A cloud platform state synchronization method, a multi-cloud management platform and a storage medium

Abstract

The application discloses a cloud platform state synchronization method, a multi-cloud management platform and a storage medium. The method comprises the following steps: receiving dynamic resource monitoring information of a cloud platform having created dynamic resources when platform operation information is not received; determining a target cloud platform according to the dynamic resource monitoring information; and acquiring and synchronizing the dynamic resource state of the target cloud platform through the bottom interface of the target cloud platform. According to the technical scheme of the embodiment of the application, the resources of each cloud platform managed by the multi-cloud management platform are divided into static resources and dynamic resources, different synchronization modes are adopted for different types of resources, the problem that it is difficult to set an interval time for timing or periodic synchronization is avoided, the data amount of single synchronization is reduced, the data resources are saved on the basis of efficient synchronization, and the load of the multi-cloud management platform is reduced.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a cloud platform status synchronization method, a multi-cloud management platform, and a storage medium. Background Technology

[0002] Multi-cloud management platforms can be used to manage different types of cloud platforms. In today's era of digital transformation, various industries often build cloud platforms to achieve cloud-based data processing. However, the coexistence of public, private, and hybrid cloud resources increases the complexity of managing multiple cloud platforms. To manage multiple cloud platforms, resources from each platform need to be synchronized within the multi-cloud management platform.

[0003] In related technologies, there are two methods to achieve resource synchronization across multiple cloud platforms under a multi-cloud management platform: one is to continuously query and update the resource data of the cloud platforms managed by the multi-cloud management platform by setting up a scheduled query task. However, this method synchronizes all underlying resources of multiple cloud platforms, requiring a large amount of data to be synchronized, consuming a lot of computing and network resources, which may cause system blockage and affect the normal operation of the system. The other method is to obtain the status of cloud platform resources managed by the multi-cloud management platform through periodic polling. However, the resource synchronization effect of this method is greatly affected by the set period. If the period is too short, it will lead to a waste of system resources; if the period is too long, it will lead to a delay in resource synchronization and affect the accuracy of cloud platform status synchronization. Summary of the Invention

[0004] This invention provides a cloud platform state synchronization method, a multi-cloud management platform, and a storage medium, which can realize adaptive synchronous management of different types of resources in multiple cloud platforms managed by the multi-cloud management platform, reduce the computing and network resources required for state synchronization, and improve the accuracy of state synchronization.

[0005] In a first aspect, embodiments of the present invention provide a cloud platform state synchronization method, applied in a multi-cloud management platform, wherein the multi-cloud management platform is communicatively connected to at least one cloud platform for managing each cloud platform, and the resources of the cloud platforms include at least static resources; the method includes:

[0006] When no platform operation information is received, receive dynamic resource monitoring information from the cloud platform for which dynamic resources have been created;

[0007] The target cloud platform is determined based on the dynamic resource monitoring information.

[0008] The dynamic resource status of the target cloud platform is obtained and synchronized through the underlying interface of the target cloud platform.

[0009] Optionally, the target cloud platform can be determined based on various dynamic resource monitoring information, including:

[0010] Dynamic resource monitoring information that includes information on changes in dynamic resource status is identified as the target dynamic resource monitoring information.

[0011] The cloud platform corresponding to the target dynamic resource monitoring information is identified as the target cloud platform.

[0012] The above technical solution monitors the dynamic resource status of multiple cloud platforms managed by the multi-cloud management platform by acquiring dynamic resource monitoring information. The determination of the target cloud platform and subsequent dynamic resource status synchronization are triggered only when the dynamic resource monitoring information includes dynamic resource change information. This ensures the timeliness of resource synchronization once dynamic resources change and are synchronized, while avoiding the waste of computing network resources caused by timed synchronization when resources have not changed.

[0013] Optionally, the method further includes:

[0014] Upon receiving platform operation information, the target cloud platform is determined based on the platform operation information;

[0015] Obtain the current platform status information of the target cloud platform, and determine the synchronization interval based on the operation type corresponding to the current platform status information and platform operation information;

[0016] After the synchronization interval, the resource status of the target cloud platform is obtained and synchronized through the underlying interface of the target cloud platform.

[0017] The above technical solution addresses the issue of a multi-cloud management platform synchronizing resource changes on the cloud platform after receiving platform operation information. Since the cloud platform resources receiving the operation will become non-steady-state, meaning that cloud platform resources in a non-steady-state state will inevitably change, the solution clarifies the trigger time for resource synchronization on the cloud platform under this condition, ensuring the timeliness and integrity of resource synchronization.

[0018] Optionally, the synchronization interval can be determined based on the current platform status information and the operation type corresponding to the platform operation information, including:

[0019] Input the current platform status information and operation type into the preset operation time prediction model, and determine the synchronization interval time based on the output operation time prediction results.

