135 results about "Network Load Balancing" patented technology

A multi-cloud overlay network for supporting communications between a first public cloud network and a second public cloud network. The overlay network features a management virtual private network, which includes a network load balancing (NLB) component and a controller registered as a target on a port of the NLB component. The overlay network further includes one or more spoke or transit gateways and a multi-cloud access virtual private cloud (VPC) operating within the first public cloud network, and a remote cloud load balancer component operating the second public cloud network. The remote cloud load balancer component is communicatively coupled between the multi-cloud access VPC and one or more remote spoke or transit gateways. The multi-cloud access VPC includes a VPC endpoint that is assigned a private IP address and communicatively coupled to the NLB component and a virtual private network (VPN) gateway communicatively coupled to a private transport.

Serverless containerization of a collaboration application improves deployment and security of the collaboration application. A system may generate a container within a private cloud. The container may execute the collaboration application. The container is assigned an Internet Protocol (IP) address and is associated with a security group that controls network traffic to and from the container. The system may include a network load balancer configured with the IP address assigned to the container that allocates computational resources for executing the collaboration application. The system may include serverless resources such as a database and filesystem that is accessible to the container within the private cloud. The system may include a private cloud (PC) endpoint configured to establish a private connection with an end user interface, wherein the private connection provides secure network traffic between the PC endpoint the end user interface within a private network of the PC endpoint.

The invention discloses an intelligent load balancing method and system based on multipath fusion, and relates to the field of network load balancing. A distributed monitoring system is constructed to collect network performance indexes, and a Transform model is used to predict traffic; a hierarchical reinforcement learning architecture is adopted, a global strategy is generated according to a macroscopic network state, and flow distribution is optimized for a single path; kalman filtering and particle filtering are automatically switched according to the path stability; the flow is flexibly migrated based on a genetic algorithm; an index weight is calculated by using a Shapley value method and an entropy weight method, and path quality is evaluated; the distribution strategy is executed through the SDN controller, and the overall performance of the network is improved. According to the invention, network abnormity is quickly responded, the traffic migration efficiency is improved, and bandwidth waste is reduced; service differentiation scheduling is supported, the service quality is guaranteed, and the attack defense capability is enhanced; operation and maintenance efficiency is improved, fault positioning time is shortened, and efficient utilization of network resources and guarantee of service quality are realized.

The invention relates to the technical field of remote control, in particular to an intelligent device remote cooperative control method and system based on the Internet of Things, and the method comprises the following steps: monitoring and adjusting the CPU utilization rate and memory usage of an intelligent device based on an Internet of Things platform, optimizing the data collection and network resource distribution, and improving the intelligent device remote cooperative control efficiency. By analyzing equipment performance and a network load balance index, equipment connection priority ranking and task load management are realized, and task execution and connection frequency is dynamically adjusted, so that real-time task requirements are met, and system efficiency is improved. According to the method, the performance index of the equipment is monitored in real time, the data transmission interval is dynamically adjusted, the data processing speed is remarkably increased, resource waste is reduced, efficient use of network resources is ensured through real-time load analysis and optimization of network nodes, the problem of data delay caused by network congestion is avoided, and in the aspect of equipment connection management, the data transmission efficiency is improved. An intelligent priority decision mechanism allows the key device to quickly access the network in an emergency.

The invention relates to a VNF migration method based on space-time large model resource demand prediction for digital twinning assistance in an industrial Internet of Things, and belongs to the technical field of mobile communication. Aiming at the problems of poor service continuity, migration strategy lagging and high synchronization delay caused by resource demand fluctuation, the following scheme is provided: constructing a digital twin network architecture to collect node information in real time; spatial features are extracted through a graph convolutional network, time dependence is captured in combination with a multi-head self-attention mechanism, and future resource requirements are predicted by using a large language model; and establishing a system optimization model fusing energy consumption, time delay and load balancing, and adopting a heterogeneous multi-agent reinforcement learning algorithm to decide VNF migration and digital twin node re-association. According to the method, the resource prediction precision is remarkably improved, the migration energy consumption is reduced by more than 15%, the synchronization delay is reduced by 20%, the network load balance is effectively optimized, and the overall effectiveness of the system is improved by 30%.

