698 results about "Network routing" patented technology

The invention relates to the technical field of network routing, and discloses a dynamic load balancing method and system for network routing, and the method comprises the steps: carrying out the periodic sampling and feature extraction of a processing load of a network node and a transmission load of a network link, and obtaining a load state vector and a network load degree; constructing a load balancing decision matrix, and performing comparison operation on the load balancing decision matrix and the multi-stage load threshold to obtain a load optimization instruction sequence; identifying a high-load source node set and a low-load target node set based on the load optimization instruction sequence, and performing load prediction modeling to obtain a task migration scheme; performing path optimization of reinforcement learning based on the task migration scheme to obtain a multi-routing path set; and carrying out load partition clustering and routing strategy distribution on the multi-routing path set to obtain a regional routing mapping table, and the method reduces the network delay, improves the resource utilization rate and realizes dynamic load balance.

The invention discloses a cloud edge computing power network routing optimization method in the technical field of computing power networks. The method comprises the following steps: acquiring high-dimensional computing power node embedding representation and edge embedding representation of a network topology structure of a cloud edge computing power network; acquiring a hidden state set of each link in the network topology structure when message transmission is carried out between the computing power nodes; according to the high-dimensional computing power node embedding representation, the edge embedding representation and the hidden state set, predicting future traffic of each link in the network topology structure to obtain state information of each link; a pre-constructed routing scheduling problem is converted into a Markov decision process; based on a Markov decision process, a multi-agent near-end strategy optimization algorithm is adopted to train agents, and an optimal routing strategy is obtained; wherein the routing scheduling problem is constructed and acquired by taking minimization of a link utilization rate standard deviation and task transmission time as targets. According to the method, the problems of low routing optimization efficiency and poor flexibility in the existing cloud edge computing power network are solved.

A system and method are provided for routing traffic through a network to ensure load balancing and avoid untrustworthy nodes. Based on network data (e.g., telemetry data), a machine learning model generates trust scores, which are used for routing decisions by determining preferred routes from a source to a destination. The trust scores for nodes along a potential route can be combined into a cumulative trust score. The potential route with the lowest cumulative trust score (i.e., most trustworthy) is preferred, when all other factors are equal. Traffic is routed along the preferred routes, until their capacity is exceeded. Then to achieve load balancing, traffic flows are extended to the next most preferred routes (e.g., the next lowest cumulative trust score), and so forth. When traffic flows include a mix of sensitive and non-sensitive data, the sensitive data is preferentially directed along the most preferred routes.

The invention relates to the technical field of multi-path information processing, and provides a multi-path optimization method, device and system for network routing, and the method comprises the steps: obtaining network node information, obtaining a plurality of available paths based on the network node information, evaluating all available paths, and obtaining network bandwidth data and an initial weight; adjusting the initial weight to obtain an initial path weight and a path priority table, distributing and identifying data packets of network flow by using the path priority table, obtaining a flow data packet with a path identifier, and adjusting the initial path weight after obtaining a flow load log and network delay data based on the flow data packet; and generating a flow scheduling scheme to update the path priority table. Through obtaining network node information and utilizing a bandwidth evaluation algorithm, initial path evaluation reflecting network conditions is generated, and through real-time data feedback of a dynamic monitoring module, a flow scheduling scheme capable of carrying out adaptive adjustment at any time is generated, so that the defects of the prior art in the aspect of dynamic adaptability are improved.

The invention discloses a multi-agent collaborative data exchange dynamic routing optimization method and system, and relates to the technical field of network communication, and the method comprises the steps: obtaining the real-time state information of each node in a network, and outputting a node state data set; constructing a historical state sequence of each node, calculating a load change trend, and outputting a routing adjustment signal when the load change trend reaches a load critical value; calculating a collaborative weight according to the network contribution degree of each agent, establishing a task allocation mapping relationship among the agents based on the collaborative weight, and outputting a collaborative scheduling result; determining a candidate path set, performing index evaluation on paths in the candidate path set, and outputting an optimal routing path; and executing data transmission, obtaining a quality difference between an actual transmission effect and an expected effect, and performing parameter correction. According to the invention, active optimization and multi-target balance of network routing are realized, and routing efficiency and system stability in a dynamic network environment are improved.

