32 results about "Network traffic control" patented technology

The invention discloses a flow control method based on a neural network, and relates to the technical field of network flow control, and the method comprises the steps: collecting network flow metadata, constructing a standardized feature data set, carrying out the flow data classification and prediction based on a hybrid neural network, generating a differential flow control strategy, and carrying out the strategy optimization and dynamic adjustment; statistical features are extracted by using flow metadata acquired in multiple environments, spatial-temporal feature fusion and attention weighting are carried out in combination with a convolutional neural network and a long-short-term memory network, and network state recognition and flow trend analysis are realized; based on an analysis result, generating a control strategy adaptive to different network environments, and performing strategy optimization and real-time adjustment through reinforcement learning; the problems of accurate flow control and dynamic strategy adjustment in a complex network environment are effectively solved, and the accuracy, the adaptivity and the system stability of flow control are improved.

The present application relates to the technical field of network communication, and particularly relates to a RoCE network flow control method and system, the method comprising: performing static code analysis on source code of a RoCE protocol stack, identifying key function points on a data processing path, and generating a probe point set; processing historical monitoring data corresponding to the probe point set, calculating an utility value for each probe point; screening the probe points based on the utility values; deploying probes at the screened probe points, collecting network layer monitoring data and host layer monitoring data, and controlling network flow based on the network layer monitoring data and the host layer monitoring data.

Embodiments of the present disclosure provide a network traffic flow control method and apparatus based on a distributed storage system, an electrical device, and a storage medium. The method includes: acquiring a transmission link for transmitting target data in response to a first data server in the distributed storage system receiving a transmission request for the target data; configuring a first priority parameter for the transmission link by the first data server according to the priority information of the business type to which the target data belongs; and in response to transmitting the target data through the transmission link, configuring a second priority parameter for the target data by the first data server according to the first priority parameter of the transmission link, so as to enable a data transmission device to allocate a network bandwidth to the target data according to the second priority parameter.

This invention provides a network traffic control method, apparatus, and readable storage medium. The method includes: acquiring target information of a target router within a first AS and resource pool computing power information transmitted by a third AS, wherein the resource pool computing power information includes computing power information of at least one computing service node, and the target information includes the segment routing identifier (SID) of the target router; determining a target computing service node that meets the computing power requirements based on the target router's target information and the resource pool computing power information; identifying routers in the third AS connected to the target computing service node as last-hop egress routers, and generating a list of segment identifiers (SIDs) corresponding to packet forwarding paths based on the last-hop egress routers; and sending the SID list to the ingress router of the first AS.

Methods and apparatuses for network traffic control are disclosed. According to an embodiment, a first network function (NF) receives, from a second NF, control information for controlling non-access stratum (NAS) traffic that is to be processed by the first NF. The control information instructs the first NF to perform a predetermined action on the NAS traffic when the NAS traffic satisfies a predetermined condition. The first NF sends, to the second NF, a response to the control information.

The invention discloses a micro-service dynamic network flow control method based on multi-factor authentication, and belongs to the field of containerized network security management. Comprising the following steps of: prohibiting flow forwarding among all pods except a multi-factor authentication service pod; the multi-factor authentication service pod receives and verifies the access request; the authentication result is converted into a NetworkPolicy rule, and the NetworkPolicy rule is obtained; calling a kube-api-server to update the NetworkPolicy rule, and calling a Kube-api-server to update the NetworkPolicy rule; the method comprises the following steps of: automatically calling a kube-api-server to remove a Network Policy rule after a timer expires; and finally, automatically calling the kube-api-server to remove the Network Policy rule. According to the method, an attacker can be effectively prevented from breaking through container isolation and transversely moving to diffuse threats by using kernel vulnerabilities, and the security of the whole cloud environment is improved.

