176 results about "Traffic shaping" patented technology

The invention discloses a power grid abnormal flow detection method based on multi-modal data fusion, and the method comprises the steps: collecting the multi-source heterogeneous data of a power grid through an edge calculation node, including the current waveform of an intelligent electric meter, the state variable of an SCADA system, network protocol metadata and an equipment log event; performing space-time alignment preprocessing on the original data, converting an unstructured log into a time sequence by adopting a sliding window mechanism, and eliminating sensor noise through an LSTM auto-encoder; constructing a multi-dimensional feature space which comprises a frequency domain feature, a spatial-temporal feature and a protocol feature, and obtaining a feature vector; the feature vectors are input into a hybrid detection model, the model is composed of an isolated forest algorithm, an improved CNN-LSTM classifier and an information entropy-based rule engine which are connected in parallel, and a dynamic weight fusion strategy is adopted to output an abnormal probability; and when the abnormal probability exceeds a dynamic threshold, triggering a multi-level response mechanism: sending a traffic shaping instruction to the edge device, generating a device fingerprint portrait on the cloud platform, and storing abnormal event features through a block chain.

The invention relates to an industrial control system security policy adaptive configuration method and device and a readable storage medium. According to the method, a control instruction stream, a process variable sampling value and link layer message metadata are synchronously collected, an association hypergraph fusing network semantics, control semantics and physical semantics is constructed to represent a system state, and a sampling period is divided into a plurality of time slots with fixed lengths. In each time slot, self-supervised learning is utilized to extract a topology embedded vector group, and a system operation rule is represented. A robustness envelope interval is generated by calculating a statistical distance between a current time slot and a historical baseline, and is used for driving a reinforcement learning model to generate a security policy parameter set. And executing corresponding access control, traffic shaping and integrity verification operations in the next time slot, generating receipt vector feedback model update according to an execution result, outputting a micro-policy patch with a hash signature and a timestamp, and sending the micro-policy patch to the edge security gateway to realize deployment, thereby realizing closed-loop adaptive optimization of the security policy in the dynamic environment.

The invention discloses a data transmission method and device, equipment, a storage medium and a product, and relates to the technical field of information security, and the method comprises the steps: obtaining an original data packet and a current network state; embedding and packaging the original data packet to obtain a to-be-transmitted data packet; predicting according to the current network state and the to-be-transmitted data packet through the data transmission model to obtain a target encapsulation level, a target traffic shaping parameter and a sending window parameter; sending the to-be-transmitted data packet based on the target encapsulation level, the target traffic shaping parameter and the sending window parameter; according to the method, the current data packet is embedded and packaged, and the network state and the application requirement of the packaged data packet are analyzed in real time according to the data transmission model to obtain the target packaging level, the target traffic shaping parameter and the sending window parameter, so that the optimal packaging strategy, the traffic shaping parameter and the congestion control strategy are dynamically selected; flexible adjustment can be performed according to actual network conditions, and communication performance and resource utilization rate are improved.

The invention discloses a traffic shaping method and system based on deep learning and an antagonism strategy. The shaping method comprises the following steps: monitoring network traffic in real time through a traffic monitor, capturing information of traffic data, carrying out data preprocessing, carrying out traffic feature extraction through the preprocessed traffic data, and training a DNN classifier; jointly inputting the trained DNN classifier and traffic data into a patch generation module, preliminarily generating an antagonistic patch according to a dual simulated annealing algorithm added with a multi-objective optimization algorithm, and optimizing the antagonistic patch through a self-adaptive adjustment strategy and a bandwidth overhead adjustment strategy; and patch injection: injecting the optimized antagonistic patch into the original flow data through an injection controller, and shaping the original flow data. According to the invention, the problem that the confrontation capability and the privacy security protection capability of the Internet of Things equipment when facing equipment identification and traffic analysis attacks need to be enhanced is solved.

Packets received at a routing intermediary and directed to a resource are transmitted to a data plane node of a partition of a traffic management service. The data plane node transmits an indication of a high-arrival-rate destination group, identified based on a metric of network packets received at the data plane node, to a control plane node of the service. A resource is identified at the control plane node as a rate limiting target based on high-arrival-rate group information from several data plane nodes. A limit is applied to the rate at which packets are received at the target. The control plane node changes the number of data plane nodes in the partition based on a triggering condition.