The invention relates to the technical field of network communication, and discloses a network load balancing evaluation method based on a deep learning model, which comprises the following steps: S1, data preprocessing: collecting flow data, and carrying out normalization, noise reduction and enhancement processing; s2, manifold modeling: dimension reduction is performed on high-dimensional flow data, and a local structure is reserved; s3, feature extraction: extracting optimized low-dimensional features, and reducing redundancy; s4, performance evaluation: comprehensively evaluating throughput, delay and balance; s5, anomaly detection: detecting abnormal traffic and outputting a result; s6, feedback optimization: dynamically adjusting a load strategy, and improving the performance. Through deep learning, variational information bottleneck and manifold learning, accurate modeling and optimization of dynamic network traffic features are realized, the extracted features accurately reflect the traffic change trend, the adaptive capacity of the model to sudden and abnormal traffic is enhanced, performance reduction caused by feature distortion in a traditional method is effectively avoided, and the method has a good application prospect. And reliable support is provided for a load balancing strategy.

The invention provides a network load balancing algorithm based on a credential platform and a credential terminal, and relates to the technical field of credential, and the algorithm comprises the following steps: collecting real-time traffic data from a plurality of ports; carrying out preprocessing of normalization, abnormal value elimination and protocol field standardization on the collected data; performing multi-dimensional protocol fingerprint feature extraction on the flow of each port based on a protocol analysis algorithm; modeling historical and real-time traffic of each port by using a time sequence neural network, and outputting a traffic prediction value and abnormal traffic probability assessment in a future short time period; dynamically generating a multi-port distribution weight factor based on the protocol characteristics and the flow change trend of each port; inputting the prediction result and the real-time load state into a reinforcement learning scheduling agent; upon detection of an abnormal traffic trend, feed-forward load distribution adjustments are performed immediately. According to the method, the data processing efficiency can be improved, the load scheduling intelligence is realized, the resource allocation priority of the port of the credential terminal is ensured, and the service continuity is guaranteed.

The invention discloses a network resource allocation method and system based on a network load, and relates to the technical field of network resource allocation, and the method comprises the following steps: 1, determining a service demand satisfaction degree; step 2, calculating a network load balancing degree; 3, predicting future business demand data; and 4, defining constraint conditions, maximizing the service demand satisfaction degree and the network balancing load degree, and formulating a network resource allocation strategy. According to the method, the situation that only the overall average level is concerned and individual key indexes are ignored to be seriously substandard when the business requirements are evaluated in the prior art can be effectively improved.

The invention relates to an Internet of Vehicles multi-domain intelligent optimization method and system under a computing network convergence architecture, and the method comprises the steps: receiving a task request sent by an edge layer road side unit based on a pre-configured cloud edge end cooperation architecture, optimizing the user service time delay and network load balance through a differential evolution algorithm, and obtaining a network load balance optimization result. Generating an original intelligent model distribution path and distributing the pre-trained original intelligent model to an edge layer road side unit; receiving an original intelligent model and vehicle mobility data, optimizing vehicle mobility, model quality and alliance establishment cost through an alliance game algorithm, and constructing a vehicle fine-tuning alliance structure suitable for federal learning; on the basis of a vehicle fine tuning alliance structure, local model updating data and trust evaluation data of vehicles in an alliance are obtained, dynamic weighted federated learning is executed through multi-dimensional trust evaluation, security is enhanced through differential privacy and a block chain tracking mechanism, and a high-precision global intelligent model is generated and deployed to the vehicles. Reliable support is provided for automatic driving, V2X communication and the like.

The invention discloses a fault analysis method for a low earth orbit satellite, and relates to the technical field of electric data analysis. The method comprises the following steps: collecting related data of influence of space debris on low-orbit satellite signal transmission, analyzing the related data of influence of the space debris on low-orbit satellite signal transmission to obtain an influence coefficient of the space debris on low-orbit satellite signal transmission, collecting related data of inter-satellite link network load balancing, and analyzing the related data of influence of the space debris on low-orbit satellite signal transmission. The inter-satellite link network load balancing related data is analyzed to obtain an inter-satellite link network load balancing evaluation coefficient, the IQ signal related data is collected, and the IQ signal related data is analyzed to obtain a difference evaluation coefficient between an IQ signal real-time value and a standard value. According to the invention, the problem that the communication interference between the low-orbit satellite and the ground station is too high in the prior art is solved.

