Task processing method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a task processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring data processing tasks from an edge processing node, and acquiring task demand information corresponding to each data processing task; acquiring node state information of each processing node in the computing power network; according to the node state information and task demand information carried by the data processing task, determining a target processing node by taking resource utilization rate maximization of each processing node in the computing power network as a target; determining a target transmission path and a target transmission protocol by taking maximization of transmission bandwidth and minimization of transmission delay as targets according to data size information and time delay requirements carried by the data processing task, and a network state and a transmission protocol of a target processing node; and transmitting the data processing task to the target processing node through the target transmission path and the target transmission protocol. By adopting the method, the task processing efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a task processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] With the rapid development of technologies such as cloud computing, edge computing, 5G, and AI, the distribution of computing resources has become increasingly widespread and diversified, and computing power networks have emerged. Computing power networks integrate widely distributed computing nodes (including cloud data centers, edge nodes, terminal devices, etc.), and then share the computing resources of different nodes; however, the current computing power networks are less efficient in task processing. Summary of the invention

[0003] Based on this, it is necessary to provide a task processing method, apparatus, computer equipment, computer-readable storage medium and computer program product that can improve task processing efficiency in response to the above technical problems.

[0004] In a first aspect, the present application provides a task processing method, comprising:

[0005] Obtain data processing tasks from edge processing nodes, and obtain task requirement information corresponding to each of the data processing tasks; the task requirement information includes data volume information, computational complexity, and latency requirements;

[0006] Obtain node status information of each processing node in the computing power network, wherein the node status information includes CPU status, memory load status and network delay information; the processing nodes include central processing nodes and edge processing nodes;

[0007] According to the node status information and the task requirement information carried by the data processing task, with the goal of maximizing the resource utilization of each processing node in the computing power network, determining the target processing node corresponding to each data processing task;

[0008] According to the data volume information and delay requirements carried by the data processing tasks, as well as the network status and transmission protocol of the target processing node, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay, determine the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node;

[0009] The data processing task is transmitted to the target processing node through the target transmission path and the target transmission protocol.

[0010] In one embodiment, the determining of the target processing node corresponding to each data processing task based on the node status information and the task requirement information carried by the data processing task with the goal of maximizing the resource utilization of each processing node in the computing power network includes:

[0011] The CPU state, the memory load state, the network delay information, the computational complexity and the delay requirement are input into a pre-trained task scheduling model, with the goal of maximizing the resource utilization of each processing node in the computing power network, to obtain the target processing node corresponding to each data processing task.

[0012] In one embodiment, the target transmission path and target transmission protocol of each data processing task to the corresponding target processing node are determined based on the data volume information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay, including:

[0013] The data volume information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, are input into a pre-trained transmission path and protocol selection model, with the goal of maximizing transmission bandwidth and minimizing transmission delay, to obtain the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node.

[0014] In one of the embodiments, the transmission protocol includes at least one of the QUIC protocol and the RDMA protocol.

[0015] In one of the embodiments, the data corresponding to the data processing task includes at least one of sensor data and video stream.

[0016] In one of the embodiments, the task requirement information also includes a priority;

[0017] The transmitting the data processing task to the target processing node through the target transmission path and the target transmission protocol includes:

[0018] Obtaining the priority corresponding to the data processing task;

[0019] According to the priority corresponding to the data processing task, the data processing task is transmitted to the target processing node through the target transmission path and the target transmission protocol.

[0020] In a second aspect, the present application further provides a task processing device, comprising:

[0021] A task acquisition module is used to acquire data processing tasks from edge processing nodes and acquire task requirement information corresponding to each of the data processing tasks; the task requirement information includes data volume information, computational complexity and latency requirements;

[0022] A node status acquisition module is used to acquire node status information of each processing node in the computing network, wherein the node status information includes CPU status, memory load status and network delay information; the processing nodes include central processing nodes and edge processing nodes;

[0023] A processing node determination module is used to determine the target processing node corresponding to each data processing task based on the node status information and the task requirement information carried by the data processing task, with the goal of maximizing the resource utilization of each processing node in the computing power network;

[0024] A transmission mode determination module is used to determine the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node based on the data volume information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay;

[0025] The task transmission module is used to transmit the data processing task to the target processing node through the target transmission path and the target transmission protocol.

