公开(公告)号：US20240356853A1

公开(公告)日：2024-10-24

申请号：US18137536

申请日：2023-04-21

Applicant: VMware, Inc.

Inventor： Chandan Ghosh ,  Anantha Mohan Raj, M.D. ,  Gaurav Jindal ,  Siddhant Verma ,  Saurabh Garg

IPC: H04L47/12 ,  H04L41/147 ,  H04L43/0876

CPC classification number: H04L47/12 ,  H04L41/147 ,  H04L43/0876

Abstract: Some embodiments provide a novel method for preemptively deploying gateways in a first network to one or more external networks. The first network of some embodiments includes a first gateway connecting to the one or more external networks. The method collects a set of statistics for the first gateway associated with bandwidth usage of the first gateway. The method determines that a second gateway needs to be deployed in the first network (1) by using the collected set of statistics to perform predictive modeling computations to predict a future load on the first gateway, and (2) by determining that the predicted future load exceeds a particular threshold. The method distributes a set of one or more forwarding rules to forward data message flows from a subset of machines in the first network to a particular external network through the second gateway.

公开(公告)号：US20240356816A1

公开(公告)日：2024-10-24

申请号：US18686174

申请日：2021-08-31

Applicant: Robert Bosch GmbH

Inventor： Maximilian Stark ,  Hugues Narcisse Tchouankem

IPC: H04L41/16 ,  H04L41/147 ,  H04L41/5009 ,  H04W36/30

CPC classification number: H04L41/16 ,  H04L41/147 ,  H04L41/5009 ,  H04W36/305

Abstract: According to a general aspect. the present disclosure relates to a method for predicting a quality of service (QOS) of a communication service. The method includes receiving data for predicting the quality of service of the communication service and processing the data for predicting the quality of service of the communication service by a hybrid machine learning model to generate a prediction of the quality of service (QOS) of the communication service. The hybrid machine-learning model includes a first module configured to determine and/or predict one or more characteristics of the communication service. The first module encodes expert knowledge concerning the communication service in an algorithm configured to determine and/or predict the one or more characteristics of the communication module. The hybrid machine-learning model further includes a trained second module coupled to the first module. the trained second module receiving data from the first module and/or providing data to the first module for predicting of the quality of service (QOS) of the communication service.

公开(公告)号：US20240348516A1

公开(公告)日：2024-10-17

申请号：US18748456

申请日：2024-06-20

Applicant: Avago Technologies International Sales Pte. Limited

Inventor： Bhaswar Mitra ,  Chi Ho Fredrek Choi ,  Nitin Vinay Isloorkar

IPC: H04L43/026 ,  H04L41/147

CPC classification number: H04L43/026 ,  H04L41/147

Abstract: Described herein are a device and a method for performing a network analysis. In one aspect, the device includes a feature extraction circuit, an input processing circuit, and a reconfigurable neural network circuit. In one aspect, the feature extraction circuit receives a raw packet stream, and obtains temporal statistics of a flow, according to a first packet attribute or a first flow attribute of the raw packet stream. In one aspect, the feature extraction circuit generates a feature data including one or more statistical features based on the temporal statistics of the flow. In one aspect, the input processing circuit scales the feature data to generate an adjusted feature data. In one aspect, the reconfigurable neural network circuit performs computations corresponding to a neural network on the adjusted feature data to determine a predicted network characteristic.

公开(公告)号：US12113679B2

公开(公告)日：2024-10-08

申请号：US17581935

申请日：2022-01-23

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Shaofeng Kuai

IPC: H04L41/14 ,  H04L41/147 ,  H04L41/5009 ,  H04W24/08

CPC classification number: H04L41/145 ,  H04L41/147 ,  H04L41/5009 ,  H04W24/08

Abstract: Embodiments of this application provide a training method for an application MOS model, and related device and system. A central network data analytics function (C-NWDAF) entity sends a first subscription request to an edge network data analytics function (E-NWDAF) entity, where the first subscription request is used to subscribe to a quality of service MOS level of a target service and a corresponding first network performance indicator. The first network performance indicator is a network performance indicator of a transmission network that carries the target service. The C-NWDAF entity receives the quality of service MOS level and the first network performance indicator from the E-NWDAF entity, and establishes a MOS model of the target service based on the received quality of service MOS level and the first network performance indicator.

公开(公告)号：US12101230B2

公开(公告)日：2024-09-24

申请号：US18061513

申请日：2022-12-05

Applicant: Dell Products L.P.

Inventor： Bijan Kumar Mohanty ,  Shamik Kacker ,  Hung Dinh

IPC: H04L41/147 ,  H04L41/0668 ,  H04L41/16

CPC classification number: H04L41/147 ,  H04L41/0668 ,  H04L41/16

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving a message for delivery to a message-oriented middleware (MOM) server and determining whether an anomaly is predicted in the MOM server. The method also includes, by the computing device, responsive to a determination that an anomaly is predicted in the MOM server, identifying an alternate MOM server for delivery of the message, and routing the message to the alternate MOM server. The method may also include, by the computing device, responsive to a determination that an anomaly is not predicted in the MOM server, delivering the message to the MOM server.

