217 results about "Flow scheduling" patented technology

The invention discloses a near-end strategy optimization method for heterogeneous supercomputing cluster job flow dynamic scheduling, which comprises the following steps of: abstracting a directed graph job flow scheduling problem in a heterogeneous supercomputing cluster, determining a scheduling target and an evaluation index, and constructing a reinforcement learning framework; and adopting a near-end strategy to optimize and realize heterogeneous supercomputing cluster job flow dynamic scheduling. According to the method, a hierarchical scheduling method is provided, processing of a scheduler on state information is reduced through a sparse scheduling decision, efficient task scheduling is achieved, task dependency relationship characteristics are extracted through a GCN and serve as a part of the state information, and communication overhead between tasks is reduced by introducing resource inheritance and an intra-task parallel mechanism; and the optimal scheduling strategy is trained and learned by using the improved PPO method, and the trained scheduling strategy can make an optimal scheduling action decision according to the dynamic change task and the state information of the computing resources.

Disclosed in the present application are a service flow scheduling method, apparatus and device based on deep reinforcement learning, and a medium. The method comprises: acquiring target data, wherein the target data comprises a current target topology state, current target service flow information and current target link information of a time-sensitive network; and inputting the target data into a deep reinforcement learning model to obtain a scheduling scheme, so as to implement scheduling of service flows on the basis of the scheduling scheme, wherein the deep reinforcement learning model comprises an agent and an environment, the agent is configured to determine gate control orchestration of the scheduling scheme, and the environment is configured to determine slot orchestration of the scheduling scheme on the basis of the gate control orchestration.

The invention provides an ordered traffic scheduling method based on dynamic priorities in a data center network. The method comprises the following steps of: performing priority distribution on each data packet of a sender; the switch collects flow information in real time and detects whether the priority queue is congested or not; and when the switch receives the data packet, the forwarding of the data packet is scheduled according to the priority of the data packet and the congestion condition of the priority queue. According to the method, different priority queues are adopted to cache data packets of different types of flows, it is ensured that data packets of urgent flows are not blocked by data packets of non-urgent flows, meanwhile, a switch utilizes a non-disorder flow scheduling mechanism based on dynamic priorities to ensure that the data packets arrive at a receiver in sequence, and the data packets are not blocked by the non-urgent flows. The flow completion time and the deadline missing rate are reduced to the maximum extent, and the transmission performance of a data center network can be greatly improved.

The invention relates to a side cloud collaborative adaptive workflow scheduling method and system, and belongs to the technical field of distributed computing and artificial intelligence. The method comprises the following steps of: firstly, in a macroscopic candidate screening stage, reducing problem granularity through task clustering, and obtaining balance between utilization and exploration based on a weighted distance probabilistic preferential strategy; then, in a collaborative scheduling decision-making stage, a global network state diagram is constructed through a graph neural network, deep spatial features of nodes and neighborhoods of the nodes are extracted, context-aware state representation is formed, and a reinforcement learning agent makes an optimal collaborative decision in multiple options such as local execution, edge migration or cloud unloading according to the state representation; and finally, in a local adaptive optimization stage, performing fine-grained optimization after the task is issued, dynamically adjusting a scheduling frequency and a multi-target weight through an online learning mechanism, realizing balance between a task deadline and a resource utilization rate, and ensuring efficient and robust execution of a node level.

A routing and scheduling method based on multi-cycle CSQF mechanism and GDRL is provided. The routing and scheduling method includes the following steps: S1, initializing a cycle index detection mechanism, a queue mapping and a queue mapping constraint of a multi-cycle CSQF; S2, constructing a DFRLLS model; S3, optimizing the DFRLLS model based on GDRL; S4, off-line training a learning strategy of a GDRL model; S5, making a decision on-line based on a trained GDRL model. The routing and scheduling method based on multi-cycle CSQF mechanism and GDRL is adopted, and GCN network is used to extract topology information between networks. Compared with the method of only using reinforcement learning, the routing and scheduling method can achieve more flow scheduling, and its performance is also stable under complex network topology, multi-cycle CSQF can reduce the start-to-end delay of the flow compared to the CSQF.

