Cloud platform-based basic environment arrangement method and device, equipment and medium

Abstract

The present disclosure provides a cloud platform-based basic environment arrangement method and device, equipment and medium, relates to the field of data processing, and particularly relates to the field of cloud platform technology. The specific implementation scheme is as follows: obtaining function requirement information and a target network segment of a user for a cloud platform to issue an execution task; determining corresponding platform resource information from the cloud platform according to the function requirement information; generating a basic environment arrangement result according to the platform resource information and a plurality of subnets obtained by dividing the target network segment, and providing the basic environment arrangement result to the user for task execution. The scheme of the present disclosure realizes the user to perform the task execution meeting the user requirement without paying attention to the underlying resource composition of the cloud platform, improves the user's task attention and the experience of performing the task execution on the cloud platform; and effectively integrates the available resources on the cloud platform, and improves the resource utilization rate of the cloud platform.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of data processing, in particular to the field of cloud platform technology, and specifically to a cloud platform-based basic environment arrangement method, device, equipment and medium. BACKGROUND

[0002] With the continuous development of AI technology, when users need to perform complex large model training and inference tasks, they need to use the powerful underlying computing power, high-speed network and high-performance storage provided by different platforms to build a large model pre-training environment. In the face of different users, complex resources and functional requirements, how to improve the efficiency of building a basic environment for users has become a problem that needs to be solved. SUMMARY

[0003] The present disclosure provides a cloud platform-based basic environment arrangement method, device, equipment and medium.

[0004] According to an aspect of the present disclosure, a cloud platform-based basic environment arrangement method is provided, comprising:

[0005] Obtaining function requirement information and a target network segment of a user for a cloud platform to issue an execution task;

[0006] According to the function requirement information, determining corresponding platform resource information from the cloud platform;

[0007] Generating a basic environment arrangement result according to the platform resource information and a plurality of subnets obtained by dividing the target network segment, and providing the basic environment arrangement result to the user for task execution.

[0008] According to another aspect of the present disclosure, a cloud platform-based basic environment arrangement device is provided, comprising:

[0009] A function requirement obtaining module for obtaining function requirement information and a target network segment of a user for a cloud platform to issue an execution task;

[0010] A platform resource determining module for determining corresponding platform resource information from the cloud platform according to the function requirement information;

[0011] An environment arrangement module for generating a basic environment arrangement result according to the platform resource information and a plurality of subnets obtained by dividing the target network segment, and providing the basic environment arrangement result to the user for task execution.

[0012] According to another aspect of the present disclosure, an electronic device is provided, comprising:

[0013] At least one processor; and

[0014] A memory in communication connection with the at least one processor; wherein,

[0015] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the cloud platform-based basic environment orchestration method according to any one of the embodiments of the present disclosure.

[0016] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to perform the cloud platform-based basic environment orchestration method according to any one of the embodiments of the present disclosure.

[0017] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the cloud platform-based basic environment orchestration method according to any one of the embodiments of the present disclosure.

[0018] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings are used to better understand the present scheme, and do not limit the present disclosure. Among them:

[0020] Figure 1 is a schematic diagram of a cloud platform-based basic environment orchestration method according to an embodiment of the present disclosure;

[0021] Figure 2 is a schematic diagram of another cloud platform-based basic environment orchestration method according to an embodiment of the present disclosure;

[0022] Figure 3 is an architecture diagram of the cloud platform-based basic environment orchestration method;

[0023] Figure 4 is a connectivity schematic diagram of a cluster heterogeneous computing resource pool;

[0024] Figure 5 is a schematic diagram of still another cloud platform-based basic environment orchestration method according to an embodiment of the present disclosure;

[0025] Figure 6 is a target network segment division schematic diagram;

[0026] Figure 7 is a structural schematic diagram of a cloud platform-based basic environment orchestration apparatus according to an embodiment of the present disclosure;

[0027] Figure 8is a block diagram of an electronic device for implementing a cloud platform-based basic environment orchestration method according to an embodiment of the disclosure. DETAILED DESCRIPTION

[0028] Exemplary embodiments of the disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the disclosure to assist in understanding them. These should be considered as merely exemplary. Thus, those skilled in the art will recognize that variations and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, in the following description, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0029] Figure 1 is a schematic diagram of a cloud platform-based basic environment orchestration method according to an embodiment of the disclosure. The embodiment can be applied to the case of orchestrating a corresponding basic environment for a user-submitted execution task on a cloud platform. The method can be executed by a cloud platform-based basic environment orchestration apparatus. The apparatus can be implemented in software and / or hardware and integrated in an electronic device. The electronic device involved in the embodiment can be a server or the like with computing and communication capabilities. Specifically, refer to Figure 1 The method specifically includes the following.

[0030] S110, obtain the functional requirement information and target network segment of the user-submitted execution task on the cloud platform.

[0031] Since the user needs to use the powerful underlying computing power, high-speed network, and high-performance storage capability of the cloud platform for task execution, the execution task includes, for example, large model training or inference tasks. The cloud platform integrates a central cloud server and edge servers provided by third-party platforms. The central cloud server and the edge servers integrate different types of computing cards to meet the different execution tasks of the user.

[0032] Specifically, the user determines the functional requirement information of the execution task, such as the model framework to be trained by the user, the required computing resource list, and network requirements, etc. The computing resource list includes the machine type and quantity of the computing resources to be used by the user, etc. For example, the cloud platform provides multiple candidate functional requirement information, which is determined according to the attribute information of the cloud platform. The user selects the corresponding target functional requirement information from the candidate functional requirement information according to the execution task and the user's own needs.

[0033] The target network segment provided by the user is network information provided to the cloud platform for access in the user's private network. The cloud platform can access the resource data in the user's private network through the IP in the target network segment to perform the task according to the accessed resource data.

[0034] The cloud platform can provide a basic environment for pre-training of a large model required by a user. The user can use the powerful underlying computing power, high-speed network, and high-performance storage provided by the cloud platform to build a large model pre-training environment in one stop, support multiple mainstream training frameworks, and use the AI acceleration capabilities provided by the cloud platform to accelerate the large model pre-training process.

[0035] In S120, platform resource information corresponding to the function requirement information is determined from the cloud platform.

[0036] The function requirement information is requirement information determined by the user from the perspective of function implementation and the perspective of actual cost control. The matching platform resource information is determined from the cloud platform according to each item included in the function requirement information, so that the determined platform resource information can not only implement the function implementation of the user's required task, but also meet the actual cost control condition required by the user. The platform resource information includes various infrastructures required for user task execution, such as computing resources, storage, and network.

[0037] For example, the matching candidate machine is determined from the cloud platform according to the machine type and quantity in the function requirement information, and the corresponding target machine is determined from the candidate machine in combination with the cost control condition of the user, as the underlying computing power resource for executing the user's task. In addition, the corresponding target network resource and target storage resource are determined from the cloud platform according to the network and storage requirements in the function requirement information. The platform resource information is determined according to the underlying computing power resource, the target network resource, and the target storage resource.