[0020] The above technical solution uses a pre-trained operation time prediction model to predict the time required to execute the operation of that type on the cloud platform based on the current platform status information and operation type. This makes the determined synchronization interval closer to the actual time required for the operation, thus ensuring that the resource synchronization of the cloud platform for the operation will occur after the operation is completed, guaranteeing the integrity and stability of resource synchronization.

[0021] Optionally, the synchronization interval can be determined based on the current platform status information and the operation type corresponding to the platform operation information, including:

[0022] Get the time of the previous operation corresponding to the operation type, and set the time of the previous operation as the synchronization interval.

[0023] Input the current platform status information and operation type into the preset operation time prediction model, and determine the synchronization interval time to be selected based on the output operation time prediction results;

[0024] Update the synchronization interval time based on the previous operation time, the selected synchronization interval time, and the preset interval threshold.

[0025] The above technical solution prioritizes the previous operation time corresponding to the operation type as the synchronization interval time for the cloud platform to execute the corresponding operation type operation at the current moment, and then updates the synchronization interval time based on the output of the pre-trained operation time prediction model. This ensures the timeliness of the synchronization interval time determination, while making the determined synchronization interval time more consistent with the working conditions of the cloud platform and improving the accuracy of the determined synchronization interval time.

[0026] Optionally, the synchronization interval time can be updated based on the previous operation time, the selected synchronization interval time, and the preset interval threshold, including:

[0027] Determine the time difference between the previous operation time and the selected synchronization interval time;

[0028] If the time difference is greater than the preset interval threshold, the synchronization interval to be selected will be determined as the synchronization interval.

[0029] If the time difference is less than or equal to the preset interval threshold, the synchronization interval time remains unchanged.

[0030] The above technical solution reduces the amount of data processing and lowers the prediction error for the synchronization interval by prioritizing the use of existing operation time as the synchronization interval time when the error requirement is met.

[0031] Optionally, before inputting the current platform status information and operation type into the preset operation time prediction model, the following steps are also included:

[0032] Obtain historical operation types, historical cloud platform status information, and historical operation times for each cloud platform;

[0033] The real dataset is determined based on the correlation between each historical operation type and the status information of each historical cloud platform;

[0034] Based on the correspondence between each historical operation time and each set of real data in the real dataset, each set of real data in the real dataset is calibrated to determine the calibration dataset; the real data includes a set of interrelated historical operation types and historical cloud platform status information.

[0035] The operation time prediction sample set, consisting of real datasets and calibration datasets, is input into the initial operation time prediction model for training until the preset convergence condition is met to obtain the operation time prediction model.

[0036] The above technical solution trains the operation time prediction model by acquiring historical operation types, historical cloud platform status information and historical operation times of each cloud platform, thereby making the synchronization interval time predicted by the operation time prediction model more accurate.

[0037] Optionally, when no platform operation information is received, the method also includes:

[0038] Obtain static resource incremental logs from each cloud platform according to a preset time period;

[0039] Based on the incremental logs of each static resource, synchronize the status of static resources on each cloud platform.

[0040] The above technical solution only performs timed synchronization of static resources of each cloud platform managed by the multi-cloud management platform, rather than synchronizing all resources across multiple cloud platforms, thus reducing the amount of data that needs to be updated.

[0041] Secondly, embodiments of the present invention provide a multi-cloud management platform, the multi-cloud management platform comprising:

[0042] At least one processor; and

[0043] A memory that is communicatively connected to at least one processor; wherein,

[0044] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor can execute the cloud platform state synchronization method provided in the embodiments of the present invention.

[0045] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute the cloud platform state synchronization method provided in the embodiments of the present invention.

[0046] The technical solution provided by this invention divides the resources of each cloud platform managed by the multi-cloud management platform into static resources and dynamic resources. Different synchronization methods are adopted for different types of resources. When no platform operation information is received, the dynamic resource status of each cloud platform with created dynamic resources is monitored by acquiring dynamic resource monitoring information. When dynamic resources change, synchronization of dynamic resources is achieved by triggering, avoiding the problem of difficulty in setting the interval time for timed or periodic synchronization, reducing the amount of data for a single synchronization, saving data resources and reducing the load on the multi-cloud management platform on the basis of efficient synchronization.

[0047] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of a cloud platform state synchronization method provided in an embodiment of the present invention;

[0050] Figure 2 This is a flowchart of another cloud platform status synchronization method provided in an embodiment of the present invention;

[0051] Figure 3 This is a flowchart illustrating a training method for an operation time prediction model provided in an embodiment of the present invention.