The invention relates to a flexible job shop energy-saving batch flow scheduling optimization method considering machine tool deterioration and preventive maintenance, and belongs to the technical field of shop scheduling. Comprising the following steps: constructing a flexible job shop energy-saving batch flow scheduling mathematical model taking completion time, total energy consumption and total production cost as optimization targets and considering machine tool deterioration and preventive maintenance; a model solving method is put forward, a strategy is introduced based on population diversity of historical populations, and the coping capacity of the algorithm to environment changes is enhanced; developing a self-adaptive crossover mutation operator, and balancing the diversity and convergence of the algorithm; and proposing a neighborhood search strategy, and exploring a local optimal solution of the energy-saving batch flow scheduling problem. According to the method, the problem of flexible job shop energy-saving batch flow scheduling multi-objective optimization considering machine tool deterioration and preventive maintenance can be efficiently solved, under-maintenance or excessive maintenance of the machine tool is effectively avoided, production interruption and maintenance cost are reduced, and a scheduling scheme with excellent completion time, total energy consumption and total production cost is obtained.

The present disclosure relates to systems and methods for flow scheduling of machine learning workloads. The systems and methods use prior knowledge of the traffic patterns for machine learning workloads and the network topology to schedule the flows of the machine learning workloads. A centralized controller leverages the knowledge of the traffic patterns for the machine learning workloads to guide per-flow routing decisions for the machine learning workflows to schedule the flows across all available network paths to fully utilize network bandwidth capacity.

The invention discloses an active congestion avoiding method and system based on flow prediction, and relates to the technical field of network communication, and the method comprises the steps: collecting the network state information of a physical network in real time; dynamically synchronizing and mapping network state information of a physical network to a virtual mirror image by using a digital twinning technology; under the virtual mirror image, traffic prediction is carried out on the network state information based on a time domain convolutional network model, and a traffic prediction result is obtained; the traffic prediction result is transmitted into a scheduling strategy engine for dynamic analysis and evaluation, and an active traffic scheduling strategy for traffic prediction is determined; issuing the active flow scheduling strategy to a physical network for execution; and according to the latest network state information, an active flow scheduling strategy is dynamically adjusted to form a closed-loop adaptive system. According to the method, the virtual mirroring of the network state information is realized by using the digital twin technology, the traffic prediction result is obtained by using the time domain convolutional network model, and the traffic scheduling strategy is actively adjusted before traffic congestion occurs, so that congestion is effectively avoided.

The invention discloses a traffic scheduling method and electronic equipment, and relates to the technical field of traffic scheduling, and the method comprises the steps: responding to a condition that a to-be-sent data stream accords with a transport layer protocol and the traffic size of the to-be-sent data stream is greater than a preset threshold value, and generating a plurality of candidate port values used for identifying the to-be-sent data stream at a data sending end; creating a plurality of detection data packets, wherein source port fields of the detection data packets are set as different candidate port values; sending the detection data packets to a data receiving end, and obtaining performance indexes of candidate network paths corresponding to the plurality of detection data packets based on the response of the data receiving end; determining a target source port value from the plurality of candidate port values based on the performance index; according to the embodiment of the invention, the source port field of the data packet corresponding to the to-be-sent data stream is modified into the target source port value, and the modified data packet corresponding to the to-be-sent data stream is sent to the data receiving end, so that the technical problem of load imbalance of a multi-path network in related technologies is solved, and the reliability of traffic scheduling is improved.

The invention discloses a flow scheduling method and device, a medium and a program product, and relates to the technical field of heat dissipation, and the method comprises the steps: obtaining a video memory access frequency as a main parameter when a parallel computing function is executed, matching with other auxiliary parameters related to heat consumption, distributing a weight, determining a heat load scoring result, and then adjusting a cold plate micro-channel flow distribution coefficient, and determining an opening value of a flow control valve and sending a flow scheduling instruction to the liquid cooling system so as to dynamically adjust the cooling liquid flow of each cold plate micro-channel. In this way, accurate temperature control and heat dissipation of the video memory area of the computing device can be achieved, energy waste caused by unnecessary circulation of cooling liquid is avoided, the heat dissipation efficiency is remarkably improved, energy consumption is reduced, and the service life of the computing device is prolonged; in addition, the mode can autonomously complete full-flow operation such as video memory heat consumption monitoring, score calculation and flow adjustment, intelligent management of the heat dissipation process is achieved, manual operation errors and cost are reduced, and the stability and reliability of operation of computing equipment are guaranteed.

