Cloud host creation method and device, computer equipment and readable storage medium

Abstract

The invention relates to a cloud host creation method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: receiving a cloud host creation request, and analyzing to obtain resources requested by a cloud host in multiple dimensions; obtaining available resources of the plurality of computing nodes under each dimension, and determining a resource evaluation score of each computing node based on the available resources of each computing node under each dimension; based on the resources requested by the cloud host under each dimension and the available resources of each computing node under each dimension, respectively obtaining the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node; determining a target computing node matched with the cloud host from the computing nodes based on the similarities corresponding to the cloud host and the resource evaluation scores of the computing nodes; and based on the available resources of the target computing node in each dimension, creating the cloud host in the target computing node according to the resources requested by the cloud host in each dimension.

Description

Technical Field

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

[0002] In the scheduling platform of multiple cloud hosts, from the perspective of load balancing, when creating a cloud host, it is necessary to configure a reasonable load balancing scheduling method so that the resources of each computing node in the cloud platform can be reasonably allocated.

[0003] However, if the load balancing scheduling method is unreasonable, when creating a cloud host, some computing nodes may have high resource usage while other computing nodes may have idle resources, which makes the resource balancing accuracy low. Summary of the invention

[0004] Based on this, it is necessary to provide a cloud host creation method, device, computer equipment, computer-readable storage medium and computer program product that can improve the accuracy of resource balancing in response to the above technical problems.

[0005] In the first aspect, the present application provides a cloud host creation method. The method includes: receiving a cloud host creation request, parsing and obtaining the resources requested by the cloud host in multiple dimensions; obtaining the available resources of multiple computing nodes in each dimension, and determining the resource evaluation score of each computing node based on the available resources of each computing node in each dimension; based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, respectively obtaining the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node; based on the similarities corresponding to the cloud host and the resource evaluation scores of each computing node, determining the target computing node matching the cloud host from each computing node; based on the available resources of the target computing node in each dimension, creating a cloud host in the target computing node according to the resources requested by the cloud host in each dimension.

[0006] In one of the embodiments, based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, the similarities between the resources requested by the cloud host and the available resources corresponding to each computing node are obtained respectively, including: obtaining the inner products of resources between the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension; based on the statistical values ​​of the inner products of each resource corresponding to the cloud host, normalizing the inner products of each resource to obtain a first normalized result; wherein multiple results in the first normalized result respectively represent the similarities between the resources requested by the cloud host and the available resources corresponding to each computing node.

[0007] In one of the embodiments, based on the similarities corresponding to the cloud host and the resource evaluation scores of the computing nodes, a target computing node matching the cloud host is determined from each computing node, including: determining the average number of cloud hosts corresponding to each computing node based on the node quantity statistics of each computing node and the total number of cloud hosts in the available zone; determining the variance matching the currently created number of cloud hosts corresponding to each computing node based on the average number of cloud hosts corresponding to each computing node and the currently created number of cloud hosts; determining the target computing node matching the cloud host from each computing node based on the similarities corresponding to the cloud host, the resource evaluation scores of the computing nodes, and the variance corresponding to the computing nodes.

[0008] In one of the embodiments, based on the similarities corresponding to the cloud host, the resource evaluation score of each computing node, and the variance corresponding to each computing node, a target computing node matching the cloud host is determined from each computing node, including: normalizing the variance corresponding to each computing node to obtain a second normalization result; obtaining the congestion of the cloud host for each computing node based on a comparison result between an average number of cloud hosts corresponding to each computing node and a currently created number of cloud hosts, and the second normalization result; and determining the target computing node matching the cloud host from each computing node based on the congestion corresponding to the cloud host, the similarities corresponding to the cloud host, and the resource evaluation score of each computing node.

[0009] In one of the embodiments, based on the congestions corresponding to the cloud host, the similarities corresponding to the cloud host, and the resource evaluation scores of the computing nodes, a target computing node matching the cloud host is determined from the computing nodes, including: determining a similarity evaluation score corresponding to each similarity based on the product between each similarity corresponding to the cloud host and a first weight, and determining a congestion evaluation score corresponding to each congestion based on the product between each congestion corresponding to the cloud host and a second weight; determining a target score for each computing node based on the sum of the similarity evaluation score corresponding to each similarity and the congestion evaluation score corresponding to each congestion, and the resource evaluation score of each computing node; and determining a target computing node matching the cloud host based on the target score of each computing node and the computing node corresponding to the largest target score.

[0010] In one of the embodiments, a target computing node that matches the cloud host is determined based on the computing node corresponding to the maximum target score, including: when there are multiple computing nodes corresponding to the maximum target score, from among the computing nodes corresponding to the maximum target score, a computing node with the largest memory resources is determined as the target computing node that matches the cloud host.

[0011] In the second aspect, the present application also provides a cloud host creation device. The device includes: a receiving module for receiving a cloud host creation request, parsing and obtaining the resources requested by the cloud host in multiple dimensions; a determining module for obtaining the available resources of multiple computing nodes in each dimension, and determining the resource evaluation score of each computing node based on the available resources of each computing node in each dimension; a computing module for obtaining the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension; a processing module for determining a target computing node matching the cloud host from each computing node based on the similarities corresponding to the cloud host and the resource evaluation scores of each computing node; and a creating module for creating a cloud host in the target computing node according to the resources requested by the cloud host in each dimension based on the available resources of the target computing node in each dimension.

[0012] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program: receiving a cloud host creation request, parsing and obtaining the resources requested by the cloud host in multiple dimensions; obtaining the available resources of multiple computing nodes in each dimension, and determining the resource evaluation score of each computing node based on the available resources of each computing node in each dimension; based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, respectively obtaining the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node; based on the similarities corresponding to the cloud host and the resource evaluation scores of each computing node, determining the target computing node matching the cloud host from each computing node; based on the available resources of the target computing node in each dimension, creating a cloud host in the target computing node according to the resources requested by the cloud host in each dimension.

