77 results about "Network congestion control" patented technology

The invention is suitable for the technical field of network congestion control, and provides a power system RDMA network dynamic congestion control method based on a DCQCN, and the method comprises the steps: collecting the multi-source state data of a power system in real time; inputting the multi-source state data into a preset electric power feature fusion model, and generating a dynamic ECN parameter group containing an ECN marking threshold value, a marking probability and a rate regulation factor; executing dynamic ECN marking on the data packet based on the dynamic ECN parameter group, and embedding a lightweight HMAC signature based on an electric power PKI system into the electric power service message with the highest priority; when a congestion event is detected, discarding a data packet which does not pass the HMAC verification, generating a security alarm log, and migrating congestion link flow to a standby loop according to a service priority weight based on a power double-loop network topology structure; and issuing the congestion decision result to a physical equipment control layer through the electric power SDN controller. According to the invention, while the service quality of the key business is guaranteed, collaborative optimization of the communication network and the power physical system is realized.

The invention provides a network congestion control method and system based on multi-agent reinforcement learning, computer equipment and a storage medium, and relates to the technical field of network congestion control, a communication network comprising a plurality of communication nodes is constructed, the communication nodes are mapped into independent agents, and the independent agents are used for controlling the network congestion. Designing a weighted composite reward function containing indexes such as throughput and delay by taking a flow-level index as a local state and a data sending rate adjustment coefficient as an action; the intelligent agent selects action execution according to a current state based on a strategy network, calculates an environment reward based on the current state, the action, a next state after the action is executed and a weighted composite reward function, and stores the environment reward in a shared experience playback pool; training a value network and a strategy network by using the data in the pool; the trained strategy network is deployed at each node, distributed congestion control is realized, the stability of a congestion control decision in a complex network environment is improved, and bandwidth resources are distributed fairly while the throughput is ensured.

The application discloses a network congestion control method and device based on fuzzy rules, and the method comprises the following steps: setting corresponding fuzzy rules according to network types, wherein the network types include a data center network and a wide area network; obtaining network parameters and performing fuzzy processing, fuzzy reasoning and defuzzification on the network parameters to obtain an adjustment factor ΔF, wherein the network parameters include a bandwidth utilization rate, an ECN marking rate and a queue length; obtaining the size of a current congestion window and adjusting the size of the congestion window in real time according to the adjustment factor ΔF; the steps of fuzzy processing and fuzzy reasoning further comprise detecting the current network type, and selecting corresponding fuzzy rules and dynamically adjusting the weight coefficients corresponding to each network parameter in the process of fuzzy reasoning according to the detected network type. The network congestion control method based on fuzzy rules can adjust the network congestion window in real time, realizes the intelligentization and automation of network congestion control, and improves the network management efficiency.

The invention provides a congestion control fairness compensation method for a wide-area RDMA (Remote Direct Memory Access) network. The system is composed of an active notification point PNP and a temporary response point TRP which are deployed on an external switch ESW connecting a DC and a WAN. When near-source congestion occurs, the switch marks congestion for the data packet, and the active notification point clears the congestion mark and actively sends a congestion notification packet CNP through the corresponding sending end; when a receiving end receives the data packet containing the congestion mark, responding to the congestion notification packet; the temporary reaction point checks the congestion notification packet, determines the severity of the congestion, and temporarily slows the corresponding cross-DC traffic before the sender begins to decelerate. Therefore, the congestion feedback signal is fed back in advance by using the programmable switch, the congestion feedback fairness of the new-source is ensured, the congestion feedback signal is responded in advance by using the programmable switch, the congestion response fairness of the new-source is ensured, and finally, the fairness in the wide-area RDMA network is maintained, and the overall network performance is improved.