The invention provides a traffic scheduling method and apparatus, an electronic device, a storage medium and a program product. The method comprises the steps of determining a plurality of to-be-trained tasks; determining the shortest completion time for training any one of the to-be-trained tasks, and performing queue allocation on any one of the to-be-trained tasks according to the shortest completion time; in response to determining that the queue of the to-be-trained tasks is allocated to the time-sensitive queue, determining a plurality of to-be-trained sub-tasks in the to-be-trained tasks, performing priority ranking on the plurality of to-be-trained sub-tasks, and determining the priority of any one of the to-be-trained sub-tasks; and carrying out bandwidth allocation on the to-be-trained task according to the priority to complete traffic scheduling. According to the method, the to-be-trained tasks are subjected to priority ranking, bandwidth allocation is performed on the to-be-trained tasks according to the priority ranking, and flow scheduling is completed, so that the overdue risk of time-sensitive tasks is reduced.

The invention relates to the technical field of artificial intelligence, intelligent gateways and finance, and provides a flow scheduling method, device and equipment for an intelligent gateway and a medium, and the method comprises the steps: obtaining resource monitoring data and API call chain data of each node in the intelligent gateway through a distributed service cluster grid; determining resource use trend prediction information and dynamic threshold information according to the resource monitoring data and a trained time sequence prediction model; in response to the fact that the resource use trend prediction information is abnormal, determining a fault propagation path according to the API call chain data and a pre-trained causal reasoning model; and determining a dynamic flow scheduling strategy through a multi-factor weight formula, and determining an adaptive fusing strategy according to the dynamic threshold information. Through the technical scheme, the problems of difficult fault positioning and scheduling lagging in a traditional distributed system are solved, and the stability, the anti-risk capability and the resource utilization efficiency of the intelligent gateway in a complex load scene are remarkably improved.

The embodiment of the invention provides a logistics scheduling system, and belongs to the technical field of logistics. The system comprises a task layer which is used for generating an order task; the feedback layer is used for collecting dynamic resource data related to execution of the order task in a logistics site; the decision-making layer is respectively in communication connection with the task layer and the feedback layer, and the decision-making layer is used for adjusting a path planning strategy according to the dynamic resource data and generating a task execution instruction according to the adjusted path planning strategy; and the execution layer is in communication connection with the decision-making layer, and the execution layer is used for scheduling corresponding transportation equipment to execute the order task according to the task execution instruction. According to the embodiment of the invention, the scheduling flexibility of the transportation equipment can be improved.

The invention discloses a multi-dimensional information-based adaptive gateway traffic scheduling method, which comprises the following steps of: acquiring server performance indexes, network delay, bandwidth and geographic position data in real time, and establishing a historical database; performance load weights are generated through weighted calculation of CPU, memory and disk indexes, and routing priorities are dynamically adjusted by adopting exponential function mapping; performing normalization processing on network bandwidth, delay and geographic distance, and calculating a service access weight to optimize a transmission path in combination with a step function; analyzing historical traffic data based on an LSTM model to predict a future trend, and generating a traffic prediction weight through a sigmoid function to realize pre-equalization; and combining the three types of weights, weighting to generate dynamic routing configuration, eliminating abnormal nodes in real time in combination with a health check mechanism, triggering high-load early warning and linking a predictive balancing strategy. According to the method, multi-dimensional index collaborative decision-making is realized, routing distribution is dynamically optimized, network delay is effectively reduced, and system throughput and stability in a high-concurrency scene are improved.

The embodiment of the invention discloses a test system and method and chip test equipment. The test system comprises a task flow scheduling module, a plurality of distribution modules and a plurality of test execution modules, the task flow scheduling module is used for splitting and sorting test tasks to obtain a plurality of sub-task flow sequences; the distribution module is used for splitting and sorting the test items in the plurality of sub-task flow sequences to obtain a plurality of test item sequences; and the test execution module is used for controlling the chip test equipment to execute each test item in the plurality of test item sequences so as to perform parallel test on the to-be-tested device to obtain a test result. Therefore, the execution mode of the test task is automatically converted into the parallel execution mode, and the situation that the chip test equipment cannot test the test device due to parallel test item determination errors and test code modification errors caused by manual adjustment to the parallel execution mode is avoided.

