Script job method and device supporting multi-cloud resources, equipment and storage medium

Abstract

The application discloses a script job method and device supporting multiple cloud resources, equipment and a storage medium. The script execution command is obtained, the script execution command comprises a script execution scene and a script execution parameter, each script execution scene corresponds to one or more cloud resource areas, a target execution machine is determined based on a target cloud resource area corresponding to the script execution scene, the script execution command is sent to the target execution machine, the corresponding execution machine can be matched for different cloud resource areas, so that the script job supporting multiple cloud resources can be realized, finally, the target execution machine performs script job according to the script execution parameter, and script job result information returned by the target execution machine is received, so that the scheduling connection between scripts supporting multiple cloud resources is realized, and large-scale script management is realized.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a scripting method, apparatus, device and storage medium that supports multi-cloud resources. Background Technology

[0002] Because different product lines have different deployment models or different customer needs, there are situations where products are deployed on multiple cloud platforms or release regions. Therefore, when it is necessary to operate the product in a script-based manner, it is necessary to support script jobs for multiple cloud resources.

[0003] Currently, traditional script-based job creation requires confirming the machine list, operation commands, and execution commands through the console, which cannot meet the needs of dynamic script creation in multi-cloud resource scenarios, thus failing to support large-scale script management. Summary of the Invention

[0004] This application provides a script job method, apparatus, device, and storage medium that supports multi-cloud resources, in order to solve the technical problem that current script job methods cannot meet the needs of dynamic script jobs in multi-cloud resource scenarios.

[0005] To address the aforementioned technical problems, firstly, this application provides a script-based job method supporting multi-cloud resources, comprising:

[0006] Obtain script execution commands, wherein the script execution commands include script execution scenarios and script execution parameters, and each script execution scenario corresponds to one or more cloud resource regions;

[0007] Based on the target cloud resource region corresponding to the script execution scenario, the target execution machine is determined, and the script execution command is sent to the target execution machine;

[0008] The system controls the target executor to perform script tasks according to the script execution parameters and receives the script task result information returned by the target executor.

[0009] In some implementations, determining the target execution machine based on the target cloud resource region corresponding to the script execution scenario includes:

[0010] For each target cloud resource region corresponding to the script execution scenario, retrieve the list of pending execution machines for the target cloud resource region;

[0011] Based on the execution machine information and load information in the list of execution machines to be executed, at least one target execution machine in the target cloud resource region is determined.

[0012] In some implementations, sending the script execution command to the target execution machine includes:

[0013] Based on the MQ cluster of the target cloud resource region, the script execution command is sent to the target executor in the MQ cluster.

[0014] In some implementations, controlling the target executor to perform script tasks according to the script execution parameters and receiving script task result information returned by the target executor includes:

[0015] According to the preset execution concurrency strategy in the script execution parameters, the target execution machine is controlled to perform script operations with a preset execution machine concurrency level, and the script operation result information of the target execution machine is obtained;

[0016] Receive the script job result information returned by the target executor.

[0017] In some implementations, before receiving the script job result information returned by the target executor, the method further includes:

[0018] Based on a preset script timeout detection strategy, timeout detection is performed on the target execution machine;

[0019] If the target execution machine experiences a script execution timeout, a timeout alarm message will be issued.

[0020] In some implementations, obtaining the script execution command includes:

[0021] Retrieve information from multiple scripts;

[0022] The script execution scenario is obtained by assembling multiple sets of script information.

[0023] Configure the script execution parameters corresponding to the script execution scenario to obtain the script execution command.

[0024] In some implementations, assembling multiple sets of script information to obtain the script execution scenario includes:

[0025] In response to the script generation instruction, multiple preset scripts are assembled in a step-by-step manner using a preset job template to generate the script execution scenario.

[0026] Secondly, this application also provides a scripting apparatus that supports multi-cloud resources, comprising:

[0027] The acquisition module is used to acquire script execution commands, which include script execution scenarios and script execution parameters. Each script execution scenario corresponds to one or more cloud resource regions.

[0028] The determination module is used to determine the target execution machine based on the target cloud resource region corresponding to the script execution scenario, and to send the script execution command to the target execution machine;

[0029] The task module is used to control the target executor to perform script tasks according to the script execution parameters, and to receive the script task result information returned by the target executor.

[0030] Thirdly, this application also provides a computer device, including a processor and a memory, the memory being used to store a computer program, which, when executed by the processor, implements the script job method supporting multi-cloud resources as described in the first aspect.