[0052] Figure 4 This is a flowchart of another cloud platform status synchronization method provided in an embodiment of the present invention;

[0053] Figure 5 This is a schematic diagram of the structure of a multi-cloud management platform provided in an embodiment of the present invention. Detailed Implementation

[0054] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0055] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0056] Figure 1 This is a flowchart illustrating a cloud platform state synchronization method provided by an embodiment of the present invention. This embodiment is applicable to situations where a multi-cloud management platform synchronizes the resources of multiple cloud platforms it manages. The method can be executed by a cloud platform state synchronization device, which can be implemented in software and / or hardware and can be configured within the multi-cloud management platform. The multi-cloud management platform is communicatively connected to at least one cloud platform for managing each cloud platform. The resources of each cloud platform include at least static resources. The cloud platform can be any type of public cloud, private cloud, or hybrid cloud; this embodiment of the present invention does not impose any limitations on this.

[0057] In this embodiment, the Cloud Management Platform (CMP) can be specifically understood as a product that provides unified integrated management of public, private, and hybrid clouds, offering services across multiple cloud infrastructures to enterprises using it. Static resources can be specifically understood as hardware and software resources of the cloud platform that are difficult to change. For example, static resources can be cloud infrastructure, including logical network boundaries, virtual servers, cloud storage devices, cloud usage monitoring, resource replication, and ready environments, etc., and this embodiment of the invention does not impose limitations on this. Generally, the state of static resources only changes under the operation of the administrator, and the frequency of updates required is relatively low.

[0058] Furthermore, for cloud platforms that are accessing the multi-cloud management platform for the first time, the multi-cloud management platform will first save the connection information of the cloud platform to the database of the multi-cloud management platform, and then back up the static resources of the cloud platform to the database of the multi-cloud management platform in order to achieve the initial synchronization of the resources of the cloud platforms managed by the multi-cloud management platform.

[0059] like Figure 1 As shown in the figure, the cloud platform state synchronization method provided by this embodiment of the invention specifically includes the following steps:

[0060] S101. When no platform operation information is received, receive dynamic resource monitoring information from the cloud platform where dynamic resources have been created.

[0061] In this embodiment, dynamic resources can be specifically understood as hardware and software resources of the cloud platform that can change in response to user operations. For example, dynamic resources can be cloud services, such as cloud hosts or cloud disks, and this embodiment of the invention does not impose any limitations on this. It should be noted that, compared to static resources, dynamic resources are characterized by their large quantity, frequent operations, and rapid updates. Therefore, if the same synchronization management method as for static resources is used to synchronize dynamic resources when managing various cloud platforms in a multi-cloud management platform, it will lead to problems such as excessively low synchronization frequency or untimely synchronization.

[0062] In this embodiment, platform operation information can be specifically understood as information received by the multi-cloud management platform for operating the cloud platforms it manages. For example, platform operation information may include query information, disk creation information, data deletion information, and data addition information, etc., and this embodiment of the invention does not impose limitations on this. Dynamic resource monitoring information can be specifically understood as information used to monitor the dynamic resource status in the cloud platform to determine whether dynamic resources have been modified, that is, to determine whether the dynamic resource status in the cloud platform is the same as the dynamic resource status stored in the multi-cloud management platform, and whether the multi-cloud management platform needs to synchronize the corresponding dynamic resource status of the cloud platform.

[0063] Specifically, when the multi-cloud management platform does not receive platform operation information, it can be assumed that no operational requests for the multiple cloud platforms it manages have been received from outside the platform. This means the resources of the multiple cloud platforms managed by the multi-cloud management platform should be in a stable state, and the resource status of the cloud platforms will not change due to external user operations. In this case, for cloud platforms with dynamically created resources, the multi-cloud management platform can receive dynamic resource monitoring information sent by the monitoring system set up in the cloud platform to determine whether the dynamic resources in the cloud platform have undergone status changes due to instability of the cloud platform or the underlying infrastructure.

[0064] In this embodiment of the invention, the dynamic resource status of the cloud platform in a stable state is monitored by acquiring dynamic resource monitoring information. Since the dynamic resource status in a stable state will not be updated automatically, the multi-cloud management platform is guaranteed to detect changes in the dynamic resource status in a timely manner, thereby improving the synchronization efficiency of cloud platform resources.

[0065] S102. Determine the target cloud platform based on the monitoring information of each dynamic resource.

[0066] Specifically, based on the monitoring information of each dynamic resource, the status changes of dynamic resources in each cloud platform are determined, and the cloud platform where the dynamic resources have changed is identified as the target cloud platform.

[0067] In one embodiment of the present invention, optionally, determining the target cloud platform based on each dynamic resource monitoring information includes: determining the dynamic resource monitoring information containing dynamic resource status change information as the target dynamic resource monitoring information; and determining the cloud platform corresponding to the target dynamic resource monitoring information as the target cloud platform.

