1577 results about "Goodput" patented technology

The invention provides a data management method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining key value cache data generated by a first type of computing power unit in a pre-filling stage, transmitting the key value cache data to a memory pool, and caching the key value cache data in the memory pool; and in response to a data acquisition demand of the second type of computing power unit in a decoding stage, transmitting the specified key value cache data cached in the memory pool to the second type of computing power unit according to the data acquisition demand. According to the technical scheme of the invention, the memory pool is arranged and the aggregated transmission channel is utilized, so that the transmission efficiency of the key value cache data between the computing power devices is effectively improved, the data transmission delay is reduced, and the network transmission protocol overhead is reduced, thereby improving the data interaction efficiency of the computing power devices in a pre-filling stage and a decoding stage, and improving the user experience. The utilization of computing power resources is optimized, and the throughput and performance of large model reasoning are improved.

The invention relates to the technical field of software defined networks, in particular to an SRv6-based SDN (Software Defined Network) data center load balancing method and system, and the method comprises the steps: obtaining node real-time load and SRv6 routing available resource information through an SDN controller, constructing a load balancing model, and recognizing an overload node and a congestion link; generating a flow scheduling path in combination with segmented routing characteristics; in combination with network state information, monitoring the load condition in real time when the node is unbalanced or the link packet loss rate is gt; when 5%, a load balancing adjustment mechanism is triggered, unbalance types are distinguished, a related flow set is determined, optimization is performed by using a DQN algorithm, and a flow scheduling strategy is generated and dynamically adjusted by taking throughput maximization and unbalance degree minimization as targets; and issuing the scheduling strategy to the data center network equipment, updating the flow table through the Netconf protocol, and redistributing the flow. Therefore, the problems of low load balancing efficiency, insufficient dynamic adaptability, low resource utilization rate and the like in the prior art are solved.

The invention discloses a high-dynamic mobile ad hoc network dynamic time slot allocation method and device, equipment and a storage medium, and belongs to the technical field of wireless communication and networks. The device is deployed between an upper-layer application module and a bottom-layer communication module of a communication node, and comprises a topology sensing module, a time frame construction module, an idle time slot distribution module, a GNSS module and an information interaction module. According to the method, neighbor nodes are determined among nodes through interaction information, and node topology change indexes and link states are calculated; the coordination node performs idle time slot allocation according to a request sent by the node in the control time slot, and dynamically adjusts a sensing period according to the current network topology change; and the node sends data in the information time slot according to the time slot allocation table, and carries out information interaction and topology perception according to the perception period. According to the method, the packet loss rate in the link unstable environment is effectively reduced, the data transmission reliability is higher, the method can adapt to link and topology dynamic changes, and the network throughput and the resource utilization rate are improved.

The present disclosure provides connection management techniques based on graph neural networks (GNN) and deep reinforcement learning (DRL) to optimize user association and load balancing. A graph structure of a communication network is considered for the GNN architecture and DRL is used to learn parameters of the GNN algorithm / model. Connection management is defined as a combinatorial graph optimization problem, and the DRL mechanism uses the underlying graph to learn weights of the GNN for an optimal user connections or associations. The connection management techniques can consider local network features to make better decisions to balance network traffic load while network throughput is also maximized. Implementations are provided based on edge computing frameworks include the Open RAN (O-RAN) architecture. Other embodiments may be described and / or claimed.

Various methods and systems of the present disclosure provide a protocol data unit (PDU) set based quality of service (QoS) handling in a wireline access. To support the PDU Set based QoS handling in the wireline access, one or more PDU Set QoS parameters and / or PDU Set information may be provided to one or more wireline nodes (e.g., a wireline gateway access function (W-AGF) and / or a residential gateway (RG) etc.). When the W-AGF receives one or more N2 requests associated with one or more PDU session resources, the W-AGF maps one or more QoS profiles received from 5G core (5GC) to a wireline access user plane (W-UP) QoS level. The W-AGF sets up one or more W-UP resources based on the W-UP QoS level for a PDU session. Further, various methods and systems of the present disclosure also provide low latency, low loss, and scalable throughput (L4S) enabled wireline access.

