594 results about "Network component" patented technology

An adapter layer may be used to customize a machine learning component by transforming data flowing into, out of, and / or within the machine learning component. The adapter layer may include a number of neural network components, or “adapters,” configured to perform a transformation on input data. Neural network components may be configured into adapter groups. A router component can, based on the input data, select one or more neural network components for transforming the input data. The input layer may combine the results of any such transformations to yield adapted data. Different adapter groups can include adapters of different complexity (e.g., involving different amounts of computation and / or latency). Thus, the amount of computation or latency added by an adapter layer can be reduced for simpler transformations of the input data.

A method may include accessing data associated with a failure of a 5G network component. The method may include providing the data associated with the failure of the 5G network component to a first machine learning model, configured to determine and output data indicating a root cause of the failure. The method may include providing data indicating the root cause of the failure to a second machine learning model, configured to determine and output data indicating one or more service providers and respective destinations associated with the root cause. The method may include generating a service ticket may include data indicating the root cause and the respective destinations associated with the root cause. The method may include transmitting the service ticket to the respective destinations of the one or more service providers associated with the root cause of the failure.

Described herein are devices, systems, methods, and processes for optimizing network traffic distribution across multiple paths in a manner that is energy-efficient and environmental sustainability-aware. This may be achieved by leveraging time-series analytics and capacity planning based on seasonalities. Data associated with the Layer 3 topology of the network can be collected. Bandwidth can be pre-reserved on an energy-aware traffic engineering tunnel. The time-series data can be used to build a capacity plan based on the seasonalities. Nodes may be clustered based on usage patterns and network utilization seasonality. The data can be used to make decisions about when and where to combine or shut down paths for energy efficiency, while maintaining optimal network performance. A hysteresis mechanism may be incorporated to avoid oscillation when changing active links. Power savings can be achieved by fully turning off or depowering certain network components when they are not needed.

A machine learning model may be configured for training using an associated learning technique. A model configured for end-to-end backpropagation may adapted for associated learning by introducing functions for projecting hidden vectors and labels to a shared representation space and for reconstructing labels from representation vectors. An associated learning loss may be calculated at each layer, with the resulting gradients backpropagated locally through that layer rather than all layers. A reconstruction loss may be calculated using each layer's output including the predicted label. Training by associated learning may be parallelized (e.g., layer by layer) to yield efficiency gains. In addition, associated learning training may be more robust to training label errors. The resulting model may be used to, for example, predict data sequences in an autoregressive manner in which subsequent portions of the output data sequence are predicted in part based on previous predicted portions of the output data sequence.

System and techniques for network flow-based hardware allocation are described herein. A workload for is obtained for execution. Here, the workload includes a flow that has a processing component and a network component. Then, during execution of the workload, the flow is repeatedly profiled and assigned a network service and a processing service during a next execution based on a network metric and a processing metric obtained from the profiling.

Systems, devices, and automated processes are described to provide collection of wireless network status data, such as a 5G or other mobile network, and to automatically respond to queries regarding the status of the network. Systems and automated processes may obtain status data from a plurality of network components, for example a radio unit (RU), a distributed unit (DU), and a centralized unit (CU) associated with a cell site, ingest the obtained status data, including processing the status data with a machine learning model system, store the ingested status data in a data store, receive a user query related to the network, ingest the received user query using the MLM system, retrieve a result corresponding to the ingested query from the data store using the MLM system, generate, using the MLM system, a summary of the retrieved status data, and present the generated summary via a user interface.

Embodiments manage data in a file system over a network. A plurality of file system operations in the file system may be executed based on a file system client action or a file system administrative action such that the file system may be integrated with a network. Characteristics of the plurality network components in the network infrastructure that may be associated with the file system may be determined. Tests may be generated based on the characteristics of the network components such that the tests may be executed to evaluate the network components. Results of the tests may be employed to perform further actions, including determining non-compliant network components based on the results; modifying the network infrastructure based on the non-compliant network components such that one or more of file system operations are modified based on the non-compliant network devices; executing the modified file system operations on the modified network infrastructure.