The invention discloses a satellite communication cross-station switching and flow scheduling method based on affiliation, and relates to the technical field of satellite communication. User satellite terminal landing path perception is realized based on network measurement; and path replanning and flow redirection of inter-station services are realized by adopting a tunnel drainage strategy reconfiguration mode through a centralized control mode. According to the invention, automatic switching of the user satellite terminal among different ground stations, inter-station path dynamic replanning and flow redirection after dynamic selection of a service landing station can be realized, the method has the advantages of supporting subnet movement, automatic switching control of an end-to-end network outgoing path and the like, and the application and automation level of a satellite communication network can be improved.

The invention relates to a warehouse logistics scheduling method and system based on cloud computing, and relates to the technical field of warehouse logistics scheduling, and the method comprises the steps: jointly building a logistics scheduling model through a plurality of edge cloud computing nodes, and the logistics scheduling model comprises an edge scheduling scheme generation model and a comprehensive scheduling scheme generation model; determining at least one first optimal edge cloud computing node based on the computing power resource demand of the warehouse logistics scheduling task; each first optimal edge cloud computing node generates a model through an edge scheduling scheme, generates a plurality of candidate warehouse logistics scheduling schemes based on the warehouse logistics scheduling task, the warehouse node state information and the logistics vehicle state information, screens the plurality of candidate warehouse logistics scheduling schemes, and determines a plurality of target warehouse logistics scheduling schemes; through the comprehensive scheduling scheme generation model, the optimal warehouse logistics scheduling scheme is generated based on the multiple target warehouse logistics scheduling schemes, warehouse logistics scheduling is carried out, and the method has the advantage of improving the intelligent level of warehouse logistics scheduling.

The invention relates to the technical field of satellite time-sensitive network communication, in particular to a time-sensitive traffic scheduling method for joint routing in a satellite scene. According to the method, the joint routing algorithm is introduced into the low-orbit satellite network, so that efficient scheduling and path optimization of time-sensitive traffic are realized. In the initial configuration stage, path calculation and time slot resource allocation are carried out by using a particle swarm optimization algorithm so as to maximize the transmission efficiency of the time-sensitive flow. And in the real-time correction stage, a time slot margin and priority adjustment mechanism is dynamically monitored to adapt to frequently changing satellite link conditions, and stable transmission of traffic is ensured. According to the method, the problems of time delay and flow conflict caused by instability of a satellite link are effectively solved, and the service quality and the system performance of a satellite network are improved.

The invention relates to the technical field of cloud fusion application operation support platforms, and discloses a session flow intelligent scheduling method under a fusion communication architecture, which comprises the following steps: monitoring a session flow logic sequence number of a current node; constructing an asynchronous mirror instance at the target node, and obtaining a computing power comparison parameter; adjusting an asynchronous mirror instance instruction execution rate to execute serial number chasing; after the sequence number difference value enters a synchronous threshold value, processor assembly line branch prediction data is injected, and instruction prefetching sequence filling is driven; according to the instruction level feature injection and execution phase alignment method, hardware execution momentum imbalance in a heterogeneous computing power environment is eliminated through instruction level feature injection and execution phase alignment, and progress connection and logic consistency of session streams at a migration interface are guaranteed.

The embodiment of the invention provides a workflow scheduling method and device, equipment, a storage medium and a program product, and is applied to the technical field of cloud computing. The method comprises the steps of obtaining target attribute data corresponding to a target workflow; according to the target attribute data, constructing a multi-target optimization model corresponding to the target workflow; the multi-target optimization model comprises a target hybrid optimization algorithm; determining a target solution set corresponding to the target workflow according to the target attribute data and a multi-target optimization model; and decoding the target solution set to obtain a target workflow scheduling mode corresponding to the target workflow. Therefore, the electronic equipment calculates and optimizes the workflow scheduling based on the multi-objective optimization model, so that the diversity of the workflow scheduling process and the scheduling result can be improved, and meanwhile, the optimization effect of the workflow scheduling can also be improved.

