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1. 发明授权

US11681945B2 Distributed learning model for fog computing 有权
公开(公告)号：US11681945B2
公开(公告)日：2023-06-20
申请号：US16298465
申请日：2019-03-11
申请人： Cisco Technology, Inc.
发明人： Robert Edgar Barton , Jerome Henry , Abhishek Kumar
IPC分类号： G06N20/00 , G06F9/50 , G06F11/30 , G06N5/043
CPC分类号： G06N20/00 , G06F9/505 , G06F11/3006 , G06N5/043
摘要： The disclosed technology relates to a process for metered training of fog nodes within the fog layer. The metered training allows the fog nodes to be continually trained within the fog layer without the need for the cloud. Furthermore, the metered training allows the fog node to operate normally as the training is performed only when spare resources are available at the fog node. The disclosed technology also relates to a process of sharing better trained machine learning models of a fog node with other similar fog nodes thereby speeding up the training process for other fog nodes within the fog layer.

2. 发明授权

US11049033B2 Deriving highly interpretable cognitive patterns for network assurance 有权
公开(公告)号：US11049033B2
公开(公告)日：2021-06-29
申请号：US15869639
申请日：2018-01-12
申请人： Cisco Technology, Inc.
发明人： Vinay Kumar Kolar , Vikram Kumaran , Abhishek Kumar , Santosh Ghanshyam Pandey , Jean-Philippe Vasseur , Grégory Mermoud
IPC分类号： G06N7/00 , H04L12/24 , G06N20/00 , H04L12/26
摘要： In one embodiment, a network assurance system that monitors a network labels time periods with positive labels, based on the network assurance system detecting problems in the network during the time periods. The network assurance system assigns tags to discrete portions of a feature space of measurements from the monitored network, based on whether a particular range of values in the feature space has a threshold probability of occurring during a positively-labeled time period. The network assurance system determines a set of the assigned tags that frequently co-occur with the positively-labeled time periods in which problems are detected in the network. The network assurance system causes performance of a mitigation action in the network based on the set of assigned tags that frequently co-occur with the positively-labeled time periods.

3. 发明申请

US20210067430A1 ANALYZING THE IMPACT OF NETWORK EVENTS ACROSS TIME 有权
公开(公告)号：US20210067430A1
公开(公告)日：2021-03-04
申请号：US16560748
申请日：2019-09-04
申请人： Cisco Technology, Inc.
发明人： Javier Cruz Mota , Erwan Barry Tarik Zerhouni , Abhishek Kumar
IPC分类号： H04L12/26
摘要： The present technology pertains to a system, method, and non-transitory computer-readable medium for evaluating the impact of network changes. The technology can detect a temporal event, wherein the temporal event is associated with a change in a network configuration, implementation, or utilization. The technology defines, based on a nature of the temporal event, a first period prior to the temporal event or a second period posterior to the temporal event. The technology compares network data collected in the first period and network data collected in the second period.

4. 发明申请

US20200293925A1 DISTRIBUTED LEARNING MODEL FOR FOG COMPUTING 审中-公开
公开(公告)号：US20200293925A1
公开(公告)日：2020-09-17
申请号：US16298465
申请日：2019-03-11
申请人： Cisco Technology, Inc.
发明人： Robert Edgar Barton , Jerome Henry , Abhishek Kumar
IPC分类号： G06N5/04 , G06N20/00 , G06F9/50 , G06F11/30
摘要： The disclosed technology relates to a process for metered training of fog nodes within the fog layer. The metered training allows the fog nodes to be continually trained within the fog layer without the need for the cloud. Furthermore, the metered training allows the fog node to operate normally as the training is performed only when spare resources are available at the fog node. The disclosed technology also relates to a process of sharing better trained machine learning models of a fog node with other similar fog nodes thereby speeding up the training process for other fog nodes within the fog layer.

5. 发明授权

US10742486B2 Analyzing common traits in a network assurance system 有权
公开(公告)号：US10742486B2
公开(公告)日：2020-08-11
申请号：US15864565
申请日：2018-01-08
申请人： Cisco Technology, Inc.
发明人： Grégory Mermoud , Abhishek Kumar , Jean-Philippe Vasseur
IPC分类号： H04L12/24
摘要： In one embodiment, a network assurance system discretizes parameter values of a plurality of time series of measurements obtained from a monitored network by assigning tags to the parameter values. The network assurance system detects occurrences of a particular type of failure event in the monitored network. The network assurance system identifies a set of the assigned tags that frequently co-occur with the occurrences of the particular type of failure event. The network assurance system determines, using a Bayesian framework, rankings for the tags in the identified set based on how well each of the tags acts as a predictor of the failure event. The network assurance system initiates performance of a corrective measure for the failure event based in part on the determined rankings for the tags in the identified set.

6. 发明授权

US11769075B2 Dynamic machine learning on premise model selection based on entity clustering and feedback 有权
公开(公告)号：US11769075B2
公开(公告)日：2023-09-26
申请号：US16548710
申请日：2019-08-22
申请人： Cisco Technology, Inc.
发明人： Erwan Barry Tarik Zerhouni , Abhishek Kumar , Javier Cruz Mota
IPC分类号： G06N20/00 , G06Q10/067
CPC分类号： G06N20/00 , G06Q10/067
摘要： The disclosed technology relates to a process of providing dynamic machine learning on premise model selection. In particular, a set of machine learned models are generated and provided to an on premise computing device. The machine learned models are generated using a cluster of customer data (e.g. telemetric data) stored on a computing network having different ranges of computational complexity. One of the machine learned models from the set of machine learned models will be selected based on the current available computational resources detected at the on premise computing device. Different machine learned models from the set of machine learned models can then be selected based on changes in the available computational resources and/or customer feedback.