Various methods and processing systems for the effective management of network traffic and facilitating data forwarding within distributed computing environments. Network components may be configured to amalgamate the Border Gateway Protocol (BGP) with Source Network Address Translation (SNAT) or network address port translation (NAPT) technologies to facilitate dynamic notification of network address accessibility and use port index identifiers to augment the precision and resilience of routing. A distributed SNAT / NAPT virtual network function (VNF) or cloud-native network function (CNF) may collaborate with LEAF switches or server aggregation devices to refine data transmission via adaptable address mapping and forwarding and enhance network scalability and resilience.

Systems, devices, and methods for disaggregating networking components are provided. An example networking chassis includes a first disaggregated server device supported by the networking chassis that includes a first central processing unit (CPU) and a first graphics processing unit (GPU) coupled with the first CPU. The networking chassis further includes a first insertable switch module communicably coupled with the first disaggregated server device that includes first switching chipsets and a first fabric management controller coupled with the first switching chipsets. The first insertable switch module at least partially controls data transmission associated with the first disaggregated server device. The first GPU of the first disaggregated server device is isolated on the first disaggregated server device, supported on the first disaggregated server device in the absence of other GPUs, or is otherwise the only GPU on the first disaggregated server device so as to provide modularity in networking applications.

Exemplary methods and apparatus are provided for implementing a deep learning accelerator (DLA) or other neural network components within the die of a non-volatile memory (NVM) apparatus using, for example, under-the-array circuit components within the die. Some aspects disclosed herein relate to configuring the under-the-array components to implement feedforward DLA operations. Other aspects relate to backpropagation operations. Still other aspects relate to using an NAND-based on-chip copy with update function to facilitate updating synaptic weights of a neural network stored on a die. Other aspects disclosed herein relate to configuring a solid state device (SSD) controller for use with the NVM. In some aspects, the SSD controller includes flash translation layer (FTL) tables configured specifically for use with neural network data stored in the NVM.

A method may include accessing event data corresponding to an event affecting a region covered by a 5G network including a plurality of network components. The method may include accessing user data corresponding to a user equipment within the region covered by the 5G network. The method may include generating, using a machine learning model, an expected network load. The method may include accessing, a dynamic threshold associated with the 5G network. The dynamic threshold may include one or more limits associated with the plurality of network components. The method may include determining that the expected network load will cause the 5G network to exceed at least one limit of the dynamic threshold. In response to determining that the expected network load will exceed the limit, the method may include generating a new network component in the 5G network based at least in part on the expected network load.

The invention provides a network security assessment method and system based on threat intelligence, and the method comprises the steps: obtaining a threat intelligence data set and a to-be-assessed network data set, and carrying out the feature association extraction processing, thereby obtaining a target security feature set; the target security feature set covers threat and component association matching, threat and behavior influence conduction and network component protection capability features, calling a pre-training security assessment model to carry out risk situation analysis on the target security feature set, and generating a network security assessment result including a potential threat triggering probability and a threat influence propagation path, and determining a to-be-evaluated network risk level and key risk point distribution information according to the network security evaluation result, generating a network security evaluation report containing a risk positioning identifier, and outputting the network security evaluation report to the network management terminal to support security policy adjustment operation, so that the network security condition can be comprehensively and accurately evaluated, and the user experience is improved. And the pertinence and effectiveness of safety protection are improved.

Systems, devices, and automated processes are described to provide collection of wireless network status data, such as for a 5G or other mobile network, and to automatically created user interactable dashboards providing various status information regarding the operation of the network. Systems and automated processes may obtain status data from a plurality of network components, for example radio units (RU), distributed units (DU), and centralized units (CU) associated with a cell sites, determine an operational state for each of the network components based on the obtained data, aggregate the determined operational states to determine an operational state of the cell sites, generate a dashboard having an indication of the operational states, provide the dashboard to a user interface, receive a user selection from the dashboard, and provide a report detailing the determined operational state.

Techniques for adaptive network connectivity in a cloud provider network are described. A managed networking service obtains metadata associated with an application deployed in the cloud provider network and uses the metadata to construct a graph including application components and network components. Network flows are identified from the metadata and used to construct flow metadata that is linked to paths of the graph. The flow metadata is further enhanced with capability information for the network components on the path. The graph can be efficiently analyzed to identify alternative, superior networking configurations for the application.