[0068] In this embodiment, dynamic resource status change information can be specifically understood as information used to indicate that the dynamic resource status of the cloud platform has changed.

[0069] Specifically, the multi-cloud management platform extracts the content from the acquired dynamic resource monitoring information to determine whether each dynamic resource monitoring information includes dynamic resource status change information. If dynamic resource status change information is present, it can be considered that the dynamic resource status in the cloud platform corresponding to the dynamic resource monitoring information has changed and needs to be synchronized. At this time, the dynamic resource monitoring information containing dynamic resource status change information is identified as the target dynamic resource monitoring information, and the cloud platform corresponding to the target dynamic resource monitoring information is identified as the target cloud platform, so that the multi-cloud management platform can perform dynamic resource synchronization on the target cloud platform where the dynamic resources have changed.

[0070] In this embodiment of the invention, the dynamic resource status of multiple cloud platforms managed by the multi-cloud management platform is monitored by acquiring dynamic resource monitoring information. The determination of the target cloud platform and subsequent dynamic resource status synchronization are triggered only when the dynamic resource monitoring information includes dynamic resource change information. This ensures the timeliness of resource synchronization once dynamic resources change and are synchronized, while avoiding the waste of computing network resources caused by timed synchronization when resources have not changed.

[0071] S103. Obtain and synchronize the dynamic resource status of the target cloud platform through the underlying interface of the target cloud platform.

[0072] In this embodiment, the underlying interface can be specifically understood as the interface connecting the target cloud platform and the multi-cloud management platform. For example, the underlying interface can be an API interface, and this embodiment of the invention does not limit the type of the underlying interface. The dynamic resource status can be specifically understood as the usage status and modification status of the dynamic resources corresponding to the cloud platform.

[0073] Specifically, after identifying the target cloud platform, the multi-cloud management platform can determine the underlying interface of the target cloud platform based on the connection information saved when the target cloud platform first connects to the database. Then, it can obtain the dynamic resource status of the target cloud platform through the underlying interface and update the dynamic resource status corresponding to the target cloud platform stored in the multi-cloud management platform database based on the obtained dynamic resource status.

[0074] The technical solution of this invention divides the resources of each cloud platform managed by the multi-cloud management platform into static resources and dynamic resources, and adopts different synchronization methods for different types of resources. When no platform operation information is received, for cloud platforms with created dynamic resources, the dynamic resource status of each cloud platform is monitored by acquiring dynamic resource monitoring information. When dynamic resources change, synchronization of dynamic resources is achieved by triggering, avoiding the problem of difficulty in setting the interval time for timed or periodic synchronization, reducing the amount of data for a single synchronization, saving data resources and reducing the load on the multi-cloud management platform on the basis of efficient synchronization.

[0075] Figure 2 This is a flowchart of another cloud platform state synchronization method provided by an embodiment of the present invention. Based on the above technical solution, this embodiment further provides a technical solution for when a multi-cloud management platform receives platform operation information. For example... Figure 2 As shown, the cloud platform status synchronization method provided in this embodiment of the invention specifically includes the following steps:

[0076] S201. Upon receiving platform operation information, determine the target cloud platform based on the platform operation information.

[0077] Specifically, since the platform operation information includes the cloud platform that needs to perform the corresponding operation, when the multi-cloud management platform receives the platform operation information, it can identify the cloud platform that needs to perform the corresponding operation as the target cloud platform.

[0078] S202. Obtain the current platform status information of the target cloud platform, and determine the synchronization interval based on the operation type corresponding to the current platform status information and platform operation information.

[0079] In this embodiment, the current platform status information can be specifically understood as factors that indicate the working status of the cloud platform at the current moment, or information that will affect the working efficiency of the cloud platform. For example, the current platform status information may include CPU utilization, storage resource utilization, network status, memory utilization, and ambient temperature, etc., and this embodiment of the invention does not impose any limitations on this. The synchronization interval time can be specifically understood as the estimated time required for the target cloud platform to complete the operation corresponding to the platform operation information in the current platform status, or the time when the resource status in the cloud platform changes due to the operation corresponding to the platform operation information.

[0080] In this embodiment, the operation type can be specifically understood as the type of task that the cloud platform needs to execute. Different operation types correspond to tasks with different levels of complexity. Accordingly, the cloud platform requires different storage resources, memory usage, and CPU usage, etc., for tasks with different time and space complexities. For example, if the operation type is cloud server power on / off, it can be considered that this operation type corresponds to a short-duration task with low resource usage; if the operation type is backup, it can be considered that this operation type corresponds to a long-duration task with high resource usage and a large amount of resource modification.

