Edge node streaming computing method and device and electronic equipment

Abstract

The invention provides an edge node streaming computing method and device and electronic equipment, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining a streaming computing starting request; distributing corresponding edge node data to an edge computing cluster through the task configuration management module according to the streaming computing starting request; and distributing the edge node data in the edge computing cluster to an edge computing sub-cluster through the task streaming computing module to complete computing. According to the method, the calculation efficiency is improved, the resource consumption is reduced, and powerful support is provided for development of the edge calculation field.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to an edge node stream computing method, device and electronic device. Background Art

[0002] With the rapid development of information technology, especially the widespread application of the Internet of Things (IoT), 5G communications, and artificial intelligence (AI) technologies, modern society is gradually moving towards an intelligent era. In this context, edge computing, as a distributed computing architecture, has gained increasing attention because it can effectively reduce data transmission delays, alleviate computing pressure on the central cloud, and protect user privacy. Edge computing pushes computing and data storage tasks from the central cloud to the edge of the network, that is, to devices or nodes close to the data source or user terminal, thereby achieving real-time processing and analysis of massive data.

[0003] However, traditional streaming computing platforms, methods, and frameworks are mainly designed for central cloud environments, which usually have large-scale clusters, sufficient underlying resources, and professional operation and maintenance team support, and can efficiently process PB-level or even EB-level data streams. They significantly improve the efficiency and throughput of data processing through technical means such as data segmentation and parallel computing. But in edge computing scenarios, the situation is completely different. Edge nodes are often resource-constrained, including computing power, storage space, and network connection bandwidth, and are widely distributed and highly heterogeneous, lacking the centralized management and rich resources of the central cloud. Therefore, directly migrating the existing central cloud streaming computing platform and architecture to the edge faces huge challenges.

[0004] With the popularization and accelerated replacement of smart devices, more and more data are generated at the edge, such as smart home devices, smart wearable devices, self-driving vehicles, smart street lights, etc. These data contain rich value and are essential for making instant decisions, optimizing user experience, and improving system efficiency. For example, in intelligent transportation systems, real-time data exchange between vehicles and road infrastructure is essential for achieving vehicle-road collaboration and improving traffic flow and safety; in the field of industrial automation, real-time data analysis at the edge can quickly adjust production line parameters, reduce downtime, and improve production efficiency.

[0005] Therefore, an edge node stream computing method, device and electronic device are proposed. Summary of the invention

[0006] This specification provides an edge node streaming computing method, device, and electronic device, which improves computing efficiency and reduces resource consumption, providing strong support for the development of the edge computing field.

[0007] This specification provides an edge node stream computing method, including: a task configuration management module and an edge multi-task stream computing module; Get the streaming computing start request; Allocate the corresponding edge node data to the edge computing cluster according to the streaming computing startup request through the task configuration management module; The task stream computing module distributes the edge node data in the edge computing cluster to the edge computing sub-cluster to complete the computing.

[0008] Optionally, allocating corresponding edge node data to an edge computing cluster according to the streaming computing startup request through the task configuration management module includes: The streaming computing request includes a splitting request and a computing request; Splitting the edge node data according to the splitting request through the task configuration management module to obtain a plurality of edge node sub-data; The task configuration management module distributes a plurality of edge node sub-data to an edge computing cluster according to the computing request.

[0009] Optionally, before the task configuration management module divides the edge node data according to the division request to obtain a plurality of edge node sub-data, the method includes: Determine, by the task configuration management module, whether the edge node data meets a preset data size value; When the task configuration management module determines that the edge node data meets the preset data size value, the task configuration management module directly distributes the corresponding edge node data to the edge computing cluster according to the computing request.

[0010] Optionally, the allocating the edge node data in the edge computing cluster to an edge computing sub-cluster to complete the computing by the task stream computing module includes: The resource situation of the edge computing sub-cluster that is recovered and exited by the task stream computing module is updated with the CPU weight sum of all the edge computing sub-clusters, and the bandwidth weight sum of the edge node sub-data is obtained; Allocating the edge node sub-data to the corresponding edge computing sub-cluster based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data; Allocate CPU and bandwidth weights for the edge node sub-data according to the resource situation of the allocated edge computing sub-cluster through the task stream computing module; The edge computing sub-cluster completes the calculation of the edge node sub-data based on the allocated CPU and bandwidth weight.