The invention relates to a Decision Transform-based website fingerprint defense method, and solves the problems that a traditional defense method mainly adopts a traffic shaping or fixed mode data packet filling strategy, only aims at static traffic characteristics to interfere, and cannot realize data packet filling; a time sequence analysis mode of an attacker is difficult to model effectively; and a defense rule is solidified and is easy to suffer from adaptive attacks. The method comprises the following steps: generating a defense track data set; training a DT decision model; monitoring a network fingerprint attack; and defending the website fingerprints. According to the method, time sequence behavior characteristics of network flow are extracted, a long-term dependency relationship of flow interaction is modeled by adopting a Transform architecture, and a virtual data packet sequence matched with real service flow time-space characteristics is generated by utilizing a Decision Transform; according to the method, the defense strategy is dynamically optimized through the reinforcement learning framework, intelligent evolution of defense rules in the confrontation environment is achieved, flow feature analysis of attackers is effectively confused, and the defense success rate of website fingerprint attacks is remarkably increased.

A system includes a scheduling device of a data distribution service (DDS) that determines bandwidth for communication of time sensitive communications between devices using the DDS in a time sensitive network (TSN). The scheduling device determines available bandwidth for communication of non-time sensitive communications of the control system using the DDS in the TSN, and controls communication of both the time sensitive and non-time sensitive communications in the TSN. The system also includes a traffic shaper of the TSN that receives a communication change from the control system at the TSN. The scheduling device changes one or more of the bandwidth for the communication of the time sensitive communications or the available bandwidth for the communication of the non-time sensitive communications in the TSN without restarting at least one network device of the TSN and while ensuring communications of both the time sensitive communications and the non-time sensitive communications.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for shaping backhaul traffic. In some implementations, a system receives forward channel data to transmit to one or more terminals. The system receives a message from a gateway over a backhaul network, wherein the message includes buffer information for a data buffer of the gateway. The system determines a data transfer rate for transmitting the forward channel data to the gateway over the backhaul network, wherein the data transfer rate is determined at least in part based on the buffer information in the message from the gateway. The system transmits the forward channel data to the gateway over the backhaul network at the determined data transfer rate.

The invention relates to a priority configuration method and device based on an intelligent synchronous bus iRAX, and the method comprises the steps: configuring a service quality strategy on a router and a switch, and distributing the highest priority for time synchronization flow; based on DiffServ or other marking mechanisms, time synchronization traffic is marked; and configuring traffic shaping and queuing management of the time synchronization traffic according to a traffic shaping technology and a queuing management mechanism. By marking the time synchronization flow and distributing the highest priority for the time synchronization flow, the router and the switch can identify and preferentially process the time synchronization flow, so that delay and jitter are reduced, the synchronization precision of the system is ensured, meanwhile, the transmission rate of a data packet can be effectively controlled by implementing flow shaping and queuing management, and the transmission efficiency of the system is improved. Therefore, network jitter caused by burst traffic is avoided, high-precision synchronization of the system is ensured, and the accuracy of the time synchronization signal and the overall performance of the iRAX system are improved.

The invention discloses a traffic shaping scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a target service type of a first data stream at a first time, and obtaining a second data stream at a second time corresponding to the target service type; the second time is before the first time and comprises flow data of a plurality of time periods; determining flow characteristic data of a third data stream at a third time according to the second data stream; the flow characteristic data is used for describing the flow rate and the burst degree of the third data flow; and determining a control instruction of a token bucket shaper of the flow queue based on the flow characteristic data, and adding a token to the token bucket shaper based on the control instruction to meet the flow transmission of the third data flow. According to the invention, through real-time adjustment of the token bucket parameters, accurate control of network congestion is realized.

The invention relates to the technical field of industrial networks, in particular to a clustering network flow scheduling method based on star flash, which comprises the following steps: S1, collecting service flow information and cluster node state information according to a management G node and a terminal T node; s2, establishing a clustering network flow scheduling model according to the service flow information and the cluster node state information; s3, establishing target optimization and constraint conditions according to the clustering network flow scheduling model; and S4, designing a Markov decision according to target optimization and constraint conditions, training a neural network model according to the Markov decision and a multi-agent near-end strategy, outputting a scheduling decision according to the trained neural network, and obtaining a multi-dimensional resource adaptation result by a solver according to the scheduling decision. And the service flow is transmitted according to the multi-dimensional resource adaptation result and a multi-queue unicast / multicast flow shaping mechanism. According to the invention, the load balancing capability of the star flash clustering network system can be improved, and the end-to-end transmission delay of the service flow can be reduced at the same time.

