135 results about "Network congestion control" patented technology

The invention discloses an air-space-ground network congestion control method based on deep reinforcement learning. The method comprises the following steps: acquiring data information of a target air-space-ground network; setting an optimization target and carrying out modeling; setting a state space, an action space and a reward function of the decision process; constructing a state prediction module, a reward redistribution module and an optimization strategy module of the decision process; and performing congestion control of the space-air-ground network based on deep reinforcement learning according to the constructed module. According to the invention, through obtaining the data information of the plurality of target air-space-ground networks, based on the setting of the state space, the action space and the reward function, and the realization of state prediction, reward redistribution and optimization strategies, the congestion control of the air-space-ground network based on deep reinforcement learning is realized, the reliability is higher, and the method is more suitable for large-scale popularization and application. The precision is better, and the effect is better.

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 belongs to the technical field of power systems, and more particularly relates to a quick-start network congestion control method suitable for a power system, which ensures the accuracy and reliability of data by collecting and preprocessing power equipment and network flow data. Then, a traffic prediction model is trained by using the preprocessed data, and the model can accurately predict the network traffic in a period of time in the future, thereby providing a basis for real-time decision making. In the starting process of the power system, network congestion detection is carried out in combination with real-time network state and equipment state data, and potential congestion risks are found in time. And finally, dynamically adjusting a flow scheduling and control strategy based on a network congestion detection result and a prediction result of the flow prediction model, thereby optimizing resource allocation and avoiding an improper decision. The series of steps ensure that network congestion can be timely and accurately dealt with under the condition that the network state is rapidly changed, and the efficiency and the stability of rapid starting of the power system are improved.

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 invention discloses a network congestion control method, a storage medium and electronic equipment. Relates to the technical field of network communication, and the method comprises the following steps: in a process that a sending end sends a target data packet to a receiving end through a switch, metadata is measured based on an in-band telemetering function integrated by the switch, and the metadata is used for indicating state information of a link; the link is used for indicating a transmission path of the data packet from the sending end to the receiving end through the switch; detecting a congestion state of the link based on the metadata; and when the congestion state is that congestion exists, sending the congestion information of the link to the sending end, so that the sending end adjusts the data packet sending rate based on the congestion information. According to the invention, the technical problems of low congestion identification accuracy and much network link bandwidth waste in the prior art are solved.

The invention provides a centralized control RoCEv2 network congestion control method, which relates to the technical field of network congestion control, and comprises the following steps: S1, when a data center network is started, a network controller calculates and stores a positive forwarding table and an alternative forwarding table of a switch according to network topology; s2, the network controller judges a network operation state according to the link information; s3, if the network operation state is a network congestion state, performing flow regulation and control by using a flow rate adjustment method, and if the congestion condition is improved after flow regulation and control, turning to the step S2; and S4, the network controller notifies the switch with congestion to set the alternative forwarding table as a positive forwarding table for data forwarding according to the comprehensive link cost, and the step S2 is executed. According to the invention, the global flow control strategy is generated through the network controller, the consistency and effectiveness of the strategy are ensured, the forwarding path is automatically switched when the global flow control strategy fails, and the efficiency and accuracy of congestion control are improved.

The invention provides a network congestion control method, device and equipment and a storage medium, and relates to the technical field of network communication, and the method can sense the congestion state of upstream and downstream network equipment based on a network forwarding node, and achieves the quick alleviation of network congestion. The method comprises the following steps: determining congestion states of a first node and a neighbor node of the first node; the first node and the neighbor node are nodes on the same forwarding path; determining a network congestion control scheme at least based on the congestion states of the first node and the neighbor node; the network congestion control scheme is used for relieving the congestion degree of the first node and / or the neighbor node; and executing the network congestion control scheme.

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 present application discloses a network congestion control method, a sending network card and a receiving network card, and belongs to the field of network communication technology. Among them, the method includes: the sending network card obtains the first timestamp of the sending request message, and obtains the second timestamp of the receiving response message, extracts the internal processing delay of the receiving network card from the response message, calculates the round-trip delay RTT between the sending network card and the receiving network card according to the first timestamp, the second timestamp and the internal processing delay; calculates the packet sending rate according to the RTT, and controls the network congestion according to the packet sending rate. Through this application, the internal delay of the network card is taken into account in measuring RTT, which eliminates the error caused by the internal delay of the receiving network card to the RTT measurement, and realizes the precise control of network congestion.