[0038] In another optional implementation of the embodiment, S120 includes:

[0039] The execution task is decomposed according to the function requirement information to obtain a plurality of sub-tasks.

[0040] A corresponding target platform resource is determined for each sub-task from the candidate platform resources of the cloud platform.

[0041] Since the cloud platform includes powerful underlying computing power resources, in order to improve the task execution efficiency, the execution task can be decomposed to obtain a plurality of sub-tasks, and the plurality of sub-tasks can be distributed to a plurality of devices for training to improve the utilization rate of the underlying computing resources in the cloud platform and the execution efficiency of the user's task.

[0042] Specifically, the execution task is decomposed according to the function requirement information of the user, so that the plurality of sub-tasks after decomposition can be processed in parallel to the greatest extent to improve the efficiency of task execution. For example, the execution task is divided according to the execution order according to the function requirement information of the user, and the corresponding sub-task is determined according to the divided sub-function.

[0043] According to the specific execution content of each subtask, the corresponding target platform resource is determined from the candidate platform resources of the cloud platform. For example, according to the specific execution content of each subtask, a plurality of platform resources that match are determined from the candidate platform resources that are currently idle in the cloud platform, and then, in combination with the target function requirement associated with the subtask in the function requirement information of the user, the target platform resource corresponding to the subtask is determined from the plurality of platform resources. Wherein, according to the specific execution content of each subtask, a plurality of platform resources that match are determined from the candidate platform resources that are currently idle in the cloud platform, which can be considered from the perspective of task execution efficiency.

[0044] The embodiment refines the granularity of the basis for determining the target platform resource by decomposing the execution task and then determining the corresponding target platform resource according to the decomposed subtask, improves the matching degree between the target platform resource and the function requirement of the user, and further improves the accuracy and efficiency of the determination of the target platform resource.

[0045] S130, generating a basic environment arrangement result according to the platform resource information and the plurality of subnets obtained by dividing the target network segment, and providing the basic environment arrangement result to the user for task execution.

[0046] Since different tasks of the cloud platform access the target network segment provided to the user at the same time when executing the task to obtain the data resource stored in the private network of the user, in order to avoid access conflict, the target network segment is divided to obtain a plurality of subnets, and different tasks can access the private network of the user through different subnets.

[0047] The relationship between the platform resource information determined in the cloud platform and the plurality of subnets is arranged and integrated to generate a basic environment arrangement result required for the user to execute the task, and the cloud platform provides the arranged basic environment to the user, so that the user can directly execute the task through the arranged basic environment.

[0048] The user executes the task through the basic environment arrangement result, so that the user only needs to focus on the execution progress of the task itself and does not need to manage the underlying platform resource, such as not needing to focus on the specific type and quantity of the machine used; and the basic environment arrangement result can meet the diverse and complex resource and function requirements of the user, while integrating the available resources inside the cloud platform, improving the resource utilization efficiency inside the cloud platform.

[0049] The scheme of the embodiment determines corresponding platform resource information through the function requirement information of the task execution, divides the target network segment provided by the user, and further generates a basic environment arrangement result of the task execution according to the platform resource information and the subnets obtained through the division, so that the user can perform the task execution that meets the user's demand without paying attention to the underlying resource composition of the cloud platform, and the user's task attention and experience of performing the task execution on the cloud platform are improved. In addition, the available resources on the cloud platform are effectively integrated during the generation of the basic environment arrangement result, and the resource utilization rate of the cloud platform is improved.

[0050] Figure 2 FIG. 2 is a schematic diagram of another cloud platform-based basic environment arrangement method according to an embodiment of the present disclosure. The embodiment is a further refinement of the above technical solution, and the technical solution in the embodiment can be combined with each of the optional schemes in one or more of the above embodiments. As shown in FIG. 2, the cloud platform-based basic environment arrangement method includes the following steps. Figure 2

[0051] S210, function requirement information and a target network segment of a task execution issued by a user on a cloud platform are obtained.

[0052] S220, the task execution is decomposed according to the function requirement information, and a plurality of subtasks are obtained.

[0053] S230, corresponding user individual requirements and task inherent requirements are determined according to execution attribute information of the subtasks.

[0054] The execution attribute information is used to determine the execution process requirement and execution purpose of the subtask, which includes the user individual requirements for representing the execution process requirement and execution purpose of the subtask by the user, and includes the task inherent requirements for representing the features that are indispensable for the task execution and the features that are best for the running implementation. For example, the user individual requirements refer to the requirements of the user on the machine type of the subtask execution or the requirements on the execution efficiency, and the task inherent requirements refer to the best running scheme of the subtask on the cloud platform, that is, which machine on the cloud platform is used to execute the subtask to make the execution efficiency of the subtask best.

[0055] Specifically, the user individual requirements and the task inherent requirements corresponding to the subtask are obtained by decomposing the execution attribute information according to the content in the function requirement information that matches the subtask.

[0056] S240, target platform resources corresponding to each subtask are determined from candidate platform resources of the cloud platform according to the user individual requirements and the task inherent requirements.

[0057] ​According to the user individual demand and the task inherent demand, a matching target platform resource is selected from the candidate platform resources. If the user individual demand and the task inherent demand conflict for the same type of platform resource, the corresponding target platform resource is determined according to the user individual demand.

[0058] For example, different types of computing cards are configured for sub-tasks of different task types according to the task inherent demand of each sub-task, and different storage systems are used for sub-tasks with different requirements according to the user individual demand of each sub-task, such as using products like parallel file systems to accelerate model training, and compliance security settings are made for corresponding sub-tasks according to the user individual demand of each sub-task to prevent sensitive data leakage.

[0059] Based on different types of candidate platform resources provided by the cloud platform, the underlying platform resources can be flexibly combined according to the different requirements of the user and the task itself. On the premise of meeting the task execution function, the cost is controlled according to the user's requirements, and the user is exempted from the operation and maintenance management of various candidate platform resources.

[0060] As shown in FIG. 1, the AI-Infra module is the main module for executing the basic environment arrangement method, which implements the cloud computing infrastructure, flexibly combines the edge computing resources and the central cloud computing resources, and realizes various functions for terminal users. The terminal user trains and deploys the AI task through the business layer to realize various functions for users, such as creating a training cluster, a queue, deploying an inference service, etc., and calls AI-Infra according to user demand to require resource combination that meets user demand. Figure 3

[0061] AI-Infra is responsible for producing and managing the user cluster of the platform data surface according to the demand of the business layer, shielding the complexity of the basic environment arrangement for the business layer. According to the resource arrangement specification submitted by the upper business layer user, the edge computing resources and the central cloud computing resources are automatically called to complete the construction of the computing power foundation.