[0011] Optionally, after the task configuration management module divides the edge node data according to the division request to obtain a plurality of edge node sub-data, the method further includes: Calculating similarity of the edge node sub-data one by one; The edge node sub-data whose similarity exceeds the clustering threshold are clustered.

[0012] Optionally, after allocating the edge node sub-data to the corresponding edge computing sub-cluster based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data, the method further comprises: The edge node sub-data of the same type as the current edge node sub-data are allocated to the edge computing sub-cluster corresponding to the current edge node sub-data.

[0013] Optionally, the allocating the edge node sub-data of the same cluster as the current edge node sub-data to the edge computing sub-cluster corresponding to the current edge node sub-data further includes: Determine whether the resource situation of the edge computing sub-cluster corresponding to the current edge node sub-data satisfies all the edge node sub-data of the same cluster as the current edge node sub-data; If it cannot be satisfied, the edge node sub-data that exceeds the resource situation of the edge computing sub-cluster corresponding to the current edge node sub-data will be returned based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data, and the edge node sub-data will be allocated to the corresponding edge computing sub-cluster.

[0014] This specification provides an edge node stream computing device, including: The task configuration management module is used to obtain a streaming computing startup request and distribute the corresponding edge node data to the edge computing cluster according to the streaming computing startup request; The task stream computing module is used to distribute the edge node data in the edge computing cluster to the edge computing sub-cluster to complete the computing.

[0015] Optionally, the task configuration management module includes: The streaming computing request includes a splitting request and a computing request; Splitting the edge node data according to the splitting request through the task configuration management module to obtain a plurality of edge node sub-data; The task configuration management module distributes a plurality of edge node sub-data to an edge computing cluster according to the computing request.

[0016] Optionally, before the task configuration management module divides the edge node data according to the division request to obtain a plurality of edge node sub-data, the method includes: Determine, by the task configuration management module, whether the edge node data meets a preset data size value; When the task configuration management module determines that the edge node data meets the preset data size value, the task configuration management module directly distributes the corresponding edge node data to the edge computing cluster according to the computing request.

[0017] Optionally, the task stream computing module includes: The resource situation of the edge computing sub-cluster that is recovered and exited by the task stream computing module is updated with the CPU weight sum of all the edge computing sub-clusters, and the bandwidth weight sum of the edge node sub-data is obtained; Allocating the edge node sub-data to the corresponding edge computing sub-cluster based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data; Allocate CPU and bandwidth weights for the edge node sub-data according to the resource situation of the allocated edge computing sub-cluster through the task stream computing module; The edge computing sub-cluster completes the calculation of the edge node sub-data based on the allocated CPU and bandwidth weight.

[0018] Optionally, after the task configuration management module divides the edge node data according to the division request to obtain a plurality of edge node sub-data, the method further includes: Calculating similarity of the edge node sub-data one by one; The edge node sub-data whose similarity exceeds the clustering threshold are clustered.

[0019] Optionally, after allocating the edge node sub-data to the corresponding edge computing sub-cluster based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data, the method further comprises: The edge node sub-data of the same type as the current edge node sub-data are allocated to the edge computing sub-cluster corresponding to the current edge node sub-data.

[0020] Optionally, the allocating the edge node sub-data of the same cluster as the current edge node sub-data to the edge computing sub-cluster corresponding to the current edge node sub-data further includes: Determine whether the resource situation of the edge computing sub-cluster corresponding to the current edge node sub-data satisfies all the edge node sub-data of the same cluster as the current edge node sub-data; If it cannot be satisfied, the edge node sub-data that exceeds the resource situation of the edge computing sub-cluster corresponding to the current edge node sub-data will be returned based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data, and the edge node sub-data will be allocated to the corresponding edge computing sub-cluster.

[0021] This specification also provides an electronic device, wherein the electronic device includes: processor; and, A memory storing computer executable instructions, which when executed cause the processor to perform any of the above methods.

[0022] The present specification also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, any of the above methods is implemented.

