Workflow execution method and device based on distributed cluster, equipment and medium

Abstract

The invention relates to a workflow execution method and device based on a distributed cluster, equipment and a storage medium, and the method comprises the steps: obtaining a service attribute and an expected resource occupancy rate corresponding to each workflow, the expected resource occupancy rate representing a ratio of expected occupation of server cluster resources when a server cluster executes the workflow, and the expected resource occupancy rate representing the ratio of the server cluster resources when the server cluster executes the workflow; and determining a target server cluster corresponding to execution of each workflow according to the service attribute and the expected resource occupancy rate, and distributing each workflow to the target server cluster, so that the target server cluster loads configuration information corresponding to the workflow and executes the workflow. According to the method and the device, the corresponding server clusters can be effectively distributed to different workflows, the workflow execution efficiency of the whole system is improved, workflow execution is distributed to a plurality of clusters, and the risk of a single point of failure is reduced.

Description

Technical Field

[0001] The present application relates to the field of computer technologies, and in particular, to a workflow execution method, apparatus, device, and storage medium based on a distributed cluster. Background Art

[0002] A workflow refers to the automation of a partial or entire business process in a computer application environment. When multiple workflows with different attributes run on the same server, since the server resources required for executing different workflows are different and different-attribute workflows share the server, the server may crash due to the influence of a certain workflow, thereby affecting the normal execution of other workflows and resulting in low workflow execution efficiency.

[0003] Therefore, how to provide a technical solution to improve the execution efficiency of workflows has become a technical problem that needs to be urgently solved by those skilled in the art. It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] In view of the above, the present application provides a workflow execution method, apparatus, device, and storage medium based on a distributed cluster, aiming to solve the above technical problems.

[0005] In a first aspect, the present application provides a workflow execution method based on a distributed cluster, the method including:

[0006] Obtaining the service attribute and expected resource occupancy rate corresponding to each workflow, where the expected resource occupancy rate represents the ratio of the resources of the server cluster expected to be occupied when the server cluster executes the workflow;

[0007] Determining the target server cluster corresponding to each workflow according to the service attribute and the expected resource occupancy rate;

[0008] Distributing each workflow to the target server cluster respectively for the target server cluster to load the configuration information corresponding to the workflow and execute the workflow.

[0009] In a second aspect, the present application provides a workflow execution apparatus based on a distributed cluster, the apparatus including:

[0010] An obtaining module: configured to obtain the service attribute and expected resource occupancy rate corresponding to each workflow, where the expected resource occupancy rate represents the ratio of the resources of the server cluster expected to be occupied when the server cluster executes the workflow;

[0011] Determination module: configured to determine a target server cluster corresponding to each of the workflows according to the service attribute and the expected resource occupancy rate;

[0012] Execution module: configured to distribute each of the workflows to the target server cluster respectively, so that the target server cluster loads the configuration information corresponding to the workflow and executes the workflow.

[0013] In a third aspect, the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus;

[0014] The memory is used to store a computer program;

[0015] The processor is configured to implement the workflow execution method based on a distributed cluster according to any one of the embodiments in the first aspect when executing the program stored on the memory.

[0016] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the workflow execution method based on a distributed cluster according to any one of the embodiments in the first aspect.

[0017] The above technical solutions provided by the embodiments of the present application have the following advantages compared with the prior art:

[0018] In the present application, by obtaining the service attribute and the expected resource occupancy rate corresponding to each workflow, where the expected resource occupancy rate represents the ratio of the expected occupancy of the server cluster resources when the server cluster executes the workflow, according to the service attribute and the expected resource occupancy rate, determining the target server cluster corresponding to each workflow, and distributing each workflow to the target server cluster respectively, so that the target server cluster loads the configuration information corresponding to the workflow and executes the workflow, it can effectively allocate corresponding server resources for different workflows, improve the overall system workflow execution efficiency, and distribute the execution of workflows to different clusters, which can reduce the risk of single-point failure. Even if a certain server fails, it will only affect the server cluster to which it belongs, and will not affect the execution of workflows in other server clusters. This fault isolation mechanism improves the stability and availability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0020] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a schematic flowchart of an embodiment of a workflow execution method based on a distributed cluster of the present application;

[0022] Figure 2 It is a schematic diagram of modules of an embodiment of a workflow execution device based on a distributed cluster of the present application;

[0023] Figure 3 It is a schematic diagram of an embodiment of an electronic device of the present application;