The invention discloses a multi-level network congestion control method for an intelligent computing center, and relates to the technical field of network communication. The method comprises the following steps: monitoring flow state parameters of each level of network of an intelligent computing center in real time; respectively calculating the congestion coefficient of each level based on the network flow state parameter of each level; setting a dynamic congestion threshold value of each level of network; when the single-layer congestion coefficient exceeds a dynamic congestion threshold value, congestion control is carried out on each level of network; and when the congestion coefficients of two or more levels exceed a dynamic congestion threshold, cross-level collaborative optimization is carried out. According to the method provided by the invention, through single-layer independent congestion control and multi-level combined congestion control, the congestion can be quickly solved when the single-layer network is congested, and when the multi-level network is congested, the performance optimization of the whole network is higher through combined optimization. Through cross-level collaborative optimization, when the intelligent computing center network is in global congestion, network conditions of other levels can be better coordinated, and sensitivity and stability are effectively balanced.

The invention discloses a network congestion control method and device based on a fuzzy rule, and the method comprises the following steps: setting a corresponding fuzzy rule according to network types which comprise a data center network and a wide area network; network parameters are obtained, fuzzification processing, fuzzy reasoning and defuzzification are carried out on the network parameters, an adjustment factor delta F is obtained, and the network parameters comprise the bandwidth utilization rate, the ECN marking rate and the queue length; obtaining the size of the current congestion window, and adjusting the size of the congestion window in real time according to the delta F; a current network type is detected between the fuzzification processing step and the fuzzy reasoning step, a corresponding fuzzy rule is selected according to the detected network type in the fuzzy reasoning process, and a weight coefficient corresponding to each network parameter is dynamically adjusted. According to the network congestion control method based on the fuzzy rule, the network congestion window can be adjusted in real time, intelligence and automation of network congestion control are achieved, and the network management efficiency is improved.

Systems, computer program products, and methods are described for advanced congestion control using multiple congestion indicators in a networking environment. An example system may include an intelligent agent configured to learn congestion control policies. The agent may interact with real-world or simulated environments replicating real-world benchmarks. Congestion indicators such as telemetry information, packet drop metrics, congestion notification packet rate, pause frame rate, port utilization metrics, and / or the like form a comprehensive state representation of the network, enabling congestion state of the network environment. The intelligent agent evaluates these conditions using a reward function to optimize network performance. The intelligent agent may then implement a behavioral policy in response to the captured congestion indicators, thereby changing the congestion state of the network environment.

The invention discloses a network card, a network congestion control method and device, electronic equipment and a storage medium, and relates to the technical field of networks. The method comprises the following steps: when entering a bandwidth detection stage or a time delay detection stage, determining a current congestion window value according to a bottleneck bandwidth and a minimum round-trip delay at a current moment, and obtaining a target congestion window value closest to the current moment in a historical record; if the current congestion window value is the same as the target congestion window value, obtaining a new and old congestion window data comparison record, and determining a target number of times when the new and old congestion window values are the same from the new and old congestion window data comparison record; and determining a window adjustment value according to the target number of times, and determining a congestion window value matched with the current network state according to the window adjustment value and the current congestion window value. The method can solve the problem that the related technology cannot adapt to the network change condition, and can dynamically adjust the congestion window value according to the actual state of the network to adapt to the network change.

The invention provides a network traffic matrix prediction method based on multi-scale convolution and attention collaborative enhancement LSTM (Long Short Term Memory), and solves the problems that an existing traffic matrix prediction model is insufficient in multi-scale feature capture, weak in space-time correlation modeling and poor in generalization. A dynamic multi-scale convolution module (adopting a 3 * 3 / 5 * 5 / 7 * 7 two-dimensional convolution kernel) is used to extract local fine-grained association and global coarse-grained trend of a two-dimensional traffic matrix, deep fusion of LSTM time sequence features and two-dimensional multi-scale spatial features is realized in combination with a cross attention mechanism, and time-space cross-dimension association is enhanced. And meanwhile, the problem of gradient disappearance of the deep network is relieved by relying on residual connection, and topology independence is realized based on a flow-by-flow method so as to adapt to traffic matrixes of different dimensions. The method specifically comprises the following steps: performing stream-by-stream division and normalization preprocessing on an original two-dimensional traffic matrix, and after processing by modules such as dynamic multi-scale convolution, cross attention enhancement LSTM, residual block and gating screening and the like, outputting a single OD stream predicted value and reconstructing a complete traffic matrix; and integrating objective functions of the encoder and the decoder, taking the MSE as a loss function, and adopting an Adam optimizer to iteratively update model parameters until the model converges. Experiments show that the MAE and the MSE of the method on real data sets such as Ailene, GEANT and the like are better than those of mainstream models such as SVR, LSTM, MTGNN and the like, the reconstruction precision of a high-dimensional traffic matrix is particularly remarkable, and a reliable basis can be provided for network congestion control and resource optimization.