[0023] In the present invention, through the collaborative work of the task configuration management module and the edge multi-task streaming computing module, it is possible to quickly respond to streaming computing startup requests, and to segment and distribute edge node data according to actual needs. It can not only perform clustering according to the similarity of the data to optimize the execution efficiency of the computing tasks, but also dynamically evaluate and adjust the resource situation of the edge computing sub-cluster to ensure that each computing task can obtain sufficient resource support. In addition, the method also has the ability to judge whether the resources meet the needs, and can automatically adjust the allocation strategy when the resources are insufficient, thereby avoiding delays or failures of computing tasks. This streaming computing method not only improves computing efficiency, but also reduces resource consumption, providing strong support for the development of the edge computing field. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments 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 drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A schematic diagram of the principle of an edge node stream computing method provided in an embodiment of this specification; Figure 2 A schematic diagram of the structure of an edge node stream computing device provided in an embodiment of this specification; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification; Figure 4A schematic diagram of a computer-readable medium provided in accordance with an embodiment of the present specification. DETAILED DESCRIPTION

[0026] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0027] The following is combined with Figure 1-4 The exemplary embodiments of the present invention are described more fully. However, the exemplary embodiments can be implemented in various forms, and it should not be understood that the present invention is limited to the embodiments set forth herein. On the contrary, providing these exemplary embodiments can make the present invention more comprehensive and complete, and it is more convenient to fully convey the inventive concept to those skilled in the art. The same reference numerals in the figures represent the same or similar elements, components or parts, and thus their repeated description will be omitted.

[0028] Under the premise of being consistent with the technical concept of the present invention, the features, structures, characteristics or other details described in a specific embodiment do not exclude that they can be combined in one or more other embodiments in a suitable manner.

[0029] In the description of specific embodiments, the features, structures, characteristics or other details described in the present invention are intended to enable those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can practice the technical solutions of the present invention without one or more of the specific features, structures, characteristics or other details.

[0030] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0031] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0032] The term "and / or" or "and / or" includes all combinations of any one or more of the associated listed items.

[0033] Figure 1A schematic diagram of the principle of an edge node stream computing method provided in an embodiment of this specification, the method may include: Task configuration management module and edge multi-task streaming computing module; S110: Obtain a streaming computing start request; In the specific implementation of this specification, the client usually starts the streaming computing task by sending an HTTP request or other network protocol request. The client can specify the relevant parameters of the streaming computing task in the request, such as data source, computing logic, output target, etc. After receiving the request from the client, the server will parse the parameters in the request and configure the streaming computing task according to these parameters. The server will also check the validity of the request, such as verifying the user identity and checking whether the parameters are legal.

[0034] S120: Allocating corresponding edge node data to an edge computing cluster according to the streaming computing startup request through the task configuration management module; Optionally, the S120 includes: The streaming computing request includes a splitting request and a computing request; Splitting the edge node data according to the splitting request through the task configuration management module to obtain a plurality of edge node sub-data; The task configuration management module distributes a plurality of edge node sub-data to an edge computing cluster according to the computing request.

[0035] In a specific implementation of this specification, the parameters in the split request are parsed to ensure the validity and correctness of the parameters. According to the parsed parameters, the data stored in the edge node is split to obtain a number of edge node sub-data. The split edge node sub-data is stored in the corresponding position and marked so that subsequent computing requests can be correctly identified and accessed. Through the detailed processing of split requests and computing requests, and the effective management of the task configuration management module, the flexible configuration and efficient execution of streaming computing tasks can be achieved.

[0036] Optionally, before the task configuration management module divides the edge node data according to the division request to obtain a plurality of edge node sub-data, the method includes: Determine, by the task configuration management module, whether the edge node data meets a preset data size value; When the task configuration management module determines that the edge node data meets the preset data size value, the task configuration management module directly distributes the corresponding edge node data to the edge computing cluster according to the computing request.

[0037] In the specific implementation of this specification, the preset data size value is a threshold value used to determine whether the data on the edge node is large enough to be split or directly calculated. This value can be set according to various factors such as system performance, availability of computing resources, and data processing requirements.

[0038] The task configuration management module collects data on edge nodes, including data obtained from sensors, databases, or other data sources, calculates the size of the collected data, and compares it with the preset data size value. If the data size is less than or equal to the preset value, the task configuration management module will decide to perform the calculation directly; if the data size is greater than the preset value, it may be necessary to execute a split request to split the data into smaller parts for more efficient processing.

[0039] S130: Allocate the edge node data in the edge computing cluster to the edge computing sub-cluster through the task stream computing module to complete the computing.

