43 results about "Traffic prioritization" patented technology

The invention discloses a cloud edge cooperative multi-channel converged communication control method and system, belongs to the technical field of wireless communication networks, and aims to improve the high efficiency, reliability and real-time performance of multi-channel converged communication in a complex scene. The method specifically comprises the following steps: constructing a hierarchical collaborative architecture of a cloud end and an edge end; the edge end collects network state parameters of various communication channels, outputs a channel quality evaluation value based on a time sequence prediction model, and performs multi-channel data transmission through an extended communication protocol when triggering channel switching; the edge end executes network parameter configuration, flow priority scheduling and running state data acquisition operation, and the cloud end generates a soft routing optimization instruction based on running state data and issues the soft routing optimization instruction to the edge end to execute and adjust network transmission performance parameters; and monitoring the container operation state and the equipment state of the edge end in real time, when a monitoring result is matched with a preset abnormal rule, generating alarm information by the edge end, returning the alarm information to the cloud end to generate a global security policy, and issuing the global security policy to the edge end to update the preset abnormal rule.

The invention discloses a spaceflight electromagnetic relay failure early warning and diagnosis method based on multi-physics field coupling, relates to the technical field of spacecraft electrical system reliability monitoring, and is used for solving the problems of lack of comprehensive consideration of multi-physics field coupling characteristics under spaceflight special working conditions and low interference resistance to complex environments. The method comprises the following steps: monitoring a network state, obtaining real-time data, calculating an optimal transmission strategy by using an intelligent regulation and control model, dynamically adjusting a coding mode, adaptively adjusting a transmission rate, further ensuring data integrity through an error code detection and error correction mechanism, and adjusting transmission parameters according to a real-time network condition in combination with packet optimization and flow priority management. Effective recognition and early warning of typical failure modes such as contact adhesion and coil burnout are achieved, and the failure early warning accuracy and diagnosis efficiency of the spaceflight electromagnetic relay in complex environments such as high radiation, extreme temperature and strong vibration are improved.

A system and computer-implemented method for routing an encrypted packet through a cloud enforcement network based on a metadata tag. The cloud enforcement network applies policy and routing attributions or tags outside of the encrypted packet payload in such a way as to not require an inner packet to first be decrypted. Traffic prioritization, data protection, and per application policies are achieved by using such metadata tags for internode routing without the need for DPI or decryption. Furthermore, the metadata itself can also be signed or encrypted depending on the provenance of the data. As such, applying meta-tagging external to an encrypted packet, the payload would not be needed to be decrypted during transit of the packet to express end-to-end policy and routing decisions.

The invention discloses a network security policy full-life-cycle adaptive tuning system based on intelligent computation driving, relates to the technical field of network security, and is used for solving the problems of policy adaptation and efficiency balance under resource limitation. The method comprises the following steps: firstly, predicting an attack probability and mapping a topological weight by using an LSTM combined with an attention mechanism, and generating a risk prediction vector; then, performing weighted pruning on the rule based on the computing power constraint and the flow priority, and generating an execution state strategy packet; after issuing, monitoring a delay increment and an interception hit rate during operation, and generating a regular degradation instruction for a low-efficiency and high-consumption rule; and finally, regular scanning parameters are adjusted through gradient optimization, a self-adaptive system from risk pre-judgment to closed-loop optimization is constructed, and intelligent support is provided for network security defense.

The application discloses a kind of network security situation awareness system and method based on multidimension, it is related to network security situation awareness technical field, by abnormal flow comprehensive score calculation unit, in combination with abnormal score Sy, risk index Ry, data packet influence factor Iy and duration factor Dy, system can calculate accurate priority Py for each abnormal flow.This kind of comprehensive evaluation method makes system be able to consider the abnormal degree of flow, potential threat to system and duration comprehensively, improves the accuracy of abnormal flow detection.Through abnormal flow sequencing and security response triggering unit, system carries out descending order sequencing to the flow priority Py calculated, and classifies flow event according to priority threshold TPth.Through flow event according to priority sequencing, system can preferentially process the most urgent security threat, ensure that security personnel can respond to the most important event in time, improve response efficiency and processing timeliness.