[0026] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in the first aspect when executing the computer program.

[0027] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect.

[0028] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements the steps of the method described in the first aspect when executed by a processor.

[0029] The above-mentioned task processing method, device, computer equipment, computer-readable storage medium and computer program product, the scheduling server obtains data processing tasks from edge processing nodes, and obtains task requirement information corresponding to each of the data processing tasks; the task requirement information includes data volume information, computational complexity and delay requirements; obtains node status information of each processing node in the computing power network, and the node status information includes CPU status, memory load status and network delay information; the processing nodes include central processing nodes and edge processing nodes; according to the node status information and the task requirement information carried by the data processing tasks, with the goal of maximizing the resource utilization of each processing node in the computing power network, the target processing node corresponding to each of the data processing tasks is determined; according to the data volume information and delay requirements carried by the data processing tasks, as well as the network status and transmission protocol of the target processing node, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay, the target transmission path and target transmission protocol from each of the data processing tasks to the corresponding target processing node are determined; the data processing tasks are transmitted to the target processing node through the target transmission path and target transmission protocol. In this application, by real-time acquisition and analysis of node status information (such as CPU status, memory load status and network delay), the method can effectively select the most suitable processing node to handle data processing tasks and realize flexible allocation of data processing tasks; and determine the specific transmission path and protocol according to the data volume, computational complexity and delay requirements, so that the data transmission process is smoother and the processing time is effectively reduced, thereby improving the overall task execution efficiency, as well as improving the resource utilization and response speed of the computing network. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0031] FIG1 is a schematic diagram of a task processing method in one embodiment;

[0032] FIG2 is a schematic diagram of a flow chart of a task processing method in another embodiment;

[0033] FIG3 is a block diagram of a task processing device in one embodiment;

[0034] FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] In an exemplary embodiment, as shown in FIG1 , a task processing method is provided, and the method is applied to a scheduling server in a computing network as an example for explanation. The scheduling server can be any processing node in the computing network or an additional server. The task processing method includes steps S202 to S210. Among them:

[0037] Step S202: Obtain data processing tasks from the edge processing node, and obtain task requirement information corresponding to each data processing task.

[0038] The task requirement information includes data size information, computational complexity and latency requirements. Data size information may refer to the size of the input data involved in the task. Computational complexity may refer to the computational resources required to execute the task, which is related to the complexity of the algorithm. Latency requirements may refer to how long the task must be completed, which may refer to the acceptable delay range.

[0039] Among them, the computing power network may refer to a network composed of multiple processing nodes, such as servers, edge devices, etc., which can provide computing and storage capabilities. The computing power network can allow tasks to be distributed and executed between nodes in different locations to improve resource utilization efficiency and system response speed. The scheduling server can be used to manage and distribute data processing tasks. Data processing tasks may refer to specific work units that need to be executed, involving requests for processing certain input data. These tasks may include data analysis, computing tasks or other computing-intensive operations. Edge processing nodes may refer to a type of computing node close to data sources or users, which can process data locally, thereby reducing latency and bandwidth usage.

[0040] Exemplarily, the scheduling server obtains data processing tasks from the edge processing node. The data processing tasks may originate from user requests or equipment monitoring processes. The scheduling server obtains task requirement information corresponding to each data processing task.

[0041] Step S204, obtaining node status information of each processing node in the computing power network;

[0042] Among them, the node status information includes CPU status, memory load status and network delay information; processing nodes include central processing nodes and edge processing nodes. CPU status can refer to the load or usage of the processor, indicating the computing power available to the node. Memory load status can refer to the current usage of memory resources of the node, including used memory and available memory. Network delay information can refer to the delay in communication between nodes, affecting the speed and performance of data transmission.