The invention relates to a network congestion control method, device and equipment, a medium and a program product, and relates to the technical field of communication. The method comprises the following steps: receiving a target message sent by a data sending end; acquiring historical congestion data of a target transmission path in the network equipment corresponding to a preset historical time period, and a target queue length of the target transmission path corresponding to a current moment; determining a reference queue length threshold according to the historical congestion data; under the condition that the target queue length is greater than the reference queue length threshold, adding speed regulation mark information into the target message to update the target message; the updated target message is sent to a data receiving end through the target transmission path, so that the data receiving end responds to the updated target message and sends feedback information to a data sending end, and the feedback information comprises speed regulation mark information. By adopting the method, the control effect of congestion control can be improved.

The invention provides a network congestion processing method and related equipment, and relates to the technical field of network communication. According to the method and the device, the to-be-discarded message which originally needs to execute the discarding strategy at the switch is converted into the target truncated message which has the ECN setting mark and is short in length, and the target truncated message is transmitted in time by scheduling the queue which is additionally configured on the basis of the configuration of the original message forwarding buffer queue and has a higher priority. A receiving end device timely notifies a sending end device to carry out message sending rate reduction processing and to-be-discarded message rapid retransmission processing based on a traditional ECN information feedback mechanism, so that in a network congestion control process based on an ECN technology, a network communication load is effectively reduced by using a message truncation operation; meanwhile, it is ensured that the sending end equipment can reduce the message sending rate in time, retransmission processing is carried out on the message to be discarded in time, and the zero packet loss requirement of the switch is met.

The application discloses a network congestion control method for minimum bandwidth guarantee, and comprises the following steps: (1) distinguishing the types of network flows, wherein the types comprise deadline flows and non-deadline flows; (2) setting minimum window values according to the types of network flows; (3) adjusting the congestion window values according to the minimum window values and the congestion degrees when adjusting the congestion windows in each stage of the congestion control. The application further discloses a network congestion control device for minimum bandwidth guarantee and a computer readable storage medium, which are used for realizing the network congestion control method for minimum bandwidth guarantee. The application can guarantee the minimum bandwidth of TCP deadline flows and achieve the purpose of congestion control by distinguishing the deadline flows and the non-deadline flows.

Mechanisms, including systems, methods, and media, for implementing network congestion control are provided, the methods including: imposing a delay on traffic on a connection between a sender and a receiver; determining a minimum round-trip time for traffic on the connection with the delay imposed; and adjusting the delay imposed on the traffic to an adjusted delay based on a minimum propagation delay goal and the round-trip time. In some of these embodiments, the methods further comprise setting an initial value of the delay to the propagation delay goal. In some of these embodiments, the delay is imposed on an acknowledgment message that is part of the traffic. In some of these embodiments, the adjusted delay is equal to the propagation delay goal minus the minimum round-trip time minus the delay.