This application provides a method and apparatus for optimizing packet loss in a game accelerator based on traffic priority management. The method includes: performing periodic link probing on at least one proxy node and generating corresponding link monitoring results; determining whether the packet loss rate for two consecutive monitoring periods exceeds a first threshold based on the link monitoring results; classifying the data stream to be sent in real time to obtain priority labels for different traffic categories; inputting the link monitoring results into a prediction algorithm to obtain a network quality prediction value for the next monitoring period, and adaptively adjusting the redundancy ratio, sending queue bandwidth constraint parameters, and proxy nodes according to the network quality prediction value; and sending the sorted source data packets and redundant data packets to the target game server or game client according to the updated proxy nodes to achieve accelerated forwarding of game data. This application can improve the reliability, latency stability, and bandwidth utilization of game data transmission in cross-border high-packet-loss network environments.

A device includes a plurality of digital signal processors (DSPs) and analog crossbars in communication with the DSPs. The DSPs and crossbars dynamically adjust power consumption based on traffic load and / or a signal quality, leveraging traffic prediction models to optimize resource allocation. The system supports features such as powering down unused lanes, asymmetrically deactivating transmission or receiving lanes, and preemptively scaling resources during predicted traffic increases. Dynamic voltage and frequency scaling (DVFS) optimizes power usage based on link utilization and traffic priority, minimizing latency impacts during transitions. The switch controller integrates traffic prioritization algorithms, including weighted round-robin (WRR), to allocate power and bandwidth efficiently to high-priority traffic flows. Redundant crossbars handle overflow traffic or failover scenarios, transitioning between standby and active states dynamically.

The application discloses a campus network flow prediction method and system, relates to the technical field of flow prediction, and comprises the following steps: step one, historical flow analysis, step two, flow distribution and step three, flow scheduling. The flow information of each region in each time period in each historical preset period is analyzed, the total demand of the current period campus flow is predicted, the flow priority and the flow threshold of each region in each time period are obtained, the flow distribution of each region is carried out based on the flow priority of each region in each time period, the speed of each region in each time period is analyzed when the flow threshold is reached, the network management strategy is refined, the flow resource is dynamically adjusted, the network resource utilization is greatly improved, and finally, the flow of each region in each time period is scheduled, so that the on-demand distribution and dynamic optimization of the campus flow are ensured.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, by a modem and from an application processor of the UE, application traffic, wherein the application traffic is associated with a prioritization bit. The UE may direct, via the modem, the application traffic to a priority queue based at least in part on the prioritization bit. The UE may monitor, via the modem, a metric associated with the application traffic when the application traffic is on the priority queue. The UE may keep, via the modem, the application traffic on the priority queue or move the application traffic to a normal queue based at least in part on the metric. Numerous other aspects are described

The present invention relates to the field of industrial control technology, and in particular to a TSN-based industrial equipment collaborative control method, system, and readable storage medium. The method comprises the following steps: a switch obtains the TSN network from a preset start detection time t1 to the current time t K The present invention adopts a static traffic scheduling strategy to ensure that critical control traffic is transmitted first; it uses an intelligent traffic dynamic scheduling algorithm, combined with the ant colony optimization algorithm, to analyze network status in real time and optimize network resource allocation. This effectively solves the problem that traditional traffic scheduling cannot adapt to network changes in real time.

Techniques for machine learning for prioritizing traffic in multi-purpose inline cloud analysis (MICA) to enhance malware detection are disclosed. In some embodiments, a system, a process, and / or a computer program product for machine learning for prioritizing traffic in multi-purpose inline cloud analysis (MICA) to enhance malware detection includes processing a set of data for network security analysis to extract a file; determining that the file is to be offloaded to a cloud security entity for security processing based at least in part on a prefilter model that is implemented as a machine learning model; forwarding the file to the cloud security entity using a multi-purpose inline cloud analysis (MICA) channel; and performing an action in response to receiving a verdict from the cloud security entity.

Methods include receiving an interrupt request from a second station at a serving access point while ongoing traffic of a first station in an active data session with serving access point on first radio channel or first link. The second station is associated with a pre-emptive higher priority LL traffic compared to the first station. The serving access point determines a second radio channel or the second link for handling ongoing traffic. The second radio channel or the second link is affiliated with an intra-AP MLD and offloading via LLR or affiliated with an inter-AP and offloading via AP level redirection. The serving access point switches the ongoing traffic from a first radio channel or first link to a second radio channel or second link, when the second radio channel or second link is available for handling the ongoing traffic.