[0024] The realization of the purpose of the present application, functional features and advantages will be further described in conjunction with the embodiments with reference to the drawings. Detailed implementation manners

[0025] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0026] The following disclosure provides many different embodiments or examples for implementing different structures of the present application. To simplify the disclosure of the present application, the components and settings of specific examples are described below. Of course, they are only examples and are not intended to limit the present application. In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0027] The present application provides a workflow execution method based on a distributed cluster. Refer to Figure 1 As shown, it is a schematic flowchart of the method of an embodiment of a workflow execution method based on a distributed cluster of the present application. This method can be executed by an electronic device, and the electronic device can be any one of the clusters in a distributed server cluster or any one server, and the electronic device is implemented by software and / or hardware. The workflow execution method based on a distributed cluster includes:

[0028] Step S10: Obtain the business attributes and expected resource occupancy rates corresponding to each workflow, where the expected resource occupancy rate represents the ratio of the resources expected to be occupied by the server cluster when executing the workflow;

[0029] Step S20: Determine the target server cluster corresponding to each workflow execution according to the business attributes and the expected resource occupancy rate;

[0030] Step S30: Distribute each workflow to the target server cluster respectively, so that the target server cluster can load the configuration information corresponding to the workflow and execute the workflow.

[0031] In this embodiment, a workflow refers to the automation of part or the whole business process in a computer application environment. A workflow includes a set of tasks and the sequential relationships between them, as well as the start and end conditions of the process and tasks.

[0032] When workflows of multiple business types run on the same server, since the server resources required to execute different workflows are different, it may cause some workflows with large resource consumption to affect the execution efficiency of other workflows. Therefore, it is necessary to schedule the workflows to different server clusters for execution by combining the business attributes and expected resource occupancy rates of the workflows to ensure the reasonable allocation of server cluster resources, so that even if some servers go down, it will not affect the normal execution of the remaining workflows.

[0033] Specifically, obtaining the business attributes and expected resource occupancy rates corresponding to each workflow, the business attributes corresponding to the workflow may include attribute information such as workflow name, workflow type, release time of the workflow, and urgency of the workflow. The expected resource occupancy rate corresponding to the workflow can be determined in advance according to past similar workflows. The expected resource occupancy rate corresponding to the workflow refers to the ratio of the resources required by the workflow to the total resources of the benchmark server cluster when the server cluster executes the workflow. The benchmark server cluster can be any arbitrarily selected server cluster.

[0034] Server cluster resources include but are not limited to CPU resources, memory resources, IO resources, and network resources. Since server cluster resources include various types of resources, the expected resource occupancy rate corresponding to the workflow can be obtained by weighted summing the occupancy rates of each type of resource.

[0035] Specifically, the obtaining of the business attributes and expected resource occupancy rates corresponding to each workflow includes:

[0036] Obtain the business attributes corresponding to the workflow from the configuration information of the workflow;

[0037] Obtain the running information of the workflow from the configuration information of the workflow;

[0038] Input the running information of the workflow into a pre-trained resource prediction network to obtain the corresponding expected resource occupancy rate of the workflow.

[0039] Since users can input relevant business attribute information in the configuration information corresponding to the workflow, the business attributes corresponding to the workflow can be obtained from the configuration information of the workflow. Since the running information of the workflow can characterize the features of the workflow, the running information of the workflow is used as the input of the model, and the corresponding expected resource occupancy rate of the workflow can be obtained through the prediction of the model. Among them, the resource prediction network is trained based on a convolutional neural network. It can be understood that the resource prediction network can also be trained through other types of neural network models. The training process determines the structural parameters of the model by minimizing the loss value between the prediction result of the model and the annotation result of the sample, so as to obtain a trained resource prediction network.

[0040] After obtaining the business attributes and expected resource occupancy rates corresponding to each workflow, since different workflows require different server resources, and when workflows with different business attributes are distributed to the corresponding type of server cluster for execution, the execution efficiency of the workflow can be improved. For example, compute-intensive workflows can be distributed to GPU-type server clusters for execution, while I / O-intensive workflows can be distributed to database server clusters for execution. Therefore, it is necessary to determine the target server cluster corresponding to each workflow according to the business attributes and expected resource occupancy rate.