Devices, networks, systems, methods, and processes for identifying, classifying, and scheduling Low Latency, Low Loss, and Scalable throughput (LAS) traffic are described herein. A device may receive a data flow comprising one or more data packets. The device may parse the one or more data packets to check for a congestion indicator. Upon detecting the congestion indicator in any data packet, the device can classify or mark the data flow as LAS. The device may schedule the data flow by utilizing an Active Queue Management (AQM) queue for L4S data flows. The device can also mark other uplink or downlink data flows associated with the data flow as L4S. The device can also facilitate LAS capability sharing while associating with one or more wireless devices. The device may utilize a management frame with an extended capabilities field to inform the LAS capability of the device.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention provides a stream computing resource scheduling method and system based on a dynamic time window, and relates to the field of stream computing resource scheduling, and the method comprises the following steps: constructing a master-slave event time monitoring network to obtain a task feature vector, and establishing an adaptive time window scheduling mechanism to dynamically adjust the window size; and constructing a multi-level task priority queue, carrying out state transition by adopting a remote direct memory access mechanism of incremental transmission, and finally coordinating computing nodes through a distributed task arrangement protocol to complete resource switching. According to the method, computing resource competition can be effectively reduced, the flow computing task processing efficiency is improved, dynamic optimal configuration of resources is realized, and the system throughput is enhanced.

The invention discloses a multi-resonator filter collaborative optimization method and system based on cloud computing, particularly relates to the field of cloud computing task processing, is used for solving the problem of resource scheduling resources, and can realize intelligent grouping of tasks in a task input stage through a dynamic density clustering method. A dynamic weight coefficient is calculated in combination with the data association degree and the resource conflict probability, so that priority division of tasks is completed before scheduling, and the accuracy of scheduling decision making is improved; a capacity expansion threshold is adjusted in combination with a historical load trend prediction model and a dynamic weight, and a resource capacity expansion strategy based on benefit driving is realized, so that the energy consumption of the system is reduced, and meanwhile, resource expansion is ensured to accurately match actual requirements; task subblock disassembly and a greedy distribution strategy are carried out based on the real-time energy efficiency ratio and the network topology cost, so that the calculation load tends to the optimal node distribution all the time, the calculation throughput is improved, and the delay loss of cross-node data transmission is reduced.

Disclosed is a method for transmitting and receiving a physical protocol data unit (PPDU) in a wireless communication system. A terminal receives a preamble of the PPDU from an access point (AP). The preamble includes a universal signal (U-SIG) field and an extremely high throughput (EHT)-SIG field, wherein the EHT-SIG field may be configured with at least one content channel on a frequency axis. Here, the number of the at least one content channel may be recognized on the basis of the U-SIG field. Subsequently, the terminal receives data of the PPDU on the basis of the U-SIG field and the EHT-SIG field.

Extended Reality (XR) applications may require high throughput and very low latency. New Radio (NR) Wireless receiver / transmitter units (WTRUs) running XR applications (XR-WTRUs) may be confronted with link failures when these applications are used in dense urban or indoor environments because of these devices operating in the millimeter band. Methods and devices are provided therefore for performing cell selection and beam management proactively to avoid link failure due to objects being in line of sight between the XR-WTRU and a network node with which it has established a connection. XR-WTRUs may therefore perform visual sensing that may enable visualizing current and future radio environment and using this information to prevent link failures by handing the XR-WTRU over to an alternative network node in line of sight of the XR-WTRU before the XR-WTRU is no longer in line of sight of the network node with which it has established a connection.

The invention discloses a CPE router, a data transmission method and device, a medium and a product, and relates to the technical field of network communication and data transmission optimizing.According to the scheme, the method comprises the steps of collecting and preprocessing inlet flow and wireless channel data of the CPE router, extracting transmission urgency and a channel quality index, constructing a transmission decision model and outputting service priority classification; and meanwhile, a dynamic transmission strategy and an early warning mechanism are formulated in combination with a channel prediction model result, and network resource allocation is optimized. According to the scheme, accurate matching and intelligent scheduling of network resources are achieved by dynamically sensing the channel state and the service characteristics, key service transmission delay is effectively reduced, the throughput of high-priority data is improved, meanwhile, transmission stability in a complex environment is enhanced, data retransmission caused by channel fluctuation is reduced, and the transmission efficiency is improved. Smoother multi-service concurrent experience is provided for users, potential transmission risks are warned in advance, and network service quality and equipment operation reliability are guaranteed.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive, at a first modem and from a first device, one or more data packets. Based on an explicit congestion notification (ECN) capable transport (ECT) codepoint indication in respective header portions of the data packets, the UE may route the data packets from the first modem to two or more queues associated with a second modem of the UE. Based on receiving buffer and link state information associated with the second modem, the UE may perform a congestion indication procedure that is associated with congestion of a communication link associated with the UE. The UE may thereafter transmit, to a second device, packet data from the two or more queues associated with the second modem of the UE based on a respective priority associated with the two or more queues.