[0043] Exemplarily, the scheduling server obtains the status information of all processing nodes (including central nodes and edge nodes) in the computing power network in real time; the CPU status, memory load status and network delay information can be obtained through monitoring tools or self-reporting mechanisms.

[0044] Step S206, based on the node status information and the task requirement information carried by the data processing task, with the goal of maximizing the resource utilization of each processing node in the computing power network, determine the target processing node corresponding to each data processing task.

[0045] The resource utilization rate may refer to the usage of resources (such as CPU, memory, and bandwidth) when a processing node executes a task. The target processing node may refer to the processing node that is most suitable for executing a specific data processing task. When selecting a target processing node, the current state of the node and the task requirements need to be considered to improve processing efficiency.

[0046] Exemplarily, the scheduling server determines the target processing node that is most suitable for executing each data processing task based on the task requirement information carried by the data processing task and the node status information of each node, and then uses an algorithm (such as a load balancing algorithm) to maximize the resource utilization of each processing node in the computing power network.

[0047] Step S208, based on the data volume information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay, determine the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node.

[0048] Among them, maximizing transmission bandwidth can refer to optimizing the data transmission path to make the best use of the network's bandwidth capacity, thereby speeding up data transmission; minimizing transmission delay can refer to optimizing the data transmission process to reduce the time delay from the source node to the target node and improve the system response speed; the target transmission path can refer to the actual network path for data transmission from the source node to the target processing node, that is, the network devices and nodes through which the data packet passes. The target transmission protocol can refer to the communication protocol used in the data transmission process, such as TCP / IP, UDP, QUIC, RDMA and other protocols.

[0049] Exemplarily, after determining the target processing node, the scheduling server determines the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node based on the data size information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay, so as to ensure the maximization of the transmission bandwidth and the minimization of the transmission delay.

[0050] Step S210, transmitting the data processing task to the target processing node via the target transmission path and the target transmission protocol.

[0051] Exemplarily, the scheduling server transmits the data processing task and related data to the target processing node via the target transmission path and the target transmission protocol.

[0052] In the above task processing method, by real-time acquisition and analysis of node status information (such as CPU status, memory load and network delay), this method can effectively select the most suitable processing node to process data processing tasks and realize flexible allocation of data processing tasks; and determine the specific transmission path and protocol according to the data volume, computational complexity and delay requirements, so that the data transmission process is smoother and the processing time is effectively reduced, thereby improving the overall task execution efficiency and improving the resource utilization and response speed of the computing network.

[0053] This task processing method effectively improves the efficiency and reliability of data processing in the computing network through intelligent scheduling, dynamic monitoring, load balancing and optimized transmission paths, reduces latency and task completion time, and maximizes resource utilization.

[0054] In an exemplary embodiment, the above-mentioned step S206 may specifically include: inputting the CPU status, memory load status, network delay information, computational complexity and latency requirements into a pre-trained task scheduling model, with the goal of maximizing the resource utilization of each processing node in the computing power network, and obtaining the target processing node corresponding to each data processing task.

[0055] Among them, the pre-trained task scheduling model can refer to an algorithm model based on machine learning or reinforcement learning, which aims to intelligently allocate data processing tasks to appropriate processing nodes based on input information, namely CPU status, memory load status, network delay information, computational complexity and latency requirements. The model can be trained through historical data to achieve the task scheduling goal of maximizing resource utilization of each processing node in the computing power network.

[0056] Exemplarily, the scheduling server inputs the CPU status, memory load status, network delay information, computational complexity, and latency requirements into a pre-trained task scheduling model, with the goal of maximizing the resource utilization of each processing node in the computing network, and obtains the target processing node corresponding to each data processing task.

[0057] In this embodiment, by inputting node status information and task requirement information into a trained task scheduling model, the target processing node is obtained, and the resource utilization in the computing power network is maximized, thereby improving the execution efficiency of data processing tasks and the overall performance of the computing power network.