[0041] Specifically, the determining the target server cluster corresponding to each workflow according to the business attributes and the expected resource occupancy rate includes:

[0042] Determine at least one candidate server cluster corresponding to the workflow according to the business attributes;

[0043] Calculate the score of each candidate server cluster according to the expected resource occupancy rate;

[0044] Take the candidate server cluster with the highest score as the target server cluster corresponding to the workflow.

[0045] For example, assume that there are 5 compute-intensive workflows and 2 GPU-type server clusters. Then these 2 server clusters are used as candidate server clusters for compute-intensive workflows. After that, according to the expected resource occupancy rate corresponding to each workflow, calculate the scores of each candidate server cluster, sort them from high to low according to the scores, and take the candidate server cluster with the highest score as the target server cluster corresponding to the workflow.

[0046] Further, calculating the score of each of the candidate server clusters according to the expected resource occupancy rate includes:

[0047] Determining the free resource space of each of the candidate server clusters before executing the workflow according to the expected resource occupancy rate;

[0048] Calculating the score of each of the candidate server clusters according to the expected resource occupancy rate and the free resource space.

[0049] Since a server needs to have corresponding free resources to execute a workflow, before distributing the workflow to a server cluster, the free resource space of the server cluster also needs to be considered. Specifically, according to the expected resource occupancy rate of the workflow, the free resource space of each candidate server cluster before executing the workflow is determined respectively, that is, the current free resource space of the candidate server cluster. According to the expected resource occupancy rate of the workflow and the free resource space, the score of each candidate server cluster is calculated, so that the score of the candidate server cluster can represent the rationality of workflow distribution.

[0050] Further, the calculation formula for the score of the candidate server cluster includes:

[0051]

[0052] where Q j represents the score of the j-th candidate server cluster, n represents the total number of workflows with the same service attribute, s ji represents the free resource space of the j-th server cluster before executing the i-th workflow, u i represents the expected resource occupancy rate of the i-th workflow, α i is the first adjustment coefficient, and β i is the second adjustment coefficient. α i and β i are used to perform non-linear adjustment on the free resource space to reflect the influence of the free resource space on the score. The selection of the adjustment coefficient will affect the calculation result. The values of α i and β i can be determined through data analysis and tuning. is used to adjust the influence of the free resource space on the score. The larger s ji , the smaller the exponential term .

[0053] After determining the target server cluster corresponding to each workflow, distribute each workflow to the corresponding target server cluster respectively, so that the target server cluster can load the configuration information corresponding to the workflow and execute the workflow. For example, when the server starts, it can load the execution configuration corresponding to the workflow assigned by the service through the configuration center, and directly read the execution configuration of the workflow through the configuration center, making the configuration of the workflow more flexible

[0054] In one embodiment, the method further includes:

[0055] When the target server cluster finishes executing the workflow, callback the execution result of the workflow based on the message queue.

[0056] When the workflow finishes execution in the target server set, the operation result of the workflow can be called back to the management end through the MQ queue or HTTP. This can reduce the connection to the database and the resource consumption of the server cluster.

[0057] In this application, according to the business attributes and resource consumption of the workflow, the workflow is divided into different server clusters for execution, effectively allocating corresponding server resources for different workflows, improving the overall workflow execution efficiency of the system. Distributing the execution of the workflow to different clusters can reduce the risk of single-point failure. Even if a certain server fails, it will only affect the server cluster to which it belongs, and will not affect the execution of the workflow in other server clusters. This fault isolation mechanism improves the stability and availability of the system.

[0058] Refer to Figure 2 As shown, it is a schematic diagram of the functional modules of the workflow execution device 100 based on a distributed cluster according to this application.

[0059] The workflow execution device 100 based on a distributed cluster according to this application can be installed in an electronic device. According to the implemented functions, the workflow execution device 100 based on a distributed cluster can include an acquisition module 110, a determination module 120, and an execution module 130. The modules in this application can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0060] In this embodiment, the functions of each module / unit are as follows:

[0061] The acquisition module 110: is used to acquire the business attributes and expected resource occupancy rate corresponding to each workflow, where the expected resource occupancy rate represents the ratio of the expected occupancy of the server cluster resources when the server cluster executes the workflow;

[0062] Determination module 120: configured to determine a target server cluster corresponding to each of the workflows according to the service attribute and the expected resource occupancy rate;

[0063] Execution module 130: configured to distribute each of the workflows to the target server cluster respectively, so that the target server cluster loads the configuration information corresponding to the workflow and executes the workflow.