Disclosed are techniques for communication. In an aspect, a network component determines that a level of heterogeneity associated with a network layer of a hierarchical network layer arrangement with multiple network layers is above a threshold. In some designs, the heterogeneity level determination may be based on heterogeneity metric(s) reported by device(s) associated with the network layer. The network component may transmit, to at least one device associated with the network layer in response to the heterogeneity level determination, a data reporting instruction associated with the network layer. The network component receives data associated with the network layer from the at least one device in accordance with the data reporting instruction.

Aspects of the disclosure are directed to registration of a target object in a target object database (e.g., maintained by a network component) in association with a target object identifier and a set of attributes associated with the target object. In an aspect, the registration process may be initiated in response to physical presence detection of the target object (e.g., by one or more sensing nodes), where the presence detection can be based on radio frequency for sensing (RF-S) or non RF-S approaches. Such aspects may provide various technical advantages, such as improved target object tracking and identification.

This system provides a technical solution for managing product sales, inventory, and deliveries through QR codes and predictive analytics. It incorporates algorithms that generate and assign unique QR codes to products based on real-time sales data from farmers. These QR codes provide direct links to product information, facilitating efficient inventory management and customer engagement. The system also leverages predictive analytics to forecast product demand by analyzing historical sales data, adjust inventory levels, and optimize delivery driver assignments based on proximity to farms. The hardware supporting the system includes servers, processors, network components, and scanning devices to ensure reliable operation and data synchronization. By automating QR code generation, integrating sales data, and dynamically assigning drivers, the system improves operational efficiency, minimizes errors, and enhances the customer experience.

Integrating a software defined perimeter (SDP) for network applications in a 5G / 6G telecommunications network includes receiving a registration request from a radio access network (RAN) component of the network into an access and mobility management function (AMF) of the network. The request routes to an SDP controller in the core network component and the SDP controller in turn transmits an authentication request over a virtual private network (VPN) coupling between a main controller and the SDP controller, on behalf of user equipment (UE) and an end user associated with the UE. Finally, responsive to the authentication, the SDP controller generates both an SDP encapsulating a set of network resources of the network supporting microservices accessible by the UE, and a secure tunnel between the UE and a gateway in a data plane of the network that moderates subsequent data flows between the UE and the microservices.

A system and method are provided for implementing a network component, such as a software-defined wide area network, a firewall, a router, or a load balancer. The network component can be an embedded network edge device that is implemented, e.g., in software, in circuitry, or using hardware acceleration (e.g., a data processing unit (DPU), a smart network interface card (SmartNIC), etc.). The system can include multiple dataplanes, including a primary dataplane and a shadow dataplane. A packet dispatcher relays received data packets to a primary dataplane and the shadow dataplane. The primary dataplane applies a current version of the network component to data packets, and the secondary dataplane applies a new version of the network component to identical replicas of the data packets. A control plane agent compares performance data gathered from the respective dataplanes to perform verification testing on the new version of the network component.

Methods, and systems for authentication via web components. One of the methods includes: submitting a token request, by a browser application, to a first server entity for a token from an API hosted by a second server entity; receiving, at the browser application, the token and a time to live (TTL) for the token from the first server entity, the first server entity having added an API key to the token request to produce an appended token request and forwarded the appended token request to the API at the second server entity, the API at the second server entity having authenticated the API key and returned the token and the TTL to the first server entity, and the first server entity having returned the token and the TTL to the browser application; and sending, by the browser application to the second server entity, a data request with the token.