The invention provides a vehicle-mounted system traffic shaping method and device, a medium and an electronic terminal. The traffic shaping method comprises the following steps: acquiring a queue request signal; the queue request signal is processed to obtain a random access memory number and an address number associated with the queue request signal, the random access memory stores counters of queues in a port of the vehicle-mounted system, and the counters are used for recording credits or tokens of the queues; and performing traffic shaping on a queue in the vehicle-mounted system port based on the random access memory number and the address number. According to the traffic shaping method, resources of the vehicle-mounted system can be saved, the chip area can be reduced, the chip cost can be further reduced, two kinds of traffic shaping of credits and tokens can be supported, and the flexibility of traffic shaping can be improved.

A service flow scheduling method and apparatus, and a system. When transmission quality of a service flow with a higher priority does not meet a service level requirement corresponding to the service flow, a first network device adjusts, to a first threshold, a transmission rate threshold of an HQoS model for a service flow with a lower priority, where the first threshold is less than a current data transmission rate of the service flow with a lower priority. By performing traffic shaping on the service flow with a lower priority, a bandwidth resource of a downlink port of the first network device may give way to the service flow with a higher priority, to ensure that the service flow with a higher priority meets the service level requirement of the service flow with a higher priority.

The invention discloses a communication method and electronic equipment, and relates to the technical field of communication, and the method adopts a two-stage QoS control strategy, and combines a bottom layer hierarchical token bucket algorithm with a top layer deficit polling algorithm. And the bottom layer QoS adopts a hierarchical token bucket to realize bandwidth allocation and traffic shaping based on traffic categories. The top layer distributes bandwidth resources by adopting deficit polling on the basis of functions, so that the problem that the waiting time of data packets in transmission queues of other functions is too long due to the fact that large data packet transmission of a certain function occupies more bandwidth in function scheduling can be solved. According to the method, end-to-end QoS guarantee can be realized, and bandwidth requirements of different traffic categories and functions are considered, so that under the scene of cross-domain sharing of PCIe equipment, the hardware resource utilization rate is maximized, fine bandwidth allocation and traffic control are performed, efficient transmission and scheduling are ensured, and bandwidth fluctuation caused by burst cross-domain access is smoothly processed.

The present application provides a scheduling method, device and system for service flows. In this solution, when the transmission quality of a service flow with a higher priority does not meet the service level requirement corresponding to the service flow, the first network device adjusts the transmission rate threshold of the HQoS model for the service flow with a lower priority to a first threshold, wherein the first threshold is less than the current data transmission rate of the service flow with a lower priority. By shaping the traffic of the service flow with a lower priority, the bandwidth resources of the downlink port of the first network device can be given to the service flow with a higher priority to ensure that the service flow with a higher priority meets its service level requirement.

Systems, methods, and instrumentalities are described herein associated with multi-flow prioritization and shaping. Per packet treatment for traffic shaping (e.g., UL traffic shaping) may be performed. Per packet treatment for traffic shaping may be performed based on a characteristic / condition associated with a data packet. Per packet treatment for traffic shaping may be performed based on a network signaling. Exclusion from traffic shaping may be determined, for example, on a per packet basis (e.g., based on a characteristic / condition associated with a data packet).

The invention discloses a data transmission method based on low-delay optimization, which comprises the following steps of data preprocessing, data classification, partitioning, compression and encryption preprocessing operation, transmission scheduling, multi-path selection, priority scheduling and flow shaping function realization, network transmission, actual data packet sending and receiving processing and basic transmission control. According to the data transmission method and system based on low-delay optimization and the computer readable storage medium, through real-time network awareness and machine learning technologies, the system can dynamically adjust a transmission strategy and adapt to diversified network environments, and the data transmission efficiency is improved. And a multi-dimensional priority model and an intelligent scheduling algorithm ensure that key data obtains an optimal transmission path and resources.

The invention discloses a network data transmission deterministic time delay guarantee method of a spatial characteristic enhanced Informer model, which comprises the following steps of: 1, predicting a time sequence of key service traffic based on the Informer model, and extracting a long-period time characteristic and a future traffic trend through a Prob sparse self-attention mechanism; 2, a graph neural network is introduced into the model to perform modeling on an underground switch topological structure, the defect that Informer only depends on time characteristics and neglects network topological information is improved, and a spatial state matrix is generated and is combined with a future flow trend to realize enhancement of spatial characteristics; 3, an asynchronous scheduling mechanism is designed, prediction calculation and short-period scheduling execution are decoupled, and real-time performance is guaranteed; and 4, generating an optimal step traffic shaper parameter by using the spatio-temporal characteristics to smooth burst traffic, allocate bandwidth and reduce delay jitter, and meanwhile, combining burst detection and redundancy scheduling protection to guarantee deterministic delay. The method is suitable for industrial networks with high real-time requirements such as coal mines and rail transit.