[0062] ​Specifically, the AI-Infra is located between the business layer and the infrastructure layer in the architecture hierarchy, and automatically utilizes the capabilities of the infrastructure layer to generate resource orchestration that meets the business layer. The AI-Infra module is divided into two parts: AI-Infra-Service: used to provide functional requirement information to the upper business layer to generate basic environment orchestration specifications; AI-Infra-operator: used to automatically call the API of the infrastructure according to the basic environment orchestration specifications to complete the automatic organization of computing resources; wherein the infrastructure at least includes edge computing resources and central cloud computing resources. In addition, the AI-Infra also has a component scheduling function according to the functional requirement information provided by the business layer, and the specific component scheduling function is not limited in the embodiments of the present application.

[0063] In another optional implementation of the present embodiment, the candidate platform resources include candidate computing resources, candidate storage resources, candidate network resources, and candidate component resources.

[0064] The cloud platform supports the automatic orchestration of multiple types of resources, including candidate computing resources, candidate storage resources, candidate network resources, and candidate component resources.

[0065] The candidate computing resources include central cloud computing resources and third-party computing resources of a third-party resource provider, and the computing resources include various GPUs, CPUs, and other computing resources. The candidate storage resources include storage systems supporting object storage and parallel file storage. The candidate network resources include basic network infrastructures such as peer-to-peer connection, elastic network card, dedicated line, and routing table. The candidate component resources include various components of the cloud-native ecosystem to complete functional requirements such as AI scheduling, GPU virtualization, log collection, and observability. The candidate cluster resources are used to interface with the Kubernetes ecosystem to build a cloud-native ecosystem consistent with the open source community.

[0066] The present embodiment improves the completeness of the basic environment of the orchestration by implementing the automatic orchestration of multiple types of platform resources in the cloud platform, thereby improving the user experience.

[0067] In another optional implementation of the present embodiment, S240 includes:

[0068] According to the user individual needs and the task inherent needs, the target computing resource corresponding to each subtask is determined from the candidate computing resources of the cloud platform;

[0069] According to the type of the target computing resource, the target network resource for interaction between the subtasks is determined from the candidate network resources.

[0070] First, according to the user individual demand and the task inherent demand corresponding to the subtask, the target computing resource for executing the subtask is determined, and then the target network resource for intercommunication between the subtasks is determined according to the type of the computing resource.

[0071] Further, according to the user individual demand and the task inherent demand, the target computing resource corresponding to each subtask is determined from the candidate computing resources of the cloud platform, and the target storage resource corresponding to each subtask is determined from the candidate storage resources of the cloud platform, and the target network resource for intercommunication between the subtasks is determined from the candidate network resources according to the type of the target computing resource and the type of the target storage resource. For example, the network connectivity and isolation scheme is automatically built according to the user network connectivity demand, computing power demand, storage demand, etc.

[0072] The embodiment first determines the target computing resource corresponding to each subtask, and then determines the target network resource corresponding to each subtask according to the target computing resource, thereby improving the accuracy of the target network resource determination and improving the precision of the basic environment arrangement.

[0073] In another optional implementation manner of the embodiment, the target network resource for intercommunication between the subtasks is determined from the candidate network resources according to the type of the target computing resource, including:

[0074] If the type of the target computing resource of the subtask is the central cloud computing resource, the elastic network card is used as the target network resource for intercommunication with other subtasks using the central cloud computing resource; the intelligent network is used as the target network resource for intercommunication with other subtasks using the edge computing resource; the peer-to-peer connection or the elastic network card is used as the target network resource for intercommunication with the user resource.

[0075] If the type of the target computing resource of the subtask is the edge computing resource, the service network card is used as the target network resource for intercommunication with other subtasks using the central cloud computing resource.

[0076] The cluster heterogeneous computing power determined for the user's execution task includes various GPU resources, which are provided by the central cloud and the third-party supplier; the elastic network card (ENI) is used to realize intercommunication between the Kubernetes cluster computing control surface and the heterogeneous computing power; the intelligent network (dedicated line, CSN) is used to realize intercommunication between the central cloud and the third-party computing power supplier; the third-party computing power supplier can use the service network card to access the central cloud computing resource; the peer-to-peer connection or the elastic network card is used to realize intercommunication between the GPU heterogeneous computing power and the user VPC, and the object storage and the parallel file storage in the user VPC can be accessed. At the same time, the security group and the ACL are automatically configured, which saves the network connectivity of the heterogeneous computing power and also guarantees the security to prevent possible information leakage. For example,Figure 4 The figure shows the connection diagram of the cluster heterogeneous computing resource pool. The cloud platform provides users with maintenance-free, exclusive resource pools, i.e., managed resource pools. Users can submit training tasks and deploy inference services without worrying about the composition of the underlying resources. From the perspective of resource orchestration, it realizes: automatic deployment of managed computing clusters and CCE clusters for users, the control plane of the computing cluster is managed by the CCE cluster, which is deployed in the CCE service VPC; automatically configure the use of container networks for computing.

[0077] As shown in the example, Figure 4 As shown, user A provides two VPCs to form the target network segment, and deploys one resource pool in each VPC. A central cloud cluster is deployed for resource pool 1, which includes multiple nodes, two nodes are shown in the figure, and each node executes the corresponding task service of the user through the user container. Each resource pool is managed by the corresponding CCE VPC, i.e., the nodes in CCE VPC-1 are management nodes for managing the computing nodes in resource pool 1. The primary network interface ENI of the management node in CCE VPC-1 is used for internal communication of the node. The managed Master is used to execute the task corresponding to the management node. The auxiliary network interface ENI is used to establish communication between the management node in CCE VPC-1 and all computing nodes in the corresponding resource pool 1. Similarly, a central cloud cluster and an edge cluster are deployed for resource pool 2. The central cloud cluster and the edge cluster in a resource pool are connected through a CSN private line. The auxiliary network interface ENI in CCE VPC-2 is used to establish communication between the management node in CCE VPC-2 and all computing nodes in the corresponding resource pool 2, as well as all computing nodes in the central cloud cluster and the edge cluster.

[0078] The embodiment determines the target network resource for interaction between sub-tasks according to the type of target computing resource corresponding to different sub-tasks, thereby improving the efficiency of network interaction between sub-tasks.

[0079] S250, generating a basic environment orchestration result according to the target platform resource and the multiple subnets obtained by dividing the target network segment, and providing the basic environment orchestration result to the user for task execution.

[0080] The scheme of the embodiment determines the corresponding target platform resource as the basic environment for task execution according to the user individual demand and task inherent demand of each sub-task by decomposing the executed task, thereby improving the accuracy of basic environment composition determination.