[0031] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the scripting method for supporting multi-cloud resources as described in the first aspect.

[0032] Fifthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the scripting method for supporting multi-cloud resources as described in the first aspect.

[0033] Compared with the prior art, this application has at least the following beneficial effects:

[0034] By acquiring script execution commands, which include script execution scenarios and script execution parameters, and each script execution scenario corresponding to one or more cloud resource regions, a target execution machine is determined based on the target cloud resource region corresponding to the script execution scenario. The script execution command is then sent to the target execution machine to match the corresponding execution machine for different cloud resource regions, thereby supporting script jobs across multiple cloud resources. Finally, the target execution machine is controlled to perform script jobs according to the script execution parameters, and the script job result information returned by the target execution machine is received to achieve scheduling and connection between scripts supporting multiple cloud resources, thereby enabling large-scale script management. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating a script job method supporting multi-cloud resources, as shown in an embodiment of this application.

[0036] Figure 2 This is a schematic diagram illustrating the distribution of script execution commands in an embodiment of this application;

[0037] Figure 3 This is a schematic diagram illustrating a script job scenario supporting multiple cloud resources, as shown in an embodiment of this application.

[0038] Figure 4 This is a schematic diagram of the structure of a scripting device supporting multi-cloud resources, as shown in an embodiment of this application.

[0039] Figure 5 This is a schematic diagram of the structure of a computer device shown in an embodiment of this application. Detailed Implementation

[0040] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0041] As documented in relevant technical records, product lines may be deployed simultaneously on multiple cloud platforms or deployment regions, such as Amazon Web Services, Huawei Cloud, Alibaba Cloud, Tencent Cloud, and JD Cloud. Therefore, scripting support for multi-cloud resources is required. However, traditional scripting methods cannot meet the needs of dynamic scripting in multi-cloud resource scenarios, thus failing to support large-scale script management.

[0042] To this end, this application obtains script execution commands, which include script execution scenarios and script execution parameters. Each script execution scenario corresponds to one or more cloud resource regions. Based on the target cloud resource region corresponding to the script execution scenario, a target execution machine is determined, and the script execution command is sent to the target execution machine. This enables matching the corresponding execution machine for different cloud resource regions, thereby supporting script operations across multiple cloud resources. Finally, the application controls the target execution machine to perform script operations according to the script execution parameters and receives the script operation result information returned by the target execution machine. This achieves scheduling and connection between scripts supporting multiple cloud resources, thereby enabling large-scale script management.

[0043] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a script job method supporting multi-cloud resources, provided as an embodiment of this application. The script job method supporting multi-cloud resources in this embodiment can be applied to computer devices, including but not limited to smartphones, laptops, tablets, desktop computers, physical servers, and cloud servers. Figure 1 As shown, the script job method supporting multi-cloud resources in this embodiment includes steps S101 to S103, which are described in detail below:

[0044] Step S101: Obtain the script execution command. The script execution command includes the script execution scenario and script execution parameters. Each script execution scenario corresponds to one or more cloud resource regions.

[0045] In this step, the script execution command is the job flow command that initiates the script job. It can be a command assembled from internal script execution instructions of the job platform, or a command assembled from external script execution instructions received through a preset open API. The script execution scenario is the script execution target, and the script execution parameters include the script file, execution time, execution cycle, and script execution strategy. Optionally, external commands can be received through a message queue to achieve asynchronous processing and rate limiting.

[0046] It is understood that a cloud resource region refers to a cloud resource or deployment area provided by a cloud vendor, such as Tencent Cloud, Huawei Cloud, Alibaba Cloud, and Amazon Web Services. Since product lines may be deployed in one or more cloud resource regions, the script execution scenario during script jobs may correspond to one or more cloud resource regions. This application assigns script execution commands for script jobs in each cloud resource region.

[0047] Step S102: Based on the target cloud resource region corresponding to the script execution scenario, determine the target execution machine and send the script execution command to the target execution machine.

[0048] In this step, each cloud resource region has multiple execution machines. Script execution commands are distributed to the corresponding execution machines to perform script jobs, thereby completing the script job for the corresponding cloud resource region. Optionally, a list of execution machines is matched based on the cloud resource region where the execution target is located. The target execution machine is matched according to the cloud host information and load conditions, and finally, the script execution command is issued to the corresponding target execution machine through the job platform.

[0049] Optionally, the execution machine can be an Agent execution machine. An Agent is server software that works between the client and the server to proxy requests between the user and the browser, and is responsible for the browser and the communication between the browser and the server.

