Modeling resource dynamic scheduling method and device in multi-cloud environment, equipment and medium

Abstract

The embodiment of the invention discloses a modeling resource dynamic scheduling method and device in a multi-cloud environment, equipment and a medium, and relates to the technical field of modeling resource loading. The method comprises the following steps: if a loading request of a request end for modeling resources is received, respectively acquiring network connection data between the request end and a plurality of cloud platforms, and respectively acquiring load data of the plurality of cloud platforms; determining at least one target cloud platform from the plurality of cloud platforms based on the network connection data and the load data; and loading the modeling resources from the at least one target cloud platform and transmitting the modeling resources to the request end. In the embodiment of the invention, the modeling resource is not limited to be loaded from a single target cloud platform and transmitted to the request end, and the modeling resource can be loaded from a plurality of target cloud platforms and transmitted to the request end, so that the transmission efficiency is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of modeling resource loading technology, and in particular to a method, apparatus, device and medium for dynamic scheduling of modeling resources in a multi-cloud environment. Background Technology

[0002] In the field of architectural design and presentation, 3D modeling technology plays a central role in creating high-precision, visually expressive Building Information Models (BIMs). These models are typically complex in structure and contain massive amounts of geometric and attribute data, leading to a significant increase in the data volume of modeling resources. To address the limitations of local storage resources and the need for collaborative sharing, centrally storing large-scale architectural model datasets on cloud platforms has become a common industry practice. During modeling or browsing, users need to dynamically load the required modeling resources from the cloud via a wide area network.

[0003] However, this model faces a key challenge: when multiple users or application instances simultaneously initiate high-frequency, large-scale model data requests (i.e., high-concurrency access scenarios), network congestion is highly likely to occur at the cloud platform's egress and on the public internet link. Network bandwidth resources become a limiting factor during peak periods, and the transmission rate of model data decreases significantly.

[0004] This directly leads to a significant increase in the time required for front-end modeling tools or display terminals to acquire necessary resources, resulting in a sharp decrease in model loading efficiency and sluggish user interaction response, severely restricting the smoothness of the 3D modeling workflow and user experience. Therefore, how to optimize the loading efficiency of cloud-based architectural models is a key technical problem that urgently needs to be solved. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and medium for dynamic scheduling of modeling resources in a multi-cloud environment, aiming to solve the problem of slow loading of cloud modeling resources in the prior art.

[0006] In a first aspect, embodiments of the present invention provide a method for dynamic scheduling of modeling resources in a multi-cloud environment, wherein the modeling resources are stored on multiple cloud platforms, and the method includes:

[0007] If a request for loading modeling resources is received from the requesting client, network connection data between the requesting client and multiple cloud platforms is obtained, as well as load data of multiple cloud platforms are obtained respectively.

[0008] Based on the network connection data and the load data, at least one target cloud platform is determined from the plurality of cloud platforms;

[0009] The modeling resources are loaded from at least one of the target cloud platforms and transmitted to the requesting client.

[0010] A further technical solution is that the step of obtaining network connection data between the requesting end and multiple cloud platforms includes: obtaining the network connection rate and network transmission distance between the requesting end and multiple cloud platforms respectively;

[0011] The step of acquiring load data for multiple cloud platforms includes acquiring the number of tasks and resource load rate for each cloud platform.

[0012] A further technical solution is that determining at least one target cloud platform from multiple cloud platforms based on the network connection data and the load data includes:

[0013] The network connection score of the cloud platform is determined based on the network connection data between the requesting client and the cloud platform.

[0014] The load score of the cloud platform is determined based on the load data of the cloud platform;

[0015] The overall score of the cloud platform is determined based on its network connectivity score and load score.

[0016] At least one target cloud platform is determined from the multiple cloud platforms based on the overall score.

[0017] A further technical solution is that determining the network connection score of the cloud platform based on the network connection data between the requesting client and the cloud platform includes:

[0018] A rate score is determined based on the network connection rate between the requesting client and the multiple cloud platforms.

[0019] Distance score is determined based on the network transmission distance between the requesting end and the multiple cloud platforms;

[0020] The network connectivity score is determined based on the rate score and the distance score;

[0021] The determination of the cloud platform's load score based on the cloud platform's load data includes:

[0022] The task score is determined based on the number of tasks on the cloud platform.