[0081] Figure 5is a schematic diagram of still another cloud platform-based basic environment orchestration method according to an embodiment of the present disclosure. The present embodiment is a further refinement of the target network segment division of the above technical solution. The technical solution in the present embodiment can be combined with each optional solution in one or more of the above embodiments.

[0082] As shown in Figure 5 The cloud platform-based basic environment orchestration method includes the following.

[0083] S310, obtain function requirement information of a user for a cloud platform to issue an execution task and a target network segment.

[0084] S320, determine corresponding platform resource information from the cloud platform according to the function requirement information.

[0085] S330, divide the target network segment based on the pre-determined multiple network segment resource requirements to obtain multiple sub-networks.

[0086] The specific objects of the network segment IP usage requirements provided by the user in the cloud platform during the task execution process are pre-statistically summarized and divided according to the content of the specific objects to obtain multiple network segment resource requirements, i.e., the specific objects of the network segment resource requirements have usage requirements for the IP provided by the user to the target network segment.

[0087] Specifically, the target network segment provided by the user is divided according to the network segment resource size information required by the multiple network segment resource requirements, and a corresponding size suitable sub-network is divided for each network segment resource requirement to improve the efficiency and security of the user private network access from the corresponding sub-network for each network segment resource requirement. For example, the network segment resource size information required by each network segment resource requirement is determined according to the specific content of the network segment resource requirement, which is not limited herein.

[0088] In another optional implementation manner of the present embodiment, the pre-determined multiple network segment resource requirements at least include the requirements of a management node of a computing node, a center cloud computing node, a user access entry address management, an edge computing node, a center cloud public service access to an edge computing node, a container, and inter-container communication to a network segment.

[0089] The computing node refers to the underlying computing power in the platform resource information determined for the user. For example, one computing node can correspond to one machine. Since the amount of tasks executed on the cloud platform is large, the platform resource information determined for the user includes multiple computing nodes. The management node of the computing node refers to a node that manages all computing nodes in the platform resource information. The management node can also be deployed on at least one machine in the platform resource information, and manages the task distribution and scheduling of the computing nodes. The demand of the management node of the computing node for the network segment refers to the access demand of the management node for the target network segment provided by the user when the management node manages the computing nodes. For example, the access demand when the management node obtains data from the private network of the user.

[0090] The center cloud computing node refers to a computer machine directly provided by the cloud platform. The demand of the center cloud computing node for the network segment refers to the access demand of the center cloud computing node in the cloud platform for the target network segment provided by the user when the center cloud computing node is running.

[0091] The user access entry address refers to a management address provided by the cloud platform for the user to access tasks. The demand of the user access entry address management for the network segment refers to the access demand for the target network segment provided by the user generated when multiple access addresses generated in the process of executing tasks are managed.

[0092] The edge computing node refers to a computing machine provided by a third party other than the cloud platform. Since the type and quantity of computing machines on the cloud platform are limited, in order to improve the task execution efficiency of the cloud platform and reduce costs, the cloud platform provides computing machines of the third party to the user, that is, the center cloud computing node and the edge computing node of the cloud platform are integrated to improve the type and quantity of machines that can be selected by the user. The demand of the edge computing node for the network segment refers to the access demand of the edge computing node in the cloud platform for the target network segment provided by the user when the edge computing node is running.

[0093] The center cloud public service refers to a public service provided by the public network where the center cloud is located. The center cloud computing node can directly access the center cloud public service. Since the edge computing node does not have a public network by default, some public services (such as yum / apt software sources) dependent on the edge computing node and the container cluster itself need to access the public services of the center cloud through the CSN (Cloud Society Network). Since the network segment used by the public service has a default internal routing (invisible to the user) inside the VPC, CSN and other gateways, it will cause the routing of the network segment added explicitly on the product to be invalid, so the center cloud needs to proxy the access of the edge computing node to the public services of the center cloud through the service network card or the proxy access mode of the BLB.

[0094] The container refers to a minimum running management unit created for task execution, multiple containers constitute a computing node, and multiple computing nodes constitute a cluster. In the task execution process, the container also needs to access the data of the network segment provided by the user in the subtask execution process. In the task execution process, the containers also need to access the user resources in the communication interaction.

[0095] The embodiment of the application determines the multiple network segment resource requirements according to the specific process of executing the task, improves the accuracy of the network segment resource requirement determination, and further improves the accuracy of the network segment division.

[0096] In another optional implementation of the embodiment, the target network segment is divided based on the multiple network segment resource requirements determined in advance, to obtain multiple subnets, including:

[0097] Based on the demand of the management node of the computing node for the network segment, the first IP reservation quantity is determined from the target network segment according to the management cluster size and the preset available redundancy;

[0098] Based on the demand of the center cloud computing node for the network segment, the second IP reservation quantity is determined according to the maximum center cloud resource pool quantity supported by a single user and the maximum node quantity supported by a single center cloud resource pool;

[0099] Based on the demand of the user access entry address management for the network segment, the third IP reservation quantity is determined according to the business scenario of the task executed by the user;

[0100] Based on the demand of the edge computing node for the network segment, the fourth IP reservation quantity is determined according to the maximum edge resource pool quantity supported by a single user and the maximum node quantity supported by a single edge resource pool;

[0101] Based on the demand of providing the center cloud public service access to the edge computing node for the network segment, the fifth IP reservation quantity is determined according to the proxy access mode;

[0102] Based on the demand of the container for the network segment, the sixth IP reservation quantity is determined according to the maximum container quantity supported by a single cluster;

[0103] Based on the demand of the container for the network segment, the sixth IP reservation quantity is determined according to the maximum container quantity supported by a single cluster;

[0104] The target network segment is divided according to the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity, to obtain multiple subnets.

[0105] The management cluster size refers to the number of computing nodes managed by the management node, and the preset available redundancy refers to the amount of redundancy set for the access demand of the management node to improve the access efficiency of the management node.

[0106] Specifically, the management cluster size is determined according to the number of computing nodes managed by the management node, for example, a mapping relationship between a plurality of computing node quantity intervals and candidate management cluster sizes is established in advance, the corresponding IP reservation quantity is determined according to the management cluster size, for example, a mapping relationship between candidate management cluster sizes and IP reservation quantities is established in advance, and finally the corresponding IP reservation quantity and the IP reservation quantity corresponding to the preset available redundancy are determined according to the management cluster size to determine the first IP reservation quantity, that is, the management node can realize efficient access to the user private network through the first IP reservation quantity of IP in the target network segment. For example, considering that the LB IP reservation quantity is evaluated according to the Master LB (Load Balancer, LB) high-availability redundancy, the apiserver ENI (Elastic Network Interface) IP reservation quantity is evaluated according to the WanKu managed cluster size, and the first IP reservation quantity is determined according to the LB IP reservation quantity and the apiserver ENI IP reservation quantity.