[0050] Step S103: Control the target executor to perform script operations according to the script execution parameters, and receive the script operation result information returned by the target executor.

[0051] In this step, the target execution machine performs the script job according to the execution concurrency strategy configured in the script execution parameters, generates script job result information, and returns the script job result information to the job platform. For script execution commands originating from open APIs or command job platforms, the script job result information is then returned to the corresponding external system.

[0052] In some embodiments, determining the target execution machine based on the target cloud resource region corresponding to the script execution scenario in step S102 includes:

[0053] For each target cloud resource region corresponding to the script execution scenario, retrieve the list of pending execution machines for the target cloud resource region;

[0054] Based on the execution machine information and load information in the list of execution machines to be executed, at least one target execution machine in the target cloud resource region is determined.

[0055] In this embodiment, each target cloud resource region has multiple execution machines. Based on the execution machine's operating status and the load information of the cloud resource region, a target execution machine whose operating status meets the load balancing strategy is selected. It should be noted that this embodiment dynamically selects execution machines to configure multiple execution targets for a single cloud resource region or multiple cloud resource regions, thereby enabling concurrent execution of script execution commands in a load-balanced manner.

[0056] In some embodiments, step S102, which involves sending the script execution command to the target execution machine, includes:

[0057] Based on the MQ cluster of the target cloud resource region, the script execution command is sent to the target executor in the MQ cluster.

[0058] In this embodiment, the MQ cluster is a message queue cluster deployed in a clustered manner, which can be a RabbitMQ cluster. Optionally, in normal cluster mode, the RabbitMQ cluster is a distributed cluster, which distributes message queues to various nodes (i.e., target executors) of the cluster, thereby improving the concurrency capability of the entire cluster. Optionally, in mirrored cluster mode, the RabbitMQ cluster is a master-slave cluster, which adds master-slave backup functionality to the normal cluster, improving the data availability of the cluster.

[0059] like Figure 2 As shown, depending on the deployment network of different products, the job platform sends script execution commands to different MQ clusters in the form of messages to ensure network isolation. Meanwhile, to adapt to different product deployment environments, it supports different versions of Agent Execution Machines (Windows and Linux), and the script files are placed on multiple Agent Execution Machines for concurrent execution.

[0060] Optionally, for Agent execution machine management, it supports one-click installation, automatic upgrades, and a heartbeat health check mechanism; and it can encapsulate the Agent interface of cloud vendors as a plugin to achieve transparency of multi-cloud differences and simplify script logic. For execution management, it supports controlling the execution time range, forcibly killing the execution thread if the range is exceeded, and supports concurrent execution of scripts by Agents.

[0061] In some embodiments, step S103 includes:

[0062] According to the preset execution concurrency strategy in the script execution parameters, the target execution machine is controlled to perform script operations with a preset execution machine concurrency level, and the script operation result information of the target execution machine is obtained;

[0063] Receive the script job result information returned by the target executor.

[0064] In this embodiment, the preset execution concurrency strategy may include an execution machine concurrency distribution strategy and an RDS (Online Cloud Database Service) concurrency connection strategy. The execution machine concurrency distribution strategy controls the number of tasks executed simultaneously by each execution machine, while the RDS concurrency connection strategy controls the number of execution machines connected to the RDS. It should be understood that RDS is an online cloud database service based on a cloud computing platform, and one RDS instance can be deployed simultaneously in multiple cloud resource regions.

[0065] In some embodiments, before receiving the script job result information returned by the target executor, the method further includes:

[0066] Based on a preset script timeout detection strategy, timeout detection is performed on the target execution machine;

[0067] If the target execution machine experiences a script execution timeout, a timeout alarm message will be issued.

[0068] In this embodiment, the execution results, script execution logs, script execution duration, and script queuing duration can be viewed in the execution history. Utilizing a preset script timeout detection strategy, based on the script timeout configuration and notification configuration, and combined with message service capabilities, timeout alerts are automatically generated, and script re-execution is supported to prevent task suspension.

[0069] In some embodiments, step S101 includes:

[0070] Retrieve information from multiple scripts;

[0071] The script execution scenario is obtained by assembling multiple sets of script information.

[0072] Configure the script execution parameters corresponding to the script execution scenario to obtain the script execution command.

[0073] In this embodiment, the script information to be executed can be determined based on the product name or command name. The script information can be an operation and maintenance script of type Python, IronPython, or Shell. Multiple script information are assembled into a complete script execution scenario, and the assembled script execution scenario is used as the execution target. After confirming the execution target, the configuration management database CMDB is queried to assemble the script parameters. Code logic is inserted before and after the script execution to support external systems to call via API.