The invention provides an air-space heterogeneous network cross-domain link selection and message distribution method, and belongs to the field of communication ad hoc networks. According to the method disclosed by the invention, the cross-domain path capability and the flow condition of each subnet are summarized through a heterogeneous network situation awareness technology, and the obtained network situation information provides a basis for subsequent link planning; carrying out capability evaluation on each path through a heterogeneous network cross-domain link selection technology, and selecting an optimal cross-domain path by comprehensively considering link time delay, packet loss and network load so as to meet the communication requirements of services and realize network load balancing; through a segment routing technology, protocol format conversion of cross-domain data packets is realized by routing nodes, and cross-domain message distribution is realized. According to the invention, interconnection and intercommunication of space and air heterogeneous networks can be realized, network overhead of message forwarding among the heterogeneous networks is reduced, meanwhile, limited network resources are fully utilized, load balancing is achieved, and an effective networking technical solution is provided for the fields of emergency rescue and the like.

The invention relates to an optical network topology dynamic adjustment method and system, computer equipment, a medium and a product. The method comprises the following steps: acquiring a computing resource association degree parameter according to equipment resource allocation information, acquiring a data transmission delay parameter according to global topology information of a network topology structure, and transmitting the data transmission delay parameter to a computing node under the condition that communication activity data generated by computing equipment of each computing node in a target iteration process passes verification, acquiring a communication traffic matrix according to the communication activity data, acquiring actual communication traffic according to the communication traffic matrix and the global topological information, acquiring a network load balancing parameter according to the actual communication traffic, and acquiring an evaluation function value based on a computing resource association degree parameter, a data transmission delay parameter and the network load balancing parameter; and according to the evaluation function values corresponding to all the network topology structures, determining a target network topology structure, and adjusting the optical connection path to realize optical network topology adjustment. By adopting the method, the network performance can be improved.

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.

The invention relates to the technical field of flow management, in particular to a communication network flow management system which comprises a flow monitoring module, an abnormal flow processing module, a flow priority adjusting module and a network load balancing module. According to the method, the abnormal data flow can be identified by analyzing the distribution characteristics of the communication data flow in real time, the abnormal level can be quantitatively evaluated according to the abnormal influence, the flow limiting is more targeted by positioning the source node of the abnormal flow and implementing the bandwidth limitation according to the abnormal level, the flow limiting degree can be adjusted as required, unnecessary broadband waste is avoided, and the flow limiting efficiency is improved. By considering the importance and real-time performance indexes of services, when the priority of communication data is adjusted, the performance of key applications is guaranteed preferentially, and by monitoring the delay and packet loss conditions of nodes in real time and intelligently adjusting node bandwidth allocation according to the flow priority, the overall operation efficiency and stability of a network are improved, and the network performance is improved. And the network performance management is optimized.

The invention provides a data center network load balancing method and device. The method comprises the following steps: acquiring information of a plurality of computing nodes; forwarding paths corresponding to the computing nodes are determined according to the information of the computing nodes, the forwarding paths corresponding to the computing nodes are equivalent forwarding paths, and the forwarding paths corresponding to the computing nodes are different; and when the data stream is received, forwarding the data stream according to the forwarding path corresponding to the target computing node of the data stream. By adopting the method, the data flow can be uniformly distributed on the whole path, and the network load balancing is achieved.

The invention discloses a power distribution network edge calculation load balancing method. According to the method, overload equipment is identified and task migration is carried out by continuously monitoring the load performance ratio of edge computing equipment. The distribution network edge computing load balancing method is characterized in that an optimal task migration target device is selected by adopting a graph model search algorithm, and a task migration scheme is generated in combination with a priority-based heuristic algorithm. According to the method, a deep reinforcement learning algorithm is introduced, and a task allocation strategy is continuously optimized by taking a whole network load balancing effect, task execution quality and a network bandwidth utilization rate as optimization targets. According to the method, the problems of unbalanced load of edge computing equipment and limited network bandwidth resources in the power distribution network are effectively solved, efficient utilization of computing resources and improvement of task execution efficiency are realized, and powerful support is provided for intelligent operation and maintenance of the power distribution network.

Techniques for load balancing secure network traffic are disclosed. A system, process, and / or computer program product for load balancing secure network traffic includes monitoring network traffic for each of a plurality of branches for an enterprise network, and performing load balancing for the plurality of monitored branches to a plurality of security processing nodes (SPNs) executing in a cloud-based security service using a network load balancer (NLB) in communication with a plurality of Network Processing Nodes (NPNs), the plurality of monitored branches being distributed to the plurality of SPNs via a plurality of tunnels.