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 present application provides a congestion control method and an apparatus. The method is applied to a data transmission server-side, and the data transmission server-side is connected to a plurality of application servers and is used for providing data transmission services for the plurality of application servers. The method comprises: when it is determined that network congestion occurs during transmission of a plurality of downlink data streams belonging to different applications or belonging to different users, performing different congestion control on the plurality of downlink data streams by combining the respective degree of demand for low-latency transmission by the plurality of downlink data streams, thereby improving the network congestion control effect.

In various embodiments, a congestion control module within a transport stack limits the rate at which packets are transmitted from a server to a client device based on a percentage of the available capacity of a network path through which the packets are transmitted. In some embodiments, the available network path capacity can be determined by first performing a linear regression using (1) send durations over which packets associated with encoded frames are transmitted, and (2) corresponding reception durations over which the packets associated with the encoded frames are received, in order to determine a line that relates send duration and reception duration. After the line is determined, the available network path capacity can be computed as an estimated intersection between the determined line and the line y=x, with the intersection being approached as a limit.

Congestion control by adding a congestion signal tag header to each of one or more transmission packets prior to transmission of the transmission packets by the first node to a second node, the congestion signal tag header specifying one or more congestion signal types and, for each of the congestion signal types, specifying a congestion signal value by providing an initial congestion signal value for the congestion signal value; receiving one or more return packets generated by the second node in response to receipt of the transmission packets, the return packets including a congestion signal reflection header having one or more return congestion signal values, and the return congestion signal values corresponding respectively to the congestion signal types; and determining whether transmission rate control is necessary based on the return congestion signal values.

The present invention proposes a data center network congestion control method and apparatus based on the coupling of data and credit. The method includes: obtaining the structure of the data center network, including switches, senders, and receivers; developing a credit rate control mechanism based on ECN for managing data congestion and credit congestion and performing congestion control for the entire loop of the data center network according to the principle of designing a data and credit coupling mechanism in the DCEF framework; solving the problems of credit waste and data and credit loss by designing a speculative detection mechanism and formulating a retransmission strategy; and realizing flow scheduling control by using a credit-driven flow scheduling mechanism, further reducing the flow completion time and the deadline miss rate. The method of the present invention improves the congestion control performance through congestion control based on the coupling of data and credit, reduces resource waste, meets the requirements of the data center network in terms of low latency, high throughput, and flow deadline, and has wide applicability.

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.

Aspects of the disclosure provide for congestion control mechanisms to reduce data transmission queues and to increase link utilization through precise congestion signals and reduction of control loop delay. A congestion control system (CC) system can utilize Back-To-Sender (BTS) packets over (round trip time) RTT control loop delay to react to congestion faster. The CC system can apply Proactive Ramp-up (PRU) to identify flow completions to occupy released bandwidth right on time, e.g., as bandwidth is made available. The CC system can perform supply matching (SM) through network calculus concepts to increase link utilization. The CC system can apply some or all of the use of BTS packets, PRU, and / or SM to reduce network latency and improve data flow completion time as compared with other approaches.

Provided in the embodiments of the present disclosure are a network congestion control method and apparatus. The method comprises: a receiving end acquiring congestion information of a data stream; the receiving end calculating a target sending rate of the data stream on the basis of the congestion information of the data stream; and the receiving end notifying a sending end of the target sending rate of the data stream, such that the sending end sends the data stream according to the target sending rate.

The invention discloses an air-space-ground network congestion control method based on deep reinforcement learning, which realizes rapid dynamic response and global resource optimization through distributed node cooperation and dynamic service rate adjustment. According to the method, modeling is carried out based on a multi-node agent Markov decision process, a composite reward function is designed, and strategy optimization algorithm training is driven by the composite reward function; a'centralized training-distributed execution 'framework is adopted in the framework, strategy network parameters are optimized in the centralized training stage, the service rate of each node is independently adjusted by each node based on local observation in the distributed execution stage, and global communication is not needed. Aiming at the characteristics of high dynamic topology and resource limitation of a satellite network, a label switching path is constructed by using an MPLS traffic engineering protocol, and link capacity fluctuation and satellite orbit movement can be dynamically adapted. According to the method, accurate queue control of air-space-ground node congestion control is realized, the throughput of the network is remarkably improved, and a reliable solution is provided for efficient congestion control of the air-space-ground network.