The invention relates to the technical field of computers, in particular to a rate control method and device for a Docker container virtual network. The method comprises the following steps: acquiring event data corresponding to a Docker internal event, and extracting network event characteristics in the event data; judging whether the Docker internal event is a virtual network creation event or a virtual network abnormal event based on the network event characteristics; if the Docker internal event is a virtual network creation event or a virtual network abnormal event, obtaining a network flow control parameter corresponding to the virtual network creation event or the virtual network abnormal event; and performing traffic control on the Docker internal event in a preset mode according to the network traffic control parameter. According to the invention, fairness and flow distribution of the container in the same network environment can be ensured, and an individual task container is prevented from occupying excessive bandwidth resources.

The invention relates to a network flow control method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: generating a classification program; the classification program is an eBPF program running in a kernel mode; controlling the network flow through the classification program; wherein the classification program is configured to classify the network traffic, and if the network traffic belongs to a first category, the network traffic is sent to an auxiliary container; and if the network traffic belongs to the second category, sending the network traffic to a service container. By adopting the method, the communication efficiency can be improved.

The application discloses a special network data security detection method based on big data, belongs to the safety detection field, and comprises the following steps: acquiring real-time data packets from special network traffic, encoding the data packets through a forward error correction code technology, generating redundant check information, and obtaining encoded data packets; if bit errors are detected in the encoded data packets during transmission, requesting the sender to retransmit the damaged data packets through an automatic repeat request mechanism to obtain retransmitted data packets; according to adaptive error correction parameters, adjusting the redundancy of the forward error correction code and the trigger threshold of the automatic repeat request, generating an optimized error correction scheme, and obtaining repaired data packets; for an alarm processing instruction, automatically adjusting a network traffic control strategy, limiting the transmission bandwidth of an abnormal destination, updating a sensitive data fingerprint library, and obtaining an optimized protection configuration.

A network flow control method includes: a first network device receives a target packet from a second network device, where the target packet carries packet flow control information, and the packet flow control information includes identification information of the second network device, information about a port from which the target packet flows into the second network device, information about a queue in which the target packet is located in the second network device, and / or priority information of the target packet. The first network device determines that the second network device needs to perform a flow control operation, then generates a first flow control notification based on the packet flow control information. The first network device sends the first flow control notification to the second network device, where the first flow control notification indicates the second network device to perform the flow control operation.

The invention belongs to the technical field of digital communication and network flow control, and particularly relates to an electric power communication resource elastic allocation method and system based on multi-objective optimization, and the method comprises the steps: extracting a spatial distribution difference according to real-time flow state data, and calculating a flow space-time evolution gradient in combination with a historical flow rate surge state; a physical cache boundary is extracted according to node attribute features, and information physical vulnerability is calculated by fusing with a flow spatio-temporal evolution gradient; according to the physical vulnerability of the information and the service priority, carrying out self-adaptive privilege lifting processing, and generating an elastic distribution factor; and allocating the actual available bandwidth quota according to the elastic allocation factor, and refreshing the rate configuration parameter. According to the invention, the key communication node which is endangered to the congestion limit can be identified and the physical bandwidth compensation is carried out, so that the key signaling is ensured to obtain the priority forwarding resource and the network communication robustness is improved.

This disclosure provides a method, apparatus, electronic device, and storage medium for intelligent computing network traffic control, relating to the field of intelligent computing network technology. The method includes: generating work queue elements within a first preset time period; matching each work queue element with a corresponding first token; sequentially sending each work queue element carrying a first token in the first work queue to a receiving network interface card (NIC), wherein the receiving NIC receives each work queue element when it determines that the first token carried by each work queue element is valid; and when the current time enters a second preset time period, if the first work queue contains unsent work queue elements, placing the unsent work queue elements into the second work queue. This disclosure stores data in a work queue, thereby controlling the transmission of work queue elements through tokens and preset time periods, avoiding network traffic congestion, packet loss, and other problems, and improving the effectiveness and reliability of network traffic control.