[0013] In a fourth aspect, the present application also provides a computer-readable storage medium. A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented: receiving a cloud host creation request, parsing and obtaining the resources requested by the cloud host in multiple dimensions; obtaining the available resources of multiple computing nodes in each dimension, and determining the resource evaluation score of each computing node based on the available resources of each computing node in each dimension; based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, respectively obtaining the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node; based on the similarities corresponding to the cloud host and the resource evaluation scores of each computing node, determining a target computing node matching the cloud host from each computing node; based on the available resources of the target computing node in each dimension, creating a cloud host in the target computing node according to the resources requested by the cloud host in each dimension.

[0014] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, which implements the following steps when executed by a processor: receiving a cloud host creation request, parsing and obtaining the resources requested by the cloud host in multiple dimensions; obtaining the available resources of multiple computing nodes in each dimension, and determining the resource evaluation score of each computing node based on the available resources of each computing node in each dimension; based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, respectively obtaining the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node; based on the similarities corresponding to the cloud host and the resource evaluation scores of each computing node, determining a target computing node matching the cloud host from each computing node; based on the available resources of the target computing node in each dimension, creating a cloud host in the target computing node according to the resources requested by the cloud host in each dimension.

[0015] The cloud host creation method, apparatus, computer device, storage medium and computer program product receive a cloud host creation request, parse and obtain the resources requested by the cloud host in multiple dimensions, obtain the available resources of multiple computing nodes in each dimension, and determine the resource evaluation score of each computing node based on the available resources of each computing node in each dimension, so that the available resources of the computing node can be effectively evaluated based on the resource evaluation score of the computing node; based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, respectively obtain the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node, so that, in the case of considering the similarity, In this way, computing nodes that match the cloud host can be found more accurately, and then based on the similarities corresponding to the cloud host and the resource evaluation scores of each computing node, the target computing node that matches the cloud host can be determined from each computing node. This can improve the resource balancing accuracy of the computing nodes while balancing the computing node resources, as well as the accuracy of the determined computing nodes that match the cloud host. Furthermore, based on the available resources of the target computing node in each dimension, a cloud host is created in the target computing node according to the resources requested by the cloud host in each dimension, which can achieve load balancing scheduling in high-concurrency cloud host creation scenarios, while reducing the load on the cloud platform and improving the reliability and stability of the cloud platform during operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] FIG1 is an application environment diagram of a cloud host creation method in one embodiment;

[0017] FIG2 is a schematic diagram of a process of creating a cloud host in one embodiment;

[0018] FIG3 is a flow chart of obtaining the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension in one embodiment;

[0019] FIG4 is a schematic diagram of a process for determining a target computing node matching a cloud host from among the computing nodes based on the similarities corresponding to the cloud host and the resource evaluation scores of the computing nodes in one embodiment;

[0020] FIG5 is a flow chart of determining a target computing node matching a cloud host from among the computing nodes based on the similarities corresponding to the cloud host, the resource evaluation scores of the computing nodes, and the variances corresponding to the computing nodes in one embodiment;

[0021] FIG6 is a flow chart of determining a target computing node matching a cloud host from among the computing nodes based on the congestion levels corresponding to the cloud host, the similarities corresponding to the cloud host, and the resource evaluation scores of the computing nodes in one embodiment;

[0022] FIG7 is a schematic diagram of a process of creating a cloud host in another embodiment;

[0023] FIG8 is a structural block diagram of a cloud host creation device in one embodiment;

[0024] FIG. 9 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations; and the acquisition, storage, processing, transmission, etc. of data must comply with relevant laws and regulations.

[0027] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0028] The cloud host method provided in the embodiment of the present application can be applied to the application environment shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers.

[0029] Specifically, server 104 receives a cloud host creation request from terminal 102, parses and obtains the resources requested by the cloud host in multiple dimensions, obtains the available resources of multiple computing nodes in each dimension, and determines the resource evaluation score of each computing node based on the available resources of each computing node in each dimension, and obtains the similarities between the resources requested by the cloud host and the available resources corresponding to each computing node based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, and then, based on the similarities corresponding to the cloud host and the resource evaluation scores of each computing node, determines the target computing node that matches the cloud host from each computing node, and creates the cloud host in the target computing node according to the resources requested by the cloud host in each dimension based on the available resources of the target computing node in each dimension.

[0030] The terminal 102 may be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0031] In one embodiment, as shown in FIG. 2 , a cloud host creation method is provided, which is described by taking the method applied to the server 104 in FIG. 1 as an example, and includes the following steps:

[0032] S202, receiving a cloud host creation request, parsing and obtaining resources requested by the cloud host in multiple dimensions.

[0033] The cloud host creation request refers to a request initiated by a terminal for creating a cloud host, and the number of cloud host creation requests is one or more.

[0034] In some embodiments, the cloud host creation request includes resources requested by the cloud host in multiple dimensions. By parsing the cloud host creation request, the resources requested by the cloud host in multiple dimensions can be obtained.

[0035] Among them, the multiple dimensions may include but are not limited to: CPU dimension, memory dimension, network dimension, and storage dimension, etc., then the resources requested by the cloud host under the CPU dimension may include CPU resources, etc., the resources requested by the cloud host under the memory dimension may include memory resources, etc., the resources requested by the cloud host under the network dimension may include network resources, etc., and the resources requested by the cloud host under the storage dimension may include storage resources, etc. CPU resources may include the number of CPU cores available in the tenant area, etc.