The invention discloses a dynamic traffic scheduling method and system based on an SD-WAN technology, which can timely discover potential problems in a network by monitoring performance parameters of network links and nodes in real time, and can effectively relieve network congestion and improve the overall network performance based on analysis of the performance parameters and dynamic adjustment of network routing and bandwidth allocation strategies. According to the method, a high-flow time period, a low-flow time period and key service flow are identified, so that the distribution of network resources is more reasonable and efficient, the adjusted network performance is evaluated in real time, the expected effect of each adjustment can be ensured, and if the performance does not reach a preset standard, the scheduling strategy is continuously optimized, and the scheduling efficiency is improved. According to the technical scheme, the method is beneficial to adapting to rapidly changing network environments and service requirements, the stability and reliability of the network are enhanced, the network states, scheduling strategies and performance evaluation results before and after adjustment are recorded in logs, and operation and maintenance personnel can rapidly position network problems by analyzing log data, so that the operation and maintenance efficiency and accuracy are improved.

The invention relates to the technical field of ad-hoc network routing, in particular to an ad-hoc network routing method based on wireless link perception, which comprises the following steps of: calculating a link quality score of a node by monitoring and analyzing a wireless communication signal and spectrum environment data of the node, recording signal quality, interference condition and bandwidth use state of the node, and calculating a link quality score of the node; and generating link quality score data of the nodes. According to the method, the wireless link state of the node is accurately monitored and analyzed, so that the signal strength can be reflected in real time, the routing decision can be dynamically adjusted, in the aspect of spectrum resource allocation, the spectrum use efficiency of the network is optimized, optimization is performed based on a dynamic spectrum adjustment scheme and real-time feedback, and through a task migration mechanism, the resource allocation efficiency of the network is improved. The task can be automatically migrated to the optimal node, through the link state, the transmission delay is reduced, the network reliability is improved, the calculation efficiency is improved through the intelligent migration of the task load, and the calculation bottleneck problem in the network is effectively solved.

The invention relates to the technical field of network monitoring, in particular to a method for constructing a dynamic self-adaptive alarm monitoring network based on LoRa and Ad-hoc ad hoc network technology, which comprises the following steps: (1) integrating a dynamic Ad-hoc ad hoc network routing protocol on each LoRa node, so that each node can automatically discover neighbor nodes, establish and maintain a dynamic routing table, and automatically discover the neighbor nodes; a non-centralized network architecture is realized; (2) through a multi-hop communication mode, the alarm information can be flexibly transmitted among the nodes, the coverage range of the network is expanded, and the system adapts to a complex geographical environment; and (3) by utilizing the characteristics of low power consumption and long-distance transmission of the LoRa technology, stable communication between the nodes in a large range is ensured, meanwhile, the energy consumption is reduced, and the working life of the nodes is prolonged. Through the dynamic self-adaptive Ad-hoc routing protocol, the network can automatically adjust the routing according to the node state and the environment change, the dynamic change of the network topology is adapted, and the reliable transmission of the alarm information is ensured.

The invention belongs to the technical field of communication, and discloses a network transmission management method and system, and the method comprises the steps that a data collection module collects multi-dimensional data in real time; the decision planning module optimizes a network route; the decision planning module performs protocol and encryption switching; the execution module dynamically adjusts the transmission strategy; the safety protection module performs four-dimensional dynamic protection on the data; the hybrid cloud deployment module guarantees the confidentiality of core data; the multi-active data center module realizes data synchronization among a plurality of data centers; the cross-domain collaborative module realizes privacy protection collaborative training between edge nodes through federated learning; and the block chain auditing traceability module records the key operation log. According to the method, routing and flow prediction are optimized through a mixed quantum classical calculation engine, multi-protocol heterogeneous scheduling and neural mimicry congestion control are combined, the network transmission efficiency is greatly improved, the data transmission speed is improved, and the data leakage risk is reduced through application of a zero-trust security matrix and a quantum key distribution technology.

