold improvement between loops is detected.

公开(公告)号：US20190196879A1

公开(公告)日：2019-06-27

申请号：US15850230

申请日：2017-12-21

Applicant: Cisco Technology, Inc.

Inventor： Debojyoti Dutta ,  Xinyuan Huang

IPC: G06F9/50 ,  H04L12/24

CPC classification number: G06F9/5083 ,  G06F9/5038 ,  G06F9/5044 ,  G06F9/505 ,  H04L41/0803

Abstract: Systems, methods, computer-readable media are disclosed for determining a point of delivery (POD) device or network component on a cloud for workload and resource placement in a multi-cloud environment. A method includes determining a first amount of data for transitioning from performing a first function on input data to performing a second function on a first outcome of the first function; determining a second amount of data for transitioning from performing the second function on the first outcome to performing a third function on a second outcome of the second function; determining a processing capacity for each of one or more network nodes on which the first function and the third function are implemented; and selecting the network node for implementing the second function based on the first amount of data, the second amount of data, and the processing capacity for each of the network nodes.

公开(公告)号：US10333958B2

公开(公告)日：2019-06-25

申请号：US15350717

申请日：2016-11-14

Applicant: Cisco Technology, Inc.

Inventor： Xinyuan Huang ,  Sarvesh Ranjan ,  Olivia Zhang ,  Yathiraj B. Udupi ,  Debojyoti Dutta

IPC: H04L29/06 ,  H04L12/26 ,  H04L12/24

Abstract: In one embodiment, a device in a network receives a first plurality of measurements for network metrics captured during a first time period. The device determines a first set of correlations between the network metrics using the first plurality of measurements captured during the first time period. The device receives a second plurality of measurements for the network metrics captured during a second time period. The device determines a second set of correlations between the network metrics using the second plurality of measurements captured during the second time period. The device identifies a difference between the first and second sets of correlations between the network metrics as a network anomaly.

公开(公告)号：US10205677B2

公开(公告)日：2019-02-12

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: H04L29/08 ,  H04L12/911

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：US20180300173A1

公开(公告)日：2018-10-18

申请号：US15485910

申请日：2017-04-12

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Komei Shimamura ,  Timothy Okwii ,  Debojyoti Dutta ,  Yathiraj B. Udupi ,  Rahul Ramakrishna ,  Xinyuan Huang

IPC: G06F9/48 ,  G06F9/50

Abstract: In one embodiment, a method for serverless computing comprises: receiving a task definition, wherein the task definition comprises a first task and a second task chained to the first task; adding the first task and the second task to a task queue; executing the first task from the task queue using hardware computing resources in a first serverless environment associated with a first serverless environment provider; and executing the second task from the task queue using hardware computing resources in a second serverless environment selected based on a condition on an output of the first task.

公开(公告)号：US09846589B2

公开(公告)日：2017-12-19

申请号：US14731166

申请日：2015-06-04

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Yathiraj B. Udupi ,  Debojyoti Dutta ,  Xinyuan Huang ,  Rishabh Jain ,  Aparupa Das Gupta

IPC: G06F15/16 ,  G06F9/455 ,  H04L29/08

CPC classification number: G06F9/45533 ,  G06F9/45558 ,  G06F2009/4557 ,  H04L67/10

Abstract: The present disclosure describes a method for virtual machine placement optimization based on generalized organizational scenarios. The method involves defining a variable matrix (wherein each entry of the variable matrix indicate whether a particular virtual machine is to be placed on a particular host server), a first set of variables (wherein each variable of the first set of variables indicate whether a particular host server has at least one virtual machine to be placed thereon), a second set of variables (wherein the second set of variables indicates for all possible pairs of host servers whether two particular host servers both have at least one virtual machine to be placed thereon). The method further involves determining a set of virtual machine to host server allocations by solving a constraints optimization problem over the first set of variables and the second set of variables based on a generalized organizational scenario.

公开(公告)号：US20160359668A1

公开(公告)日：2016-12-08

申请号：US14731166

申请日：2015-06-04

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Yathiraj B. Udupi ,  Debojyoti Dutta ,  Xinyuan Huang ,  Rishabh Jain ,  Aparupa Das Gupta

IPC: H04L12/24 ,  H04L29/08

CPC classification number: G06F9/45533 ,  G06F9/45558 ,  G06F2009/4557 ,  H04L67/10

Abstract: The present disclosure describes a method for virtual machine placement optimization based on generalized organizational scenarios. The method involves defining a variable matrix (wherein each entry of the variable matrix indicate whether a particular virtual machine is to be placed on a particular host server), a first set of variables (wherein each variable of the first set of variables indicate whether a particular host server has at least one virtual machine to be placed thereon), a second set of variables (wherein the second set of variables indicates for all possible pairs of host servers whether two particular host servers both have at least one virtual machine to be placed thereon). The method further involves determining a set of virtual machine to host server allocations by solving a constraints optimization problem over the first set of variables and the second set of variables based on a generalized organizational scenario.

Abstract translation: 本公开描述了基于广义组织场景的用于虚拟机放置优化的方法。 该方法涉及定义可变矩阵（其中变量矩阵的每个条目表示特定虚拟机是否要被放置在特定主机服务器上），第一组变量（其中，第一组变量的每个变量指示是否 特定主机服务器具有至少一个要放置在其上的虚拟机），第二组变量（其中第二组变量指示所有可能的主机服务器对，无论两个特定主机服务器是否具有至少一个待放置的虚拟机 ）。 该方法还包括通过基于广义组织场景解决第一组变量和第二组变量来解决约束优化问题来确定一组虚拟机以主机服务器分配。