[0036] For example, if the number of cloud host creation requests is j, that is, the number of cloud hosts that need to be created is j, and the resources requested by each cloud host in multiple dimensions include CPU resources, memory resources, network resources, and storage resources, in the scenario of high-concurrency creation of cloud hosts on the cloud platform, for j cloud host creation requests, the resource requirement matrix F can be established based on the specification resources requested by different cloud hosts, such as resources including CPU resources, memory resources, network resources, and storage resources:

[0037]

[0038] in, to Respectively represent the specification resources of each cloud host, which can be further refined into:

[0039]

[0040] in, represents the CPU resources requested by the jth cloud host, represents the memory resources requested by the jth cloud host, represents the network resources requested by the jth cloud host, Represents the storage resources requested by the jth cloud host.

[0041] S204, obtaining available resources of multiple computing nodes in each dimension, and determining a resource evaluation score of each computing node based on the available resources of each computing node in each dimension.

[0042] Among them, computing nodes refer to candidate computing nodes that can be used to create cloud hosts in the cloud platform. Computing nodes can represent physical servers. The available resources of computing nodes can include CPU resources, memory resources, network resources, storage resources, etc. For example, assuming that the number of available computing nodes in the cloud platform is i, based on the CPU resources, memory resources, network resources, and storage resources corresponding to each computing node, the cloud platform resource model matrix H can be established:

[0043]

[0044] in, to Respectively represent the available resources of each computing node in each dimension, which can be further refined into:

[0045]

[0046] in, represents the CPU resources available on the i-th computing node, represents the memory resources available to the i-th computing node, represents the network resources available to the i-th computing node, Represents the storage resources available to the i computing nodes.

[0047] There is no limitation on the implementation method of determining the resource evaluation score of each computing node based on the available resources of each computing node in each dimension, and several of the methods are described below with examples.

[0048] In one embodiment, for each computing node, a resource evaluation score that matches the available resources of the computing node in each dimension can be determined based on the mapping relationship between the available resources in different dimensions and the resource evaluation scores; the mean of the resource evaluation scores of the computing node in each dimension is calculated to obtain the resource evaluation score of the computing node.

[0049] In one embodiment, for each computing node, the resource evaluation score of each computing node is determined based on the available resources of the computing node in each dimension and the resource weight corresponding to each dimension. Specifically, the available resources of the computing node in each dimension and the resource weight corresponding to each dimension are weighted and summed to obtain the resource evaluation score of the computing node.

[0050] For example, taking the available resources of a computing node including CPU resources, memory resources, network resources, and storage resources as an example, a resource weight vector W can be established:

[0051]

[0052] That is, we can assign weight ratios of 0.3, 0.4, 0.2, and 0.1 to CPU resources, memory resources, network resources, and storage resources. Furthermore, combined with the above cloud platform resource model matrix H, we can construct a resource evaluation score matrix S:

[0053]

[0054] Furthermore, the resource evaluation score matrix S can be refined as follows:

[0055]

[0056] in, , Represents the resource evaluation score of the i-th computing node.

[0057] S206, based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, respectively obtain the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node.

[0058] Among them, based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, there is no limitation on the implementation method of obtaining the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node. The following is an example of possible methods.

[0059] In one embodiment, for each computing node, the resource ratio between the resource requested by the cloud host in each dimension and the available resource of the computing node in each dimension is determined; based on the statistical value between the resource ratios corresponding to each dimension, the similarity corresponding to the statistical value is determined as the similarity between the resource requested by the cloud host and the available resource corresponding to the computing node. The similarity corresponding to the statistical value is obtained based on the mapping relationship between the statistical value and the similarity.

[0060] S208, based on the similarities corresponding to the cloud host and the resource evaluation scores of the computing nodes, determine a target computing node that matches the cloud host from among the computing nodes.

[0061] The target computing node refers to the computing node used to create the cloud host. Based on the similarities corresponding to the cloud host and the resource evaluation scores of each computing node, the implementation method of determining the target computing node matching the cloud host from each computing node is not limited, and the following is an example of possible implementation methods.

[0062] In one embodiment, the similarities corresponding to the cloud host are sorted in descending order to determine the computing nodes corresponding to the top M similarities; from the resource evaluation scores of each computing node, the computing node corresponding to the largest resource evaluation is determined as the second computing node; when the M computing nodes include the second computing node, the second computing node is determined as the target computing node that matches the cloud host; when the M computing nodes do not include the second computing node, any one of the M computing nodes is determined as the target computing node that matches the cloud host.

[0063] S210, based on the available resources of the target computing node in each dimension, create a cloud host in the target computing node according to the resources requested by the cloud host in each dimension.

[0064] Among them, based on the available resources of the target computing node in each dimension, a cloud host is created in the target computing node according to the resources requested by the cloud host in each dimension, so that the cloud host executes the corresponding processing process based on the requested resources.

[0065] Based on the method shown in FIG. 2 , a cloud host creation request is received, the resources requested by the cloud host in multiple dimensions are parsed, and the available resources of multiple computing nodes in each dimension are obtained, and the resource evaluation score of each computing node is determined based on the available resources of each computing node in each dimension. Thus, based on the resource evaluation score of the computing node, the available resources of the computing node can be effectively evaluated; based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node is obtained respectively. Thus, in the case of considering the similarity, the resources corresponding to the computing node can be found more accurately. The computing nodes matched with the cloud host are then determined from the computing nodes based on the similarities corresponding to the cloud hosts and the resource evaluation scores of the computing nodes. This can improve the resource balancing accuracy of the computing nodes while balancing the computing node resources, as well as the accuracy of the determined computing nodes matched with the cloud host. Furthermore, based on the available resources of the target computing node in each dimension, a cloud host is created in the target computing node according to the resources requested by the cloud host in each dimension. This can achieve load balancing scheduling in high-concurrency cloud host creation scenarios, while reducing the load on the cloud platform, while improving the reliability and stability of the cloud platform during operation.