[0107] The maximum number of central cloud resource pools supported by a single user is determined according to the functional demand information of the user in the history of executing tasks, and the maximum number of nodes supported by a single central cloud resource pool is determined according to the attribute information set by the central cloud resource pool, and then the second IP reservation quantity is determined according to the maximum number of central cloud resource pools supported by a single user and the maximum number of nodes supported by a single central cloud resource pool. For example, the total number of central cloud nodes is determined according to the product of the maximum number of central cloud resource pools supported by a single user and the maximum number of nodes supported by a single central cloud resource pool, and the mapping relationship between the central cloud node quantity interval and the IP reservation quantity is determined according to the mapping relationship between the central cloud node quantity interval and the IP reservation quantity. For example, in the task execution process, the subnet is divided into a central cloud resource pool granularity, the peer-to-peer connection route is established to a subnet granularity, and the user's perception of the resource pool is actually connected to the subnet. Therefore, the subnet is divided according to the maximum number of resource pools supported by a single user and the maximum number of nodes supported by a single resource pool.

[0108] The user issues a service scenario of executing a task, and the type of the task is determined, for example, the service scenario includes a development machine, an inference service, a training task, and the like. Specifically, a mapping relationship between different service scenarios and IP reservation quantities is established in advance, the corresponding service scenario is determined according to the specific type of the task issued by the user to execute the task, and then the third IP reservation quantity corresponding to the service scenario is determined according to the mapping relationship. For example, the number of independent BLBs that may be used according to the service scenario (development machine, inference service, training task TensorBoard) is considered to estimate the third IP reservation quantity, and meanwhile, the BLB multiplexing (single-instance multi-port) in some scenarios is considered.

[0109] The maximum edge resource pool quantity supported by a single user is determined according to the usage information of the third-party computing nodes of the cloud platform, the maximum node quantity supported by a single edge resource pool is determined according to the attribute information of the edge resource pool, and then the fourth IP reservation quantity is determined according to the maximum edge resource pool quantity supported by a single user and the maximum node quantity supported by a single edge resource pool. For example, the total edge node quantity is determined according to the product of the maximum edge resource pool quantity supported by a single user and the maximum node quantity supported by a single edge resource pool, and the fourth IP reservation quantity is determined according to the mapping relationship between the pre-determined edge node quantity interval and the IP reservation quantity. For example, the edge VPC (Virtual Private Cloud, VPC) and the center VPC are in a large network segment, and in the view of the user, they are the same network segment. The edge VPC network segment size and the subnet division are determined according to the single-tenant edge resource pool quantity and the resource pool size supported by the plan.

[0110] The proxy access mode of the edge node to the center cloud public service is determined, and the fifth IP reservation quantity is determined according to the mapping relationship between the pre-determined different proxy access modes and the IP reservation quantity.

[0111] The maximum container quantity supported by a single cluster is determined according to the attribute information of the center cloud, and then the sixth IP reservation quantity is determined according to the mapping relationship between the pre-determined container quantity and the IP reservation quantity. For example, the container network uses an Overlay network to avoid conflicts when the container accesses the user VPC network, and therefore a special network segment is also needed, and the container subnet is divided according to the maximum container quantity supported by a single cluster.

[0112] During the task execution process, the communication interaction between the containers also needs to access the user resources. Therefore, when the container accesses the user resources through the target network segment of the user, the seventh IP reservation quantity is determined according to the network access quantity, wherein the network access quantity between the container and the user resources can be determined according to the historical access data. A network segment is separately set for the communication between the containers to avoid conflicts with the user VPC network, which causes the Service to be unable to be used.

[0113] According to the first IP reservation number, a corresponding subnet is divided from the target network as a Master subnet for hosting clusters, which is used to provide IP access support for Master LB and apiserver ENI. According to the second IP reservation number, a corresponding subnet is divided from the target network as a resource pool subnet, which is used to provide IP access support for node ENI in the center cloud. According to the third IP reservation number, a corresponding subnet is divided from the target network as a LB Service subnet, which is used to provide BLB IP access support. According to the fourth IP reservation number, a corresponding subnet is divided from the target network as an edge VPC subnet, which is used to provide IP access support for edge nodes. According to the fifth IP reservation number, a corresponding subnet is divided from the target network as a public service subnet, which is used to provide IP access support for edge nodes to access the center cloud public service. According to the sixth IP reservation number, a corresponding subnet is divided from the target network as a container subnet, and according to the seventh IP reservation number, a corresponding subnet is divided from the target network as a cluster subnet, which is used for IP address access of cluster internal container communication.

[0114] As shown in Figure 6 is a target network segment division schematic diagram. The target network segment of user A includes three VPC network segments: VPC-1, VPC-2 and VPC-3. According to the user's demand, corresponding resource pools are set in each VPC network segment. The cloud platform includes platform resource information determined for the user. According to different network segment resource demands, the three VPC network segments are divided, wherein the VPC-1 network segment is divided to the first center cloud VPC, and the first center cloud VPC includes node group 1, node group system and management node. The demand of the user's private network for access in the first center cloud VPC is realized through the VPC-1 network segment. Further, the VPC-1 network segment can be further divided according to the respective demands of the node group and the management node. Similarly, the VPC-2 network segment is divided to the second center cloud VPC and the second edge VPC, wherein the demand of the user's private network for access in the second center cloud VPC and the second edge VPC is realized through the VPC-2 network segment. Further, the VPC-2 network segment can be further divided according to the respective demands of the node group and the management node. The VPC-3 network segment is divided to the third center cloud VPC and the third edge VPC, wherein the demand of the user's private network for access in the third center cloud VPC and the third edge VPC is realized through the VPC-3 network segment. Further, the VPC-3 network segment can be further divided according to the respective demands of the node group and the management node.

[0115] If a center cloud node in the first center cloud VPC, the second center cloud VPC and the third center cloud VPC is offline or missing, a standby center cloud node is determined from a standby pool through a node changing VPC; similarly, if an edge node in the second edge VPC and the third edge VPC is offline or missing, a standby edge node is determined from a standby pool through a node changing VPC.

[0116] In another optional implementation of the embodiment, before the target network segment is divided according to the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity to obtain a plurality of subnets, the method further includes:

[0117] determining a comparison result of a predicted IP sum of the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity and a target IP sum included in the target network segment;

[0118] If the quantity of the target IP sum is greater than the predicted IP sum, a remaining IP quantity is determined according to the target IP sum and the predicted IP sum;

[0119] The first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity are updated according to the remaining IP quantity.

[0120] In order to avoid redundancy and waste of the IP of the target network segment provided by the user, after the corresponding IP reservation quantity is determined according to different network segment resource requirements, since the currently determined IP reservation quantity is the minimum access requirement of different network segment resource requirements, after the predicted IP sum is determined according to the determination of the minimum access requirement, it is determined whether there is a remaining IP of the target network segment according to the predicted IP sum.