Provided is a communication system comprising: at least one base station accommodating a wireless terminal; at least one transfer device which transfers, to a higher level, an uplink communication of the wireless terminal received via the base station; and a transfer device controller which controls the at least one transfer device, and the communication system further includes: an information acquisition unit for acquiring information regarding a wireless communication for each quality-of-service class between the base station and the wireless terminal; a rate determination unit for determining, for each of the quality-of-service classes, a shaping rate for traffic shaping such that a packet loss caused by congestion does not occur, on the basis of the information regarding the wireless communication for each quality-of-service class; and a communication control unit for executing, for each quality-of-service class, the traffic shaping at the higher level to the base station on the basis of the shaping rate determined by the rate determination unit.

The invention provides a multi-sensor fusion estimation method and system based on a token bucket traffic shaping mechanism, and belongs to the field of multi-sensor control. The problem of scheduling communication resources in existing multi-sensor fusion estimation and the problem of how to process state estimation and censoring measurement of a nonlinear system are solved. According to the method, a new communication protocol is designed, a trace of an upper bound of a local estimation error covariance and a trigger function designed based on a token are combined to serve as a judgment condition for whether transmission measurement is carried out or not, whether a measurement value of each node is transmitted to a local estimator or not is determined, and the characteristic of token traffic shaping is fully exerted; designing a local Tobit Kalman filter gain of each node by minimizing a trace of a filtering error covariance matrix; local estimates from each node are transmitted to a fusion center, global estimates are generated using federated fusion rules, and global estimates with appropriate weights are sent back to each node for prediction at subsequent local filters.

This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for use case aware network traffic shaping techniques for power reduction. An AR device may align a set of transmission periodicities associated with a set of transmission types to a common periodicity corresponding to a master transmission type of the set of transmission types based on a modification to at least one transmission periodicity of the set of transmission periodicities. A UE may align a set of transmission periodicities associated with a set of transmission types to a common periodicity, where a packet size associated with a transmission type may be based on a latency threshold associated with the transmission type. The UE may buffer one or more transmissions of a set of transmissions corresponding to the set of transmission types prior to performing packetization associated with at least one transmission.

A constellation of satellites provides communication services to user terminals (UTs). Downstream data addressed to a UT is received at a point-of-presence (POP) and tokenized before sending to a satellite serving the UT. Tokens are associated with resource blocks (RBs), each RB indicative of a particular combination of downlink frequency and timeslot. Tokens are then allocated to downstream data. This tokenized downstream data is sent to the satellite. Untokenized downstream data may be buffered for later tokenization or discarded. A satellite may use information in the token to schedule transmission on a downlink to the UT. The supply of tokens may be based on shaper input data such as gateway queue depth, estimated latency from the POP to the satellite, estimated time to empty a buffer onboard the satellite, and so forth. The supply of tokens may be adjusted to minimize data loss during handovers from one satellite to another.

The invention discloses a secure data transmission method based on multi-thread control, and relates to the technical field of network information security, and the method comprises the following steps: extracting a sensitivity identifier from a data packet header, adopting a lookup algorithm to match a preset sensitivity grading table, dividing data into a high-sensitivity type, a medium-sensitivity type and a low-sensitivity type, determining a priority queue distribution proportion according to the sensitivity, obtaining an initial flow shaping parameter, obtaining current network flow data according to the initial flow shaping parameter, analyzing a preset congestion detection threshold value and a historical transmission record, judging a network congestion degree by adopting a token bucket algorithm, and if the congestion degree is higher than the preset congestion detection threshold value, sending the current network flow data to the priority queue; if yes, the token generation rate of the high-sensitivity data is adjusted, and an adjusted traffic shaping strategy is obtained; according to the secure data transmission method based on multi-thread control, the network transmission efficiency can be effectively improved while the data security is ensured, and the method is particularly suitable for a complex network environment in which data with different sensitivities needs to be processed at the same time.

This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for use case aware network traffic shaping techniques for power reduction. An AR device may align a set of transmission periodicities associated with a set of transmission types to a common periodicity corresponding to a master transmission type of the set of transmission types based on a modification to at least one transmission periodicity of the set of transmission periodicities. A UE may align a set of transmission periodicities associated with a set of transmission types to a common periodicity, where a packet size associated with a transmission type may be based on a latency threshold associated with the transmission type. The UE may buffer one or more transmissions of a set of transmissions corresponding to the set of transmission types prior to performing packetization associated with at least one transmission.