The invention discloses a data model dual-drive-based data center network congestion control method, which combines the self-adaptive capability of deep reinforcement learning and the quick response capability of traditional model mapping to solve the problems of slow response speed and insufficient adaptability in a dynamic network environment in the prior art. According to the method, a layered architecture is adopted, and an optimization engine module, an adjustment control module and a statistical information module are included. The optimization engine module learns network state data based on a deep reinforcement learning algorithm, and dynamically optimizes a congestion control strategy; the regulation control module quickly executes a strategy by using a model mapping mechanism to realize real-time regulation of the transmission rate; the statistical information module continuously collects network state information to ensure that the system adapts to complex traffic changes. According to the method, communication delay can be effectively reduced in a complex dynamic network environment, queue backlog is reduced, the throughput is improved, the stability of a data center network is enhanced, and the method is particularly suitable for high-load scenes such as AI large model training.

The invention belongs to the field of RDMA and cloud memory pooling, and provides a cloud memory pool priority flow control method and system based on RDMA, a cloud server is used as a cloud cloud memory pool of a plurality of local nodes, the local nodes read and write memories in a cloud memory pool server through RDMA, the memory capacity of the local nodes is logically expanded, and the priority flow control of the cloud memory pool is realized. The memory demand and the memory utilization rate of each local node on the cloud memory pool are monitored through a dynamic regulation and control algorithm, the memory capacity allocated to each local node by the cloud memory pool is dynamically regulated and controlled, the memory utilization rate is improved, and meanwhile, by maintaining a table containing the priority of each local node, when network congestion occurs, the network congestion rate is improved through priority scheduling. Network congestion control is realized, high-speed and stable data transmission and remote memory access are provided for high-priority nodes while the network throughput is ensured, and the processing speed of important transactions is ensured.

A communication method, a communication apparatus, a communication device and a storage medium are provided. The method includes: receiving, by a first network function, first information from a first function, the first information being information related to network congestion control; generating, by the first network function, policy information at least based on the first information, and sends, by the first network function, the policy information to another network function and / or User Equipment (UE).

The present invention provides a centralized RoCEv2 network congestion control method, which relates to the technical field of network congestion control, including: S1, when a data center network is started, a network controller calculates and saves a switch forwarding table and an alternative forwarding table according to the network topology; S2, the network controller determines the network operation state according to link information; S3, if the network operation state is a network congestion state, a flow rate adjustment method is used to perform flow control, and if the congestion situation is improved after flow control, the method goes to step S2; S4, the network controller notifies the congested switch according to the comprehensive link cost to set the alternative forwarding table as the positive forwarding table for data forwarding, and goes to step S2. The present invention generates a global flow control strategy through the network controller, ensures the consistency and effectiveness of the strategy, and automatically switches the forwarding path when the global flow control strategy fails, thereby improving the efficiency and accuracy of congestion control.

The invention provides a congestion control method and device, and the method is applied to a data transmission server, and the data transmission server is connected with a plurality of application servers, and is used for providing a data transmission service for the plurality of application servers. The method comprises the following steps: when determining that network congestion occurs in the transmission process of a plurality of downlink data streams belonging to different applications or different users, executing different congestion control on the plurality of downlink data streams by combining the demand degrees of the plurality of downlink data streams on low transmission delay, thereby improving the effect of network congestion control.

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.

This application discloses a network congestion control method and a network device, and relates to the field of communication technologies, to improve continuity and completion of a packet in a transmission process of a Transmission Control Protocol (TCP) flow. In this application, when pre-congestion (that is, congestion may occur) occurs in a forwarding direction of the TCP flow, a forwarding device reduces a value of a receive window (RWND) field in an acknowledge (ACK) packet, to reduce a sending rate of a transmit end for a packet in the TCP flow or delay sending of a packet by a transmit end. This relieves buffer pressure of the forwarding device, reduces a packet loss, and improves the continuity and the completion of the packet in the transmission process of the TCP flow.

The invention provides a network congestion control method, electronic equipment and a storage medium, and the method comprises the steps that a network controller receives the information of a target port, the information of a to-be-paused service queue corresponding to the target port and the pause duration sent by a target network element, the network controller inquires and determines a target service path according to the information of the target port and the information of the to-be-paused service queue, the target service path is used for indicating the information of a network element through which a data stream in the to-be-paused service queue passes when the data stream is transmitted to the target port, and the network controller pauses the to-be-paused service queue according to the pause duration. And sending a pause command to an initial network element in the passing network elements, so that the initial network element pauses data stream transmission with the target port within the pause duration. Through the congestion control of the data flow level, the data flow passing through the target port can be accurately suspended, the innocent data flow is enabled to continue to be transmitted, and the network transmission quality is remarkably improved.