[0121] Specifically, the sum of the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity is taken as the predicted IP sum. The comparison result of the predicted IP sum and the target IP sum included in the target network segment is determined, if the predicted IP sum is the same as the target IP sum, the network segment planning is performed according to the currently determined first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity, and a plurality of subnets are obtained.

[0122] If the target IP sum is greater than the expected IP sum, it indicates that there are remaining IPs in the target network segment provided by the user, and the remaining IPs are respectively allocated to different network resource requirements, that is, the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity are updated. For example, the priority of different network resource requirements is determined in advance, and the different IP reservation quantities are updated from high to low according to the priority; or the remaining IPs are divided according to the proportion of the current corresponding IP reservation quantity of different network resource requirements, and the remaining IPs are allocated to different IP reservation quantities according to the proportion.

[0123] If the target IP sum is less than the expected IP sum, it indicates that there is a problem of insufficient target network segment according to the current planning for network segment division, and therefore the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity are reduced. For example, the priority of different network resource requirements is determined in advance, and the different IP reservation quantities are reduced from low to high according to the priority; or the remaining IPs are divided according to the proportion of the current corresponding IP reservation quantity of different network resource requirements, and the IP reservation quantities corresponding to different network resource requirements are reduced according to the proportion, so that the total IP quantity to be reduced is the difference between the expected IP sum and the target IP sum.

[0124] The embodiment determines the size comparison result between the target IP sum and the expected IP sum, realizes the full use of the target network segment provided by the user, and avoids the waste of the user-provided resources.

[0125] In S340, a basic environment arrangement result is generated according to the platform resource information and the multiple subnets, and the basic environment arrangement result is provided to the user for task execution.

[0126] The scheme of the embodiment divides the corresponding subnets for the multiple network resource requirements from different angles respectively, realizes the access of different network resource requirements to the target network segment provided by the user through different subnets, improves the access efficiency of the target network segment, avoids the conflict of different requirements to the target network segment in the task execution process, and improves the division accuracy of the target network segment.

[0127] Figure 7 FIG. 7 is a structural schematic diagram of a basic environment arrangement device based on a cloud platform according to an embodiment of the present disclosure. The device can execute the basic environment arrangement method based on the cloud platform involved in any embodiment of the present disclosure. Referring to FIG. 7, the basic environment arrangement device 700 based on the cloud platform comprises a functional requirement acquisition module 710, a platform resource determination module 720 and an environment arrangement module 730. Figure 7 The basic environment arrangement device 700 based on the cloud platform comprises a functional requirement acquisition module 710, a platform resource determination module 720 and an environment arrangement module 730.

[0128] The function requirement obtaining module 710 is configured to obtain function requirement information of a user for a cloud platform to issue an execution task and a target network segment.

[0129] The platform resource determining module 720 is configured to determine corresponding platform resource information from the cloud platform according to the function requirement information.

[0130] The environment arrangement module 730 is configured to generate a basic environment arrangement result according to the platform resource information and a plurality of sub-networks obtained by dividing the target network segment, and provide the basic environment arrangement result to the user for task execution.

[0131] The scheme of the embodiment determines corresponding platform resource information according to function requirement information of an execution task, divides a target network segment provided by a user, and then generates a basic environment arrangement result for task execution according to platform resource information and sub-networks obtained by division, so as to realize task execution of the user to meet user requirements without the user needing to pay attention to the underlying resource composition of the cloud platform, improve the user's task attention and experience of task execution on the cloud platform, and effectively integrate available resources on the cloud platform in the process of generating the basic environment arrangement result, thereby improving the resource utilization rate of the cloud platform.

[0132] In an optional implementation manner of the embodiment, the platform resource determining module comprises:

[0133] The sub-task decomposition sub-module is configured to decompose the execution task according to the function requirement information to obtain a plurality of sub-tasks.

[0134] The target platform resource determining sub-module is configured to determine, for each sub-task, a corresponding target platform resource from candidate platform resources of the cloud platform.

[0135] In an optional implementation manner of the embodiment, the target platform resource determining sub-module comprises:

[0136] The requirement determining unit is configured to determine, according to execution attribute information of the sub-task, a user individual requirement and a task inherent requirement.

[0137] The target platform resource determining unit is configured to determine, according to the user individual requirement and the task inherent requirement, a target platform resource corresponding to each sub-task from candidate platform resources of the cloud platform.

[0138] In an optional implementation manner of the embodiment, the candidate platform resources comprise candidate computing resources, candidate storage resources, candidate network resources, and candidate component resources.

[0139] In an optional implementation of the embodiment, the target platform resource determination unit is specifically configured to:

[0140] The computing resource determination subunit is configured to determine, according to the user individual demand and the task inherent demand, a target computing resource corresponding to each of the subtasks from the candidate computing resources of the cloud platform;

[0141] The network resource determination subunit is configured to determine, according to a type of the target computing resource, a target network resource for interaction between the subtasks from candidate network resources.

[0142] In an optional implementation of the embodiment, the network resource determination subunit includes:

[0143] If the type of the target computing resource of the subtask is the central cloud computing resource, the subtask communicates and interacts with other subtasks using the elastic network card as the target network resource; the subtask communicates and interacts with other subtasks using the intelligent network as the target network resource; and the subtask communicates and interacts with the user resource using the peer-to-peer connection or the elastic network card as the target network resource.

[0144] If the type of the target computing resource of the subtask is the edge computing resource, the subtask communicates and interacts with other subtasks using the service network card as the target network resource.

[0145] In an optional implementation of the embodiment, the apparatus further includes a subnetwork division module configured to, before the generating of the basic environment arrangement result according to the platform resource information and the multiple subnetworks obtained by dividing the target network segment, specifically:

[0146] Divide the target network segment based on a plurality of predetermined network segment resource demands to obtain multiple subnetworks.

[0147] In an optional implementation of the embodiment, the plurality of predetermined network segment resource demands at least include a demand of a management node of a computing node for a network segment, a demand of a central cloud computing node for a network segment, a demand of a user access entry address management for a network segment, a demand of an edge computing node for a network segment, a demand of a central cloud public service access provided to the edge computing node for a network segment, a demand of a container for a network segment, and a demand of inter-container communication for a network segment.