[0058] In an exemplary embodiment, the above-mentioned step S208 may specifically include: inputting the data volume information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, into a pre-trained transmission path and protocol selection model, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay, to obtain the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node.

[0059] Among them, the pre-trained transmission path and protocol selection model can be a model based on machine learning or optimization algorithm, which is used to predict the best transmission path and transmission protocol, that is, to maximize the transmission bandwidth and minimize the transmission delay.

[0060] Exemplarily, the scheduling server inputs the data size information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, into a pre-trained transmission path and protocol selection model, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay, to obtain the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node.

[0061] In this embodiment, the optimal target transmission path and target transmission protocol are selected through a pre-trained transmission path and protocol selection model to maximize the transmission bandwidth and minimize the transmission delay. This can optimize the data transmission path between different nodes, reduce network delays, improve bandwidth utilization, and reduce data transmission bottlenecks.

[0062] In an exemplary embodiment, the transmission protocol includes at least one of the QUIC protocol and the RDMA protocol. Among them, the QUIC (Quick UDP Internet Connections) protocol may refer to the fast UDP Internet connection protocol, which is a protocol used to reduce latency and increase transmission speed. The RDMA (Remote Direct Memory Access) protocol may refer to the remote direct memory access protocol, which allows data to be transmitted directly from memory to memory, reducing latency. In this embodiment, lower latency, higher bandwidth and throughput, enhanced reliability, and better dynamic adaptability can be achieved by introducing improved transmission protocols (such as the QUIC protocol, the RDMA protocol, etc.).

[0063] In an exemplary embodiment, the data corresponding to the data processing task includes at least one of sensor data and video stream. Among them, the sensor data can be information collected from various sensors (such as temperature sensors, humidity sensors, acceleration sensors, gas sensors, etc.). For example, it is commonly used in environmental monitoring, smart homes, industrial automation, agricultural monitoring and other fields of Internet of Things (IoT) devices. Sensor data may include real-time data streams, such as temperature, humidity, light, pressure, etc.; it can also be data generated by various sensors on autonomous driving vehicles. Video streams can refer to dynamic visual information transmitted by a series of continuous image frames, such as video streams generated in scenarios such as security monitoring, traffic monitoring, video conferencing systems, streaming media services, and video stream data collected by cameras on autonomous driving vehicles.

[0064] In an exemplary embodiment, the task requirement information also includes a priority; as shown in FIG2 , transmitting the data processing task to the target processing node through the target transmission path and the target transmission protocol may specifically include:

[0065] Step S302, obtaining the priority corresponding to the data processing task.

[0066] Step S304: according to the priority corresponding to the data processing task, the data processing task is transmitted to the target processing node through the target transmission path and the target transmission protocol.

[0067] Among them, the task requirement information may also include priority; the priority may refer to a priority pre-set for the data processing task type, for example, sensor data is a first priority task, and video stream is a second priority task, or for another example, sensor data is a first priority task, and video stream is a second priority task, and the first priority is higher than the second priority.

[0068] Exemplarily, the scheduling server obtains the priority corresponding to the data processing task, and transmits the data processing task to the target processing node through the target transmission path and the target transmission protocol according to the priority corresponding to the data processing task.

[0069] In this embodiment, by setting different data processing task priorities, it can be ensured that important tasks are processed first. The scheduling server makes decisions based on the priority of the task when transmitting data, thereby optimizing the use of transmission resources and ensuring that high-priority tasks can reach the target processing node faster.

[0070] Technical problems in the application of existing technologies in computing power networks include: data transmission delay and insufficient bandwidth utilization, insufficient flexibility and efficiency in computing task scheduling, lack of cross-level collaborative optimization mechanism, and insufficient coordination between edge computing nodes and central nodes.