[0066] In the process of scheduling multiple cloud hosts, from the perspective of load balancing, it is hoped that the resources of each computing node of the cloud platform can be reasonably allocated to avoid the situation where some computing nodes have high resource usage and other computing nodes have idle resources. A reasonable cloud host load balancing scheduling algorithm should meet two scenarios. On the one hand, the cloud host should call the computing node that can provide its specification resource requirements. Furthermore, the scheduled computing node should be the node that makes the cloud host most satisfied. For example, for CPU-enhanced cloud hosts or cloud hosts with more CPU core requests, among the computing nodes that meet the scheduling requirements, it is more hoped that the cloud host will be scheduled to the node with the most available CPU resources among the computing nodes. The same is true for other types of cloud hosts. On the other hand, the load balancing algorithm should also meet the load balancing of the number of cloud hosts as much as possible under the premise of meeting the resource load balancing, that is, the number of cloud hosts on each computing node should be as close as possible. Among them, the inner product (i.e., dot product) of vectors can geometrically represent the projection of a vector on another vector. The larger the inner product of two vectors, the closer the vector projection is to the other vector, that is, the more similar the two vectors are.

[0067] Based on this, the resource inner product between the resources requested by the cloud host and the available resources of each computing node can be considered to determine the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node. Specifically, in one embodiment, based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, the implementation method of obtaining the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node (i.e., S206) can be shown in Figure 3, including the following steps:

[0068] S302, obtaining the inner product of resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension.

[0069] For example, taking the number of cloud hosts as j and the number of computing nodes as i, combined with the above resource demand matrix F and cloud platform resource model matrix H, we can get:

[0070]

[0071] Further, it can be refined into:

[0072]

[0073] in, , which is the inner product of the resources requested by the j-th cloud host and the available resources of the i-th computing node.

[0074] S304, based on the statistical values ​​of the inner products of the resources corresponding to the cloud host, normalize the inner products of the resources to obtain a first normalized result; multiple results in the first normalized result respectively represent the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node.

[0075] It should be understood that the inner product of each resource corresponding to the cloud host is a quantitative value, and it cannot intuitively show the similarity between the resource conditions of the cloud host and each computing node. Therefore, a normalization function can be used, such as Functions, etc., linearly change the inner products of each resource corresponding to the cloud host to convert the corresponding similarity into a probability distribution, which can intuitively represent which computing node the cloud host has a stronger sense of belonging to.

[0076] For example, the first normalized result corresponding to each cloud host can be obtained through the profit-seeking sense of belonging matrix It means that the cloud host has a stronger sense of belonging to the computing nodes with the most abundant resources, and will tend to schedule the computing nodes with the most abundant resources. Specifically:

[0077]

[0078] Furthermore, the profit-seeking sense of belonging matrix L can be refined as follows:

[0079]

[0080] From the above, we can see that the profit-seeking sense of belonging matrix Each value in is normalized to the interval [0,1], and the profit-seeking sense of belonging matrix The values ​​in each row of respectively represent the similarity between the resources requested by the corresponding cloud host and the available resources corresponding to each computing node, and also represent the cloud host's preference for the resources of each computing node.

[0081] Based on the content shown in Figure 3, the cosine similarity between the resources requested by the cloud host and the available resources of each computing node can be determined by considering the inner product of the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension. Then, based on the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node, the cloud host's sense of belonging to the available resources of each computing node is reflected. When determining the target computing node that matches the cloud host, the resource balancing accuracy of the computing nodes can be improved while balancing the computing node resources, and the accuracy of the computing nodes determined to match the cloud host can be improved.

[0082] In one embodiment, based on the similarities corresponding to the cloud host and the resource evaluation scores of the computing nodes, the implementation method of determining the target computing node matching the cloud host from the computing nodes (i.e., S208) may include the following steps as shown in FIG. 4:

[0083] S402, based on the node quantity statistics of each computing node and the total number of cloud hosts in the available zone, determine the average number of cloud hosts corresponding to each computing node.

[0084] Among them, Availability Zone (AZ) is an important concept in cloud computing platforms. It refers to a physical area with highly redundant and independent power, network and hardware facilities in the same region. Various computing nodes are deployed in the availability zone.

[0085] Specifically, the ratio of the total number of cloud hosts to the number of nodes is determined as the average number of cloud hosts corresponding to each computing node, that is, the average number of cloud hosts corresponding to all computing nodes is the same. For example, the total number of cloud hosts in the available zone is N, the number of nodes is i, and the average number of cloud hosts is .

[0086] S404: Determine a variance matching the number of currently created cloud hosts corresponding to each computing node based on the average number of cloud hosts corresponding to each computing node and the number of currently created cloud hosts.

[0087] Among them, for a cloud platform with load balancing of the number of cloud hosts, the number of cloud hosts currently created on its computing nodes should be very close to the average number of cloud hosts. Therefore, this application uses variance to measure the degree of balance of the number of cloud hosts created on the computing nodes. For example, a large variance may indicate that the number of cloud hosts currently created on the computing node is significantly different from the average number of cloud hosts; a small variance indicates that the number of cloud hosts currently created on the computing node is not much different from the average number of cloud hosts.

[0088] Specifically, for each computing node, the difference between the number of currently created cloud hosts corresponding to the computing node and the average number of cloud hosts is determined; and the square of the difference is determined as the variance matching the number of currently created cloud hosts corresponding to each computing node.

[0089] for example, Indicates the number of currently created cloud hosts corresponding to the i-th computing node. represents the variance of the number of currently created cloud hosts corresponding to the i-th computing node, then .