The invention discloses an optical communication network route optimization method and system, particularly relates to the technical field of multi-domain optical network collaborative networking, and is used for solving the problem of path selection deviation caused by topology abstraction and simplification in an existing multi-domain optical network. Intra-domain topology compensation parameters representing potential path resource consumption in sub-domains are reversely generated by acquiring inter-domain topology abstract information of a cross-domain optical network, and a hidden link competition relationship shared by cross-domain optical paths is dynamically monitored in combination with real-time link resource data to generate a competition intensity index; evaluating a port resource consumption weight based on a compensation parameter and a competition intensity index, fusing a load state to generate an optimal port selection weight, and finally screening a global path through a weight priority and triggering an optical switching node instruction to establish a cross-domain optical path; dynamic collaborative optimization of resource consumption and competition conflicts in the multi-domain optical network is realized, the accuracy of cross-domain path selection and the global utilization rate of network resources are effectively improved, and the risks of redundancy jump and local link congestion are reduced.

The invention relates to a network routing optimization method and device based on particle swarm optimization and simulated annealing, and the method comprises the steps: taking a network device as a node, taking a communication link as an edge, representing a distributed network as a graph, endowing each edge with a corresponding weight, and enabling the weights to represent the parameters of delay, bandwidth and packet loss rate; a plurality of particles are created, a particle swarm is initialized, and each particle of the particle swarm represents a path combination from a source node to a destination node; adjusting the state of each particle based on a speed and position updating rule of a particle swarm optimization algorithm; and carrying out local optimization on the current global optimal particle according to a simulated annealing algorithm. According to the network routing optimization method based on particle swarm optimization and simulated annealing, through a hybrid optimization method combining a particle swarm optimization algorithm and a simulated annealing algorithm, the problem of local optimization and the problem of insufficient adaptability in existing network routing are solved.

The invention provides an intelligent computing fusion network routing system and a routing method thereof. The system comprises an entrance gateway, a routing forwarding node, a computing node and a computing network controller, the computing network controller is used for carrying out path selection and computing resource allocation in combination with a real-time network state and load information of the computing node, dynamically generating and updating a computing network routing table through a computing network fusion routing algorithm, and issuing the computing network routing table to the entry gateway and the routing forwarding node; and the entry gateway receives and stores the computing network routing table issued by the computing network controller as a data stream access point, receives a computing power data stream which is input from the outside and carries a computing power service request, executes computing power routing table matching according to the computing power service request, and forwards the computing power data stream to a proper routing forwarding node according to a matching result. According to the invention, by optimizing the joint scheduling of the computing power service paths and the computing nodes, a plurality of paths of the anycast computing power routing and the weights of the paths are generated, the service flow level load balancing is realized, and the end-to-end time delay is reduced.

The invention relates to the technical field of network routing, and provides a real-time traffic analysis method and system for network routing, and the method comprises the steps: obtaining a network data packet of the network routing, carrying out the data preprocessing, generating a traffic event atlas, generating a dynamic threat score, carrying out the feature extraction of the traffic event atlas, and generating a behavior abnormality matrix; analyzing the abnormal behavior matrix and the dynamic threat score, generating an initial network routing strategy, and analyzing the initial network routing strategy to obtain network performance and security evaluation indexes; and adjusting the network routing based on the evaluation index to obtain an adjustment strategy, carrying out iterative optimization on the adjustment strategy to obtain a target network routing strategy, and carrying out forwarding processing on the network data packet based on the target network routing strategy to obtain a real-time flow analysis result of the network routing. Through the above scheme, real-time, dynamic and accurate analysis of the network traffic is realized, and the problems of low detection precision, slow response speed and lack of real-time adjustment capability in a highly dynamic and complex network environment are improved.