[0071] In order to solve the technical problems existing in the application of the prior art in the computing power network, in an exemplary embodiment, a scheduling server is provided, which can be composed of the following four modules: Data generation module: responsible for task generation, data input and processing. Transmission optimization module: responsible for efficient data transmission, including transmission protocol optimization and data flow control. Computing scheduling module: intelligently allocates computing tasks, balances the load of each computing node, and considers network conditions and node resources. Task execution and feedback module: executes scheduling tasks, and returns the calculation results to the source end or downstream node.

[0072] Based on the above-mentioned scheduling server, a task processing method can be implemented in the computing network, wherein the computing network includes edge nodes and central nodes. Edge nodes: close to the data source, performing simple or real-time-required tasks. Central nodes: strong computing power, suitable for processing complex computing tasks. Scheduling server: global control, responsible for monitoring network status, node load, optimizing task scheduling and transmission path; the scheduling server can be any node in the computing network or an additional server. The above-mentioned task processing method can specifically include:

[0073] Step S1, the scheduling server obtains input data and generates data processing tasks. The input data can be generated by a terminal device or an edge node. The data may come from various real-time application scenarios, such as sensor data in autonomous driving, video streams collected by edge devices, etc. Each task is accompanied by parameters such as data size and latency requirements; specific scenarios may include: the camera on the autonomous driving vehicle continuously generates video stream data, which is passed to the edge node or central computing node as input data.

[0074] Step S2, computing task scheduling. The scheduling server monitors the computing resource status (node ​​status information) of the network and each node in real time, and assigns tasks to appropriate nodes (edge ​​or center) through an intelligent scheduling algorithm. Scheduling decisions take into account the computational complexity of the task, latency requirements, load of each node, and network conditions. Among them, the node load can be predicted by an algorithm based on reinforcement learning or deep learning, and the appropriate node can be selected based on task requirements and network latency. Specific scenarios may include: the scheduling server assigns computationally complex video processing tasks to the cloud computing center, while real-time simple analysis tasks are assigned to edge nodes. The intelligent scheduling algorithm used in this embodiment can be a dynamic task scheduling algorithm based on reinforcement learning, which dynamically adjusts task allocation by continuously learning the node load and task execution effect. The algorithm steps are as follows:

[0075] Input: task requirements (computational complexity, latency requirements), node status (CPU, memory load, network latency, etc.).

[0076] Output: The optimal node allocation strategy.

[0077] Training process: By simulating the execution of tasks in the environment, the scheduling strategy is continuously adjusted to maximize resource utilization.

[0078] Step S3, transmission path selection. After the data processing task is generated, the transmission optimization module calculates the best transmission path and protocol selection based on the real-time network status and the optimization algorithm of the transmission protocol (such as QUIC, RDMA) to maximize the transmission bandwidth and minimize the transmission delay. For example, an optimization algorithm (such as an AI algorithm based on path prediction) can be used to select the transmission path. Specific scenarios may include: the vehicle transmits the video stream to a nearby edge node, and transmits it to the cloud server through the optimized transmission protocol and path to ensure low latency.

[0079] Step S4, transmission task. Data processing tasks are transmitted between different nodes through optimized transmission paths. Fast data transmission between memories is achieved through RDMA technology, reducing the delay of system calls during transmission. For example, QUIC or RDMA protocol is used to send and receive data packets, and flow control is performed according to the network status. Specific scenarios may include: edge nodes transmit video streams to central servers through QUIC protocol for calculation of deep learning models.

[0080] After step S4, after the task is assigned to the appropriate node, the computing task is executed on the node and the result is generated. After the edge node completes the real-time processing task, the result is fed back to the terminal or downstream node; the result of complex calculation is fed back by the central node. Specific scenarios may include: after the edge node completes the preliminary video processing, the analysis result is returned to the vehicle terminal to help the vehicle make real-time decisions; the complex video processing results are processed by the central node and fed back to the system.