The invention relates to the technical field of computers, and discloses a process scheduling method based on a tenant isolation strategy, and the method comprises the steps: receiving a process scheduling request of a tenant, and carrying out the standardization processing and legality verification; querying a predefined tenant mapping table, and determining an internal unified tenant identifier corresponding to the flow scheduling request and associated single-tenant concurrent quota, tenant resource weight, isolation level and strategy rule; obtaining a single-tenant concurrent quota and a tenant resource weight, generating a tenant isolation strategy in combination with the calculated rate limiting parameter and the tenant correction priority, and performing admission judgment; and based on the judgment result, distributing the flow scheduling request to the corresponding tenant scheduling unit according to the local priority, selecting a target tenant scheduling unit according to the scheduling qualification value and the global scheduling priority, generating an execution instance and triggering the execution instance. Through differentiated services, resource monopoly is prevented, task hunger is eliminated, system overload is avoided, multi-dimensional mutual restriction is carried out, and isolation between tenants is achieved.

The invention relates to a time-sensitive network routing and scheduling method based on a genetic algorithm, and belongs to the technical field of time-sensitive networks. The method comprises the following steps: reading constraint condition information of TSN network topology, node processing delay and flow; calculating the least common multiple of the periods of all the flows; sorting each flow according to the flow transmission period, the maximum cut-off time delay, the source node-destination node and the frame size; searching all possible paths from a source node to a destination node for each flow by using a mode based on depth-first search; initializing population chromosomes, wherein each chromosome represents a potential flow scheduling scheme; planning appropriate transmission starting time for each flow through a wait-free scheduling algorithm; evaluating the performance or fitness of each chromosome through a fitness function; and executing a genetic algorithm to iteratively improve a scheduling scheme in the population, and finally generating and printing a routing table and a gating list of each switch node.

The invention discloses a multi-link aggregated cloud native network system and a flow scheduling method. The system comprises an application sensing layer, a virtual link layer and a physical link layer, the application sensing layer is connected with the virtual link layer and used for analyzing network traffic to be distributed to obtain features of the network traffic, and the features comprise traffic types, priorities and bandwidth requirements; the virtual link layer is connected with the physical link layer, and is used for detecting the states of the plurality of links to obtain the state information of the plurality of links, and determining the flow distribution strategy of the plurality of links based on the characteristics and the state information; and the physical link layer is used for carrying out flow distribution on the plurality of links based on the flow distribution strategy. According to the invention, the technical problem that the traditional cloud native network depends on single link transmission, so that network congestion is easy to occur when the service flow is increased sharply is solved.

The invention discloses a real-time signaling flow intelligent arrangement system based on queue optimization, and relates to the technical field of communication networks, and the system comprises a multi-dimensional feature perception and fusion module which is used for collecting and outputting various dynamic attribute parameters of signaling in real time; and the self-adaptive scheduling decision module is connected with the multi-dimensional feature perception and fusion module and is used for receiving the dynamic attribute parameters and generating a scheduling instruction. According to the real-time signaling flow intelligent arrangement system based on queue optimization, by introducing a double-loop intelligent architecture of prediction and decision verification, the perspectiveness and reliability of signaling flow scheduling are effectively improved. According to the technical scheme, the system can make decisions by integrating the multi-dimensional dynamic factors, and through double inspection of the rule base and micro-simulation, the reasonability of the scheduling strategy and the system stability are still kept in the face of complex burst traffic, and the end-to-end service quality is guaranteed.

The invention discloses a CQF mechanism train communication network scheduling method based on a time sensitive network, comprising the following steps: establishing a train communication network topology model based on a TSN, defining each performance parameter in the train communication network topology model, establishing a traffic scheduling model based on the train communication network topology model, and scheduling the traffic scheduling model based on the train communication network topology model. Defining performance parameters in a communication flow scheduling model, defining related constraints based on a flow scheduling process, constructing an overall flow of a JFRF algorithm, and establishing a flow scheduling model with a scheduling success rate as an optimization target on the basis of scheduling constraint conditions; the method comprises the following steps of: constructing a sequencing equation consisting of flow features and route features by combining flow, offset and route through the combination of the flow, the offset and the route, determining and selecting all flow scheduling priorities according to the sequencing equation, and selecting the maximum priority from all the scheduling priorities for scheduling. According to the invention, the process of traffic scheduling can be more efficiently planned, load balancing with a higher level can be realized, and scheduling resource allocation can be optimized. The problem that part of time trigger flows cannot be scheduled under the background that the data volume is increased in a blowout mode after train intelligentization can be effectively solved.