This invention relates to the field of network communication technology, specifically to a VLAN configuration method for time-sensitive networks (TSNs) supporting deterministic latency. The method includes the following steps: extracting key data flow paths and link load distribution based on the TSN, identifying latency spikes and bandwidth anomalies, analyzing load and bandwidth matching to filter bottleneck links, extracting priority and time slot characteristics to evaluate VLAN performance, calibrating latency deviations, and outputting configuration adjustment control command sets for abnormal links. In this invention, by constructing a link status anomaly identification dataset and analyzing load and bandwidth trends, rapid identification of link bottlenecks and latency anomaly location are achieved. Traffic priority and time slot characteristics are extracted, and anomaly levels are calibrated in latency trend analysis. This enables balanced distribution and optimized control of key traffic, improves the adaptability and accuracy of VLAN configuration, strengthens traffic scheduling coordination, optimizes bandwidth utilization, reduces latency fluctuations, and enhances the determinism of key data flow transmission and network stability.

Systems and procedures are provided to maximize / optimize the quality of experience (QoE) associated with applications. A network controller can receive telemetry data from an access point (AP). A resource manager, operationally connected to the network controller, can estimate a QoE value for individual application traffic streams from one or more application traffic streams passing through the AP, based on the telemetry data. An access category and traffic priority, based on the estimated QoE value, can be calculated and jointly assigned to a specific application traffic stream.

A device includes a plurality of digital signal processors (DSPs), analog crossbars in communication with the DSPs, and a switch controller. The switch controller facilitates control signals using in-band signaling within payload traffic or out-of-band signaling over a dedicated management interface. The system supports dynamic traffic prioritization based on real-time metrics, such as latency and traffic load, ensuring efficient resource allocation and seamless operation during congestion or reconfiguration. Redundant crossbars and failover mechanisms enhance fault tolerance, while granular backpressure selectively throttles traffic flows to prevent bottlenecks. Integrated diagnostic tools monitor performance and optimize traffic flow, enabling high-speed and reliable communication.

A method and system for distributing leftover bandwidth in a satellite communication network is provided. The method involves monitoring utilization rates of real-time leftover bandwidth allocations for terminals, calculating leftover bandwidth allocations based on these rates, and adjusting them with a scale that prioritizes allocations based on advertised demand. The system includes a bandwidth allocator configured to perform these tasks and a receiver to handle bursts transmitted within the allocated bandwidth. The method and system aim to optimize bandwidth distribution by considering factors such as anticipated demand, traffic priorities, and historical utilization rates.

A method and system for distributing leftover bandwidth in a satellite communication network is provided. The method involves monitoring utilization rates of real-time leftover bandwidth allocations for terminals, calculating leftover bandwidth allocations based on these rates, and adjusting them with a scale that prioritizes allocations based on advertised demand. The system includes a bandwidth allocator configured to perform these tasks and a receiver to handle bursts transmitted within the allocated bandwidth. The method and system aim to optimize bandwidth distribution by considering factors such as anticipated demand, traffic priorities, and historical utilization rates.

Example embodiments of the present disclosure relate to devices, methods, apparatuses and computer readable storage media of traffic priority-based energy detection threshold adaption in unlicensed band. The method comprises determining a target traffic priority for transmitting data from a first device to a second device; obtaining at least one candidate configuration parameter of an energy detection threshold for channel clearance assessment for the target traffic priority; determining a target configuration parameter for transmitting the data based on the at least one candidate configuration parameter; and transmitting the data to the second device based on the target configuration parameter. In this way, the maximum EDT can be associated with the traffic priority, to facilitate flexible channel access requirement of different uplink traffic priority.

The utility model discloses an MES system integrated output interface assembly, comprising a controller, the controller is respectively connected with an LVDS sending interface circuit, an LVDS receiving interface circuit module, and a synchronous circuit 422 interface circuit, the LVDS receiving interface circuit module comprises an LVDS receiving interface circuit 1 and an LVDS receiving interface circuit 2, and the LVDS sending interface circuit; according to the utility model, the dynamic bandwidth management module is introduced, real-time monitoring and dynamic bandwidth distribution of an Ethernet interface circuit are realized, data streams of key tasks are processed preferentially through the flow priority control unit, tasks with high real-time requirements can be ensured to be completed rapidly, the enhanced power supply module adopts a double-path redundant power supply design, and the power supply efficiency is improved. The paths of the main power supply unit and the standby power supply unit are automatically switched, the influence of a single-path power supply fault on system operation is effectively avoided, the power management unit monitors the states of power supply voltage, current, temperature and the like in real time, and potential power supply faults are found and alarmed in time.