[0081] The timing of this embodiment may include: Data generation phase: the terminal device or edge node collects real-time data and triggers computing tasks. Computing task scheduling phase: the scheduling server monitors the network status and node resources in real time and dynamically allocates tasks. Transmission path selection phase: the scheduling server selects the optimal transmission path and performs data transmission, and the data begins to be transmitted to the processing node. Task execution and feedback phase: the computing node executes the task, the edge or central node generates the result and returns it to the requester. For example, the terminal device generates data → edge node → scheduling server determines the transmission path → data transmission → central node / edge node → executes the task → result feedback.

[0082] The above-mentioned embodiment introduces an end-to-end collaborative optimization mechanism for data transmission and computing scheduling. By combining an improved transmission protocol (such as optimization based on QUIC or RDMA) and an intelligent dynamic computing task scheduling algorithm, it breaks the limitation of independent optimization of transmission and computing in the prior art. This solution can adjust the data transmission path and computing task allocation in real time according to the network status, node load and task requirements, realize efficient collaboration in a cross-regional and multi-node environment, and significantly improve the resource utilization of the computing network and the overall system performance. Through the collaborative mechanism of data transmission optimization and computing task scheduling, the following technical effects are achieved:

[0083] 1) Improve data transmission efficiency: By introducing improved transmission protocols (such as QUIC, RDMA) and intelligent transmission path selection algorithms, the data transmission path between different nodes is optimized, network latency is reduced, and bandwidth utilization is improved, especially in cross-regional and complex network environments, significantly reducing data transmission bottlenecks.

[0084] 2) Improve resource utilization of computing tasks: Through intelligent dynamic scheduling algorithms, tasks are assigned to the most suitable computing nodes (edge ​​nodes or central nodes) in real time according to task requirements, node loads, and network status, achieving efficient utilization of computing resources and avoiding resource waste and delays in task execution.

[0085] 3) Achieve end-to-end collaborative optimization: Breaking the traditional mode of independent optimization of data transmission and computing scheduling, an end-to-end collaborative optimization mechanism is established, which can dynamically adjust between data transmission and computing task allocation, maximize the performance of the entire system, and adapt to the needs of large-scale, heterogeneous computing power networks.

[0086] 4) Reduce system response time: Through the collaborative working mechanism of edge computing and central computing nodes, tasks with high real-time requirements are assigned to edge nodes first, while complex tasks are transmitted to central nodes for processing, reducing the overall delay of computing tasks and improving the response speed of the system.

[0087] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0088] Based on the same inventive concept, the embodiment of the present application also provides a task processing device for implementing the task processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more task processing device embodiments provided below can refer to the limitations on the task processing method above, and will not be repeated here.

[0089] In an exemplary embodiment, as shown in FIG3 , a task processing device 900 is provided, comprising: a task acquisition module 901, a node status acquisition module 902, a processing node determination module 903, a transmission mode determination module 904 and a task transmission module 905, wherein:

[0090] The task acquisition module 901 is used to acquire data processing tasks from edge processing nodes and acquire task requirement information corresponding to each data processing task; the task requirement information includes data volume information, computational complexity and latency requirements.

[0091] The node status acquisition module 902 is used to obtain the node status information of each processing node in the computing power network, and the node status information includes CPU status, memory load status and network delay information; the processing nodes include central processing nodes and edge processing nodes.

[0092] The processing node determination module 903 is used to determine the target processing node corresponding to each data processing task based on the node status information and the task requirement information carried by the data processing task, with the goal of maximizing the resource utilization of each processing node in the computing power network.

[0093] The transmission mode determination module 904 is used to determine the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node based on the data size information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay.

[0094] The task transmission module 905 is used to transmit the data processing task to the target processing node through the target transmission path and the target transmission protocol.

[0095] In an exemplary embodiment, the processing node determination module 903 is also used to input the CPU status, the memory load status, the network delay information, the computational complexity and the delay requirement into a pre-trained task scheduling model, with the goal of maximizing the resource utilization of each processing node in the computing power network, to obtain the target processing node corresponding to each data processing task.