The invention provides a multi-layer satellite network routing method and system based on multi-agent deep reinforcement learning. The method comprises the following steps that: a plurality of low-orbit satellites train respective executor networks by adopting a distributed multi-agent reinforcement learning algorithm to realize routing decision; and one medium-orbit satellite trains a global evaluation network according to the local parameters of the performer network uploaded by the plurality of low-orbit satellites, and provides the global parameters of the trained global evaluation network to each low-orbit satellite, so that the low-orbit satellites update respective local evaluation networks according to the global parameters. And updating respective executor networks according to the updated local evaluation network. According to the multi-layer satellite network routing method and system based on multi-agent deep reinforcement learning, dynamic optimization of a routing strategy can be realized in a medium and low orbit satellite network, and the overall performance of the satellite network is remarkably improved.

The invention provides an unmanned aerial vehicle quantum network entanglement routing method with guaranteed fidelity. The method comprises the following steps: step 1, establishing entanglement connection between adjacent quantum nodes; step 2, establishing a path selection method with end-to-end fidelity as measurement; step 3, an entanglement purification method of hop-by-hop multiple attempts; and step 4, an evaluation method based on network throughput. The method is used for solving the problem of lack of unmanned aerial vehicle quantum entanglement network routing protocols, an entanglement purification scheme for guaranteeing the fidelity is given, and the end-to-end fidelity is effectively guaranteed. According to the method, the entanglement switching path taking the end-to-end fidelity as the routing metric is selected, so that the network throughput is further enhanced, and the quantum nodes of the unmanned aerial vehicle are protected.

The invention relates to an underwater acoustic sensor network routing method based on fuzzy logic, which comprises the following steps that: a Sink node periodically broadcasts a HELLO packet to perceive topological change, and each node locally and dynamically maintains a parameter Hcur representing the hop count of the node for representing the hop count from the node to the Sink node; in the relay selection process, the sending node broadcasts a PING packet; after the neighbor node monitors the PING packet, the neighbor node whose hop count to the Sink node is less than or equal to that of the sending node becomes a candidate node, and the candidate node calculates the link quality between the sending node and the node and replies the PONG packet in a point-to-point manner; (4) within the waiting time, the sending node receives the PONG packet replied from the candidate node, and constructs a candidate node routing table; and (5) after the timing of the waiting time is finished, selecting an optimal forwarding node based on a candidate node routing table and a fuzzy system.

The present application relates to the technical field of network routing, and discloses a method and system for adaptively adjusting network routing, the method comprising: performing detection data packet transmission and hierarchical clustering on a network interface to obtain a path cluster set; performing double-circular matrix encoding on the path cluster set to obtain a routing path encoding set; constructing a spatiotemporal feature tensor based on the routing path encoding set, and performing tensor decomposition and feature pattern extraction to obtain a time-varying weight coefficient; performing sparse-aware proportional iterative optimization calculation based on the spatiotemporal feature tensor and the time-varying weight coefficient to obtain an optimal routing configuration scheme, thereby achieving differentiated processing of different business flows and improving network resource utilization efficiency.

The present application relates to network routing between networks where private routing tables are in use and remote networks. Overlapping IP address ranges may be in use in private networks, which may result IP clashes in the remote network. A system (400) for routing internet protocol, IP, traffic is provided. The system (400) comprises one or more consumer networks (410) each comprising a gateway (420) and one or more private networks (430). Each of the plurality of private networks serves one or more endpoints (440). The system (400) further comprises a remote network (450) comprising a router (460) for each of the plurality of private networks (430) in the consumer network (410). Each router (460) in the remote network has a unique identifier and is configured to establish a tunnel (470) between the router and the gateway (420) of the corresponding consumer network, so that each tunnel corresponds to a private network (430) of the one or more private networks (430) in the one or more consumer networks (410). The gateway (420) of each consumer network (410) is configured to receive upstream IP traffic, determine from which private network (430) the upstream IP traffic originates, and send the upstream IP traffic to the remote network (450) via the tunnel (470) corresponding to the private network (430) from which the upstream IP traffic originates.