[0081] Specifically, after identifying the target cloud platform for the operation corresponding to the platform operation information, the current platform status information of the target cloud platform at the current moment is obtained. Based on the operation type corresponding to the platform operation information, the time required for the task of that operation type to be executed in the target cloud platform in the current state is estimated, and the estimated time is determined as the synchronization interval time.

[0082] In this embodiment of the invention, the time required for the target cloud platform to execute the task corresponding to the platform operation information is estimated by using the current platform status information and the operation type corresponding to the platform operation information. This allows the multi-cloud management platform to synchronize the resource status in the target cloud platform after estimating that the cloud platform operation has been completed, thus avoiding incomplete synchronization of changes in the resource status of the target cloud platform caused by synchronization before the operation is completed, which would otherwise waste data resources by requiring resynchronization of the resource status of the target cloud platform.

[0083] In one embodiment of the present invention, optionally, determining the synchronization interval time based on the operation type corresponding to the current platform status information and platform operation information includes: inputting the current platform status information and operation type into a preset operation time prediction model, and determining the synchronization interval time based on the output operation time prediction result.

[0084] In this embodiment, the operation time prediction model can be specifically understood as a neural network model used to predict the time required for the cloud platform to perform the corresponding operation type operation under the above-mentioned state, based on the input operation type and the current platform state information.

[0085] Specifically, the current platform status information and operation type are input into a pre-trained operation time prediction model used to predict operation time. The operation time prediction result output by the operation time prediction model can be considered as the operation time required for the target cloud platform to execute the corresponding operation type task under the current platform status. At this time, the operation time is determined as the synchronization interval time.

[0086] Optional, Figure 3 This is a flowchart illustrating a training method for an operation time prediction model provided in an embodiment of the present invention. The training of this operation time prediction model should occur before the current platform state information and operation type are input into the preset operation time prediction model. Figure 3 As shown, the specific steps may include the following:

[0087] S301. Obtain the historical operation types, historical cloud platform status information, and historical operation times for each cloud platform.

[0088] In this embodiment, the historical operation type can be specifically understood as the type of operation that each cloud platform receives within a preset historical period, requiring platform operation information for operation. The historical cloud platform status information can be specifically understood as the cloud platform status information corresponding to the historical operation type when executing the historical operation type operation. The historical operation time can be specifically understood as the time spent by the cloud platform executing the corresponding historical operation type operation under the state corresponding to the historical cloud platform status information.

[0089] S302. Determine the real dataset based on the correlation between each historical operation type and the status information of each historical cloud platform.

[0090] In this embodiment, the real dataset can be specifically understood as the collection of real data actually obtained for training the neural network model.

[0091] Specifically, based on the correlation between each historical operation type and the status information of each historical cloud platform, the historical operation type and the status information of each historical cloud platform are matched. The historical operation type and the status information of the same cloud platform obtained at the same time are determined as a set of real data. The set of real data obtained after matching is determined as the real dataset that can be used to train the preset neural network model.

[0092] S303. Based on the correspondence between each historical operation time and each group of real data in the real dataset, calibrate each group of real data in the real dataset to determine the calibration dataset.

[0093] The real data includes a set of interconnected historical operation types and historical cloud platform status information.

[0094] In this embodiment, the calibration dataset can be specifically understood as a set of data that is calibrated by the historical operation time of each set of real data in the actual execution process, so as to be output when the real data is input into the operation time prediction model.

[0095] Specifically, for each set of real data in the real dataset, the corresponding historical operation time is determined, and the real data is calibrated using this historical operation time. The combination of the calibrated real data is then used to determine the calibration dataset.

[0096] S304. Input the operation time prediction sample set consisting of the real dataset and the calibration dataset into the initial operation time prediction model for training until the preset convergence condition is met to obtain the operation time prediction model.

[0097] In this embodiment, the prediction sample set can be specifically understood as the set of training objects determined based on real data, input into the operation time prediction model for training. This may include a real dataset composed of historical operation types and historical cloud platform status information, and a calibration dataset obtained by calibrating the real dataset using historical operation times. The initial operation time prediction model can be specifically understood as an operation time prediction model without adjusting the weights in each convolutional layer. The preset convergence condition can be specifically understood as a pre-set condition used to determine that the model has been trained successfully. For example, the preset convergence condition may be that the model output error is less than a preset error threshold, the weight change between two iterations is less than a preset change threshold, or the number of iterations exceeds a preset number threshold, etc. This embodiment of the invention does not impose any limitations on these conditions.

[0098] Specifically, the real dataset from the operation time prediction sample set is input into the initial operation time prediction model. The intermediate results output by the initial operation time prediction model are compared with the calibration dataset to construct the corresponding loss function. Then, the weights in the initial operation time prediction model are adjusted through the loss function and backpropagation training is performed until an operation time prediction model that meets the preset convergence conditions is obtained.