The invention discloses a network load balancing distributed strategy optimization method. The method comprises the following steps: acquiring node load data; a gravity and repulsive force field is simulated to dynamically adjust load distribution; each data center node deploys a load balancing agent, and a task allocation strategy is dynamically adjusted through multi-agent reinforcement learning training; resource allocation optimization: based on a resource allocation optimization target, optimizing allocation from tasks to nodes with the target of minimizing data transmission cost and calculation delay and satisfying resource constraints at the same time; and the load index and the actual task delay of each node are monitored in real time, and the overall performance of the system is evaluated. The method can adapt to load fluctuation of the ground; the reward function ensures that the proxy not only optimizes local performance in a high dynamic scene, but also maintains the stability of a global network structure; the load attention mechanism enables the agent to dynamically pay attention to nodes with lower load and better delay.

The invention relates to the technical field of network security, and provides a smart power grid adaptive defense and flow dynamic optimization method and system, and the method comprises the following steps: obtaining the real-time network flow of a smart power grid; detecting the obtained real-time network flow based on a machine learning algorithm, and judging whether the real-time network flow is subjected to DoS attack or not; for the detected attack traffic, a DoS attack defense response measure is automatically executed; and for the detected normal flow, optimizing through network load balancing and priority queue management based on kernel density estimation, and carrying out network flow dynamic management on the network flow. According to the invention, denial of service (DoS) attacks in the smart power grid can be identified and coped with in time, and meanwhile, the overall network performance is improved and the operation stability of a power system is improved through dynamic optimization management of traffic and optimization of distribution of network resources.

The invention discloses a user access method and system in a general sensing calculation control fusion network, and a storage medium. The method comprises the following steps: collecting a communication demand and a sensing demand of a user; calculating an uplink and downlink communication signal to interference plus noise ratio and a sensing signal to interference plus noise ratio of each base station; calculating the calculation load information of each base station; calculating the priority of each base station according to the utility function, and selecting the base station with the highest priority to access; and carrying out constraint condition check, and if communication quality and computing resource constraints are met, accessing the base station by the user, and updating the system state. By adopting the technical scheme of the invention, the base station which is most suitable for executing communication, sensing and calculation tasks is selected by evaluating the sensing coverage capability of each base station and the current network sensing calculation load, so that the communication, sensing and calculation performance of the network can be effectively ensured, and the network load balancing is realized.

A packet to be forwarded over a computer network to a destination is received. A group of multiple network paths is available to forward to the packet to the destination. One or more path selection factors are determined to be used to identify a specific network load balancing algorithm to select a specific network path from the group of multiple network paths. The one or more path selection factors include at least one path selection factor determined based at least in part on a dynamic state of the computer network or a network node in the computer network. In response to selecting, by the specific network load balancing algorithm, the specific network path from among the group of multiple network paths, the packet is forwarded over the specific network path.

This invention relates to a routing method and system for low-Earth orbit satellite constellation networks. This method enhances the congestion awareness of satellite nodes to improve the routing selection and traffic distribution effect. Based on real-time link status information, routes are updated promptly to better respond to changes in satellite network topology and network traffic, resulting in a higher packet delivery success rate. It improves congestion awareness to obtain link load information within a two-hop range and enhances network load balancing by providing a wider range of routing options, enabling satellite nodes to make routing and forwarding decisions with a broader routing perspective.

The invention discloses a multi-service QoS (Quality of Service) demand-oriented load balancing routing method in a satellite-ground convergence network. The method comprises the following steps: a ground control center obtains a network topology; calculating an alternative path set; selecting a transmission path based on a genetic algorithm by taking maximization of a load balancing factor as a target; according to the method, a complete full-path route is calculated for each satellite node, and the service transmission requirement is better guaranteed; according to the technical scheme, an alternative path set mode is adopted, organic connection is established between a multi-service QoS guarantee strategy and a network load balancing strategy, selectable paths in the load balancing strategy are all schemes meeting service transmission requirements, and network loads are balanced while service QoS is guaranteed.

Techniques for load balancing secure network traffic are disclosed. A system, process, and / or computer program product for load balancing secure network traffic includes monitoring network traffic for one branch of a plurality of branches for an enterprise network, and splitting the network traffic of the one branch into a plurality of network segments based on a determination that the network traffic exceeds traffic capacity of at least one security processing node (SPN) of a plurality of SPNs using a network load balancer (NLB) in communication with a plurality of Network Processing Nodes (NPNs), the plurality of monitored branches being distributed to the plurality of SPNs via a plurality of tunnels.