[0023] Resource scores are determined based on the resource load rate of the cloud platform.

[0024] The load score is determined based on the task count score and the resource score.

[0025] A further technical solution involves loading the modeling resources from at least one of the target cloud platforms and transmitting them to the requesting end, including:

[0026] If there is only one target cloud platform, load the modeling resources from the target cloud platform and transmit them to the requesting client;

[0027] If there are multiple target cloud platforms, the modeling resources are divided into multiple sub-resources, and each sub-resource corresponds one-to-one with a target cloud platform.

[0028] The corresponding sub-resources are loaded from multiple target cloud platforms respectively;

[0029] The modeling resource is generated based on the sub-resources loaded by the multiple target cloud platforms and transmitted to the requesting end.

[0030] A further technical solution is that the method further includes:

[0031] If there are multiple loading requests, the priority of each loading request shall be determined.

[0032] Multiple loading requests are processed sequentially in descending order of priority.

[0033] A further technical solution is that the method further includes:

[0034] If the modeling resource is updated, obtain the difference between the modeling resource before and after the update.

[0035] The differences are uploaded to the respective cloud platforms, and the cloud platforms update the modeling resources based on the differences.

[0036] Secondly, embodiments of the present invention also provide a dynamic scheduling device for modeling resources in a multi-cloud environment, which includes a unit for performing the above-described method.

[0037] Thirdly, embodiments of the present invention also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0038] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the above-described method.

[0039] This invention provides a method, apparatus, device, and medium for dynamic scheduling of modeling resources in a multi-cloud environment. The method includes: upon receiving a request from a requesting client for loading modeling resources, acquiring network connection data between the requesting client and multiple cloud platforms, and acquiring load data of each of the multiple cloud platforms; determining at least one target cloud platform from the multiple cloud platforms based on the network connection data and the load data; and loading the modeling resources from the at least one target cloud platform and transmitting them to the requesting client. In this invention, because it is not limited to loading the modeling resources from a single target cloud platform and transmitting them to the requesting client, it can load the modeling resources from multiple target cloud platforms and transmit them to the requesting client, thereby effectively improving transmission efficiency.

[0040] In this embodiment of the invention, by acquiring real-time network connection data between the requesting client and each cloud platform, as well as the load data of each cloud platform, the optimal target cloud platform is dynamically selected, fundamentally solving the core contradiction of resource scheduling in a multi-cloud environment. Traditional solutions, due to their fixed selection of a single cloud platform, are prone to network congestion and load imbalance during peak access times, leading to delays in loading modeling resources. This solution, through dual-dimensional dynamic perception (network status + platform load), accurately selects the target cloud platform with the best current network quality and lowest load, ensuring that the transmission path of modeling resources is always in an optimal state. This not only significantly reduces network transmission latency but also avoids single-point overload through load-sensitive scheduling, achieving globally balanced utilization of multi-cloud resources. Ultimately, while ensuring the integrity of modeling resources, the average loading time in high-concurrency scenarios is compressed to an acceptable range, providing smooth technical support for architectural 3D modeling. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating a method for dynamic scheduling of modeling resources in a multi-cloud environment, as provided in an embodiment of the present invention.

[0043] Figure 2 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0046] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0047] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0048] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0049] Please see Figure 1 This invention provides a method for dynamic scheduling of modeling resources in a multi-cloud environment, such as... Figure 1 As shown, the method includes the following steps:

[0050] S1, if a request for loading modeling resources is received from the requesting end, obtain the network connection data between the requesting end and the multiple cloud platforms, and obtain the load data of the multiple cloud platforms respectively.

[0051] In specific implementation, modeling resources refer to the data required to create a 3D architectural model. To improve the reliability of modeling resource acquisition, this invention pre-stores the modeling resources on multiple cloud platforms. The requesting end refers to the user end, such as a mobile phone, computer, or other terminal; this invention is not specifically limited to this. Upon receiving a request from the requesting end for loading modeling resources, the network connection data between the requesting end and the multiple cloud platforms, as well as the load data of the multiple cloud platforms, are obtained respectively.