[0040] Optionally, the S130 includes: The resource situation of the edge computing sub-cluster that is recovered and exited by the task stream computing module is updated with the CPU weight sum of all the edge computing sub-clusters, and the bandwidth weight sum of the edge node sub-data is obtained; Allocating the edge node sub-data to the corresponding edge computing sub-cluster based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data; Allocate CPU and bandwidth weights for the edge node sub-data according to the resource situation of the allocated edge computing sub-cluster through the task stream computing module; The edge computing sub-cluster completes the calculation of the edge node sub-data based on the allocated CPU and bandwidth weight.

[0041] In a specific implementation of this specification, when a node or task in an edge computing subcluster exits, the task stream computing module will be responsible for recovering the resources occupied by the node or task, including CPU, memory, storage, and network bandwidth. The task stream computing module calculates the current CPU weight sum of all edge computing subclusters, which is usually obtained by adding the CPU weights of all nodes in each subcluster. If a node in a subcluster exits and recycles resources, the CPU weight sum of the subcluster will be reduced accordingly. At the same time, the task stream computing module also obtains the bandwidth weight sum of all edge node sub-data, which reflects the current demand for network bandwidth for all pending data. The task stream computing module considers multiple factors to allocate edge node sub-data to the corresponding edge computing subcluster, including the current load of the subcluster, the urgency of the data, the priority of the computing task, etc. After the data is allocated to the corresponding edge computing subcluster, the task stream computing module will allocate the corresponding CPU and bandwidth weights to it according to the resource situation of the subcluster and the bandwidth requirements of the data, which ensures that each data block can obtain sufficient computing resources and network resources to complete its computing task. If it is found during the actual computing process that a subcluster has insufficient resources or the computing requirements of a data block have changed, the task stream computing module can also dynamically adjust the allocation of resources, including increasing or decreasing CPU weight, adjusting bandwidth allocation, etc.

[0042] Optionally, after the task configuration management module divides the edge node data according to the division request to obtain a plurality of edge node sub-data, the method further includes: Calculating similarity of the edge node sub-data one by one; The edge node sub-data whose similarity exceeds the clustering threshold are clustered.

[0043] Optionally, after allocating the edge node sub-data to the corresponding edge computing sub-cluster based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data, the method further comprises: The edge node sub-data of the same type as the current edge node sub-data are allocated to the edge computing sub-cluster corresponding to the current edge node sub-data.

[0044] In the specific implementation of this specification, similarity calculation methods include cosine similarity, Euclidean distance, Manhattan distance, Jaccard similarity coefficient, etc. After resource allocation is completed, the system may adjust resources according to the actual load and computing requirements of the sub-cluster, including increasing or decreasing the computing resources of the sub-cluster, adjusting the network bandwidth, etc., to ensure the smooth completion of the computing task.

[0045] During the calculation process, the system will continuously monitor the calculation progress and resource usage of the sub-cluster. If any abnormality or insufficient resources are found, the system will make timely adjustments or issue warnings. At the same time, the system will also collect feedback data during the calculation process in order to optimize and improve subsequent calculation tasks.

[0046] Optionally, the allocating the edge node sub-data of the same cluster as the current edge node sub-data to the edge computing sub-cluster corresponding to the current edge node sub-data further includes: Determine whether the resource situation of the edge computing sub-cluster corresponding to the current edge node sub-data satisfies all the edge node sub-data of the same cluster as the current edge node sub-data; If it cannot be satisfied, the edge node sub-data that exceeds the resource situation of the edge computing sub-cluster corresponding to the current edge node sub-data will be returned based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data, and the edge node sub-data will be allocated to the corresponding edge computing sub-cluster.

[0047] In the specific implementation of this specification, the total resource requirements of all edge node sub-data in the same cluster are compared with the resource situation of the current edge computing sub-cluster. If the resources of the sub-cluster can meet the requirements of all sub-data, the subsequent computing tasks can be continued; if not, resources need to be adjusted or reallocated.

[0048] In the present invention, through the collaborative work of the task configuration management module and the edge multi-task streaming computing module, it is possible to quickly respond to streaming computing startup requests, and to segment and distribute edge node data according to actual needs. It can not only perform clustering according to the similarity of the data to optimize the execution efficiency of the computing tasks, but also dynamically evaluate and adjust the resource situation of the edge computing sub-cluster to ensure that each computing task can obtain sufficient resource support. In addition, the method also has the ability to judge whether the resources meet the needs, and can automatically adjust the allocation strategy when the resources are insufficient, thereby avoiding delays or failures of computing tasks. This streaming computing method not only improves computing efficiency, but also reduces resource consumption, providing strong support for the development of the edge computing field.