The embodiments of the present specification disclose a URPF check method and device based on QoS traffic priority, equipment and medium, comprising: dividing traffic into multiple priorities according to QoS mechanism; configuring multiple loopback interfaces to realize the redirection of traffic to each loopback interface and the enablement or disablement of URPF check in each loopback interface; after receiving specified traffic, redirecting the specified traffic to a specified loopback interface according to the priority corresponding to the specified traffic; when the specified loopback interface enables URPF check, performing URPF check through the specified loopback interface and forwarding the specified traffic; when the specified loopback interface disables URPF check, forwarding the specified traffic through the specified loopback interface.

A protective intelligent electronic device (IED), such as a protective relay, includes a processor to execute protection routines and / or data acquisition routines. The protective IED or an associated networking device may include, for example, a measurement module to monitor processor burden and control logic to detect Ethernet storms based on a dynamic burden function threshold. In response to storm detection, non-critical Ethernet traffic is throttled while critical protection functions are maintained. Auxiliary communication subsystems and / or critical protocols such as RSTP may continue to function during a storm mitigation mode. The systems and methods described herein leverage dynamic CPU burden assessments, traffic prioritization, and hysteresis logic to limit toggling between storm mitigation and normal operation modes.

The invention discloses a network security situation awareness system and method based on multiple dimensions, and relates to the technical field of network security situation awareness, the system can calculate an accurate priority Py for each abnormal flow through an abnormal flow comprehensive score calculation unit in combination with an abnormal score Sy, a risk index Ry, a data packet influence factor Iy and a duration factor Dy. According to the comprehensive evaluation method, the system can comprehensively consider the abnormal degree of the traffic, the potential threat to the system and the duration time, and the accuracy of abnormal traffic detection is improved. And through an abnormal traffic sorting and safety response triggering unit, the system sorts the calculated traffic priority Py in a descending order, and classifies traffic events according to a priority threshold TPth. The traffic events are sorted according to the priorities, so that the system can process the most urgent security threat preferentially, security personnel can respond to the most important events in time, and the response efficiency and the processing timeliness are improved.

This application provides a traffic loading method, apparatus, device, medium, and program product, applied in the field of computer technology. The method includes: during the process of loading target traffic by a gateway, obtaining a first traffic priority coefficient, a first traffic loading duration, and an initial traffic ratio of the gateway; determining a smoothing coefficient based on the initial traffic ratio and the first traffic loading duration; determining a second traffic priority coefficient of the gateway at different times during the loading of target traffic based on the smoothing coefficient and the first traffic priority coefficient, wherein the second traffic priority coefficient is a non-linear function; and controlling the gateway to load the target traffic based on the second traffic priority coefficient. Through the method of this application, the traffic growth in the initial stage is more gradual, effectively reducing the load pressure on the gateway during the initial startup phase, thereby improving the stability of gateway operation.

The invention relates to the technical field of flow analysis, in particular to a web service quality control method based on flow analysis. The method comprises the following steps: S1, monitoring a network in real time to collect flow data, capture request, response, data transmission speed and delay information by adopting a Promeheus and Grafana combined technology, and summarizing and feeding back the flow data; s2, using an AI-driven traffic analysis technology to deeply analyze traffic data, and analyzing key performance indexes; s3, performing mode anomaly recognition by using a rule engine and threshold detection; and S4, dynamically adjusting resource allocation and traffic priority strategies according to traffic analysis and anomaly detection results. According to the web service quality control method based on the flow analysis, the AI-driven flow analysis technology is used for deeply analyzing the flow data, the response efficiency and safety of a network can be improved, and network resource use can be optimized by dynamically adjusting resource allocation and a flow priority strategy.