[0148] In an optional implementation of the embodiment, the subnetwork division module includes:

[0149] a first demand division submodule, configured to determine a first IP reservation number from the target network segment based on a demand of a management node of a computing node for the network segment according to a management cluster size and a preset available redundancy, and determine a first division subnet according to the first IP reservation number;

[0150] a second demand division submodule, configured to determine a second IP reservation number according to a maximum central cloud resource pool number supported by a single user and a maximum node number supported by a single central cloud resource pool based on a demand of a central cloud computing node for the network segment, and determine a second division subnet according to the second IP reservation number;

[0151] a third demand division submodule, configured to determine a third IP reservation number according to a business scenario of a user issuing an execution task based on a demand of a user access portal address management for the network segment, and determine a third division subnet according to the third IP reservation number;

[0152] a fourth demand division submodule, configured to determine a fourth IP reservation number according to a maximum edge resource pool number supported by a single user and a maximum node number supported by a single edge resource pool based on a demand of an edge computing node for the network segment, and determine a fourth division subnet according to the fourth IP reservation number;

[0153] a fifth demand division submodule, configured to determine a fifth IP reservation number according to a proxy access mode based on a demand of providing central cloud public service access to an edge computing node for the network segment, and determine a fifth division subnet according to the fifth IP reservation number;

[0154] a sixth demand division submodule, configured to determine a sixth IP reservation number according to a maximum container number supported by a single cluster based on a demand of a container for the network segment, and determine a sixth division subnet according to the sixth IP reservation number;

[0155] a seventh demand division submodule, configured to determine a seventh IP reservation number according to a network access amount between a container and a user resource based on a demand of inter-container communication for the network segment, and determine a seventh division subnet according to the seventh IP reservation number;

[0156] a target network segment division submodule, configured to divide the target network segment according to the first IP reservation number, the second IP reservation number, the third IP reservation number, the fourth IP reservation number, the fifth IP reservation number, the sixth IP reservation number and the seventh IP reservation number, to obtain a plurality of subnets.

[0157] In an optional implementation of the embodiment, the device further comprises a remaining network segment division sub-module, configured to, before dividing the target network segment according to the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity to obtain a plurality of sub-networks, comprising:

[0158] an IP comparison unit configured to determine a comparison result of a total IP quantity of the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity and a target total IP quantity included in the target network segment;

[0159] a remaining IP determination unit configured to, if the target total IP quantity is greater than the total IP quantity, determine a remaining IP quantity according to the target total IP quantity and the total IP quantity;

[0160] a remaining IP division unit configured to update the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity and the seventh IP reservation quantity according to the remaining IP quantity.

[0161] The cloud platform-based basic environment arrangement device described above can execute the cloud platform-based basic environment arrangement method provided by any embodiment of the present disclosure, and has the corresponding function modules and beneficial effects of the execution method. Technical details not described in detail in the embodiment can be referred to the cloud platform-based basic environment arrangement method provided by any embodiment of the present disclosure.

[0162] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good customs.

[0163] According to the embodiments of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium and a computer program product.

[0164] Figure 8A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0165] like Figure 8 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. Various programs and data required for the operation of the device 500 can also be stored in the RAM 503. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0166] Various components in device 500 are connected to I / O interface 505, including: an input unit 506, such as a keyboard, mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, optical disk, etc.; and a communication unit 509, such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0167] The computing unit 501 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs various methods and processes described above, such as the cloud platform based base environment orchestration method. For example, in some embodiments, the cloud platform based base environment orchestration method can be implemented as a computer software program tangibly embodied in a machine readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded onto the RAM 503 and executed by the computing unit 501, one or more steps of the cloud platform based base environment orchestration method described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform the method cloud platform based base environment orchestration by any other appropriate means, such as by means of firmware.

[0168] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0169] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0170] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0171] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0172] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain networks, and the Internet.

[0173] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0174] It should be understood that the various forms of flow shown above can be re-ordered, added to, or have steps deleted, using the flow. For example, the steps described in the present disclosure can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, which are not limited herein.

[0175] The specific implementation described above does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A cloud platform-based basic environment orchestration method, comprising: obtaining function requirement information and a target network segment of a user for a cloud platform to issue an execution task; determining corresponding platform resource information from the cloud platform according to the function requirement information; generating a basic environment orchestration result according to the platform resource information and a plurality of subnets obtained by dividing the target network segment, and providing the basic environment orchestration result to the user for task execution; wherein the determining of the corresponding platform resource information from the cloud platform according to the function requirement information comprises: decomposing the execution task according to the function requirement information to obtain a plurality of subtasks; and determining corresponding target platform resources for each subtask from candidate platform resources of the cloud platform; wherein the candidate platform resources include candidate computing resources and candidate network resources; wherein the determining of the corresponding target platform resources for each subtask from the candidate platform resources of the cloud platform comprises: determining corresponding user individual requirements and task inherent requirements according to execution attribute information of the subtask; and determining the corresponding target platform resources for each subtask from the candidate platform resources of the cloud platform according to the user individual requirements and the task inherent requirements; wherein the determining of the corresponding target platform resources for each subtask from the candidate platform resources of the cloud platform according to the user individual requirements and the task inherent requirements comprises: determining corresponding target computing resources for each subtask from the candidate computing resources of the cloud platform according to the user individual requirements and the task inherent requirements; and determining target network resources for interaction between the subtasks from the candidate network resources according to types of the target computing resources; wherein the determining of the target network resources for interaction between the subtasks from the candidate network resources according to the types of the target computing resources comprises: if the type of the target computing resource of the subtask is a central cloud computing resource, using an elastic network card as the target network resource to communicate and interact with other subtasks using the central cloud computing resource; using an intelligent network as the target network resource to communicate and interact with other subtasks using an edge computing resource; and using a peer-to-peer connection or an elastic network card as the target network resource to communicate and interact with user resources; and if the type of the target computing resource of the subtask is an edge computing resource, using a service network card as the target network resource to communicate and interact with other subtasks using the central cloud computing resource.

2. The method of claim 1, wherein, The candidate platform resources further include candidate storage resources and candidate component resources.

3. The method of claim 1, wherein, Before the generating of the basic environment orchestration result according to the platform resource information and the plurality of subnets obtained by dividing the target network segment, the method further comprises: dividing the target network segment to obtain a plurality of subnets based on a plurality of network segment resource requirements determined in advance.

4. The method of claim 3, wherein, The predetermined multiple network segment resource requirements at least include requirements of a management node of a computing node, requirements of a central cloud computing node, requirements of a user access portal address management, requirements of an edge computing node, requirements of a central cloud public service access to the edge computing node, requirements of a container, and requirements of inter-container communication.

5. The method of claim 4, wherein, The target network segment is divided based on the predetermined multiple network segment resource requirements to obtain multiple sub-networks, including: Based on the requirements of the management node of the computing node, a first IP reservation quantity is determined from the target network segment according to a management cluster size and a preset available redundancy; Based on the requirements of the central cloud computing node, a second IP reservation quantity is determined according to a maximum central cloud resource pool quantity supported by a single user and a maximum node quantity supported by a single central cloud resource pool; Based on the requirements of the user access portal address management, a third IP reservation quantity is determined according to a business scenario of a user issuing an execution task; Based on the requirements of the edge computing node, a fourth IP reservation quantity is determined according to a maximum edge resource pool quantity supported by a single user and a maximum node quantity supported by a single edge resource pool; Based on the requirements of the central cloud public service access to the edge computing node, a fifth IP reservation quantity is determined according to a proxy access mode; Based on the requirements of the container, a sixth IP reservation quantity is determined according to a maximum container quantity supported by a single cluster; Based on the requirements of the inter-container communication, a seventh IP reservation quantity is determined according to a network access quantity between the container and the user resource; The target network segment is divided according to the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity, and the seventh IP reservation quantity to obtain multiple sub-networks.