[0049] Figure 2 A schematic diagram of a principle of an edge node stream computing device provided in an embodiment of this specification, the device may include: The task configuration management module 10 is used to obtain a streaming computing startup request and distribute the corresponding edge node data to the edge computing cluster according to the streaming computing startup request; The task stream computing module 20 is used to distribute the edge node data in the edge computing cluster to the edge computing sub-cluster to complete the computing.

[0050] Optionally, the task configuration management module 10 includes: The streaming computing request includes a splitting request and a computing request; Splitting the edge node data according to the splitting request through the task configuration management module to obtain a plurality of edge node sub-data; The task configuration management module distributes a plurality of edge node sub-data to an edge computing cluster according to the computing request.

[0051] Optionally, before the task configuration management module divides the edge node data according to the division request to obtain a plurality of edge node sub-data, the method includes: Determine, by the task configuration management module, whether the edge node data meets a preset data size value; When the task configuration management module determines that the edge node data meets the preset data size value, the task configuration management module directly distributes the corresponding edge node data to the edge computing cluster according to the computing request.

[0052] Optionally, the task stream computing module 20 includes: The resource situation of the edge computing sub-cluster that is recovered and exited by the task stream computing module is updated with the CPU weight sum of all the edge computing sub-clusters, and the bandwidth weight sum of the edge node sub-data is obtained; Allocating the edge node sub-data to the corresponding edge computing sub-cluster based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data; Allocate CPU and bandwidth weights for the edge node sub-data according to the resource situation of the allocated edge computing sub-cluster through the task stream computing module; The edge computing sub-cluster completes the calculation of the edge node sub-data based on the allocated CPU and bandwidth weight.

[0053] Optionally, after the task configuration management module divides the edge node data according to the division request to obtain a plurality of edge node sub-data, the method further includes: Calculating similarity of the edge node sub-data one by one; The edge node sub-data whose similarity exceeds the clustering threshold are clustered.

[0054] Optionally, after allocating the edge node sub-data to the corresponding edge computing sub-cluster based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data, the method further comprises: The edge node sub-data of the same type as the current edge node sub-data are allocated to the edge computing sub-cluster corresponding to the current edge node sub-data.

[0055] Optionally, the allocating the edge node sub-data of the same cluster as the current edge node sub-data to the edge computing sub-cluster corresponding to the current edge node sub-data further includes: Determine whether the resource situation of the edge computing sub-cluster corresponding to the current edge node sub-data satisfies all the edge node sub-data of the same cluster as the current edge node sub-data; If it cannot be satisfied, the edge node sub-data that exceeds the resource situation of the edge computing sub-cluster corresponding to the current edge node sub-data will be returned based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data, and the edge node sub-data will be allocated to the corresponding edge computing sub-cluster.

[0056] The functions of the device in the embodiment of the present invention have been described in the above method embodiment, so for details not covered in the description of this embodiment, please refer to the relevant description in the above embodiment, and no further description will be given here.

[0057] Based on the same inventive concept, an embodiment of this specification also provides an electronic device.

[0058] The following describes an electronic device embodiment of the present invention, which can be regarded as a specific physical implementation of the method and device embodiments of the present invention. The details described in the electronic device embodiment of the present invention should be regarded as a supplement to the above method or device embodiments; details not disclosed in the electronic device embodiment of the present invention can be implemented with reference to the above method or device embodiments.

[0059] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. Figure 3 The electronic device 300 according to the embodiment of the present invention is described. Figure 3 The electronic device 300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0060] like Figure 3As shown, the electronic device 300 is in the form of a general computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including the storage unit 320 and the processing unit 310), a display unit 340, etc.

[0061] The storage unit stores program codes, which can be executed by the processing unit 310, so that the processing unit 310 performs the steps according to various exemplary embodiments of the present invention described in the above processing method section of this specification. For example, the processing unit 310 can perform the following steps: Figure 1 Steps shown.

[0062] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache memory unit 3202 , and may further include a read-only memory unit (ROM) 3203 .

[0063] The storage unit 320 may also include a program / utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0064] Bus 330 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0065] The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable viewers to interact with the electronic device 300, and / or any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 350. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 360. The network adapter 360 may communicate with other modules of the electronic device 300 through the bus 330. It should be understood that although Figure 3Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0066] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation method of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.) or on a network, including a number of instructions to enable a computing device (which can be a personal computer, server, or network device, etc.) to execute the above method according to the present invention. When the computer program is executed by a data processing device, the computer-readable medium can implement the above method of the present invention, that is: Figure 1 The method shown.