The invention discloses a dynamic working condition-oriented data-real fusion test consistency guarantee method, which comprises the following steps of: carrying out dynamic working condition sensing and data stream preprocessing, sensing network load and physical node states in real time, and preferentially processing high-timeliness data by adopting a self-adaptive traffic shaping mechanism; the method comprises the following steps: constructing an attention-enhanced distributed multi-agent Actor-Critic network architecture based on deep reinforcement learning, and carrying out collaborative decision making by taking a communication node as an independent agent; strategy iteration training is carried out in a digital twin training environment mapped with a physical environment, key state features are extracted by using a self-attention mechanism, and scheduling behaviors are optimized through a multi-target reward function comprehensively balancing delay jitter, multi-source data consistency and network load balance; and converting the global optimization strategy into a dynamic flow scheduling table and issuing the dynamic flow scheduling table to a physical node for execution. According to the method, the problems of time delay fluctuation and consistency deviation of a data-real fusion test in a dynamic network environment are solved.

Technologies for programming flexible accelerated network pipelines include a comping device with a network controller. The computing device loads a program binary file that includes a packet processing program and a requested hint section. The binary file may be an executable and linkable format (ELF) file with an extended Berkeley packet filter (eBPF) program. The computing device determines a hardware configuration for the network controller based on the requested offload hints and programs the network controller. The network controller processes network packets with the requested offloads, such as packet classification, hashing, checksums, traffic shaping, or other offloads. The network controller returns results of the offloads as hints in metadata. The packet processing program performs actions based on the metadata, such as forwarding, dropping, packet modification, or other actions. The computing device may compile an eBPF source file to generate the binary file. Other embodiments are described and claimed.

This application provides a traffic scheduling method, apparatus, and electronic device, relating to the field of communication technology. The method includes: when a target channel group determined from a shaping group and a non-shaping group is a shaping group, determining a first target channel from the shaping group based on a filtering rule that uses the channel corresponding to the highest credit value as the target channel, and determining whether it meets preset shaping conditions; if it does, scheduling the traffic of the first target channel, adjusting the credit values ​​of the first target channel and other first channels according to a preset credit value adjustment rule, and continuing to schedule the shaping group; if all channels in the shaping group have been scheduled at least once or the preset shaping conditions are not met, re-determining the target channel group. The non-shaping group is scheduled using a round-robin scheduling method. This can alleviate the problem of uneven traffic distribution among channels with traffic shaping enabled when some channels have traffic shaping enabled and others do not.

The invention relates to the technical field of communication, and discloses a traffic shaping method and device and a storage medium, the traffic shaping device comprises a message receiving module, a plurality of cache units, a message forwarding module and a token management module, and the token management module comprises a token supplementing unit and a token deduction unit. And the token deduction unit is used for determining a corresponding deduction multiplying power coefficient according to the priority of the message to be forwarded when the message forwarding module forwards the message to be forwarded, and deducting a corresponding number of tokens from the token bucket corresponding to the cache unit in which the message to be forwarded is located. According to the invention, the differentiated deduction multiplying power coefficients are set for the messages with different priorities, so that the overall forwarding efficiency and the service quality adaptability of the system in a multi-service complex environment are improved.

The invention provides a distributed network bandwidth control system, and aims to solve the problems of coarse granularity, difficult management, poor dynamic adjustment and weak integration of a traditional bandwidth control scheme. The system adopts an architecture of a central control platform and a distributed agent program module, the central control platform provides a Web interface and an API interface, and centralized storage and issuing of an IP speed limiting strategy (including IP, uplink and downlink bandwidth and priority) and agent program state monitoring are realized; a proxy program is deployed at a network exit node, a Linux TC tool is called to realize single-IP fine-grained traffic shaping, and a heartbeat synchronization state is sent at regular time, so that strategy feedback is performed on a proxy program module and related nodes in the process, and a complete control closed loop is formed.

The application discloses an edge computing network architecture providing deterministic quality of service, which mainly comprises a service provider, a user terminal, a base station, a router and an edge computing node, wherein the service provider receives a task of the user terminal and feeds back relevant configuration parameters for processing the task to the user terminal and relevant equipment on a task transmission / processing path, the base station receives the task of the user terminal and forwards the task to the router, and the router receives the task sent by the base station and forwards the task to the edge computing node; a three-part domain deployment mode is adopted, including three independent domains of a wireless domain, a wired transmission domain and a computing domain, and an inter-domain traffic shaping mechanism is adopted. The method transplants the deterministic network technology into the edge computing network, solves the problem that the traditional deterministic network technology cannot be adapted to a heterogeneous medium scene, and can provide deterministic quality of service in a dynamic and unpredictable environment.