6. The method of claim 5, before the target network segment is divided according to the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity, and the seventh IP reservation quantity to obtain multiple sub-networks, the method further comprises: determining a comparison result of a predicted IP total sum of the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity, and the seventh IP reservation quantity and a target IP total sum included in the target network segment; if the target IP total sum is greater than the predicted IP total sum, determining a remaining IP quantity according to the target IP total sum and the predicted IP total sum; updating the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity, and the seventh IP reservation quantity according to the remaining IP quantity.

7. A cloud platform-based basic environment arrangement device, comprising: a function requirement acquisition module configured to acquire function requirement information of a user for a cloud platform to issue an execution task and a target network segment; a platform resource determination module configured to determine corresponding platform resource information from the cloud platform according to the function requirement information; an environment arrangement module configured to generate a basic environment arrangement result according to the platform resource information and a plurality of sub-networks obtained by dividing the target network segment, and provide the basic environment arrangement result to the user for task execution; wherein the platform resource determination module comprises a sub-task decomposition submodule configured to decompose the execution task according to the function requirement information to obtain a plurality of sub-tasks, and a target platform resource determination submodule configured to determine a corresponding target platform resource for each sub-task from candidate platform resources of the cloud platform; wherein the target platform resource determination submodule comprises a requirement determination unit configured to determine a user individual requirement and a task inherent requirement according to execution attribute information of the sub-task, and a target platform resource determination unit configured to determine a target platform resource corresponding to each sub-task from the candidate platform resources of the cloud platform according to the user individual requirement and the task inherent requirement; wherein the candidate platform resources comprise candidate computing resources and candidate network resources; wherein the target platform resource determination unit is specifically configured to a computing resource determination subunit configured to determine a target computing resource corresponding to each sub-task from the candidate computing resources of the cloud platform according to the user individual requirement and the task inherent requirement, and a network resource determination subunit configured to determine a target network resource for interaction between the sub-tasks from the candidate network resources according to a type of the target computing resource; wherein the network resource determination subunit comprises: if the type of the target computing resource of the sub-task is a central cloud computing resource, then the sub-task and other sub-tasks using the central cloud computing resource are connected and interacted through an elastic network card as the target network resource; the sub-task and other sub-tasks using an edge computing resource are connected and interacted through an intelligent network as the target network resource; and the sub-task and user resources are connected and interacted through peer-to-peer connection or an elastic network card as the target network resource; and if the type of the target computing resource of the sub-task is an edge computing resource, then the sub-task and other sub-tasks using the central cloud computing resource are connected and interacted through a service network card as the target network resource.

8. The apparatus of claim 7, wherein, The candidate platform resources further comprise candidate storage resources and candidate component resources.

9. The apparatus of claim 7, wherein, The device further comprises a sub-network division module configured to, before the basic environment arrangement result is generated according to the platform resource information and the plurality of sub-networks obtained by dividing the target network segment, specifically: divide the target network segment based on a plurality of network segment resource requirements determined in advance to obtain the plurality of sub-networks.

10. The apparatus of claim 9, wherein, The predetermined multiple network segment resource requirements comprise at least a requirement of a management node of a computing node for a network segment, a requirement of a central cloud computing node for a network segment, a requirement of a user access portal address management for a network segment, a requirement of an edge computing node for a network segment, a requirement of providing central cloud public service access to an edge computing node for a network segment, a requirement of a container for a network segment, and a requirement of inter-container communication for a network segment.

11. The apparatus of claim 10, wherein, The subnet division module comprises: A first requirement division submodule is configured to determine a first IP reservation quantity from the target network segment based on the requirement of the management node of the computing node for the network segment according to a management cluster size and a preset available redundancy, and determine a first division subnet according to the first IP reservation quantity; A second requirement division submodule is configured to determine a second IP reservation quantity according to a maximum central cloud resource pool quantity supported by a single user and a maximum node quantity supported by a single central cloud resource pool based on the requirement of the central cloud computing node for the network segment, and determine a second division subnet according to the second IP reservation quantity; A third requirement division submodule is configured to determine a third IP reservation quantity according to a business scenario of a user issuing an execution task based on the requirement of the user access portal address management for the network segment, and determine a third division subnet according to the third IP reservation quantity; A fourth requirement division submodule is configured to determine a fourth IP reservation quantity according to a maximum edge resource pool quantity supported by a single user and a maximum node quantity supported by a single edge resource pool based on the requirement of the edge computing node for the network segment, and determine a fourth division subnet according to the fourth IP reservation quantity; A fifth requirement division submodule is configured to determine a fifth IP reservation quantity according to a proxy access mode based on the requirement of providing central cloud public service access to the edge computing node for the network segment, and determine a fifth division subnet according to the fifth IP reservation quantity; A sixth requirement division submodule is configured to determine a sixth IP reservation quantity according to a maximum container quantity supported by a single cluster based on the requirement of the container for the network segment, and determine a sixth division subnet according to the sixth IP reservation quantity; A seventh requirement division submodule is configured to determine a seventh IP reservation quantity according to a network access quantity between a container and a user resource based on the requirement of inter-container communication for the network segment, and determine a seventh division subnet according to the seventh IP reservation quantity; A target network segment division submodule is configured to divide the target network segment according to the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity, and the seventh IP reservation quantity to obtain multiple subnets.

12. The apparatus of claim 11, wherein, The device further comprises a remaining network segment division submodule configured to, before dividing the target network segment according to the first IP reservation quantity, the second IP reservation quantity, the third IP reservation quantity, the fourth IP reservation quantity, the fifth IP reservation quantity, the sixth IP reservation quantity, and the seventh IP reservation quantity to obtain multiple subnets, comprise: An IP comparison unit is used to determine a comparison result between the sum of the expected IP addresses of the first reserved IP address, the second reserved IP address, the third reserved IP address, the fourth reserved IP address, the fifth reserved IP address, the sixth reserved IP address, and the seventh reserved IP address and the sum of the target IP addresses included in the target network segment; a remaining IP determination unit, configured to determine the number of remaining IPs based on the target IP sum and the expected IP sum if the target IP sum is greater than the expected IP sum; The remaining IP division unit is used to update the first IP reserved quantity, the second IP reserved quantity, the third IP reserved quantity, the fourth IP reserved quantity, the fifth IP reserved quantity, the sixth IP reserved quantity and the seventh IP reserved quantity according to the remaining IP quantity.

13. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

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

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