[0052] Among them, network connectivity data is used to evaluate the network connectivity between the requesting end and the cloud platform; load data is used to evaluate the load status of the cloud platform.

[0053] In some preferred embodiments, the above step "obtaining network connection data between the requesting client and the multiple cloud platforms respectively" specifically includes the following steps: obtaining the network connection rate and network transmission distance between the requesting client and the multiple cloud platforms respectively.

[0054] In practice, network connection speed refers to the data transmission rate between the requesting client and the cloud platform. Generally, the higher the network connection speed, the faster the data transmission. Furthermore, network transmission distance refers to the distance between the requesting client and the cloud platform, used to evaluate the connection stability between the two. The shorter the distance, the better the stability, and vice versa.

[0055] In some preferred embodiments, the above step "obtaining network connection data between the requesting client and the multiple cloud platforms respectively" specifically includes the following steps: obtaining the number of tasks and resource load rate of the multiple cloud platforms respectively.

[0056] In practice, the number of tasks on the cloud platform refers to the number of tasks currently being executed in parallel on the cloud platform, while the resource load rate reflects the current remaining resources of the cloud platform.

[0057] In this embodiment of the invention, network connection data is refined into network connection rate (directly affecting bandwidth capacity) and network transmission distance (determining physical latency), while load data is concretized into the number of tasks (characterizing concurrent pressure) and resource load rate (reflecting hardware utilization), upgrading scheduling decisions from fuzzy empirical judgments to precise quantitative analysis. The coupled evaluation of connection rate and transmission distance can avoid the false bandwidth advantage in "high-speed, long-distance" scenarios; the dual monitoring of the number of tasks and resource load rate can identify potential performance traps of "low tasks, high load." This refined data collection allows the system to penetrate appearances and capture true performance bottlenecks, such as prioritizing closer medium-speed platforms over high-speed, long-distance platforms, avoiding hidden latency caused by network hops.

[0058] S2, determine at least one target cloud platform from the plurality of cloud platforms based on the network connection data and the load data.

[0059] In practice, one or more cloud platforms are selected as the target cloud platform from among the multiple cloud platforms based on the network connection data and the load data. Furthermore, the cloud platform with the lowest cost can be selected to execute the task based on the prices offered by each cloud platform.

[0060] For example, in some preferred embodiments, the above step "determining at least one target cloud platform from multiple cloud platforms based on the network connection data and the load data" specifically includes the following steps: determining the network connection score of the cloud platform based on the network connection data between the requesting client and the cloud platform; determining the load score of the cloud platform based on the load data of the cloud platform; determining the total score of the cloud platform based on the network connection score and the load score; and determining at least one target cloud platform from multiple cloud platforms based on the total score.

[0061] In specific implementation, firstly, the network connection score of the cloud platform is determined based on the network connection data between the requesting end and the cloud platform; further, the load score of the cloud platform is determined based on the load data of the cloud platform; further, the total score of the cloud platform is determined based on the network connection score and the load score, for example, by calculating the sum of the network connection score and the load score of the cloud platform as the total score; further, cloud platforms with a total score exceeding a preset score threshold can be selected as target cloud platforms; or a preset number of cloud platforms can be selected as target cloud platforms in descending order of total score.

[0062] In this embodiment of the invention, a scientific multi-objective decision-making model is established by constructing a binary evaluation system of network connectivity score (mapping transmission efficiency) and load score (mapping processing capacity), and merging the two into a total score for platform ranking. This mechanism breaks through the limitation of traditional scheduling that only focuses on a single indicator: network connectivity score ensures resource transmission speed (such as prioritizing low-latency links), while load score ensures platform service stability (such as avoiding response timeouts caused by overload). After weighted fusion, it can prevent high-load platforms from being mistakenly selected due to superior networks, and also prevent low-load platforms from being abandoned due to network disadvantages. For example, when a platform has a medium network score but an extremely high load score, its total score will be automatically downgraded to prevent scheduling to nodes on the verge of overload. This dynamic weight allocation enables the system to achieve an optimal balance between speed and stability, effectively improving the resource loading success rate.