The application relates to the field of 5G uplink, and discloses a self-adaptive network congestion control system and method, wherein the self-adaptive network congestion control method comprises the following steps: forming an uplink occupation evolution record according to the sending time sequence and feedback indication of a physical uplink shared channel and a physical random access channel; introducing cross-terminal access track alignment processing around the dense overlapping section appearing in the uplink occupation evolution record to generate a heterogeneous congestion indication; embedding an access mode distinguishing identifier in the heterogeneous congestion indication to build the distribution form of the interference degree between different access modes; introducing a variable-granularity scheduling adjustment unit based on the phased change characteristics of the distribution form to form a sending behavior feedback result; and combining the sending behavior feedback result after adjustment to jointly evaluate the resource acquisition efficiency and data discharge rate of each terminal in a continuous scheduling period and dynamically correct a congestion judgment boundary. The application has the advantage of improving the network resource utilization efficiency.

The application discloses a mass data network congestion control method and device, electronic equipment and storage medium, belongs to the computer network communication technical field, method includes: according to the congestion mark byte number of the confirmation data packet, total confirmation byte number and historical congestion ratio, determine the congestion ratio estimation value, and combine the sampling data packet sending time and confirmation data packet receiving time to determine the round trip communication time and communication time gradient;According to the confirmation byte number, the sending byte number, the round trip communication time and the historical bottleneck bandwidth to determine the bandwidth utilization rate;When the round trip communication time does not exceed the minimum communication time, maintain the sending end control parameter;When the round trip communication time exceeds the minimum communication time, according to the congestion ratio estimation value, the communication time gradient, the round trip communication time and the bandwidth utilization rate to determine the congestion score, and accordingly adjust the sending end control parameter. Through the implementation of the application, the problem of inaccurate congestion control of mass data network in the prior art can be solved.

This application discloses a network congestion control method, apparatus, device, and system, and a storage medium. In an example method, a first switch receives a target signaling packet that is sent by a second switch in a case in which a network congestion status is a target network congestion status. The target signaling packet carries flow source information. The first switch sends, based on the target signaling packet, target flow control information to a network device corresponding to the flow source information. The target flow control information is used to indicate flow control.

The present application relates to the technical field of network communication congestion control, in particular to an end-network cooperative ubiquitous network congestion control method. The method is applied to a ubiquitous network comprising end nodes, intermediate nodes and content production nodes. The end nodes generate and send interest packets. The intermediate nodes obtain queue usage state, pending interest table occupation state, available bandwidth state and interest packet satisfaction state when the data packets return, generate a first congestion score or a second congestion score, and generate a congestion marking probability in combination with the occupation contribution of the target interest flow in the pending interest table to mark the return data packets with congestion. The intermediate nodes perform interest flow splitting or rate shaping according to the congestion level, and the end nodes adjust the subsequent interest packet sending window according to the congestion score information and the congestion marking result. The present application is suitable for high dynamic and multi-path ubiquitous network congestion control.

Provided in the embodiments of the present disclosure are a network congestion control method, a communication apparatus, a storage medium and a program product. The network congestion control method comprises: acquiring a service message, wherein the service message comprises a first flag bit and at least one second flag bit, the first flag bit is used for indicating the type of at least one type of congestion control signal carried in the service message, the at least one second flag bit corresponds to at least one type of congestion control signal, and one of the at least one second flag bit is used for indicating a sub-type of the type of congestion control signal corresponding to the second flag bit; and on the basis of the first flag bit and the at least one second flag bit, processing the service message.

A computer system for managing congestion control of packets includes a processor that implements a network traffic manager including a congestion controller and a socket. The congestion controller determines messages parameters for packets of a plurality of messages and assigns a congestion control algorithm setting to each packet based on the message parameters associated with each packet. The socket includes a buffer with a queue having a plurality of threshold queue positions. The congestion controller determines that a subset of packets exceeds at least a first threshold queue position and selects a congestion control action for each packet in the subset based on the assigned congestion control setting for each respective packet and a position of each respective packet relative to the plurality of threshold queue positions.

The application discloses a routing optimization method based on a graph neural network and deep reinforcement learning, which is improved from two aspects of network topology perception and intelligent decision-making by combining GNN and DRL technologies, starting from the actual needs of modern communication networks. Under the condition of ensuring network stability and user experience, the algorithm has been significantly improved in network resource allocation, routing path calculation and network congestion control, and the communication network is promoted to develop in a more intelligent and greener direction.