The application is a neural network-based traffic control method, and relates to the technical field of network traffic control.The application collects network traffic metadata, constructs a standardized feature dataset, classifies and predicts traffic data based on a hybrid neural network, generates differentiated traffic control strategies, and implements strategy optimization and dynamic adjustment.Statistical features are extracted from traffic metadata collected in multiple environments, convolutional neural networks and long short-term memory networks are combined for spatiotemporal feature fusion and attention weighting, network state recognition and traffic trend analysis are realized, control strategies adapted to different network environments are generated based on analysis results, and strategy optimization and real-time adjustment are realized through reinforcement learning.The application effectively solves the problems of accurate traffic control and dynamic strategy adjustment in complex network environments, and improves the accuracy, adaptability and system stability of traffic control.

One or more multi-service network traffic controllers are established for a distributed system. The controllers are connected to traffic control implementation units at a first service and a second service. Based at least in part on data collected from a traffic control implementation unit deployed at the second service, a modification to be made to a parameter of a traffic control implementation unit deployed at the first service is determined at the controllers. The modification is applied at the first service, and results in a change to a traffic flow of the second service.

Control of network traffic in a network is provided, including classifying a network request from a network source address using request classifiers selected from a plurality of request classifiers based on the network request satisfying classification conditions of the selected request classifiers, associating the network request with each classifier metric corresponding to the selected request classifiers, aggregating the classifier metrics associated with the network request to determine an aggregate request control metric of the network request, and instructing a network traffic controller to operate on the network request based on whether the aggregate request control metric satisfies a request control condition. Each of the plurality of request classifiers is associated in memory with a corresponding classifier metric.

One or more embodiments of the present disclosure provide an access control method and apparatus for a container network, a device and a medium, the access control method for the container network is applied to a first computing node included in a first computing cluster, and the container network of the first computing cluster is a first type of container network. When network flow is transmitted in a first type container network, the network flow does not pass through a protocol stack of each computing node in a first computing cluster, and the method comprises the following steps: acquiring a plurality of network flow control rules associated with a first computing node; screening the plurality of network flow control rules according to the container group information of the first container group, and generating a first access control strategy of the first container group; and configuring the first access control policy in a protocol stack of the first container group. In the method, an access control strategy in a protocol stack in a container group is utilized to process network traffic, and access control of the network traffic is realized in a specific type (such as MacVlan) container network.

The invention discloses a flexible flow control method, a network node, electronic equipment and a storage medium. The flexible flow control method comprises the following steps: when a first network node is in network congestion, acquiring flexible flow control node information carried in a service message; the flexible flow control node information comprises node information of a flexible flow control enabling node located at the upstream of the first network node; determining a first flexible flow control node corresponding to the first network node based on the flexible flow control node information; the first flexible flow control node is at least one of upstream flexible flow control enabling nodes; generating a first flexible flow control protocol message according to the node information of the first flexible flow control node; the first flexible flow control protocol message is used for indicating the first flexible flow control node to perform network flow control; and sending the first flexible flow control protocol message to the first flexible flow control node. According to the invention, a flexible control mechanism for the network flow is realized, so that the network flow control is not limited to a hop-by-hop flow control mechanism or a source end flow control mechanism any more, and the network flow control effect is optimized.

The application relates to the technical field of network flow control and specifically discloses a domain name and flow feature linkage game acceleration verification system which comprises the following steps: a DNS pre-grabbing module terminal device extracts a domain name, an IP address and cache-related parameters to store initial cache data; an intelligent cache management module cleans up invalid data and removes duplicate data to obtain effective cache data; a rule interaction module requests a game-specific rule set from a cloud rule server, receives a cloud rule set containing a multi-level domain name list, and determines flow processing strategies of the domain name lists at different levels; an intelligent recognition matching module calls effective cache data and the cloud rule set to link and compare, recognizes and marks hit data of domain name lists at different levels; a routing dynamic configuration module expands an IP address segment of a hit core domain name list and updates the IP address segment to a router routing table, and forwards the IP address segment to an acceleration node through a special acceleration channel; and a flow directional forwarding module directionally forwards flow; and the game acceleration stability is significantly improved.