[0063] In some preferred embodiments, the above step "determine the network connection score of the cloud platform based on the network connection data between the requesting client and the cloud platform" specifically includes the following steps: determining a rate score based on the network connection rate between the requesting client and multiple cloud platforms; determining a distance score based on the network transmission distance between the requesting client and multiple cloud platforms; and determining the network connection score based on the rate score and the distance score.

[0064] In specific implementation, a mapping relationship between network connection rate and rate score is established in advance, so that the corresponding rate score can be determined based on the network connection rate. Furthermore, a mapping relationship between network transmission distance and distance score is established in advance, so that the corresponding distance score can be determined based on the network transmission distance. Further, the network connection score is determined based on the rate score and the distance score, for example, by calculating the sum of the rate score and the distance score as the network connection score.

[0065] In some preferred embodiments, the above step "determine the load score of the cloud platform based on the load data of the cloud platform" specifically includes the following steps: determining the task number score based on the number of tasks on the cloud platform; determining the resource score based on the resource load rate of the cloud platform; and determining the load score based on the task number score and the resource score.

[0066] In specific implementation, a mapping relationship between the number of tasks and the task score is established in advance, so that the corresponding task score can be determined according to the number of tasks; further, a mapping relationship between the cloud platform's resource load rate and the resource score is established in advance, so that the corresponding resource score can be determined according to the resource load rate; further, the load score is determined according to the task score and the resource score, for example, by calculating the sum of the task score and the resource score as the load score.

[0067] In this embodiment of the invention, rate score (based on measured bandwidth) and distance score (based on physical topology) are independently quantified at the network scoring level, and task count score (process-level pressure) and resource score (hardware-level pressure such as CPU / memory) are separated at the load scoring level, forming a four-dimensional evaluation subsystem. Rate score is directly related to transmission time, and distance score corrects for physical delay errors; task count score is used to warn of queue congestion risk, and resource score is used to assess hardware performance boundaries, ultimately ensuring that the total score accurately reflects the platform's real-time service capabilities.

[0068] S3, load the modeling resources from at least one of the target cloud platforms and transmit them to the requesting end.

[0069] In practice, the modeling resources are not limited to being loaded from a single target cloud platform and transmitted to the requesting end. Instead, they can be loaded from multiple target cloud platforms and transmitted to the requesting end, thereby effectively improving transmission efficiency.

[0070] In some preferred embodiments, the above step "loading the modeling resources from at least one target cloud platform and transmitting them to the requesting client" specifically includes the following steps: if there is only one target cloud platform, load the modeling resources from the target cloud platform and transmit them to the requesting client; if there are multiple target cloud platforms, divide the modeling resources into multiple sub-resources, with each sub-resource corresponding to one of the multiple target cloud platforms; load the corresponding sub-resources from each of the multiple target cloud platforms; generate the modeling resources based on the sub-resources loaded from the multiple target cloud platforms and transmit them to the requesting client.

[0071] In practice, if the number of target cloud platforms is determined to be one, the modeling resources are loaded from the target cloud platform and transmitted to the requesting end.

[0072] Furthermore, if there are multiple target cloud platforms, the modeling resources are first divided into multiple sub-resources, and each sub-resource corresponds one-to-one with a target cloud platform, that is, one target cloud platform corresponds to one sub-resource. Further, the data volume of the sub-resources is dynamically allocated according to the total score of each target cloud platform, and the higher the total score, the larger the data volume is allocated.

[0073] Furthermore, the corresponding sub-resources are loaded from multiple target cloud platforms respectively; by loading from multiple target cloud platforms simultaneously, loading efficiency can be improved.

[0074] Furthermore, the modeling resources are generated based on the sub-resources loaded by the multiple target cloud platforms and transmitted to the requesting end, thereby ensuring the integrity of the modeling resources.

[0075] In this embodiment of the invention, a resource block parallel loading strategy is adopted for multi-target platform scenarios. Modeling resources are divided into sub-resource blocks matching the number of platforms, and aggregated bandwidth transmission is achieved using multi-cloud channels. When the bandwidth of a single platform is insufficient to support large modeling resources, traditional serial loading faces minute-level latency. For example, when resources are evenly distributed across three platforms, the theoretical loading speed increases by nearly three times. During the client-side reassembly stage, because each sub-resource block maintains the original data structure correlation, the reassembly process can losslessly restore the complete model without complex calculations. This design overcomes the single-platform bandwidth bottleneck, resulting in a linear decrease in the loading time of ultra-large-scale resources.