[0090] S406, based on the similarities corresponding to the cloud host, the resource evaluation scores of the computing nodes, and the variances corresponding to the computing nodes, determine a target computing node that matches the cloud host from among the computing nodes.

[0091] Among them, based on the similarities corresponding to the cloud host, the resource evaluation scores of each computing node, and the variances corresponding to each computing node, there is no limitation on the implementation method of determining the target computing node matching the cloud host from each computing node. The following is an example of possible implementation methods.

[0092] In one embodiment, the similarities corresponding to the cloud host are sorted in descending order to determine the computing nodes corresponding to the top M similarities; based on the resource evaluation score of each computing node, the computing node corresponding to the largest resource evaluation is determined as the second computing node; based on the variance corresponding to each computing node, the computing node corresponding to the smallest variance is determined as the third computing node; when the M computing nodes include the second computing node, and the second computing node is inconsistent with the third computing node, the second computing node is determined as the target computing node that matches the cloud host.

[0093] In one embodiment, when the M computing nodes do not include the second computing node, any one computing node among the M computing nodes is determined as a target computing node that matches the cloud host.

[0094] In one embodiment, when the M computing nodes include a second computing node, and the second computing node is consistent with the third computing node, any computing node among the M computing nodes except the second computing node is determined as a target computing node matching the cloud host.

[0095] Based on the content shown in Figure 4, by considering the number of cloud hosts currently created on each computing node, and thus determining the variance corresponding to each computing node based on the number of cloud hosts currently created, the cloud host creation status of each computing node can be evaluated. Furthermore, based on the variance corresponding to each computing node, when determining the target computing node that matches the cloud host, the determination accuracy can be improved while balancing the computing node resources.

[0096] Combined with the content shown in Figure 3, it can be seen that the variance corresponding to the computing node can measure the load balancing degree of the number of cloud hosts on the computing node, but it cannot show the congestion of the cloud host to be created for the computing node, because a large variance may indicate that the current number of cloud hosts created on the computing node is significantly different from the average number of cloud hosts, but it cannot show whether the current number of cloud hosts created is less than or more than the average number of cloud hosts. In order to characterize the congestion of the cloud host to be created for the computing node, the variance corresponding to the computing node can be normalized to reduce the variance corresponding to the computing node to a probability distribution represented as the [0,1] interval.

[0097] In one embodiment, based on the similarities corresponding to the cloud host, the resource evaluation scores of the computing nodes, and the variances corresponding to the computing nodes, a method for determining a target computing node (i.e., S406) that matches the cloud host from the computing nodes may be as shown in FIG. 5 and include the following steps:

[0098] S502, normalizing the variance corresponding to each computing node to obtain a second normalized result.

[0099] S504: Based on the comparison result between the average number of cloud hosts corresponding to each computing node and the number of currently created cloud hosts, and the second normalization result, obtain the congestion degree of the cloud hosts for each computing node.

[0100] Among them, congestion is used to characterize the suitability of cloud hosts created on computing nodes.

[0101] Specifically, based on the comparison result between the average number of cloud hosts corresponding to each computing node and the number of currently created cloud hosts, determine whether to adjust the sign of the second normalization result; if no adjustment is required, obtain the congestion degree of the cloud host for each computing node based on the second normalization result; if adjustment is required, obtain the congestion degree of the cloud host for each computing node based on the adjusted second normalization result. For example, when the number of currently created cloud hosts is less than the average number of cloud hosts, adjust the sign of the second normalization result, otherwise do not adjust.

[0102] For example, taking the number of computing nodes as i, the symbolic function can be defined as:

[0103]

[0104] in, , through the symbolic function Adjust the sign of the variance probability distribution to affect the sign of the second normalization result; the number of currently created cloud hosts corresponding to the computing node Less than the average number of cloud hosts hour, If it is positive, it indicates that the weight of the computing node needs to be increased; otherwise, if it is negative, it indicates that the weight of the computing node needs to be reduced.

[0105] Furthermore, based on the variance corresponding to each computing node, the variance matrix D can be constructed:

[0106]

[0107] Furthermore, based on the variance matrix D, the crowding aversion matrix is ​​constructed :

[0108]

[0109] Furthermore, the crowding aversion matrix Can be refined into:

[0110]

[0111] in, Each value in represents the congestion of the cloud host for each computing node.

[0112] S506, based on the congestion levels corresponding to the cloud hosts, the similarities corresponding to the cloud hosts, and the resource evaluation scores of the computing nodes, a target computing node matching the cloud host is determined from the computing nodes.

[0113] There is no limitation on the implementation method of determining the target computing node matching the cloud host from among the computing nodes based on the congestion levels, the similarities corresponding to the cloud hosts, and the resource evaluation scores of the computing nodes. The following is an example of possible implementation methods.

[0114] In one embodiment, the congestions corresponding to the cloud hosts are sorted in order from small to large, and the top Q congestions are determined; the similarities corresponding to the cloud hosts are sorted in order from large to small, and the computing nodes corresponding to the top M similarities are determined; based on the resource evaluation scores of the computing nodes, the computing node corresponding to the largest resource evaluation is determined as the second computing node; when the M computing nodes include the second computing node, and the computing nodes corresponding to the Q congestions include the second computing node, the second computing node is determined as the target computing node matching the cloud host.

[0115] In one embodiment, when the M computing nodes include the second computing node, and the computing nodes corresponding to the Q congestion levels do not include the second computing node, any computing node among the M computing nodes except the second computing node is determined as a target computing node matching the cloud host.

[0116] In some embodiments, when the M computing nodes do not include the second computing node, and the computing nodes corresponding to the Q congestion levels include the second computing node, any one of the computing nodes corresponding to the Q congestion levels is determined as a target computing node matching the cloud host.