[0067] Figure 4 A schematic diagram of a computer-readable medium provided in accordance with an embodiment of the present specification.

[0068] accomplish Figure 1 The computer program of the method shown can be stored on one or more computer readable media. The computer readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0069] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0070] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the viewer computing device, partially on the viewer device, as a separate software package, partially on the viewer computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the viewer computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0071] In summary, the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that general data processing devices such as microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all functions of some or all components in the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0072] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the present invention is not inherently related to any specific computer, virtual device or electronic device, and various general devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0073] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0074] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. An edge node stream computing method, characterized in that: include: Task configuration management module and edge multi-task streaming computing module; Get the streaming computing start request; Allocate the corresponding edge node data to the edge computing cluster according to the streaming computing startup request through the task configuration management module; The task stream computing module distributes the edge node data in the edge computing cluster to the edge computing sub-cluster to complete the computing.

2. The edge node stream computing method according to claim 1, characterized in that: The step of allocating the corresponding edge node data to the edge computing cluster according to the streaming computing startup request by the task configuration management module includes: The streaming computing request includes a splitting request and a computing request; Splitting the edge node data according to the splitting request through the task configuration management module to obtain a plurality of edge node sub-data; The task configuration management module distributes a plurality of edge node sub-data to an edge computing cluster according to the computing request.

3. The edge node stream computing method according to claim 2, characterized in that: Before the task configuration management module divides the edge node data according to the division request to obtain a plurality of edge node sub-data, the method includes: Determine, by the task configuration management module, whether the edge node data meets a preset data size value; When the task configuration management module determines that the edge node data meets the preset data size value, the task configuration management module directly distributes the corresponding edge node data to the edge computing cluster according to the computing request.

4. The edge node stream computing method according to claim 2, characterized in that: The step of allocating the edge node data in the edge computing cluster to the edge computing sub-cluster to complete the calculation by the task stream computing module includes: The resource situation of the edge computing sub-cluster that is recovered and exited by the task stream computing module is updated with the CPU weight sum of all the edge computing sub-clusters, and the bandwidth weight sum of the edge node sub-data is obtained; Allocating the edge node sub-data to the corresponding edge computing sub-cluster based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data; Allocate CPU and bandwidth weights for the edge node sub-data according to the resource situation of the allocated edge computing sub-cluster through the task stream computing module; The edge computing sub-cluster completes the calculation of the edge node sub-data based on the allocated CPU and bandwidth weight.

5. The edge node stream computing method according to claim 4, characterized in that: After the task configuration management module divides the edge node data according to the division request to obtain a plurality of edge node sub-data, the method includes: Calculating similarity of the edge node sub-data one by one; The edge node sub-data whose similarity exceeds the clustering threshold are clustered.

6. The edge node stream computing method according to claim 5, characterized in that: After allocating the edge node sub-data to the corresponding edge computing sub-cluster based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data, the method includes: The edge node sub-data of the same type as the current edge node sub-data are allocated to the edge computing sub-cluster corresponding to the current edge node sub-data.

7. The edge node stream computing method according to claim 6, characterized in that: The step of allocating the edge node sub-data of the same cluster as the current edge node sub-data to the edge computing sub-cluster corresponding to the current edge node sub-data further includes: Determine whether the resource situation of the edge computing sub-cluster corresponding to the current edge node sub-data satisfies all the edge node sub-data of the same cluster as the current edge node sub-data; If it cannot be satisfied, the edge node sub-data that exceeds the resource situation of the edge computing sub-cluster corresponding to the current edge node sub-data will be returned based on the CPU weight of the edge computing sub-cluster and the bandwidth weight of the edge node sub-data, and the edge node sub-data will be allocated to the corresponding edge computing sub-cluster.

8. An edge node stream computing device, characterized in that: include: The task configuration management module is used to obtain a streaming computing startup request and distribute the corresponding edge node data to the edge computing cluster according to the streaming computing startup request; The task stream computing module is used to distribute the edge node data in the edge computing cluster to the edge computing sub-cluster to complete the computing.

9. An electronic device, wherein: The electronic device includes: processor; and, A memory storing computer executable instructions which, when executed, cause the processor to perform a method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method of any one of claims 1 to 7 is implemented.

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