The invention discloses a network congestion control method, a network congestion control device and electronic equipment. The method comprises the following steps: acquiring network state information respectively corresponding to a plurality of agents in a data center network; determining initial adjustment strategies corresponding to the plurality of agents according to the network state information, and determining response results obtained after the initial adjustment strategies are executed; updating network parameters of the global network according to the response result to obtain a target global network; and updating the network parameters of the local networks corresponding to the plurality of agents by adopting the target global network to obtain a target local network. The technical problem that in a distributed network control scene, related technologies lack an effective mechanism to ensure coordination of network congestion control decisions among a plurality of agents, and consequently, overall performance deterioration is likely to occur is solved.

The application relates to a network congestion control method and device, computer equipment, a storage medium and a product, and relates to the technical field of communication. The method comprises the following steps: obtaining a response message for a sent message; when it is determined that a data flow where the sent message is located exists congestion based on the response message, identifying a congestion occurrence position according to congestion information carried by the response message; if the congestion occurrence position is located at a message forwarding port, obtaining path mapping information; wherein the path mapping information is used for recording the corresponding relationship between message transmission parameter values and message forwarding paths; the original message transmission parameter value of a to-be-sent message in the data flow is adjusted to a first transmission parameter; the first transmission parameter refers to other transmission parameters in each message transmission parameter, except the original message transmission parameter of the to-be-sent message; and the next to-be-sent message of the sent message in the data flow is transmitted through a message forwarding path indicated by the first transmission parameter. The method can improve network bandwidth utilization.

The application discloses a network congestion control method and device, equipment, a storage medium and a program product, and relates to the technical field of communication, so as to realize accurate network congestion control. The method comprises the following steps: a first message is sent to a second device; wherein the first message comprises the identification of a first data flow in which network congestion occurs, the information of a first queue to which the first data flow belongs, and first information used for representing congestion processing action information. The embodiment of the application can realize accurate network congestion control.

The invention provides a congestion control selection method and system based on reinforcement learning, and relates to the technical field of network congestion control. The method comprises the following steps: firstly, in an offline preparation state, dividing a network state into a plurality of grades, and matching a candidate congestion control algorithm pool for each network grade; after entering an online running state, collecting currently running network performance data, determining the grade of the currently running network performance data, loading a corresponding candidate congestion control algorithm pool, and monitoring the network dynamic state so as to feed back and adjust a network grade division basis of an offline stage; and a reinforcement learning agent is introduced, the selection of the congestion control algorithm is used as the action of the reinforcement learning agent, the reinforcement learning agent is trained, and the trained reinforcement learning agent is utilized to decide and output the congestion control algorithm. The preparation of the offline stage accelerates the convergence speed of training, can quickly and dynamically switch to the current optimal congestion control algorithm, and ensures the stability and high efficiency of network transmission.

The embodiment of the present application provides a congestion control method and device for real-time communication. The technical scheme provided by the embodiment of the present application enters a code rate restriction state according to a set code rate restriction period when network congestion is detected, determines a current restriction code rate according to a historical restriction code rate and a maximum receiving code rate of a data receiving end when network congestion occurs, and performs network congestion control based on the current restriction code rate in the code rate restriction state, wherein the detection code rate enters a detection state after the duration of network congestion reaches a set detection waiting time or after each code rate restriction period, and the detection code rate is obtained by upward detection of the code rate in the detection state. The network congestion control is performed based on the current restriction code rate in the code rate restriction period. While ensuring that the code rate is close to the code rate restriction under the current network environment, the number of upward detection of the code rate is effectively reduced, the code rate fluctuation caused by frequent upward detection of the code rate is reduced, and the quality of experience of real-time communication of a user is improved.