The invention relates to an improved DQN-based flexible job shop dynamic batch flow scheduling optimization method considering emergency order insertion and reworking, and belongs to the technical field of shop scheduling. The method comprises the following steps of: considering a flexible job shop dynamic batch flow scheduling multi-objective optimization mathematical model of emergency order insertion and reworking; based on a knowledge-driven batching method and a multi-agent DQN model solving method, a local optimal solution of a flexible job shop dynamic batch flow scheduling model problem considering workpiece batching is further explored. The problem that a traditional optimization method is poor in applicability and efficiency under dynamic event interference is solved, and flexible job shop dynamic batch flow optimization scheduling is achieved. According to the optimization method, the problem of flexible job shop dynamic batch flow scheduling optimization under the order insertion and workpiece reworking dynamic event can be efficiently solved, waste of resources such as energy, time and cost is effectively avoided, production interruption and maintenance cost are reduced, and a scheduling scheme with good completion time and order insertion workpiece delay time is obtained.

The invention discloses an online incremental flow scheduling method, device and equipment based on a time-sensitive test launch control network, which are suitable for solving the problem of mixed service flow resource allocation in a ground test launch control system network and improving the scheduling capability of the network on time-sensitive service flows and measurement service flows. The method comprises the following steps: carrying out service flow category division and attribute representation; network topology and resource detection; constructing a mixed service flow incremental transmission model; based on a mixed service flow incremental transmission model, establishing a common flow scheduling optimization target and constraint conditions of different types of service flows; configuring a Markov decision process according to the optimization target; executing an online incremental scheduling decision based on a traffic scheduling algorithm of deep reinforcement learning; according to the method and the device, the scheduling success rate of the periodic time-sensitive service flow and the measurement type service flow can be improved while the end-to-end transmission delay of the non-periodic burst time-sensitive service flow is optimized.

An embodiment of the present disclosure provides a traffic scheduling and control method, device, and storage medium based on a partitioned storage device. By determining the target ratio between the throughput of writing data during the garbage collection process of the currently to-be-reclaimed partition and the throughput of the user writing data to the partitioned storage device according to the ratio of garbage data to valid data in the currently to-be-reclaimed partition of the partitioned storage device; controlling the first type of requests for writing data during the garbage collection process and the second type of requests for the user to write data to the partitioned storage device by the partitioned storage device according to the target ratio. By controlling the garbage collection speed and the speed of the user writing data to the partitioned storage device according to the determined target ratio, the stability of the number of free partitions of the partitioned storage device is ensured, thereby ensuring the stability of the partitioned storage device and making full use of the writing data performance of the partitioned storage device.

The invention discloses a flow scheduling method and device based on machine room multi-index real-time prediction. The method comprises the following steps: predicting network environment data by using a machine room performance prediction model to generate performance prediction data; performing index weighting calculation on the performance prediction data and the network environment data to obtain a comprehensive score of each machine room; determining a score difference value between the machine rooms according to the comprehensive score, determining a traffic distribution strategy, distributing traffic for each machine room according to the traffic distribution strategy, updating the global routing table to execute traffic scheduling, and obtaining a traffic scheduling execution result; and monitoring network environment data after flow scheduling based on a flow scheduling execution result, determining a prediction deviation value based on the monitored network environment data and the performance prediction data, and updating the machine room performance prediction model according to the prediction deviation value and the network environment data. According to the method and the device, the problems of lack of perspectiveness, unbalanced resource utilization and single decision index of multi-machine-room flow scheduling in the prior art are solved.

The invention discloses a service flow scheduling method, equipment and a medium. The method comprises the following steps: acquiring business flow scheduling instruction information; analyzing the service flow scheduling instruction information to obtain a service flow scheduling constraint condition corresponding to the scheduling target and an effective condition of the service flow scheduling constraint condition; obtaining queue state information of a service flow queue of each service flow scheduling level; constructing a service flow scheduling optimization model based on the service flow scheduling constraint condition and the queue state information under the condition that the effective condition is met, and solving the service flow scheduling optimization model to obtain a scheduling strategy of the first service flow; wherein the service flow scheduling hierarchy comprises a flow hierarchy, a queue hierarchy and a port hierarchy. According to the technical scheme, the problem that the multi-layer queue business flow scheduling effect needs to be improved at present is solved, the stability of business flow scheduling can be improved through collaborative scheduling management of the multi-layer business flow queues and an intelligent decision-making mechanism for dynamically adjusting parameters, and the business transmission service effect is guaranteed.