[0117] Based on the content shown in FIG. 5 , by considering the congestion of the cloud host for each computing node, the determination accuracy can be improved when determining the target computing node that matches the cloud host from the computing nodes.

[0118] In one embodiment, based on the congestion levels corresponding to the cloud hosts, the similarities corresponding to the cloud hosts, and the resource evaluation scores of the computing nodes, a method of determining a target computing node (i.e., S506) that matches the cloud host from the computing nodes may be as shown in FIG. 6 and include the following steps:

[0119] S602, determining a similarity evaluation score corresponding to each similarity based on the product between each similarity corresponding to the cloud host and the first weight, and determining a congestion evaluation score corresponding to each congestion based on the product between each congestion corresponding to the cloud host and the second weight.

[0120] S604 : Determine a target score for each computing node based on the sum of the similarity evaluation score corresponding to each similarity, the congestion evaluation score corresponding to each congestion degree, and the resource evaluation score of each computing node.

[0121] Among them, the similarity evaluation score corresponding to each similarity, the congestion evaluation score corresponding to each congestion degree, the resource evaluation score of each computing node, and the target score of each computing node can be represented by a matrix.

[0122] Specifically, by adding the matrix corresponding to the similarity evaluation score corresponding to each similarity and the matrix corresponding to the congestion evaluation score corresponding to each congestion degree, and multiplying it by the transpose of the matrix corresponding to the resource evaluation score of each computing node, a target score matrix can be obtained, and each numerical value in the target score matrix represents the target score of each computing node.

[0123] For example, based on the resource evaluation score matrix S, we can get the resource evaluation score of each computing node; based on the profit-seeking sense of belonging matrix , we can get the similarities corresponding to each cloud host; based on the above congestion aversion matrix , we can get the congestion degree corresponding to the cloud host, then the target score matrix corresponding to the jth cloud host is :

[0124]

[0125] in, express The jth row in Each value in represents the target score of each computing node corresponding to the jth cloud host; and Respectively represented as the first weight and the second weight, and They can be 0.8 and 0.2 respectively, or other values ​​can be set.

[0126] In combination with the above content, taking the resource requested by the cloud host in multiple dimensions including CPU resources, memory resources, network resources and storage resources as an example, in one embodiment, as shown in FIG. 7 , a cloud host creation method is provided, and the method is applied to the server 104 in FIG. 1 as an example for description, including the following steps:

[0127] S702, receiving a cloud host creation request, parsing and obtaining resources requested by the cloud host in multiple dimensions.

[0128] For example, the number of cloud hosts is j, and the resources requested by each cloud host in multiple dimensions include CPU resources, memory resources, network resources, and storage resources. Then the resource demand matrix F is established:

[0129]

[0130] Among them, among them, represents the CPU resources requested by the jth cloud host, represents the memory resources requested by the jth cloud host, represents the network resources requested by the jth cloud host, Represents the storage resources requested by the jth cloud host.

[0131] S704, obtaining available resources of multiple computing nodes in each dimension, and determining a resource evaluation score of each computing node based on the available resources of each computing node in each dimension.

[0132] In the hybrid cloud platform scenario, the tenant area memory resources are usually tight. Due to the high risk of memory resource over-allocation, when the memory resource load is high, it is easy to have memory overflow (Out Of Memory, OOM), which will cause the cloud platform operating system to kill the cloud host process, causing great losses and inconvenience. In addition, CPU resources are usually under low load, and resource utilization can be improved by serialization and increasing the over-allocation ratio, while network and storage resources are more abundant than CPU and memory resources. Therefore, a resource weight vector is established for CPU resources, memory resources, network resources, and storage resources to achieve load balancing scheduling.

[0133] Memory overflow refers to the situation where there is unreclaimable memory in the application system or too much memory is used, which eventually causes the memory required for the program to run to be greater than the maximum memory available. At this point, the program cannot run, the system will prompt a memory overflow, and sometimes the software will be automatically closed.

[0134] For example, assuming that the number of computing nodes is i, the cloud platform resource model matrix H is established based on the CPU resources, memory resources, network resources, and storage resources corresponding to each computing node:

[0135]

[0136] in, represents the CPU resources of the i-th computing node, represents the memory resources of the i-th computing node, represents the network resources of the i-th computing node, Represents the storage resources of i computing nodes.

[0137] Furthermore, a resource evaluation score matrix S is constructed:

[0138]

[0139] in, , Represents the resource evaluation score of the i-th computing node.

[0140] S706, obtaining the inner product of resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension.

[0141] Among them, combining the above resource demand matrix F and cloud platform resource model matrix H, we can get:

[0142]

[0143] in, , which is the inner product of the resources requested by the j-th cloud host and the available resources of the i-th computing node.

[0144] S708, based on the statistical values ​​of the inner products of the resources corresponding to the cloud host, normalize the inner products of the resources to obtain a first normalized result; wherein the multiple results in the first normalized result respectively represent the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node.

[0145] S710, based on the node quantity statistics of each computing node and the total number of cloud hosts in the available zone, determine the average number of cloud hosts corresponding to each computing node.

[0146] For example, the total number of cloud hosts is N, the number of nodes is i, and the average number of cloud hosts is .

[0147] S712: Determine a variance that matches the number of currently created cloud hosts corresponding to each computing node based on the average number of cloud hosts corresponding to each computing node and the number of currently created cloud hosts.

[0148] for example, Indicates the number of currently created cloud hosts corresponding to the i-th computing node. represents the variance of the number of currently created cloud hosts corresponding to the i-th computing node, then .