[0074] Optionally, assembling multiple sets of script information to obtain the script execution scenario includes:

[0075] In response to the script generation instruction, multiple script information items are assembled in a step-by-step manner using a preset job template to generate the script execution scenario.

[0076] In this optional embodiment, a job template is pre-set to facilitate the arrangement of job scripts. Multiple script information is assembled into a template in the form of steps. Each step supports different script types, timeout durations, execution target pools, and script parameters.

[0077] It should be noted that this embodiment uses a process-oriented approach to assemble fragmented individual tasks into a job workflow (script job command), and executes them concurrently according to different cloud environments and deployment regions using a load-balanced method. Furthermore, the platform's API allows for the invocation and viewing of any job, enabling integration with other platforms or systems to automate scheduling and significantly improve job efficiency.

[0078] This is an example, not a limitation. Figure 3 A schematic diagram illustrating a script job scenario supporting multiple cloud resource regions is shown. Figure 3As shown, the job platform application receives script execution instructions (including internal script information initiated by the job platform or external script information received through the open API). The background task scheduling component configures the script information in multiple script execution instructions using either `vhost:cosmicqueue:ip1_script` or `vhost:cosmicqueue:ip2_script` to generate script execution commands. These commands are then sent to the Agent service in the corresponding cloud resource region (K8s node IP1 or Windows virtual machine IP2). The Agent service distributes the script execution commands to the target execution machine in the corresponding cloud resource region for script execution. After the script execution, the Agent service reports the script execution result to the script result listening component, which returns the result to the job platform application or to an external system via the open API. Simultaneously, timeout detection, consumer detection, and queue status detection are performed through the MQ service API, and the results are reported to the Agent status listening component. The Agent service supports configuration updates via Agent update sources and also reports Agent update information to the Agent information listening component.

[0079] To execute the script job method supporting multi-cloud resources corresponding to the above method embodiments, in order to achieve the corresponding functions and technical effects. See also Figure 4 , Figure 4 This diagram illustrates a structural block diagram of a scripting apparatus supporting multi-cloud resources according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The scripting apparatus supporting multi-cloud resources provided in this embodiment includes:

[0080] The acquisition module 401 is used to acquire script execution commands, the script execution commands include script execution scenarios and script execution parameters, and each script execution scenario corresponds to one or more cloud resource regions;

[0081] The determination module 402 is used to determine the target execution machine based on the target cloud resource region corresponding to the script execution scenario, and to send the script execution command to the target execution machine;

[0082] The job module 403 is used to control the target executor to perform script jobs according to the script execution parameters, and to receive the script job result information returned by the target executor.

[0083] In some embodiments, the determining module 402 is specifically used for:

[0084] For each target cloud resource region corresponding to the script execution scenario, retrieve the list of pending execution machines for the target cloud resource region;

[0085] Based on the execution machine information and load information in the list of execution machines to be executed, at least one target execution machine in the target cloud resource region is determined.

[0086] In some embodiments, the determining module 402 is further configured to:

[0087] Based on the MQ cluster of the target cloud resource region, the script execution command is sent to the target executor in the MQ cluster.

[0088] In some embodiments, the operation module 403 is specifically used for:

[0089] According to the preset execution concurrency strategy in the script execution parameters, the target execution machine is controlled to perform script operations with a preset execution machine concurrency level, and the script operation result information of the target execution machine is obtained;

[0090] Receive the script job result information returned by the target executor.

[0091] In some embodiments, the operation module 403 is further specifically used for:

[0092] Based on a preset script timeout detection strategy, timeout detection is performed on the target execution machine;

[0093] If the target execution machine experiences a script execution timeout, a timeout alarm message will be issued.

[0094] In some embodiments, the scripting apparatus further includes:

[0095] The assembly module is used to assemble multiple script information in response to the script generation command to obtain the script execution scenario;

[0096] The configuration module is used to configure the script execution parameters corresponding to the script execution scenario and obtain the script execution command.

[0097] In some embodiments, the assembly module is specifically used for:

[0098] In response to the script generation instruction, multiple preset scripts are assembled in a step-by-step manner using a preset job template to generate the script execution scenario.

[0099] The aforementioned script job apparatus supporting multi-cloud resources can implement the script job method supporting multi-cloud resources described in the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application's embodiments can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0100] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 5 As shown, the computer device 5 of this embodiment includes: at least one processor 50 ( Figure 5 (Only one is shown in the diagram), memory 51, and computer program 52 stored in the memory 51 and executable on the at least one processor 50, wherein the processor 50 executes the computer program 52 to implement the steps in any of the above method embodiments.