The invention relates to an optical network routing intelligent planning method and system based on multi-objective optimization. The method comprises the following steps: performing recursive partitioning on a target optical network to obtain a plurality of sub-network units and a boundary node set; inputting the plurality of sub-network units into a three-layer deep learning model for processing to obtain a routing decision matrix and a wavelength allocation vector; inputting the plurality of sub-network units and the boundary node set into a distributed controller for processing to obtain an error compensation matrix; based on the routing decision matrix and the wavelength allocation vector, performing dual strategy processing of time delay priority and hop count priority to obtain a time delay weighted evaluation function and a hop count weighted evaluation function; performing multi-target joint optimization to obtain an optimal routing scheme; and performing reconstruction cost calculation on the optical path set according to the error compensation matrix and the optimal routing scheme, and outputting a resource reconfiguration scheme. According to the implementation of the invention, the resource overhead in the network reconstruction process is reduced, and the overall operation efficiency of the network is improved.

The invention provides an Internet of Things cooperative routing method based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a wireless sensor network model; constructing an energy consumption model; establishing an optimization problem of network routing according to the constructed wireless sensor network model and the energy consumption model; modeling the optimization problem into a multi-agent reinforcement learning problem, and selecting an optimal routing path according to the multi-agent reinforcement learning problem; wherein in the multi-agent reinforcement learning problem, each sensor node is regarded as an independent agent, and each agent learns a strategy for optimizing the overall network performance according to a local state and a reward mechanism. According to the invention, the energy utilization efficiency of the routing protocol is improved, routing with balanced energy is realized, and the network life cycle is effectively prolonged.

The invention relates to a satellite network distributed routing method, a satellite network distributed routing system, satellite network distributed routing equipment and a storage medium, belongs to the technical field of wireless communication, and solves the problems of dynamic topological structure change and overlarge load of part of nodes caused by non-uniform service volume distribution in the existing satellite network routing method. The satellite network distributed routing method comprises the following steps: firstly, modeling a routing problem, constructing a problem model, then defining a state space, an observation space, an action space and a reward function of a Markov decision process, constructing a DDQN-based satellite network routing algorithm training framework based on the Markov decision process, and constructing a satellite network routing algorithm training framework based on the DDQN. And then a satellite network routing algorithm training framework based on DDQN is utilized to realize satellite network routing decision, and the network load is reduced with the purpose of minimizing end-to-end delay. The method is not influenced by the dynamic change of the network topology, and can adapt to the frequent network topology change of the low-orbit satellite, thereby ensuring the real-time performance and robustness of data transmission.

The invention discloses a network-on-chip routing method and device, routing nodes, equipment, a medium and a product, and relates to the technical field of network-on-chip, and the method comprises the steps: firstly, screening the routing nodes through employing the input direction of a message, and limiting the selection range of the next-hop routing node; the relative position of the target routing node relative to the current routing node is determined, the routing nodes are further screened according to the relative position, and candidate routing nodes are determined; and determining a next-hop routing node according to the cache capacity and the input direction of the candidate routing node, and transmitting the message to be transmitted to the next-hop routing node to complete routing. The problem that the routing of the network-on-chip is easy to cause network message blockage or link failure of the network-on-chip can be solved. According to the method, an input direction is adopted to limit a selection range of a next-hop routing node, a relative position is adopted to determine candidate routing nodes, the next-hop routing node is determined in the candidate routing nodes based on cache capacity, and self-adaptive deadlock-free routing is realized.

The invention discloses an attack path intelligent recommendation system based on improved MulVAL. The system comprises a network environment information acquisition module, an interaction rule module, a security configuration information acquisition module, a reasoning module and an ATTamp; according to the attack path recommendation system of the CK mapping module and the evaluation module, network description information is perfected by incorporating network routing information, security configuration information such as a firewall and host configuration information into a reasoning strategy, the accuracy of attack path planning can be effectively improved, and in addition, through a high-level attack graph model and a middle-level ATTamp, a high-level ATTamp model and a high-level ATTamp model, a high-level ATTamp model and a high-level ATTamp model are integrated. The CK framework and a low-layer vulnerability analysis knowledge base are combined to construct a multi-level threat analysis mode, attack path analysis under a global view angle and a microscopic view angle is provided for a user, and the availability of the system is improved.