The provided is an anti-radiation low-latency chip for neural network inference acceleration. The following steps are included: deploying a neural network inference acceleration chip at a frontend of a detector, using a streaming architecture to correspond each pipeline stage to each major neural network layer, balancing a limited on-chip memory resource and support for a large-sized input, and constructing layer parallelism, channel parallelism, and convolution kernel parallelism. This application proposes to deploy an anti-radiation, low-latency, and efficient chip for convolutional neural network (CNN) inference acceleration is deployed at the frontend of the detector to improve an intelligence level of future detector hardware. To this end, a fine-grained streaming architecture, and fine-grained storage management, flexible compression and quantization, and anti-radiation digital chip design technologies are proposed to achieve a high throughput and a low on-chip memory consumption while achieving anti-radiation and low-latency inference.

The invention discloses a multi-band network resource allocation method based on ML-MADDPG, and belongs to the technical field of communication. According to the technical scheme, a basic framework of a communication system is established, and the environment and condition of multi-band resource allocation are defined; an integrated MBN network is adopted, and specific modeling is carried out by considering the characteristics of each frequency band; a meta learning method is fused into an MADDPG algorithm, and a meta learning MADDPG framework is constructed; according to task release of a multi-band network, each task corresponds to one network setting, specifically, the whole design is divided into a meta-training stage and a meta-adaptation stage; and target imbalance caused by direct weighted summation is avoided through a layered reward mechanism. The method has the beneficial effects that the packet loss rate is remarkably reduced, the network throughput is improved, the dynamic environment adaptability and sample efficiency are enhanced, the multi-agent cooperation and global coordination capability is optimized, the training stability and generalization capability are improved, efficient resource scheduling under complex constraints is supported, and the actual deployment cost and complexity are reduced.

The invention relates to the technical field of network communication, and discloses a ubiquitous network dynamic fair rate cooperative control method, which aims at link time varying, topology reconstruction and multi-source and multi-path concurrency characteristics of an air-space-ground-sea fusion network. A routing node periodically collects indexes such as a request satisfaction rate, a queue length and a multi-hop queuing-free proportion to obtain link transmission quality and dynamically update an interest packet forwarding table; deducing a queue upper limit, calculating a rate shaping value in combination with an interest packet forwarding proportion, and carrying out delayed transmission on an over-limit interest packet; when the interest queue exceeds a threshold value, a congestion feedback value is generated and goes down along with a data packet, and an uncongested interface obtains a release bandwidth through probability compensation; and if the whole path is still congested, the consumer multiplicatively shrinks the sending window according to the feedback proportion. According to the method, the throughput is remarkably improved and the queuing delay is reduced while fair allocation of bandwidth resources is ensured.

The invention provides a network congestion control method and system based on multi-agent reinforcement learning, computer equipment and a storage medium, and relates to the technical field of network congestion control, a communication network comprising a plurality of communication nodes is constructed, the communication nodes are mapped into independent agents, and the independent agents are used for controlling the network congestion. Designing a weighted composite reward function containing indexes such as throughput and delay by taking a flow-level index as a local state and a data sending rate adjustment coefficient as an action; the intelligent agent selects action execution according to a current state based on a strategy network, calculates an environment reward based on the current state, the action, a next state after the action is executed and a weighted composite reward function, and stores the environment reward in a shared experience playback pool; training a value network and a strategy network by using the data in the pool; the trained strategy network is deployed at each node, distributed congestion control is realized, the stability of a congestion control decision in a complex network environment is improved, and bandwidth resources are distributed fairly while the throughput is ensured.