[0096] In an exemplary embodiment, the transmission mode determination module 904 is also used to input the data volume information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, into a pre-trained transmission path and protocol selection model, so as to maximize the transmission bandwidth and minimize the transmission delay, and obtain the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node.

[0097] In an exemplary embodiment, the task requirement information also includes a priority; the above-mentioned task transmission module 905 is also used to obtain the priority corresponding to the data processing task; according to the priority corresponding to the data processing task, the data processing task is transmitted to the target processing node through the target transmission path and the target transmission protocol.

[0098] Each module in the above task processing device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0099] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG4. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a task processing method is implemented.

[0100] Those skilled in the art will understand that the structure shown in FIG. 4 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0101] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0102] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0103] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0104] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0106] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A task processing method, characterized in that: A scheduling server applied to a computing power network; the method comprises: Obtain data processing tasks from edge processing nodes, and obtain task requirement information corresponding to each of the data processing tasks; the task requirement information includes data volume information, computational complexity, and latency requirements; Obtain node status information of each processing node in the computing power network, wherein the node status information includes CPU status, memory load status and network delay information; the processing nodes include central processing nodes and edge processing nodes; According to the node status information and the task requirement information carried by the data processing task, with the goal of maximizing the resource utilization of each processing node in the computing power network, determining the target processing node corresponding to each data processing task; According to the data volume information and delay requirements carried by the data processing tasks, as well as the network status and transmission protocol of the target processing node, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay, determine the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node; The data processing task is transmitted to the target processing node through the target transmission path and the target transmission protocol.

2. The method according to claim 1, characterized in that The determining of the target processing node corresponding to each data processing task based on the node status information and the task requirement information carried by the data processing task with the goal of maximizing the resource utilization of each processing node in the computing power network includes: The CPU state, the memory load state, the network delay information, the computational complexity and the delay requirement are input into a pre-trained task scheduling model, with the goal of maximizing the resource utilization of each processing node in the computing power network, to obtain the target processing node corresponding to each data processing task.

3. The method according to claim 1, characterized in that The method of determining a target transmission path and a target transmission protocol from each data processing task to the corresponding target processing node with the goal of maximizing transmission bandwidth and minimizing transmission delay according to the data volume information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, includes: The data volume information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, are input into a pre-trained transmission path and protocol selection model, with the goal of maximizing transmission bandwidth and minimizing transmission delay, to obtain the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node.

4. The method according to claim 1, characterized in that: The transmission protocol includes at least one of the QUIC protocol and the RDMA protocol.

5. The method according to claim 1, characterized in that The data corresponding to the data processing task includes at least one of sensor data and video stream.

6. The method according to claim 1, characterized in that The task requirement information also includes a priority; The transmitting the data processing task to the target processing node through the target transmission path and the target transmission protocol includes: Obtaining the priority corresponding to the data processing task; According to the priority corresponding to the data processing task, the data processing task is transmitted to the target processing node through the target transmission path and the target transmission protocol.

7. A task processing device, characterized in that: The device comprises: A task acquisition module is used to acquire data processing tasks from edge processing nodes and acquire task requirement information corresponding to each of the data processing tasks; the task requirement information includes data volume information, computational complexity and latency requirements; A node status acquisition module is used to acquire node status information of each processing node in the computing network, wherein the node status information includes CPU status, memory load status and network delay information; the processing nodes include central processing nodes and edge processing nodes; A processing node determination module is used to determine the target processing node corresponding to each data processing task based on the node status information and the task requirement information carried by the data processing task, with the goal of maximizing the resource utilization of each processing node in the computing power network; A transmission mode determination module is used to determine the target transmission path and target transmission protocol from each data processing task to the corresponding target processing node based on the data volume information and delay requirements carried by the data processing task, as well as the network status and transmission protocol of the target processing node, with the goal of maximizing the transmission bandwidth and minimizing the transmission delay; The task transmission module is used to transmit the data processing task to the target processing node through the target transmission path and the target transmission protocol.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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