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公开(公告)号：US20180027004A1
公开(公告)日：2018-01-25
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
CPC classification number: H04L63/1425 , H04L41/0631 , H04L41/16 , H04L43/04 , H04L43/08 , H04L63/162
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.
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公开(公告)号：US12165390B2
公开(公告)日：2024-12-10
申请号：US17582959
申请日：2022-01-24
Applicant: Cisco Technology, Inc.
Inventor： Elvira Dzhuraeva , Xinyuan Huang , Ashutosh Arwind Malegaonkar , Patrick James Riel
IPC: G06V10/774 , G06V10/776 , G06V10/82
Abstract: Systems, methods, and computer-readable media are disclosed for dynamically adjusting a configuration of a pre-processor and/or a post-processor of a machine learning system. In one aspect, a machine learning system can receive raw data at a pre-processor where the pre-processor being configured to generate pre-processed data, train a machine learning model based on the pre-processed data to generate output data, process the output data at a post-processor to generate inference data, and adjust, by a controller, configuration of one or a combination of the pre-processor and the post-processor based on the inference data.
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公开(公告)号：US11983104B2
公开(公告)日：2024-05-14
申请号：US17582997
申请日：2022-01-24
Applicant: Cisco Technology, Inc.
Inventor： Elvira Dzhuraeva , Patrick James Riel , Xinyuan Huang , Ashutosh Arwind Malegaonkar
IPC: G06F11/36
CPC classification number: G06F11/3692
Abstract: Systems, methods, and computer-readable media are disclosed for validating a machine learning model. In one aspect, a machine learning model validation system can receive a test machine learning model, analyze an output of the test machine learning model, determine a degree of similarity between the test machine learning model and one or more machine learning models stored in a database based on the output of the test machine learning model, and determining whether the test machine learning model complies with a set of validation rules based on the degree of the similarity with respect to one or more thresholds.
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公开(公告)号：US11016673B2
公开(公告)日：2021-05-25
申请号：US15931302
申请日：2020-05-13
Applicant: Cisco Technology, Inc.
Inventor： Xinyuan Huang , Johnu George , Marc Solanas Tarre , Komei Shimamura , Purushotham Kamath , Debojyoti Dutta
Abstract: Aspects of the technology provide improvements to a Serverless Computing (SLC) workflow by determining when and how to optimize SLC jobs for computing in a Distributed Computing Framework (DCF). DCF optimization can be performed by abstracting SLC tasks into different workflow configurations to determined optimal arrangements for execution in a DCF environment. A process of the technology can include steps for receiving an SLC job including one or more SLC tasks, executing one or more of the tasks to determine a latency metric and a throughput metric for the SLC tasks, and determining if the SLC tasks should be converted to a Distributed Computing Framework (DCF) format based on the latency metric and the throughput metric. Systems and machine-readable media are also provided.
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公开(公告)号：US20200272338A1
公开(公告)日：2020-08-27
申请号：US15931302
申请日：2020-05-13
Applicant: Cisco Technology, Inc.
Inventor： Xinyuan Huang , Johnu George , Marc Solanas Tarre , Komei Shimamura , Purushotham Kamath , Debojyoti Dutta
Abstract: Aspects of the technology provide improvements to a Serverless Computing (SLC) workflow by determining when and how to optimize SLC jobs for computing in a Distributed Computing Framework (DCF). DCF optimization can be performed by abstracting SLC tasks into different workflow configurations to determined optimal arrangements for execution in a DCF environment. A process of the technology can include steps for receiving an SLC job including one or more SLC tasks, executing one or more of the tasks to determine a latency metric and a throughput metric for the SLC tasks, and determining if the SLC tasks should be converted to a Distributed Computing Framework (DCF) format based on the latency metric and the throughput metric. Systems and machine-readable media are also provided.
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Patent Agency Ranking
公开(公告)号：US10705882B2
公开(公告)日：2020-07-07
申请号：US15850230
申请日：2017-12-21
Applicant: Cisco Technology, Inc.
Inventor： Debojyoti Dutta , Xinyuan Huang
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.
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公开(公告)号：US10659387B2
公开(公告)日：2020-05-19
申请号：US16232775
申请日：2018-12-26
Applicant: Cisco Technology, Inc.
Inventor： Yathiraj B. Udupi , Xinyuan Huang , Aparupa Das Gupta , Marc Solanas Tarre , Rishabh Jain
IPC: H04L12/911 , H04L29/08
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.
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公开(公告)号：US10353800B2
公开(公告)日：2019-07-16
申请号：US15786790
申请日：2017-10-18
Applicant: Cisco Technology, Inc.
Inventor： Mingye Chen , Xinyuan Huang , Debojyoti Dutta
Abstract: Systems, methods, and computer-readable media are disclosed for graph based monitoring and management of network components of a distributed streaming system. In one aspect, a method includes generating, by a processor, a first metrics and a second metrics based on data collected on a system; generating, by the processor, a topology graph representing data flow within the system; generating, by the processor, at least one first metrics graph corresponding to the first metrics based in part on the topology graph; generating, by the processor, at least one second metrics graph corresponding to the second metrics based in part on the topology graph; identifying, by the processor, a malfunction within the system based on a change in at least one of the first metrics graph and the second metrics graph; and sending, by the processor, a feedback on the malfunction to an operational management component of the system.
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公开(公告)号：US20190149481A1
公开(公告)日：2019-05-16
申请号：US16232775
申请日：2018-12-26
Applicant: Cisco Technology, Inc.
Inventor： Yathiraj B. Udupi , Xinyuan Huang , Aparupa Das Gupta , Marc Solanas Tarre , Rishabh Jain
IPC: H04L12/911 , H04L29/08
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.
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公开(公告)号：US20230236960A1
公开(公告)日：2023-07-27
申请号：US17582997
申请日：2022-01-24
Applicant: Cisco Technology, Inc.
Inventor： Elvira Dzhuraeva , Patrick James Riel , Xinyuan Huang , Ashutosh Arwind Malegaonkar
IPC: G06F11/36
CPC classification number: G06F11/3692
Abstract: Systems, methods, and computer-readable media are disclosed for validating a machine learning model. In one aspect, a machine learning model validation system can receive a test machine learning model, analyze an output of the test machine learning model, determine a degree of similarity between the test machine learning model and one or more machine learning models stored in a database based on the output of the test machine learning model, and determining whether the test machine learning model complies with a set of validation rules based on the degree of the similarity with respect to one or more thresholds.
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