Disclosed in the present application are a flexible flow control method, a network node, an electronic device and a storage medium. The flexible flow control method comprises: when network congestion occurs in a first network node, acquiring flexible flow control node information carried in a service message, wherein the flexible flow control node information comprises node information of flexible flow control enable nodes located upstream of the first network node; on the basis of the flexible flow control node information, determining a first flexible flow control node corresponding to the first network node, wherein the first flexible flow control node is at least one of upstream flexible flow control enable nodes; on the basis of node information of the first flexible flow control node, generating a first flexible flow control protocol message, wherein the first flexible flow control protocol message is used for instructing the first flexible flow control node to perform network traffic control; and sending the first flexible flow control protocol message to the first flexible flow control node.

The invention discloses a dynamic network flow control method based on large model training, which comprises the following steps of: capturing and processing intranet flow data through a data probe and a data processing server, storing the data into a flow database through a data server according to a data capturing result, forming a data set, and analyzing the characteristics of the flow through a data mining model; the method comprises the following steps: training a data processing server, calculating whether the traffic is an outlier based on an average contour coefficient method according to the trained model and the captured traffic, and finally judging whether the traffic is the outlier and whether the traffic is intercepted according to the judgment of the data processing server, thereby ensuring that the conventional traffic is normally passed and the unconventional traffic is intercepted, and preventing the server from being damaged. The method has the advantages of dynamic controllability, no influence on normal business and capability of only intercepting malicious traffic; compared with existing hard configuration through access control, the method has the advantage of being more flexible.

A system simulator simulates operations of a plurality of interconnected devices in a simulation of a computing system. The system simulator implements a communication runtime in the simulation to receive a packet generated by a simulation of a first one of the plurality of devices to be sent to a simulation of a second one of the plurality of devices in the simulation. The communication runtime buffers the packet in its internal buffer and receives a query from the simulation of the second device based on buffer capacity in the simulation of the second device has capacity. The packet is sent from the communication runtime buffer to the simulation of the second device based on the query to simulate transmission of the packet from the first device to the second device on a link.

The invention provides a method for processing an access request, a related device and a computer program product, is applied to an edge node of a content distribution network, and relates to the technical field of artificial intelligence of the content distribution network, authority management, network flow control and the like. A specific embodiment of the method comprises the following steps: in response to a received access request sent by target equipment, performing authentication based on an authentication rule list configured to a local, description information of the target equipment and demand information of the access request, and generating a first authentication result and credibility of the first authentication result; in response to the fact that the credibility is smaller than a credibility threshold value, generating authentication reference information based on the description information and the demand information; sending authentication reference information to the authentication node, and controlling the authentication node to return a processing mode corresponding to a second authentication result after generating the second authentication result based on the authentication reference information; and in response to the received processing mode, processing the access request in the processing mode.

The embodiment of the invention provides a high-throughput network flow control method, electronic equipment and a storage medium, and relates to the technical field of networks. The method is applied to a switching chip, the switching chip comprises a first channel and a second channel, the first channel is used for processing standard Ethernet traffic, the second channel is used for processing high-throughput Ethernet traffic, and the method comprises the following steps: in response to a received first traffic pause frame, analyzing a first frame start delimiter in the first traffic pause frame; if the value of the first frame start determiner is a first preset value, carrying out flow stopping processing on the first channel to stop sending the standard Ethernet flow to the first opposite-end equipment; and if the value of the first frame start determiner is a second preset value, carrying out flow stopping processing on the second channel so as to stop sending the high-throughput Ethernet flow to the first opposite-end equipment. Therefore, differential flow stop control can be carried out on the standard Ethernet flow channel and the high-throughput Ethernet flow channel in the switch chip.

