26 results about "Traffic prioritization" patented technology

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.

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

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.

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.

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.

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.

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.

The invention relates to the technical field of modern network communication, and discloses an Ethernet switch flow adaptive control method and system. The method comprises the following steps: acquiring switch port flow data containing a load value of each port; calculating an average load value of each port in groups according to time periods, and taking a standard deviation of all average values as a load balancing value; if the deviation threshold value is exceeded, determining that resource allocation is not uniform, and dynamically shunting a high-load port to obtain a load balancing target; if balance is not reached after shunting, adjusting and recording a port resource utilization rate; determining a traffic fluctuation range based on the port resource utilization rate, and predicting a traffic distribution trend if the traffic fluctuation range exceeds a threshold value; if a high-load scene is about to arrive, activating a standby port to execute preventive allocation, and obtaining an enhanced response state; and determining a risk level according to the enhanced response state, sorting flow priorities and distributing to obtain a final path configuration scheme. The method can realize flow self-adaptive balance control, and guarantee efficient transmission and continuous service.

A system and method for efficiently distributing available bandwidth of a satellite beam among network service providers (NSPs) and virtual network operators (VNOs) associated with the NSPs. The method involves defining bandwidth allocations for each NSP and VNO, receiving demands, dividing the available bandwidth among NSPs and sub-dividing it among VNOs based on respective demands, Minimum Information Rates (MIN), Committed Information Rates (CIR), and Maximum Information Rates (MAX). The method supports multiple traffic priorities, prevents bandwidth starvation, and rebalances bandwidth allocation using a throttle factor during heavy traffic. By sorting demands in ascending order and smoothing beam utilization, the method ensures fairness and efficiency in satellite beam usage.

The invention relates to the technical field of network optimization, and discloses an acceleration system and method based on game traffic feature recognition and intelligent path optimization, and the system comprises a traffic feature recognition engine which is deployed at a user client, the sub-flow classification module is used for identifying and classifying sub-flows with different priorities in a game data flow in real time through deep packet detection and a machine learning model; the real-time network probe module is composed of a plurality of acceleration nodes distributed globally, and is used for continuously detecting the network quality among the nodes and constructing a dynamic network quality topological graph; and the intelligent routing decision engine is used for receiving the sub-traffic priority information output by the traffic feature recognition engine and the network quality topological graph output by the real-time network probe module, and dynamically calculating an optimal transmission path according to the sub-traffic priority. According to the acceleration system and method based on game traffic feature recognition and intelligent path optimization, the network delay, jitter and packet loss rate of the game can be remarkably reduced, and the stability and smoothness of the game are improved.

An apparatus comprising: a classifier configured to classify, in real time, an application type of an application traffic flow based on one or more indicators received from one or more components of an edge device and associated with the application traffic flow; and a quality of service (QoS) engine configured to assign a priority to the application traffic flow by modifying a packet header of the application traffic flow, thereby enabling the network module to dynamically prioritize the application traffic flow based on the classification.

This invention discloses a cloud-edge collaborative multi-channel fusion communication control method and system, belonging to the field of wireless communication network technology, aiming to improve the efficiency, reliability, and real-time performance of multi-channel fusion communication in complex scenarios. Specifically, it includes: constructing a layered collaborative architecture between the cloud and the edge; the edge end collecting network status parameters of multiple communication channels, outputting channel quality assessment values ​​based on a time-series prediction model, and performing multi-channel data transmission through an extended communication protocol when channel switching is triggered; the edge end performing network parameter configuration, traffic priority scheduling, and operational status data collection operations; the cloud generating soft routing optimization instructions based on the operational status data and sending them to the edge end for execution to adjust network transmission performance parameters; real-time monitoring of the container operation status and device status at the edge end; when the monitoring results match preset anomaly rules, the edge end generating alarm information, sending it back to the cloud to generate a global security policy, and sending it to the edge end to update the preset anomaly rules.

The disclosure provides an approach for processing communications between connected data centers. Embodiments include receiving, at a first gateway of a first data center from a second gateway of a second data center, one or more policies associated with traffic attributes. Embodiments include programming priority routes between the first gateway and the second gateway over a virtual private network (VPN) tunnel based on the one or more policies, wherein each of the priority routes is associated with a traffic attribute of the traffic attributes. Embodiments include providing the one or more policies to a central controller of the first data center and programming, by the central controller, one or more tables associated with a centrally-managed virtual switch based on the one or more policies. Embodiments include updating a database associated with each of a plurality of hosts based on the programming of the one or more tables.

The application belongs to the technical field of industrial network, and particularly relates to a joint scheduling method based on BACnet / IP and TSN network, comprising the following steps: obtaining BAcnet / IP data frames, which are subjected to VLAN label adding processing at a data link layer, and the BAcnet / IP data frames are identified by traffic priority in the TSN network; dividing the BAcnet / IP data frames into periodic traffic and aperiodic traffic, periodically allocating periodic queues according to the traffic priority, adopting a periodic data scheduling method based on load adaptive priority, and scheduling in a periodic time period of each continuous cycle, and according to the traffic priority, allocating non-periodic queues to the aperiodic traffic, adopting a dynamic adjustment priority strategy, and scheduling in a non-periodic time period of each continuous cycle; the application realizes interconnection of two networks, preferentially guarantees periodic traffic transmission under the premise of not exceeding given resources, reduces the time delay of the BACnet / IP network, and ensures that critical tasks are completed on time.