[0101] The computer device 5 may be a smartphone, tablet, desktop computer, or cloud server, among other computing devices. This computer device may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will understand that... Figure 5 The computer device 5 is merely an example and does not constitute a limitation on the computer device 5. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0102] The processor 50 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0103] In some embodiments, the memory 51 may be an internal storage unit of the computer device 5, such as a hard disk or memory of the computer device 5. In other embodiments, the memory 51 may be an external storage device of the computer device 5, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 5. Furthermore, the memory 51 may include both internal and external storage units of the computer device 5. The memory 51 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 51 can also be used to temporarily store data that has been output or will be output.

[0104] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the above method embodiments.

[0105] This application provides a computer program product that, when run on a computer device, enables the computer device to execute the steps described in the various method embodiments above.

[0106] In the several embodiments provided in this application, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0107] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0108] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A script-based job method supporting multi-cloud resources, characterized in that, include: Obtain script execution commands, wherein the script execution commands include script execution scenarios and script execution parameters, and each script execution scenario corresponds to one or more cloud resource regions; Based on the target cloud resource region corresponding to the script execution scenario, the target execution machine is determined, and the script execution command is sent to the target execution machine; The system controls the target execution machine to perform script tasks according to the script execution parameters and receives the script task result information returned by the target execution machine. The determination of the target execution machine based on the target cloud resource region corresponding to the script execution scenario includes: For each target cloud resource region corresponding to the script execution scenario, retrieve the list of pending execution machines for the target cloud resource region; Based on the execution machine information and load information in the list of execution machines to be executed, at least one target execution machine in the target cloud resource region is determined; Sending the script execution command to the target execution machine includes: Based on the MQ cluster of the target cloud resource region, the script execution command is sent to the target executor in the MQ cluster.

2. The script job method supporting multi-cloud resources as described in claim 1, characterized in that, The process of controlling the target executor to perform script tasks according to the script execution parameters and receiving script task result information returned by the target executor includes: According to the preset execution concurrency strategy in the script execution parameters, the target execution machine is controlled to perform script operations with a preset execution machine concurrency level, and the script operation result information of the target execution machine is obtained; Receive the script job result information returned by the target executor.

3. The script job method supporting multi-cloud resources as described in claim 2, characterized in that, Before receiving the script job result information returned by the target executor, the method further includes: Based on a preset script timeout detection strategy, timeout detection is performed on the target execution machine; If the target execution machine experiences a script execution timeout, a timeout alarm message will be issued.

4. The script job method supporting multi-cloud resources as described in claim 1, characterized in that, The process of obtaining the script execution command includes: Retrieve information from multiple scripts; The script execution scenario is obtained by assembling multiple sets of script information. Configure the script execution parameters corresponding to the script execution scenario to obtain the script execution command.

5. The script job method supporting multi-cloud resources as described in claim 4, characterized in that, The assembly of multiple script information entries to obtain the script execution scenario includes: In response to the script generation instruction, multiple script information items are assembled in a step-by-step manner using a preset job template to generate the script execution scenario.

6. A scripting device supporting multi-cloud resources, characterized in that, include: The acquisition module is used to acquire script execution commands, which include script execution scenarios and script execution parameters. Each script execution scenario corresponds to one or more cloud resource regions. The determination module is used to determine the target execution machine based on the target cloud resource region corresponding to the script execution scenario, and to send the script execution command to the target execution machine; The task module is used to control the target executor to perform script tasks according to the script execution parameters, and to receive script task result information returned by the target executor; The determining module determines the target execution machine based on the target cloud resource region corresponding to the script execution scenario, including: For each target cloud resource region corresponding to the script execution scenario, the determining module retrieves the list of pending execution machines for the target cloud resource region. Based on the execution machine information and load information in the list of execution machines to be executed, at least one target execution machine in the target cloud resource region is determined; The determining module sends the script execution command to the target execution machine, including: The determining module, based on the MQ cluster of the target cloud resource region, sends the script execution command to the target execution machine in the MQ cluster.

7. A computer device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program that, when executed by the processor, implements the script job method supporting multi-cloud resources as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the script job method for supporting multi-cloud resources as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, When the computer program product is run on a computer device, it causes the computer device to execute the script job method supporting multi-cloud resources as described in any one of claims 1 to 5.

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