7. 发明申请

US20200293942A1 DISTRIBUTED LEARNING MODEL FOR FOG COMPUTING 审中-公开
公开(公告)号：US20200293942A1
公开(公告)日：2020-09-17
申请号：US16298881
申请日：2019-03-11
申请人： Cisco Technology, Inc.
发明人： Robert Edgar Barton , Jerome Henry , Abhishek Kumar
IPC分类号： G06N20/00
摘要： The disclosed technology relates to a process for metered training of fog nodes within the fog layer. The metered training allows the fog nodes to be continually trained within the fog layer without the need for the cloud. Furthermore, the metered training allows the fog node to operate normally as the training is performed only when spare resources are available at the fog node. The disclosed technology also relates to a process of sharing better trained machine learning models of a fog node with other similar fog nodes thereby speeding up the training process for other fog nodes within the fog layer.

8. 发明授权

US10091087B2 Methods and systems for load balancing based on data shard leader 有权
公开(公告)号：US10091087B2
公开(公告)日：2018-10-02
申请号：US14804195
申请日：2015-07-20
申请人： Cisco Technology, Inc.
发明人： Moiz Raja , Abhishek Kumar , Kamalkrishna Rameshan , Harmanpreet Singh
IPC分类号： G06F17/30 , H04L12/26 , H04L12/803
摘要： Disclosed are systems, methods, and computer-readable storage media for load balance resources amongst computing nodes. Various embodiments of the present technology can be used to, prior to assigning a first data shard included in a first cluster of data shards as a leader of the first cluster of data shards, determine whether a first computing node storing the first data shard also stores a second data shard designated as a leader of a second cluster of data shards, yielding a determination, and when the determination indicates that the first computing node stores the second data shard designated as the leader of the second cluster of data shards, designate the first data shard as non-electable to be the leader of the first cluster of data shards and electing an alternate data shard included in the first cluster of data shards as the leader of the first cluster of data shards.

9. 发明申请

US20170024453A1 METHODS AND SYSTEMS FOR LOAD BALANCING BASED ON DATA SHARD LEADER 审中-公开
标题翻译：基于数据引擎的负载均衡的方法和系统
公开(公告)号：US20170024453A1
公开(公告)日：2017-01-26
申请号：US14804195
申请日：2015-07-20
申请人： Cisco Technology, Inc.
发明人： Moiz Raja , Abhishek Kumar , Kamalkrishna Rameshan , Harmanpreet Singh
IPC分类号： G06F17/30 , H04L12/26 , H04L12/803
CPC分类号： H04L43/16 , G06F17/30584 , H04L41/0896 , H04L47/125
摘要： Disclosed are systems, methods, and computer-readable storage media for load balance resources amongst computing nodes. Various embodiments of the present technology can be used to, prior to assigning a first data shard included in a first cluster of data shards as a leader of the first cluster of data shards, determine whether a first computing node storing the first data shard also stores a second data shard designated as a leader of a second cluster of data shards, yielding a determination, and when the determination indicates that the first computing node stores the second data shard designated as the leader of the second cluster of data shards, designate the first data shard as non-electable to be the leader of the first cluster of data shards and electing an alternate data shard included in the first cluster of data shards as the leader of the first cluster of data shards.
摘要翻译：公开了用于计算节点之间的负载平衡资源的系统，方法和计算机可读存储介质。可以使用本技术的各种实施例，在将包括在第一数据分片簇中的第一数据分片分配为第一数据分片簇的前导者之前，确定存储第一数据分片的第一计算节点是否也存储指定为第二数据分片簇的引导者的第二数据分片，产生确定，并且当确定指示第一计算节点存储指定为第二数据分片簇的前导的第二数据片时，指定第一数据分片数据分片不可选择成为第一个数据分片集群的领导者，并选择第一个数据分片集群中包含的备用数据分片作为第一个数据分片集群的领导者。

10. 发明公开

US20230281515A1 DISTRIBUTED LEARNING MODEL FOR FOG COMPUTING 审中-公开
公开(公告)号：US20230281515A1
公开(公告)日：2023-09-07
申请号：US18315281
申请日：2023-05-10
申请人： Cisco Technology, Inc.
发明人： Robert Edgar Barton , Jerome Henry , Abhishek Kumar
IPC分类号： G06N20/00 , G06F9/50 , G06F11/30 , G06N5/043
CPC分类号： G06N20/00 , G06F9/505 , G06F11/3006 , G06N5/043
摘要： The disclosed technology relates to a process for metered training of fog nodes within the fog layer. The metered training allows the fog nodes to be continually trained within the fog layer without the need for the cloud. Furthermore, the metered training allows the fog node to operate normally as the training is performed only when spare resources are available at the fog node. The disclosed technology also relates to a process of sharing better trained machine learning models of a fog node with other similar fog nodes thereby speeding up the training process for other fog nodes within the fog layer.

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