The invention relates to a white-box device congestion control optimization method and system based on a large model technology, and the method comprises the steps: collecting network performance data and flow data through a white-box device, and transmitting the data to a central intelligent congestion control system through a bidirectional communication mechanism; the central intelligent congestion control system uses the received data to train a large model capable of predicting the network traffic trend, and constructs a knowledge database for storing traffic feature vectors and corresponding congestion control parameters; a central intelligent congestion control system adjusts congestion control parameters based on real-time flow characteristics transmitted by white-box equipment, the congestion control parameters retrieved by a knowledge database and a flow trend predicted by a large model, and issues the congestion control parameters to the white-box equipment through a two-way communication mechanism so as to optimize the network congestion control performance of the white-box equipment. According to the invention, the adaptive adjustment of the congestion control parameters of the white-box device is realized by combining the traffic characteristic fuzzy matching of the vector database and the traffic trend prediction capability of the center large model.

The invention discloses a cross-data center RDMA network congestion control method and device based on in-band telemetering, and belongs to the technical field of network transmission control. The method comprises the following steps: sending a first cross-data center flow to an upstream DCI switch at an initial rate; according to a Switch-INT message returned by an upstream DCI switch, a first fair rate is calculated, a second cross-data center flow is sent to the upstream DCI switch at the first fair rate, an ACK message of a downstream data center responding to the first cross-data center flow is obtained, and the ACK message comprises a suggested rate; and generating a final rate in combination with the first fair rate and the suggested rate, and sending a third cross-data center flow to an upstream DCI switch at the final rate. According to the method, the network sensing capability of the cross-data center flow can be improved, the fairness of the flow in the data center and the cross-data center flow is improved, and the transmission delay of the datagram is optimized.

The application discloses a dual-time-scale congestion control method of an RDMA lossless data center network and belongs to the technical field of data center network congestion control. The application constructs a dual-time-scale congestion control framework. The method introduces a reinforcement learning model in a coarse-grained layer, realizes the control of data sending rate and a sending window by multi-dimensional perception of global network state, traffic distribution characteristics and historical performance indexes, and thus achieves an optimization decision from a global perspective. In a fine-grained layer, the instantaneous link state is perceived in real time based on a fast feedback congestion mechanism, and fine adjustment of traffic and fast convergence of a micro-time scale are completed. By the optimization capability of the reinforcement learning in dual-dimensional scheduling of the rate and the window, and in combination with the low-delay response characteristics of the fast feedback congestion mechanism, the application can effectively inhibit the accumulation of switch queues, block the risk of congestion diffusion, and thus achieve a dynamic balance among high network throughput, multi-flow fairness and ultra-low queuing delay.

This invention relates to a hybrid congestion control method and system for data center networks based on RTT and INT, within the field of computer network transmission control. The invention aims to achieve high-precision network congestion control with low data center network load costs. When facing different network environments, the host and network nodes can promptly perceive the network status and select different strategies accordingly, thereby adjusting the sender's rate to an ideal state. This invention employs a congestion control scheme based on a hybrid of time-delay signals and network telemetry signals. By analyzing network information and determining the network state, time-delay-based control ensures that the algorithm can achieve overall control of the network under general conditions with low network overhead, while network telemetry-based control ensures precise control of complex network conditions. The flexible switching between the two improves the overall performance of data center network data transmission.

The invention relates to the technical field of end network congestion control, and discloses an end network convergence congestion control method and device, and the method comprises the steps: constructing and initializing a plurality of transmission queue pairs; obtaining measurement data round-trip time, confirmation data packet quantity and congestion notification packet quantity corresponding to the transmission queue pair; and based on the measured data round-trip time, the acknowledgement data packet number and the congestion notification packet number, respectively performing data distribution on the plurality of transmission queue pairs by using a congestion control algorithm so as to transmit data through the plurality of transmission queue pairs. According to the invention, the problem of poor congestion control effect caused by jitter or inaccuracy of single-dimensional information is avoided, and the accuracy of congestion control is improved.