Aspects of the present disclosure improve wireless networking performance by reducing or limiting performance issues caused by undetected or unknown interference sources and / or attenuation sources. Feedback from client devices can be utilized to reduce or limit wireless network performance issues caused by varying interference parameters and / or attenuation parameters observed by each client device. The feedback, associated with wireless communication conditions of corresponding client devices, in conjunction with a machine learning algorithm can be used to enable a dynamic selection of one or more particular operating bands, channels, and / or channel widths to avoid or limit signal interference and / or signal attenuation observed by each client device. Feedback from a plurality of client devices can be used to recommend one or more channels and / or one or more channel widths of one or more operating bands to use that reduces latency, CCI, and / or ACI and improves speed, throughput, and / or QoS.

The invention discloses a LoRa-based parallel backscatter communication method and system. The method comprises the following steps: transmitting a single-tone radio frequency source signal as a signal excitation source; designing a backscattering label, and performing backscattering modulation processing on the environmental perception data in combination with the signal excitation source to obtain a modulated standard LoRa data packet; and the receiver presets the center frequency of a communication link, and performs frequency band distinguishing and demodulation processing on the modulated standard LoRa data packet to complete parallel backscatter communication of the LoRa data. According to the invention, while a parallel backscattering LoRa communication system compatible with commercial LoRa equipment is realized, the throughput of the system is improved, and frequency division multiple access parallel transmission is realized. The LoRa-based parallel backscatter communication method and system can be widely applied to the technical field of parallel backscatter communication.

The invention discloses a GPU Box hardware decoupling system based on a Retimer card and a PCIe Switch chip, and belongs to the technical field of computer hardware architecture and high-speed interconnection. According to the system, a Retimer card and a PCIe Switch chip are integrated in an independent GPU Box, and a decoupling link of a CPU server and a GPU acceleration card is constructed; the Retimer card realizes 30-meter long-distance PCIe signal transmission and breaks through physical distance limitation; the PCIe Switch chip pools GPU resources through a dynamic routing and MRIOV technology, supports flexible allocation of computing power by multiple servers, and realizes Peer-to-Peer direct connection communication between GPUs. Aiming at a large model reasoning scene, the system optimizes KV cache bandwidth allocation and video memory and memory cooperative scheduling, so that the 100B parameter model reasoning throughput is greatly improved; and meanwhile, the usability of the system is greatly improved through fault isolation and hot plug design. According to the method, the problems of physical binding of the CPU and the GPU, limited transmission distance, rigid resource allocation and the like in a traditional architecture are solved, and the method is suitable for large-scale AI calculation and distributed GPU cluster deployment.

The invention discloses a satellite Internet of Things distributed node parallel transmission acceleration method, which relates to the technical field of satellite Internet of Things, and comprises the following steps: dynamically fragmenting, splitting into N fragments according to a network load rate and a node state, adding a serial number, a check code and redundant information, and marking a head as {INJ}; the intelligent routing is used for distributing fragments to distributed nodes through a scoring function based on satellite ephemeris, node loads and link quality; dynamic path switching: monitoring link and node states, and switching to a better path when the performance does not reach the standard; fault-tolerant recombination is carried out, recombination is carried out according to sequence numbers, if missing fragments are few, erasure codes are used for recovery, and if missing fragments are more, local retransmission is carried out. Through parallel transmission, dynamic switching and fault-tolerant recombination, the transmission rate, throughput and robustness are improved, node loads are balanced, and the performance of the satellite Internet of Things is optimized.

The invention relates to the technical field of computers, discloses a heterogeneous computing low-delay communication method and system, and aims to solve the problem of high delay caused by high communication protocol overhead, lack of dynamic scheduling collaboration, memory migration redundancy and non-uniform cross-node communication abstraction in existing heterogeneous computing. The method comprises the following steps: receiving a task scheduling request and analyzing a task dependency graph; tasks are dynamically allocated based on node loads and link states; rDMA, NVLink or PCIe straight-through protocols are adaptively selected according to node types to establish communication channels; zero-copy data exchange is realized through a shared memory mapping buffer area; hardware timestamps are utilized to synchronize feedback delays with PTP to optimize scheduling. The system comprises a heterogeneous computing node cluster, a unified communication scheduling controller, a low-delay communication protocol stack, a shared memory mapping buffer area and a communication delay sensing task distributor. According to the scheme, the communication delay is remarkably reduced, and the throughput and the task execution efficiency are improved.