[0099] In one embodiment of the present invention, optionally, determining the synchronization interval time based on the operation type corresponding to the current platform status information and platform operation information includes: obtaining the previous operation time corresponding to the operation type and determining the previous operation time as the synchronization interval time; inputting the current platform status information and operation type into a preset operation time prediction model and determining the selected synchronization interval time based on the output operation time prediction result; and updating the synchronization interval time based on the previous operation time, the selected synchronization interval time, and a preset interval threshold.

[0100] In this embodiment, the synchronization interval to be selected can be understood as the predicted operation time output by the operation time prediction model, which can be selected as the synchronization interval. The preset interval threshold can be understood as a threshold data set in advance according to the actual situation to determine the error between the output of the operation time prediction model and the prior operation time.

[0101] Specifically, when determining the operation type of platform operation information, the multi-cloud management platform can determine the previous operation time of the target cloud platform when it last performed that operation type. Since the time spent performing the same operation on the same cloud platform should be roughly the same, the previous operation time can be prioritized as the synchronization interval. Then, the current platform status information and operation type of the target cloud platform can be input into a pre-trained operation time prediction model to obtain the candidate synchronization interval output by the operation time prediction model. Because the operation time prediction model requires a certain amount of time for data processing, and the model output is only a prediction of the operation time, not an accurate operation time, the model output is only used as one possible synchronization interval. If the existence of a synchronization interval is guaranteed by the determined previous operation time, the previous operation time and the candidate synchronization interval are compared. If the error between the two is small, the currently determined synchronization interval can be kept unchanged to avoid unnecessary data processing. Otherwise, the current synchronization interval can be updated using the predicted candidate synchronization interval to ensure that the determined synchronization interval is closest to the time spent by the target cloud platform performing the operation.

[0102] In this embodiment of the invention, by considering the time of the last operation corresponding to the operation type on the target cloud platform and the prediction result of the trained operation time prediction model, the determined synchronization interval time is more consistent with the working conditions of the cloud platform, thereby improving the accuracy of the determined synchronization interval time.

[0103] In one embodiment of the present invention, optionally, updating the synchronization interval time based on the previous operation time, the selected synchronization interval time, and a preset interval threshold includes: determining the time difference between the previous operation time and the selected synchronization interval time; if the time difference is greater than the preset interval threshold, then determining the selected synchronization interval time as the synchronization interval time; if the time difference is less than or equal to the preset interval threshold, then keeping the synchronization interval time unchanged.

[0104] Specifically, the time difference between the previous operation time and the selected synchronization interval time is compared with a preset interval threshold to determine whether the error between the selected synchronization interval time predicted based on the current platform status information and operation type and the previous operation time consumed by the target cloud platform in performing this type of operation meets the requirements. When the time difference is greater than the preset interval threshold, it can be considered that the state corresponding to the current platform status information is significantly different from the state corresponding to the previous operation of this type of operation by the target cloud platform. In this case, the selected synchronization interval time is determined as the synchronization interval time to ensure the accuracy of the determined synchronization interval time. When the time difference is less than or equal to the preset interval threshold, it can be considered that the state corresponding to the current platform status information is significantly different from the state corresponding to the previous operation of this type of operation by the target cloud platform. Since the previous operation time has been determined as the synchronization interval time, there is no need to modify or replace it using the selected synchronization interval time, reducing the amount of data processing and better avoiding prediction errors caused by the operation time prediction model.

[0105] S203. After the synchronization interval, obtain and synchronize the resource status of the target cloud platform through the underlying interface of the target cloud platform.

[0106] Specifically, after specifying the synchronization interval, the multi-cloud management platform will wait for the synchronization interval to complete the corresponding operation on the target cloud platform operation information. Then, through the underlying interface connected to the target cloud platform, it will obtain the resource status within the target cloud platform and update the resource status corresponding to the target cloud platform stored in the multi-cloud management platform database with the obtained resource status. At this time, the updated resource status can be a static resource status or a dynamic resource status, and this embodiment of the invention does not impose any restrictions on this.

[0107] The technical solution of this invention determines the target cloud platform by receiving platform operation information, then estimates the time required for the target cloud platform to perform the corresponding operation based on the current platform status information and the operation type corresponding to the platform operation information, obtaining a synchronization interval. The resource status of the target cloud platform is then synchronized after the synchronization interval. Since the resources of the target cloud platform receiving the operation will become non-steady-state, meaning that the resources of the cloud platform in a non-steady-state state will inevitably change, the trigger time for resource synchronization of the cloud platform in this case is clearly defined, ensuring the timeliness and integrity of resource synchronization.