This application provides a data center network load balancing method, and system, a communication device, and a storage medium. The method includes: obtaining information of a plurality of computing nodes; determining, based on the information of the plurality of computing nodes, forwarding paths corresponding to the plurality of computing nodes, where the forwarding paths corresponding to the plurality of computing nodes are equal-cost forwarding paths, and the forwarding paths corresponding to the plurality of computing nodes are different from each other; and when receiving a data flow, forwarding the data flow based on a forwarding path corresponding to a target computing node of the data flow.

The invention provides a terminal redirection method and device, and the method comprises the steps: determining a target distributed network for a to-be-migrated terminal in a current distributed network under the condition that the current distributed network meets an internal terminal redirection condition; acquiring base station information, and determining a plurality of candidate service paths according to the base station information, the to-be-migrated terminal and the target distributed network; determining a target service path by taking the optimal comprehensive performance parameter of the road section as a target; and generating a service strategy based on the target service path, and issuing the service strategy to the to-be-migrated terminal, the to-be-migrated terminal being used for redirecting to the target distributed network according to the service strategy. Therefore, the automatic redirection of the terminal among the distributed networks is realized by dynamically evaluating the network state and intelligently selecting the optimal migration path, and the network load balancing efficiency and the terminal service quality can be improved while the energy consumption is reduced.

The invention provides an AI training cluster load balancing method and device. The method comprises the following steps: collecting performance data of a communication link occupied by a distributed training task in real time; when the performance data is lower than the expectation, planning a new communication link for the distributed training task based on the network topology information and the network flow information of the AI training cluster and the demand information of the distributed training task; evaluating a plurality of candidate load balancing schemes through network flow simulation, and determining a target load balancing scheme with the optimal load balancing capability; and issuing a corresponding network configuration instruction to the AI training cluster according to the new communication link and the target load balancing scheme. According to the method, the network traffic load is dynamically adjusted in real time without delay, and the distributed training task is ensured to be executed in the optimal network load balancing state.

The invention discloses a network load balancing method, a computer program product, equipment and a medium, relates to the technical field of data centers, is applied to a virtual switch of a data center, and obtains to-be-sent data; packaging the to-be-sent data to obtain initial packaged data; in the quintuple, determining an item to be transformed; in the initial encapsulation data, transforming a value corresponding to a to-be-transformed item in the outer layer message to obtain target encapsulation data; performing hash operation based on the quintuple of the target encapsulation data to obtain a hash value; and transmitting the target encapsulation data according to a transmission path corresponding to the hash value. The method and the device have the beneficial effects that different transmission paths can be used for transmitting the target encapsulation data, and finally, different to-be-sent data of the same stream are transmitted through different transmission paths, so that the occupation of a single transmission path by the data of the same stream is avoided, load balancing between the transmission paths is realized, and the transmission efficiency is improved. The network load balancing capability of the data center is improved; and the universality is good.

The invention provides an energy data dynamic encryption method and system based on edge computing, and relates to the technical field of information security, and the method comprises the steps: dividing terminal equipment in a power grid into a plurality of equipment groups, and enabling each equipment group to correspond to an edge computing node; constructing a block chain network according to the edge computing nodes; acquiring data features of data to be sent to a second edge computing node by a first edge computing node, and network features of the first edge computing node and the second edge computing node; generating a first encryption strategy according to the data features; adjusting the first encryption strategy according to the network characteristics to generate a second encryption strategy; and generating a network load balancing parameter according to the load states of other edge computing nodes in the block chain network, and adjusting the second encryption strategy according to the network load balancing parameter to generate a target encryption strategy. The technical effect of the application is that the security of data transmission is improved.

The invention discloses a quantum key distribution network routing method and system. The method comprises the following steps: dividing a quantum key pool in an SDN-QKD network; based on key service requests of different security levels and the divided quantum key pool, constructing a maximum key service request success model, and using the maximum key service request success model to approve the key service request; screening the approved key service requests based on a minimum key consumption model; a final path is generated based on the screened key service request and the key resource consumption routing optimization algorithm; and performing key distribution based on the final path. According to the invention, the utilization rate of key resources is improved, the transmission success rate of high-priority services is guaranteed, and the network load balance degree is effectively improved.