The invention provides an SD-WAN architecture-based intelligent global hybrid cloud routing system, and belongs to the technical field of network routing, and the system comprises a data acquisition module which is used for compressing a to-be-transmitted data stream based on a preset compression algorithm so as to obtain a compressed data stream; the node determination module is used for acquiring network performance related data and determining a plurality of dynamic distributed nodes corresponding to the SD-WAN control layer; the node connection module is used for connecting all the distributed nodes and connecting all the distributed nodes to the SD-WAN control layer at the same time; the score determination module is used for collecting real-time network state data of all the distributed nodes, performing data analysis and determining a path score of each available path in combination with a preset user demand; the path determination module is used for determining an optimal path and a redundant path; and the path switching module is used for acquiring network state data of all the distributed nodes after the optimal path performs data transmission, and further determining a redundancy switching condition and performing redundancy path switching. And the reliability and adaptability of the network are improved.

The invention discloses an electric power communication optical network routing optimization method based on a DQN model, and the method comprises the following steps: S1, collecting the information of an electric power communication optical network, and building a state space, an action space and a reward function of the electric power communication optical network routing; s2, performing offline training on the DGQN model according to the state space, the action space and the reward function to obtain a trained DGQN model; and S3, optimizing a target electric power communication optical network route according to the trained DGQN model deployment, and updating model parameters through an online learning mechanism. According to the method, the adaptive learning capability of deep reinforcement learning is utilized, so that the routing decision can dynamically adapt to the continuously changing flow load and link state in the electric power communication optical network, the defect that a traditional static routing method cannot cope with a dynamic environment is overcome, and the response speed and stability of the network are improved.

Techniques are described for routing traffic through an interconnect cloud gateway based on cloud traffic routing indicators. The interconnect cloud gateway can advertise the cloud traffic routing indicators, which can include cloud indicators and transport gateway indicators. The cloud indicators can include cloud tags utilized to route cloud traffic. The transport gateway indicators can include transport gateway flags utilized to identify private networks utilized to route the cloud traffic. The cloud traffic can routed during normal private network operation through private networks, which can be dynamically replaced by public networks due to occurrences of failures preventing the data traffic from being routed through the private networks and to cloud networks.

A system manages network traffic in a distributed system comprising a plurality of network devices. The network devices are divided into a plurality of network slices, each of the network slices including a subset of the network devices such that there is no overlap of network devices between the network slices. Individual network slices are associated with individual slice controllers, and an individual slice controller is configured to manage network routing of an individual network slice. Each of the individual slice controllers route the network traffic within each respective individual network slice. The network traffic is independently routed based on expected network conditions for each respective individual network slice, and data defining routing decisions is contained within each network slice to limit fault effects between the network slices.

Methods and systems related to parallel computing using heterogeneous networks of computational nodes are disclosed herein. A method for executing a complex computation on a heterogeneous set of computational nodes linked together by a set of links in a network is disclosed. The method includes compiling, using a table of bandwidth values for the set of links in the network, a set of instructions for routing data for the execution of the complex computation. The method also includes configuring a set of programmable controllers on the heterogeneous set of computational nodes with the set of instructions. The method also includes executing the set of instructions using the set of programmable controllers. The method also includes routing data through the network to facilitate the execution of the complex computation by the heterogeneous set of computational nodes and in response to the execution of the instructions.

The invention discloses an electric power communication network route planning method based on reinforcement learning, relates to the technical field of electric power communication networks, and solves the problem that load and risk balance of electric power communication network route planning cannot be accurately realized due to the fact that an existing electric power communication network route planning method is single in consideration factor. The method comprises the following steps: constructing a power communication network topology model G and a power communication network service model H; constructing a load balancing and risk balancing optimization model of the power communication network; constructing a PDQN network model, and performing path selection in the Pareto optimal solution set; and training the PDQN network model, and finally outputting a routing decision of a service demand. According to the method, the limitation that a traditional method mainly pays attention to a single target is avoided, the network load balance degree and the risk balance degree can be effectively reduced, the service route planning requirement of the power communication network is met, and the overall performance and reliability of the network are improved.