[0064] In one embodiment, the workflow execution device 100 based on a distributed cluster further includes a callback module, and the callback module is configured to:

[0065] When the target server cluster finishes executing the workflow, callback the execution result of the workflow based on a message queue.

[0066] In one embodiment, the determining a target server cluster corresponding to each of the workflows according to the service attribute and the expected resource occupancy rate includes:

[0067] Determine at least one candidate server cluster corresponding to the workflow according to the service attribute;

[0068] Calculate the score of each candidate server cluster according to the expected resource occupancy rate;

[0069] Use the candidate server cluster with the highest score as the target server cluster corresponding to the workflow.

[0070] In one embodiment, the calculating the score of each candidate server cluster according to the expected resource occupancy rate includes:

[0071] Determine the free resource space of each candidate server cluster before executing the workflow according to the expected resource occupancy rate;

[0072] Calculate the score of each candidate server cluster according to the expected resource occupancy rate and the free resource space.

[0073] In one embodiment, the calculation formula of the score of the candidate server cluster includes:

[0074]

[0075] where Q j represents the score of the jth candidate server cluster, n represents the total number of workflows with the same service attribute, s ji represents the free resource space of the jth server cluster before executing the ith workflow, u i represents the expected resource occupancy rate of the ith workflow, α i is a first adjustment coefficient, βi is the second adjustment coefficient.

[0076] In one embodiment, the obtaining of the service attribute and the expected resource occupancy rate corresponding to each workflow includes:

[0077] Obtaining the service attribute corresponding to the workflow from the configuration information of the workflow;

[0078] Obtaining the running information of the workflow from the configuration information of the workflow;

[0079] Inputting the running information of the workflow into a pre-trained resource prediction network to obtain the corresponding expected resource occupancy rate of the workflow.

[0080] In one embodiment, the resource prediction network is trained based on a convolutional neural network.

[0081] Referring to Figure 3 shown, is a schematic diagram of a preferred embodiment of the electronic device of the present application.

[0082] The electronic device includes a processor 111, a communication interface 112, a memory 113, and a communication bus 114. Among them, the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114;

[0083] The memory 113 is used to store computer programs, for example, a workflow execution program based on a distributed cluster;

[0084] Among them, the processor 111 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 111 is generally used to control the overall operation of the electronic device, for example, to execute control and processing related to data interaction or communication. In this embodiment, the processor 111 is used to run the program code stored in the memory 113 or process data, for example, to run the program code of a workflow execution program based on a distributed cluster.

[0085] The communication interface 112 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The communication interface 112 can also be used to establish a communication connection between the electronic device and other electronic devices.

[0086] The memory 113 includes at least one type of readable storage medium, which includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 113 may be an internal storage unit of the electronic device, such as the hard disk or memory of the electronic device. In other embodiments, the memory 113 may also be an external storage device of the electronic device, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped with the electronic device. Of course, the memory 113 may also include both the internal storage unit and the external storage device of the electronic device. In this embodiment, the memory 11 is generally used to store the operating system installed in the electronic device and various computer programs, such as the program code of the workflow execution program based on the distributed cluster. In addition, the memory 113 may also be used to temporarily store various data that have been output or will be output.

[0087] In an embodiment of the present application, when the processor 111 is used to execute the program stored on the memory 113, it implements the workflow execution method based on the distributed cluster provided by any one of the foregoing method embodiments, including:

[0088] Obtain the service attributes and expected resource occupancy rates corresponding to each workflow, where the expected resource occupancy rate represents the ratio of the expected occupancy of the server cluster resources when the server cluster executes the workflow;

[0089] Determine the target server cluster corresponding to each workflow according to the service attributes and the expected resource occupancy rates;

[0090] Distribute each workflow to the target server cluster respectively for the target server cluster to load the configuration information corresponding to the workflow and execute the workflow.

[0091] For a detailed introduction to the above steps, please refer to the above Figure 1 Explanation of the flowchart of the embodiment of the workflow execution method based on the distributed cluster.

[0092] In addition, an embodiment of the present application also proposes a computer-readable storage medium, which can be non-volatile or volatile. The computer-readable storage medium includes a storage data area and a storage program area. The storage program area stores a workflow execution program based on a distributed cluster. When the workflow execution program based on the distributed cluster is executed by a processor, the following operations are implemented:

[0093] Obtain the service attributes and expected resource occupancy rates corresponding to each workflow, where the expected resource occupancy rate represents the ratio of the expected occupancy of the server cluster resources when the server cluster executes the workflow;

[0094] Determine the target server cluster corresponding to each workflow according to the service attributes and the expected resource occupancy rates;

[0095] Distribute each workflow to the target server cluster respectively, so that the target server cluster loads the configuration information corresponding to the workflow and executes the workflow.