Techniques for machine learning for prioritizing traffic in multi-purpose inline cloud analysis (MICA) to enhance malware detection are disclosed. In some embodiments, a system, a process, and / or a computer program product for machine learning for prioritizing traffic in multi-purpose inline cloud analysis (MICA) to enhance malware detection includes processing a set of data for network security analysis to extract a file; determining that the file is to be offloaded to a cloud security entity for security processing based at least in part on a prefilter model that is implemented as a machine learning model; forwarding the file to the cloud security entity using a multi-purpose inline cloud analysis (MICA) channel; and performing an action in response to receiving a verdict from the cloud security entity.

A plurality of channels over which devices may communicate with a network access point to communicate with one or more network nodes communicatively coupled to the network access point are monitored. Based on the monitoring, a channel priority level is assigned to each channel. Data traffic on logical connections over the plurality of channels between a plurality of devices and the network access point are also monitored. A data traffic type is determined for each logical connection. Based on the type of data traffic present on a given logical connection, a traffic priority level is assigned to that logical connection. Based at least in part on the traffic priority level assigned to each logical connection and the channel priority level assigned to each channel, a first channel is assigned to the respective logical connection. The logical connection then communicates with the network access point over the first channel.

The invention relates to a substation TSN flow optimization scheduling method and system based on a near-end strategy optimization algorithm and a medium. The method comprises the following steps: environment deployment: deploying a TSN protocol stack and an AI scheduling engine in an embedded real-time operating system of a substation, and establishing a flow data acquisition and analysis environment; traffic modeling: performing feature extraction and demand modeling on multiple types of service traffic (such as telemetering, remote control and protection signals) of the transformer substation through a near-end strategy optimization algorithm, and generating a traffic priority and time delay constraint model; dynamic scheduling: based on AI model output, combining a QBV / QCI scheduling mechanism of a TSN to realize dynamic time slot allocation and priority adjustment of traffic; and hardware adaptation: designing a TSN intelligent network card based on the FPGA, integrating an AI lightweight reasoning module, and realizing real-time execution of a scheduling strategy. Through the adaptive decision-making capability of the near-end strategy optimization algorithm, the scheduling adaptability of the TSN to the complex dynamic flow of the transformer substation is improved, the deterministic transmission requirements of different services are met, and high reliability and low time delay of communication of a power system of the transformer substation are guaranteed.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, by a modem and from an application processor of the UE, application traffic, wherein the application traffic is associated with a prioritization bit. The UE may direct, via the modem, the application traffic to a priority queue based at least in part on the prioritization bit. The UE may monitor, via the modem, a metric associated with the application traffic when the application traffic is on the priority queue. The UE may keep, via the modem, the application traffic on the priority queue or move the application traffic to a normal queue based at least in part on the metric. Numerous other aspects are described.

A system and computer-implemented method for routing an encrypted packet through a cloud enforcement network based on a metadata tag. The cloud enforcement network applies policy and routing attributions or tags outside of the encrypted packet payload in such a way as to not require an inner packet to first be decrypted. Traffic prioritization, data protection, and per application policies are achieved by using such metadata tags for internode routing without the need for DPI or decryption. Furthermore, the metadata itself can also be signed or encrypted depending on the provenance of the data. As such, applying meta-tagging external to an encrypted packet, the payload would not be needed to be decrypted during transit of the packet to express end-to-end policy and routing decisions.

The invention relates to the technical field of port communication, in particular to a port mixed service traffic cooperative transmission method based on a 5G-TSN logic network bridge. The method comprises the following steps: periodically acquiring context information of a wireless channel between a 5G base station and a port mobile terminal by using the 5G base station, and collecting historical transmission feedback data; constructing a channel state evaluation model based on ridge regression, and establishing a 5G air interface retransmission time model based on the channel state information; a cluster-based collaborative learning mechanism is introduced, and a channel state evaluation model is jointly trained and updated; the method comprises the following steps: dynamically adjusting an attenuation factor and a time sensitive weight of a parameter in a channel state evaluation model according to a deviation between an actual transmission time delay and a time delay budget in a 5G domain, compensating time delay jitter of a 5G air interface through a 5G-TSN mixed flow scheduling strategy by using a deterministic transmission capability of the TSN, and determining the time delay jitter of the 5G air interface according to a service type, the time delay budget in the 5G domain and the size of a remaining time window. The flow priority is dynamically set, queue mapping is determined by 5G and TSN transmission conditions, and iterative adjustment is fed back.