This invention belongs to the field of cloud computing and network traffic control technology, and relates to an intelligent network traffic control method and system for containerized clusters. Based on pre-configured rules, it distinguishes between public network and internal network traffic in real time along the data forwarding path. It periodically monitors the total public network traffic of the cluster and dynamically calculates bandwidth limit parameters using a closed-loop feedback control algorithm. Then, it applies dynamic parameters to public network traffic for real-time shaping, while applying a completely independent static protection strategy to internal network traffic. Finally, it independently monitors the status of the control layer at the data execution layer, automatically releasing public network traffic control in case of disconnection, and ensuring business continuity by adhering to the Fail-Open principle. This invention aims to solve problems in traditional solutions such as indiscriminate control that may damage the internal network, the inability of static configuration to adapt, single points of failure in centralized architectures, and the lack of intelligent closed-loop control. It effectively improves bandwidth utilization and business performance, protects critical internal network traffic, significantly reduces operational complexity, and enhances system robustness.

The invention provides a flow control method and device of network equipment and electronic equipment. The method comprises the following steps: acquiring historical traffic data, real-time traffic data and a traffic log of a network device in a historical time period, the traffic data comprising a traffic rate, a network time delay, a packet loss rate and a service type; constructing a traffic prediction model based on LSTM, inputting the historical traffic data into the traffic prediction model to obtain predicted traffic data of a future time period, and performing anomaly analysis processing on the real-time traffic data according to the traffic log by adopting an isolated forest algorithm to obtain an anomaly analysis result; and determining a network resource scheduling strategy of a future time period by adopting a DDQN model according to the predicted flow data and an anomaly analysis result, and controlling network equipment by adopting the network resource scheduling strategy. The problem that the user experience is poor due to the fact that an existing network flow control scheme is insufficient in real-time performance, low in accuracy and poor in adaptability when facing a complex network environment is solved.

The described technology is generally directed towards data clustering for network traffic modeling. Cellular network measurement data from different geographic areas can be separated into clusters based on similarities in network performance indicators, cell traffic load data, their changing pattern over time, and / or other metrics. A machine learning model can then be assigned to each cluster, and the machine learning models can be trained to make network traffic control decisions under conditions exhibited in their respective clusters. If the error rate of the trained machine learning models is acceptable, then the machine learning models can be deployed for use at network equipment. If the overall error rate is not acceptable, then the cellular network measurement data can be re-separated into a larger number of clusters, and machine learning models can again be trained for each cluster. The re-separation of data and re-training of machine learning models can repeat until the error rate is acceptable and the machine learning models can be deployed.

Methods and apparatuses for network traffic control are disclosed. According to an embodiment, a first network function (NF) receives, from a second NF, control information for controlling non-access stratum (NAS) traffic that is to be processed by the first NF. The control information instructs the first NF to perform a predetermined action on the NAS traffic when the NAS traffic satisfies a predetermined condition. The first NF sends, to the second NF, a response to the control information.

The application provides a DPU-based network traffic flow control method and device. The method comprises the following steps: in a virtual switch in a data processor (DPU), a virtual switch flow control configuration command issued by a virtual switch agent unit corresponding to the DPU is received, and a target network traffic control strategy corresponding to the virtual switch is set based on the virtual switch flow control configuration command, wherein the virtual switch flow control configuration command is obtained by converting speed limit configuration data of a service mesh data plane by the virtual switch agent unit in advance; and network traffic speed control is performed on data packets transmitted to the DPU in the virtual switch according to the target network traffic control strategy. The application can unload network traffic flow control to hardware for execution without the need of an L4 TCP layer flow control filter of an Envoy agent, can effectively reduce CPU load, can improve the efficiency and reliability of network traffic flow control, and can significantly improve the overall performance and manageability of the network.

The invention relates to the technical field of network communication, in particular to an RoCE network flow control method and system, and the method comprises the steps: carrying out the static code analysis of a source code of an RoCE protocol stack, recognizing a key function point on a data processing path, and generating a probe point set; processing historical monitoring data corresponding to the probe point set, and calculating a utility value for each probe point; screening the probe points based on the utility values; probes are deployed at the screened probe points, network layer monitoring data and host layer monitoring data are collected, and network flow is controlled based on the network layer monitoring data and the host layer monitoring data.