[0096] The specific implementation manner of the computer-readable storage medium of the present application is substantially the same as the specific implementation manner of the above-mentioned workflow execution method based on a distributed cluster, and will not be elaborated here.

[0097] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the related technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0099] It should be noted that the descriptions involving "first", "second", etc. in this application are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. Additionally, the technical solutions between various embodiments may be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0100] It should be understood that the terms used herein are for the purpose of describing particular example embodiments only and are not intended to be limiting. Unless the context clearly dictates otherwise, the singular forms "a", "an", and "the" as used herein may also include the plural forms. The terms "comprising", "including", "containing", and "having" are inclusive and thus specify the presence of the stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring them to be performed in the particular order described or illustrated, unless the order of performance is explicitly stated. It should also be understood that alternative or additional steps may be used.

[0101] The above are only specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to these embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A workflow execution method based on distributed cluster, characterized in that: The method comprises: Obtaining the business attributes and expected resource occupancy rate corresponding to each workflow, wherein the expected resource occupancy rate represents the ratio of the server cluster resources expected to be occupied when the server cluster executes the workflow; Determine, according to the business attributes and the expected resource occupancy rate, a target server cluster corresponding to executing each of the workflows; Each of the workflows is distributed to the target server cluster respectively, so that the target server cluster loads the configuration information corresponding to the workflow and executes the workflow.

2. The distributed cluster-based workflow execution method according to claim 1, characterized in that: The method further comprises: When the target server cluster finishes executing the workflow, the execution result of the workflow is called back based on the message queue.

3. The distributed cluster-based workflow execution method according to claim 1, characterized in that: The step of determining, according to the business attributes and the expected resource occupancy rate, a target server cluster corresponding to executing each of the workflows includes: Determine at least one candidate server cluster corresponding to the workflow according to the business attribute; Calculating a score for each of the candidate server clusters according to the expected resource occupancy rate; The candidate server cluster with the largest score is used as the target server cluster corresponding to the workflow.

4. The distributed cluster-based workflow execution method according to claim 3, characterized in that: Calculating the score of each of the candidate server clusters according to the expected resource occupancy rate includes: According to the expected resource occupancy rate, respectively determining the free resource space of the candidate server cluster before executing the workflow; A score of each of the candidate server clusters is calculated according to the expected resource occupancy rate and the free resource space.

5. The distributed cluster-based workflow execution method according to claim 4, characterized in that: The calculation formula for the score of the candidate server cluster includes: Among them, Q j represents the score of the jth candidate server cluster, n represents the total number of workflows with the same business attributes, and s ji represents the free resource space before the j-th server cluster executes the i-th workflow, u i represents the expected resource occupancy rate of the i-th workflow, α i is the first adjustment coefficient, β i is the second adjustment coefficient.

6. The distributed cluster-based workflow execution method according to claim 1, characterized in that: The obtaining of the business attributes and expected resource occupancy rate corresponding to each workflow includes: Acquire the business attribute corresponding to the workflow from the configuration information of the workflow; Acquiring operation information of the workflow from configuration information of the workflow; The operation information of the workflow is input into a pre-trained resource prediction network to obtain the corresponding expected resource occupancy rate of the workflow.

7. The distributed cluster-based workflow execution method according to claim 5, characterized in that: The resource prediction network is obtained based on convolutional neural network training.

8. A workflow execution device based on a distributed cluster, characterized in that: The device comprises: Acquisition module: used to acquire the business attributes and expected resource occupancy rate corresponding to each workflow, wherein the expected resource occupancy rate represents the ratio of the server cluster resources expected to be occupied when the server cluster executes the workflow; Determination module: used for determining the target server cluster corresponding to executing each of the workflows according to the business attributes and the expected resource occupancy rate; Execution module: used to distribute each of the workflows to the target server cluster respectively, so that the target server cluster can load the configuration information corresponding to the workflow and execute the workflow.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the distributed cluster-based workflow execution method described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the distributed cluster-based workflow execution method according to any one of claims 1 to 7 is implemented.

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