[0108] Figure 4 This is a flowchart of another cloud platform state synchronization method provided by an embodiment of the present invention. Based on the above technical solution, this embodiment of the present invention provides solutions for synchronizing the cloud platform state under different circumstances. Furthermore, it provides a technical solution for updating the static resources of each cloud platform when the multi-cloud management platform does not receive cloud platform operation information. Figure 4 As shown, the cloud platform status synchronization method provided in this embodiment of the invention specifically includes the following steps:

[0109] S401. Back up the connection information of the cloud platform to the multi-cloud management platform and the static resources of the cloud platform to the database of the multi-cloud management platform for the first time.

[0110] In this embodiment, the connection information can be specifically understood as information indicating the connection method of the cloud platform to the multi-cloud management platform.

[0111] Specifically, when a cloud platform first connects to a multi-cloud management platform, in order to meet the management needs of the multi-cloud management platform, the access method information must first be stored in the database of the multi-cloud management platform, and then the corresponding static resources must be stored in the database of the multi-cloud management platform as backup storage.

[0112] S402. Determine whether platform operation information has been received. If yes, proceed to step S403; otherwise, proceed to step S406.

[0113] S403. Determine the target cloud platform based on the platform operation information.

[0114] S404. Obtain the current platform status information of the target cloud platform, and determine the synchronization interval based on the operation type corresponding to the current platform status information and platform operation information.

[0115] Furthermore, the synchronization interval time can be determined based on the operation type corresponding to the current platform status information and platform operation information. This can be achieved by inputting the current platform status information and operation type into a preset operation time prediction model, and determining the synchronization interval time based on the output operation time prediction result.

[0116] Furthermore, the synchronization interval time can be determined based on the operation type corresponding to the current platform status information and platform operation information, and can also be achieved in the following ways: obtain the previous operation time corresponding to the operation type and determine the previous operation time as the synchronization interval time; input the current platform status information and operation type into the preset operation time prediction model, and determine the synchronization interval time to be selected based on the output operation time prediction result; update the synchronization interval time based on the previous operation time, the synchronization interval time to be selected, and the preset interval threshold.

[0117] Optionally, the synchronization interval time can be updated based on the previous operation time, the selected synchronization interval time, and the preset interval threshold. This can be achieved by: determining the time difference between the previous operation time and the selected synchronization interval time; if the time difference is greater than the preset interval threshold, then the selected synchronization interval time is determined as the synchronization interval time; if the time difference is less than or equal to the preset interval threshold, then the synchronization interval time remains unchanged.

[0118] S405. After the synchronization interval, obtain and synchronize the resource status of the target cloud platform through the underlying interface of the target cloud platform.

[0119] S406. Determine whether the cloud platform has created dynamic resources. If yes, proceed to step S407; otherwise, proceed to step S411.

[0120] S407: Receive dynamic resource monitoring information from the cloud platform where dynamic resources have been created.

[0121] In this embodiment, dynamic resource monitoring information can be generated and sent through a monitoring system set up in the cloud platform, and this embodiment of the invention does not impose any restrictions on this.

[0122] S408. The dynamic resource monitoring information containing dynamic resource status change information is identified as the target dynamic resource monitoring information.

[0123] S409. The cloud platform corresponding to the target dynamic resource monitoring information is identified as the target cloud platform.

[0124] S410: Obtain and synchronize the dynamic resource status of the target cloud platform through the underlying interface of the target cloud platform.

[0125] S411. Obtain static resource incremental logs from each cloud platform according to a preset time period.

[0126] In this embodiment, the preset time period can be understood as a time interval pre-set according to actual needs, used to periodically update the static resource status of the cloud platform that is not easily changed. This embodiment of the invention does not limit the specific value of the time period. The static resource incremental log can be understood as a log recording changes in the static resources of the cloud platform.

[0127] Specifically, the multi-cloud management platform acquires static resource incremental logs for the multiple cloud platforms it manages according to a preset time period. For example, for a single cloud platform, the multi-cloud management platform acquires the static resource incremental logs for that cloud platform at preset time intervals.

[0128] S412. Synchronize the static resource status of each cloud platform based on the incremental logs of each static resource.

[0129] Specifically, the multi-cloud management platform uses the incremental logs of each static resource to identify the changes in static resources on each cloud platform, and then synchronizes the changes to the static resources stored in the multi-cloud management platform database, thereby synchronizing the status of static resources on each cloud platform.

[0130] The technical solution of this invention classifies the resources of multiple cloud platforms managed by a multi-cloud management platform into different types. For the static and dynamic resources obtained from the classification, different methods are used for state synchronization before receiving platform operation information. This ensures the timeliness of resource synchronization, reduces the frequency of resource synchronization, and reduces the amount of data required for a single synchronization. Furthermore, when the multi-cloud management platform receives platform operation information, it predicts the synchronization interval required for the corresponding operation and synchronizes the resource status of the cloud platform after the synchronization interval. This ensures the timeliness of the determined synchronization interval and makes the determined synchronization interval more consistent with the working conditions of the cloud platform, thus improving the accuracy of the determined synchronization interval.