[0149] S714, normalize the variance corresponding to each computing node to obtain a second normalized result, and based on the comparison result between the average number of cloud hosts corresponding to each computing node and the number of currently created cloud hosts, and the second normalized result, obtain the congestion degree of the cloud host for each computing node.

[0150] It should be understood that, under the premise of satisfying resource load balancing, the load balancing of the number of cloud hosts should be satisfied as much as possible, that is, the number of cloud hosts on each computing node should be as close as possible. Therefore, by establishing a congestion aversion matrix, the number of cloud hosts can be further balanced to the maximum extent on the basis of satisfying resource load balancing.

[0151] Among them, based on the variance corresponding to each computing node, the variance matrix D is constructed:

[0152]

[0153] Furthermore, based on the variance matrix D, the crowding aversion matrix is ​​constructed :

[0154]

[0155] in, Each value in represents the congestion of the cloud host for each computing node.

[0156] S716, determining a similarity evaluation score corresponding to each similarity based on the product between each similarity corresponding to the cloud host and the first weight, and determining a congestion evaluation score corresponding to each congestion based on the product between each congestion corresponding to the cloud host and the second weight.

[0157] S718, adding the matrix corresponding to the similarity evaluation score corresponding to each similarity and the matrix corresponding to the congestion evaluation score corresponding to each congestion degree, and multiplying it by the transpose of the matrix corresponding to the resource evaluation score of each computing node to obtain a target score matrix; each numerical value in the target score matrix represents the target score of each computing node.

[0158] Among them, based on the above resource evaluation score matrix S, the resource evaluation score of each computing node can be obtained; based on the above profit-seeking sense of belonging matrix , we can get the similarities corresponding to each cloud host; based on the above congestion aversion matrix , we can get the congestion degree corresponding to the cloud host, then the target score matrix corresponding to the jth cloud host is :

[0159]

[0160] in, express The jth row in Each value in represents the target score of each computing node corresponding to the jth cloud host; and Respectively represented as the first weight and the second weight.

[0161] S720, based on the available resources of the target computing node in each dimension, create a cloud host in the target computing node according to the resources requested by the cloud host in each dimension.

[0162] Among them, the specific content of S702-S720 can be referred to the content adaptation description, which will not be repeated here.

[0163] Based on the content shown in Figure 7, it can be seen that first, each computing node is initially scored according to the available resources of each computing node to obtain the resource evaluation score of each computing node; and the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node is calculated through matrix inner product operation to reflect the cloud host's sense of belonging to the resources of each computing node. Then, the sense of belonging matrix is ​​defined according to the vector inner product and cosine similarity, and the degree of belonging of each cloud host to be created to each computing node can be quickly calculated to reflect the percentage-based preference of each cloud host for each computing node. Secondly, by calculating the average number of cloud hosts and variance corresponding to the node, a congestion aversion matrix that can reflect the congestion of the cloud host to each computing node can be constructed to quickly obtain the cloud host distribution of the overall cloud platform. Finally, according to the resource evaluation score, sense of belonging matrix, and congestion aversion matrix corresponding to each computing node, the final score of each cloud host to each computing node is calculated, and the one with the highest score is selected for scheduling, realizing load balancing scheduling in the scenario of high-concurrency cloud host creation, while reducing the load of the cloud platform, improving the reliability and stability of the system.

[0164] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0165] Based on the same inventive concept, the embodiment of the present application provides a cloud host creation device for implementing the cloud host creation method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more cloud host creation device embodiments provided below can refer to the limitations of the cloud host creation method above, and will not be repeated here.

[0166] In one embodiment, as shown in FIG8 , a cloud host creation device is provided, comprising: a receiving module 802, a determining module 804, a calculating module 806, a processing module 808 and a creating module 810, wherein: the cloud host creation device. The device comprises: a receiving module 802, for receiving a cloud host creation request, parsing and obtaining the resources requested by the cloud host in multiple dimensions; a determining module 804, for obtaining the available resources of multiple computing nodes in each dimension, and determining the resource evaluation score of each computing node based on the available resources of each computing node in each dimension; a calculating module 806, for obtaining the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension; a processing module 808, for determining a target computing node matching the cloud host from each computing node based on the similarities corresponding to the cloud host and the resource evaluation scores of each computing node; and a creating module 810, for creating a cloud host in the target computing node according to the resources requested by the cloud host in each dimension based on the available resources of the target computing node in each dimension.

[0167] In one of the embodiments, the computing module is further used to: obtain the inner product of resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension; normalize the inner products of each resource based on the statistical value of the inner products of each resource corresponding to the cloud host to obtain a first normalized result; wherein multiple results in the first normalized result respectively represent the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node.

[0168] In one of the embodiments, the processing module is further used to: determine the average number of cloud hosts corresponding to each computing node based on the node quantity statistics of each computing node and the total number of cloud hosts in the available zone; determine the variance matching the currently created number of cloud hosts corresponding to each computing node based on the average number of cloud hosts corresponding to each computing node and the currently created number of cloud hosts; and determine the target computing node matching the cloud host from each computing node based on the similarities corresponding to the cloud hosts, the resource evaluation scores of each computing node, and the variance corresponding to each computing node.

[0169] In one of the embodiments, the processing module is further used to: normalize the variance corresponding to each computing node to obtain a second normalized result; obtain the congestion of the cloud host for each computing node based on the comparison result between the average number of cloud hosts corresponding to each computing node and the number of currently created cloud hosts, and the second normalized result; determine the target computing node that matches the cloud host from each computing node based on the congestion corresponding to the cloud host, the similarities corresponding to the cloud host, and the resource evaluation score of each computing node.