Dynamic slicing technology can be performed to assign network resources for various robotic repairing subtasks having different priorities, while satisfying various real-time and high throughput requirements. In some cases, M / M / 1 queuing theory is applied so as to efficiently improve the utilization of network resources, reduce queuing latency and propagation latency, and balance the load among different network slicing, so as to perform robotic repairing operations.

Logic to generate an extremely high throughput (EHT) physical layer protocol data unit (PPDU) comprising a medium access control (MAC) management frame, the MAC management frame comprising a QoS management field. the QoS management field comprising at least one bit value to indicate quality of service (QOS) management capability associated with links associated with more than one frequency bands. Logic to cause the transmission of the EHT PPDU. And logic to receive and decode the EHT PPDU.

A processing system may apply a data set comprising utilization metrics of a cells of a cell sector to a throughput prediction model to obtain a first predicted throughput for the cell sector for a designated future time period and for all cells being in an active state. The processing system may generate a first modified data set simulating a first cell being placed in an inactive state, by distributing utilization metrics of the first cell over at least one additional cell, and may apply the first modified data set to the throughput prediction model to obtain a second predicted throughput. The processing system may then determine that the second predicted throughput meets a threshold throughput that is based on the first predicted throughput, and transmit at least one instruction to place the first cell in the inactive state for the designated future time period in response to the determining.

Methods related to wireless communication systems and uplink (UL) rank adaptation are provided. A user equipment (UE) receives, from a base station (BS), a configuration indicating a resource associated with a plurality of UL reference signal ports. The UE determines, based on at least one of an overheating status, a throughput, or a latency, a quantity of transmission layers for a transmission. The UE transmits, to the BS, an indication of at least one of (1) one or more UL reference signal ports of the plurality of UL reference signal resources associated with the UL reference signal resource based on the quantity of transmission layers, or (2) a number of UL reference signal ports associated with the multi-port UL reference signal resource based on the quantity of transmission layers.

The invention relates to the technical field of access control, in particular to a method for controlling multi-user concurrent access of a server, which comprises the following steps of: analyzing task types and resource demand characteristics requested by users in real time, and generating a triple comprising a calculation mode identifier, a data topology fingerprint and a time constraint factor; performing space-time coupling matching on the triad and a server node, calculating a space-time weight score, and generating a binding execution unit based on the space-time weight score; quantum fragmentation scheduling is carried out on the bound execution unit, a reverse dependence barrier is inserted between adjacent atomic operation sequences, and execution track data is generated in execution; and adjusting calculation mode matching through an execution track feedback mechanism, and performing calculation mode identifier remapping of an unexecuted task by utilizing the path efficiency of the completed task. According to the method, the computing resource adaptation deviation of the task is effectively reduced, the remote data access overhead is reduced, and the task throughput and the execution stability are improved.

The invention relates to the technical field of computer networks, in particular to a shortest routing and deadlock avoiding method and device for a double-layer polygon topology, and the method comprises the steps: determining the routing type of the topology; xOR processing is carried out on the mark low bits of the source node and the target node, and a candidate node corresponding to the shortest path of the source node is determined; determining an available state of the anti-deadlock node, if the available state is idle, determining routing bypass information, and sending the routing bypass information to the source node; and determining an available state of the common node, and generating a route selection vector of the candidate node, so that the source node determines a next-hop node according to the route selection vector, sends the data packet to the next-hop node, and takes the next-hop node as a new source node. According to the method provided by the embodiment of the invention, the number of the shortest paths is expanded into all theoretical shortest paths, so that the number of the shortest paths among the nodes in the computer network topology structure is increased, the system transmission efficiency and the data throughput are improved, and meanwhile, deadlock is avoided.

The invention discloses a quantum network path selection and resource allocation method and system for realizing high throughput, and the method comprises the steps: initializing global variables and network parameters at least comprising a network topology structure, a request number and the maximum capacity of edges through building a quantum network model; setting the initial capacity of each edge, adjusting the capacity according to the fidelity of the edge, and removing the edge of which the fidelity is less than 0.5; designing a path selection algorithm, and selecting an optimal path by using the characteristics of the quantum state; constructing a resource allocation strategy based on quantum entanglement and quantum parallelism, and dynamically allocating network resources; and verifying the validity of the path selection and resource allocation method through the quantum network simulator. The resource allocation sequence is optimized, redundant operation in the resource allocation process is reduced, and therefore consumption of computing resources is reduced.