[0131] Figure 5 This is a schematic diagram of a multi-cloud management platform provided in an embodiment of the present invention. The multi-cloud management platform 10 can be an electronic device, which 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 processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0132] like Figure 5As shown, the multi-cloud management platform 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 and a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the multi-cloud management platform 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0133] Multiple components in the multi-cloud management platform 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the multi-cloud management platform 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0134] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as cloud platform state synchronization methods.

[0135] In some embodiments, the cloud platform state synchronization method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the multi-cloud management platform 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the cloud platform state synchronization method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the cloud platform state synchronization method by any other suitable means (e.g., by means of firmware).

[0136] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0138] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0139] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 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 pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; 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 sound input, voice input, or tactile input).

[0140] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0141] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0142] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0143] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. 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 substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A cloud platform state synchronization method, characterized in that, The method is applied in a multi-cloud management platform, which is communicatively connected to at least one cloud platform for managing each cloud platform, wherein the resources of each cloud platform include at least static resources; the method includes: When no platform operation information is received, dynamic resource monitoring information of the cloud platform that has created dynamic resources is received; wherein, the platform operation information is information received by the multi-cloud management platform for operating the cloud platforms it manages. The target cloud platform is determined based on the dynamic resource monitoring information described above; The dynamic resource status of the target cloud platform is obtained and synchronized through the underlying interface of the target cloud platform. The static resources are cloud infrastructure, including at least one of logical network boundaries, virtual servers, cloud storage devices, cloud usage monitoring, resource replication, and ready environments. The method further includes: Upon receiving platform operation information, the target cloud platform is determined based on the platform operation information; Obtain the current platform status information of the target cloud platform, and determine the synchronization interval time based on the operation type corresponding to the current platform status information and the platform operation information; wherein, the current platform status information includes CPU utilization, storage resource utilization, network status, memory utilization and ambient temperature; After the synchronization interval, the resource status of the target cloud platform is obtained and synchronized through the underlying interface of the target cloud platform. The step of determining the target cloud platform based on the dynamic resource monitoring information includes: Dynamic resource monitoring information that includes information on changes in dynamic resource status is identified as the target dynamic resource monitoring information. The cloud platform corresponding to the target dynamic resource monitoring information is identified as the target cloud platform.

2. The method according to claim 1, wherein determining the synchronization interval based on the operation type corresponding to the current platform status information and the platform operation information includes: The current platform status information and the operation type are input into a preset operation time prediction model, and the synchronization interval is determined based on the output operation time prediction result.

3. The method according to claim 1, characterized in that, The step of determining the synchronization interval based on the current platform status information and the operation type corresponding to the platform operation information includes: Obtain the previous operation time corresponding to the operation type, and determine the previous operation time as the synchronization interval time; The current platform status information and the operation type are input into the preset operation time prediction model, and the synchronization interval time to be selected is determined based on the output operation time prediction result; The synchronization interval time is updated based on the previous operation time, the selected synchronization interval time, and the preset interval threshold.

4. The method according to claim 3, characterized in that, The step of updating the synchronization interval time based on the previous operation time, the selected synchronization interval time, and a preset interval threshold includes: Determine the time difference between the previous operation time and the selected synchronization interval time; If the time difference is greater than the preset interval threshold, then the synchronization interval time to be selected is determined as the synchronization interval time. If the time difference is less than or equal to a preset interval threshold, the synchronization interval time remains unchanged.

5. The method according to claim 2 or 3, characterized in that, Before inputting the current platform status information and the operation type into the preset operation time prediction model, the method further includes: Obtain the historical operation types, historical cloud platform status information, and historical operation times for each of the aforementioned cloud platforms; The real dataset is determined based on the correlation between each of the historical operation types and each of the historical cloud platform status information; Based on the correspondence between the historical operation time and each set of real data in the real dataset, each set of real data in the real dataset is calibrated to determine the calibration dataset; wherein, the real data includes a set of interrelated historical operation types and historical cloud platform status information; The operation time prediction sample set, consisting of the real dataset and the calibration dataset, is input into the initial operation time prediction model for training until the preset convergence condition is met to obtain the operation time prediction model.

6. The method according to claim 1, characterized in that, When no platform operation information is received, the method further includes: Obtain static resource incremental logs for each cloud platform according to a preset time period; Based on the incremental logs of each static resource, synchronize the static resource status of each cloud platform.

7. A multi-cloud management platform, characterized in that, The multi-cloud management platform includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the cloud platform state synchronization method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the cloud platform state synchronization method according to any one of claims 1-6.

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