[0170] In one of the embodiments, the processing module is further used to: determine a similarity evaluation score corresponding to each similarity based on the product between each similarity corresponding to the cloud host and the first weight, and determine a congestion evaluation score corresponding to each congestion based on the product between each congestion corresponding to the cloud host and the second weight; determine a target score for each computing node based on the sum of the similarity evaluation score corresponding to each similarity and the congestion evaluation score corresponding to each congestion, and the resource evaluation score of each computing node; determine a target computing node that matches the cloud host based on the target score of each computing node and the computing node corresponding to the largest target score.

[0171] In one of the embodiments, the processing module is further used to: when there are multiple computing nodes corresponding to the maximum target score, determine the computing node with the largest memory resources from the computing nodes corresponding to the maximum target score as the target computing node matching the cloud host.

[0172] Each module in the above-mentioned cloud host creation device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0173] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG9. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as cloud host creation requests. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a cloud host creation method is implemented.

[0174] Those skilled in the art will understand that the structure shown in FIG. 9 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0175] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0176] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0177] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0178] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0179] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0180] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A cloud host creation method, characterized in that: The method comprises: Receive cloud host creation requests, parse and obtain the resources requested by the cloud host in multiple dimensions; Obtaining available resources of multiple computing nodes under each of the dimensions, and determining a resource evaluation score of each of the computing nodes based on the available resources of each of the computing nodes under each of the dimensions; Based on the resources requested by the cloud host under each of the dimensions and the available resources of each of the computing nodes under each of the dimensions, respectively obtaining similarities between the resources requested by the cloud host and the available resources corresponding to each of the computing nodes; Based on the similarities corresponding to the cloud host and the resource evaluation scores of the computing nodes, determining a target computing node matching the cloud host from the computing nodes; Based on the available resources of the target computing node in each of the dimensions, the cloud host is created in the target computing node according to the resources requested by the cloud host in each of the dimensions.

2. The method according to claim 1, characterized in that The obtaining, based on the resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension, the similarity between the resources requested by the cloud host and the available resources corresponding to each computing node, includes: Obtaining the inner product of resources requested by the cloud host in each dimension and the available resources of each computing node in each dimension; Based on the statistical value of the inner product of each resource corresponding to the cloud host, normalize each inner product of the resource to obtain a first normalization result; Among them, the multiple results in the first normalized result respectively represent the similarity between the resources requested by the cloud host and the available resources corresponding to each of the computing nodes.

3. The method according to claim 1, characterized in that The determining, from the computing nodes, a target computing node matching the cloud host based on the similarities corresponding to the cloud host and the resource evaluation scores of the computing nodes, comprises: Determine the average number of cloud hosts corresponding to each computing node based on the node quantity statistics of each computing node and the total number of cloud hosts in the available zone; Based on the average number of cloud hosts corresponding to each of the computing nodes and the number of currently created cloud hosts, determining a variance that matches the number of currently created cloud hosts corresponding to each of the computing nodes; Based on the similarities corresponding to the cloud host, the resource evaluation scores of the computing nodes, and the variances corresponding to the computing nodes, a target computing node matching the cloud host is determined from the computing nodes.

4. The method according to claim 3, characterized in that The determining, from the computing nodes, a target computing node matching the cloud host based on the similarities corresponding to the cloud host, the resource evaluation scores of the computing nodes, and the variances corresponding to the computing nodes, comprises: Normalizing the variance corresponding to each of the computing nodes to obtain a second normalized result; Obtaining the congestion degree of the cloud host for each of the computing nodes based on a comparison result between the average number of cloud hosts corresponding to each of the computing nodes and the number of currently created cloud hosts, and a second normalization result; Based on the congestion degrees corresponding to the cloud host, the similarities corresponding to the cloud host, and the resource evaluation scores of the computing nodes, a target computing node matching the cloud host is determined from the computing nodes.

5. The method according to claim 4, characterized in that The determining, from the computing nodes, a target computing node matching the cloud host based on the congestion degrees corresponding to the cloud host, the similarities corresponding to the cloud host, and the resource evaluation scores of the computing nodes, comprises: Determine a similarity evaluation score corresponding to each similarity based on the product between each similarity corresponding to the cloud host and the first weight, and determine a congestion evaluation score corresponding to each congestion based on the product between each congestion corresponding to the cloud host and the second weight; Determine a target score for each computing node based on a sum of a similarity evaluation score corresponding to each similarity, a congestion evaluation score corresponding to each congestion degree, and a resource evaluation score for each computing node; Based on the target scores of the computing nodes and based on the computing node corresponding to the largest target score, a target computing node matching the cloud host is determined.

6. The method according to claim 1, characterized in that The determining, based on the computing node corresponding to the maximum target score, a target computing node matching the cloud host comprises: When there are multiple computing nodes corresponding to the maximum target score, a computing node with the largest memory resources is determined as a target computing node matching the cloud host from among the computing nodes corresponding to the maximum target score.

7. A cloud host creation device, characterized in that: The device comprises: The receiving module is used to receive a cloud host creation request and parse and obtain the resources requested by the cloud host in multiple dimensions; A determination module, used to obtain available resources of multiple computing nodes under each of the dimensions, and determine a resource evaluation score of each of the computing nodes based on the available resources of each of the computing nodes under each of the dimensions; A calculation module, configured to obtain, based on the resources requested by the cloud host in each of the dimensions and the available resources of each of the computing nodes in each of the dimensions, the similarity between the resources requested by the cloud host and the available resources corresponding to each of the computing nodes; A processing module, configured to determine a target computing node matching the cloud host from among the computing nodes based on the similarities corresponding to the cloud host and the resource evaluation scores of the computing nodes; A creation module is used to create the cloud host in the target computing node according to the resources requested by the cloud host in each of the dimensions based on the available resources of the target computing node in each of the dimensions.

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

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

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

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