公开(公告)号：US12081454B2

公开(公告)日：2024-09-03

申请号：US18165525

申请日：2023-02-07

Applicant: Google LLC

Inventor： Alexandre Duarte ,  Yingchong Situ ,  Robert Van Gent ,  Walfredo Cime Filho ,  Ramy Abdelaal ,  Smeeta Jalan ,  Maya Haridasan

IPC: H04L47/78 ,  G06F9/50 ,  H04L41/14 ,  H04L41/147 ,  H04L41/50 ,  H04L41/5009 ,  H04L43/55

CPC classification number: H04L47/78 ,  G06F9/5005 ,  G06F9/5027 ,  G06F9/5061 ,  H04L41/14 ,  H04L41/147 ,  H04L41/50 ,  H04L41/5009 ,  H04L43/55

Abstract: Systems and methods for providing a guaranteed batch pool are described, including receiving a job request for execution on the pool of resources; determining an amount of time to be utilized for executing the job request based on available resources from the pool of resources and historical resource usage of the pool of resources; determining a resource allocation from the pool of resources, wherein the resource allocation spreads the job request over the amount of time; determining that the job request is capable of being executed for the amount of time; and executing the job request over the amount of time, according to the resource allocation.

公开(公告)号：US20240291726A1

公开(公告)日：2024-08-29

申请号：US18658928

申请日：2024-05-08

Applicant: CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.

Inventor： Zehua SHAO ,  Bin LIU ,  Yong LI ,  Lei ZHANG

IPC: H04L41/147 ,  G16Y10/35 ,  G16Y40/35

CPC classification number: H04L41/147 ,  G16Y10/35 ,  G16Y40/35

Abstract: The present disclosure provides a method and system for hierarchical processing of gas data based on smart gas Internet of Things (IoT). The method is executed via the smart gas IoT. The method includes obtaining gas data sending and receiving information of at least one node in at least one period. The method also includes predicting a gas data communication load of the at least one node in at least one future period based on the gas data sending and receiving information. The method further includes determining a time-sharing processing scheme for the at least one future period based on the gas data communication load of the at least one node in the at least one future period.

公开(公告)号：US20240291718A1

公开(公告)日：2024-08-29

申请号：US18505914

申请日：2023-11-09

Applicant: Oracle International Corporation

Inventor： Santhosh Kumar Vuda ,  Kiran Kumar Palukuri ,  Kumar G Varun ,  Jerry Paul Russell

IPC: H04L41/12 ,  H04L41/147

CPC classification number: H04L41/12 ,  H04L41/147

Abstract: Techniques for recommending plans to remediate a network topologies are disclosed. The techniques include predicting characteristics of the network using network topology information identifying relationships between entities in the network. The techniques further include determining a subset of the topology based on the predicted characteristics violating anomaly detection criteria. Additionally, the techniques include determining a remediation plan for modifying the subset and presenting the plan to a user.

公开(公告)号：US12074772B2

公开(公告)日：2024-08-27

申请号：US17155256

申请日：2021-01-22

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Zhiping Jiang ,  Yuren You ,  Mahdi Hemmati ,  Abbas Javadtalab

IPC: H04L41/147 ,  G06N5/04 ,  G06N20/00 ,  G06Q10/067 ,  H04L41/14 ,  H04L43/55 ,  G06Q10/0637 ,  G06Q10/0639 ,  G06Q40/08

CPC classification number: H04L41/147 ,  G06N5/04 ,  G06N20/00 ,  G06Q10/067 ,  H04L41/145 ,  H04L43/55 ,  G06Q10/06375 ,  G06Q10/0639 ,  G06Q40/08

Abstract: The disclosed systems, structures, and methods are directed to risk assessment of an optical network. A simulation framework includes a risk map engine including a performance prediction engine that generates a simulation of the optical network based at least in part on an input network topology and/or service map. The prediction performance engine runs the simulation to predict, based at least in part on received network telemetry data, direct and indirect impacts on the optical network of a risk factor represented in a what-if scenario. The risk map engine includes a risk assessment engine that determines a risk associated with the risk factor based at least in part on the predicted direct and indirect impacts and on a likelihood of occurrence of the risk factor. The risk assessment engine generates a risk map showing aggregate risks to the optical network from a plurality of risk factors.

公开(公告)号：US20240267300A1

公开(公告)日：2024-08-08

申请号：US18566508

申请日：2021-11-17

Applicant: Telefonaktiebolaget LM Ericsson (publ)

Inventor： Shrihari VASUDEVAN ,  M.J. PRASATH

IPC: H04L41/147 ,  H04L41/16

CPC classification number: H04L41/147 ,  H04L41/16

Abstract: The application relates to method for determining resource needs needed in a telecommunications network for providing a network function in the telecommunications network, comprising the steps of—determining training data describing a network traffic, determining domain specific features of the telecommunications network to be used as predictors of a regression model in which the predictors are weighted by model weights to determine the resource needs, wherein the training data are described by the regression model, the regression model being configured to predict the resource needs, determining domain knowledge of the telecommunications network on at least one of expected values of the model weights, expected boundaries of the model weights and constraints between the model weights, encoding the domain knowledge as at least one of a prior probability distribution, constraints between model weights and boundaries of the model weights, encoding target values of the resource needs as a likelihood probability distribution centered on outcomes of the regression model, determining the model weights of the regression model through Bayesian modeling, taking into account the likelihood probability distribution, the prior probability distribution, optionally at least one of the constraints between the model weights and boundaries of the model weights, and determining the resource needs based on the determined regression model.