The invention provides a space-air-ground three-dimensional communication cross-network cooperative reliable transmission method, device and system. The system comprises a generalized service layer, a resource adaptation layer and a fusion network layer, the generalized service layer receives a service request initiated by a user, analyzes the service request to obtain a service attribute, and sends the service attribute to the resource adaptation layer; the resource adaptation layer performs service resource adaptation mapping by using a reinforcement learning algorithm according to the service attribute and the current network state information, generates a service deployment strategy and issues the service deployment strategy to the fusion network layer; and the fusion network layer schedules a corresponding network component according to the received service deployment strategy, and completes path selection, forwarding scheduling and coding strategy application tasks corresponding to the service request. The system has multi-protocol identification and unified packaging capabilities, and can receive multiple data formats and carry out standardized processing. According to the method, multi-path flexible scheduling, network coding and covert channel methods are integrated, and the limitation of an existing communication system in the aspects of protocol compatibility, transmission efficiency and reliability is broken through.

The invention relates to a method for automatically generating a multi-professional pipe network BIM model based on a CAD drawing of a graph neural network, comprising: pipe network CAD drawing information analysis and multi-professional pipe network BIM model generation, the pipe network CAD drawing information analysis comprising graph neural network model construction, CAD drawing analysis based on the graph neural network model, legend table analysis and multi-graph information aggregation; the multi-professional pipe network BIM model is generated based on the extracted pipe network component information, and different reconstruction strategies are adopted for different types of pipe network components. According to the method, an automatic building pipe network CAD drawing analysis technical route combining deep learning driving and knowledge rule constraint is constructed, symbol primitive semantic extraction is carried out based on a graph neural network, high-accuracy symbol and layer automatic identification is realized, a pipe network system expressed by multiple CAD drawings is converted into a graph structure, and the method is suitable for large-scale popularization and application. And entity matching and information aggregation are carried out on a plurality of drawings and a plurality of graph structures, so that an automatic, efficient and high-stability building pipe network system information model reconstruction task is realized.

The invention provides a KubeVirt-based life cycle management method and device for directly connecting a physical network card to a virtual machine, and the method comprises the steps: firstly, carrying out the resource configuration of a network card of the virtual machine based on a network planning demand, and generating an SR-IOV scheme; and then, based on the SR-IOV scheme, performing automatic configuration management on physical server hardware, and generating physical network card configuration information. Based on the information, virtual function resources of the physical host machine are deployed through a Kubernetes network component, and plug-in management information is generated. And finally, based on the plug-in management information, virtual resource allocation information is generated by carrying out SR-IOV network card loading management on the virtual machine, and life cycle management of the physical network card through the virtual machine is completed. According to the embodiment of the invention, hardware-driven self-loading and starting-up self-loading of the straight-through network card are realized, the function of dynamically loading the straight-through network card by the virtual machine is ensured, the configuration process of automatic deployment and management of SR-IOV network card resources is simplified, and the utilization rate of virtual network resources is optimized.

Systems and techniques include identifying a network layer for performing a memory operation, identifying a subset of a plurality of configuration bit clusters of a non-volatile distributed memory that are mapped to the identified network layer using a cluster mapping, in response to identifying the subset of the plurality of configuration bit clusters, activating the subset of the plurality of configuration bit clusters, loading network component data from the subset of the plurality of configuration bit clusters into a local buffer, and applying the network component data to the network layer for performing the memory operation.

A modular Computer-On-Fan-Tray-Assembly (COFTA) networking device system includes a networking device chassis housing a circuit board coupled to a plurality of non-networking components and including: a networking processing system coupled to networking ports that are accessible on the networking device chassis, a networking processing system management connector coupled to the networking processing system, a non-networking component management connector coupled to the non-networking components, and a networking software management connector. At least two COFTA systems are each connected to a different subset of the networking processing system management connector, the non-networking component management connector, and the networking software management connector. Each COFTA system provides a respective operating system that manages a different subset of the networking processing system via the networking processing system management connector, the plurality of non-networking components via the non-networking component management connector, and networking software via the networking software management connector.

Disclosed are techniques for wireless signaling. In an aspect, a user equipment (UE) transmits, to a network component, an indication of a capability to estimate a carrier frequency offset (CFO) of the UE associated with radio frequency for sensing (RF-S) operations. In another aspect, the network component estimates the CFO of the UE (e.g., based on measurement information from the UE and / or wireless node(s)). In some designs, the network component further transmits, to the UE, the CFO of the UE (e.g., for CFO compensation associated with RF-S sensing operation(s)).