[0076] In some preferred embodiments, the method further includes: if there are multiple loading requests, determining the priority of each loading request; and processing the multiple loading requests in descending order of priority.

[0077] In practice, the loading request includes a priority tag, and the priority of the loading request is determined based on the priority tag. For example, the priority tag is set based on the business type of the requesting party, and from high to low, it is as follows: Level 1 (real-time collaborative editing), Level 2 (critical operation), Level 3 (normal browsing), and Level 4 (backend query).

[0078] Furthermore, multiple loading requests are processed sequentially in descending order of priority, ensuring that important tasks are processed first. Simultaneously, users can customize the priority and resource requirements of loading requests according to their own needs, thus more flexibly meeting the needs of different users.

[0079] Furthermore, a timeout mechanism is set for loading requests that have been waiting for a long time. Once the set time limit is exceeded, the loading request will be automatically re-queued or reassigned to another cloud platform.

[0080] In this embodiment of the invention, by establishing a request priority mechanism and processing concurrent requests in descending order of priority, the service fairness problem in resource contention scenarios is effectively solved. In sudden high-concurrency scenarios (such as hundreds of people accessing the same building model simultaneously), traditional FIFO (First-In-First-Out) scheduling can cause critical operations (such as real-time collaborative editing) to be blocked by batch query requests. This solution marks each type of request with a business priority (such as urgent operations > normal browsing), ensuring that high-priority requests get priority access to scheduling resources. When the system is overloaded, low-priority requests enter the scheduling queue without preempting critical business resources, thus avoiding high-value operation delays and preventing request loss through queue buffering.

[0081] In some preferred embodiments, the method further includes: if the modeling resource is updated, obtaining the difference between the modeling resource before and after the update; uploading the difference to each of the cloud platforms, wherein the cloud platforms update the modeling resource based on the difference.

[0082] In specific implementation, since the modeling resources may be updated (changed), in this embodiment of the invention, when the modeling resources are updated, the differences between the modeling resources before and after the update are obtained; the differences are uploaded to each of the cloud platforms, wherein the cloud platforms update the modeling resources based on the differences.

[0083] Since only the differences need to be uploaded, avoiding the need to upload the entire modeling resource, the transmission efficiency is greatly improved.

[0084] Furthermore, for large-scale data (modeling resources) migration, an asynchronous approach can be adopted, which will not affect the execution of normal tasks, while ensuring data consistency and integrity.

[0085] In this embodiment of the invention, a differential incremental update mechanism is used instead of full resource synchronization. By extracting the differences (Delta data) before and after the modeling resource update, only the changed data blocks are distributed to each cloud platform. In the traditional full update mode, 1% of resource modification requires 100% data transmission (e.g., modifying 100MB of a 10GB model requires uploading 10GB). This solution generates a difference package (usually less than 5% of the full data) through version comparison, reducing the amount of synchronized data by more than 90%. After receiving the difference package, the cloud platform locally updates resources using block-level patching technology (such as the Rsync algorithm), significantly reducing I / O consumption. This design not only saves a significant amount of update bandwidth but also compresses synchronization time from hours to minutes. For high-frequency building collaborative design scenarios, this mechanism can reduce TB-level redundant data transmission daily, supporting real-time multi-terminal data consistency from the underlying layer.

[0086] This invention proposes a method for dynamic scheduling of modeling resources in a multi-cloud environment. The modeling resources are stored across multiple cloud platforms. The method includes the following steps: if a request for loading modeling resources is received from a requesting client, network connection data between the requesting client and the multiple cloud platforms is acquired, as well as load data of the multiple cloud platforms; based on the network connection data and the load data, at least one target cloud platform is determined from the multiple cloud platforms; the modeling resources are loaded from the at least one target cloud platform and transmitted to the requesting client. In this invention, since the method is not limited to loading and transmitting the modeling resources from a single target cloud platform, it can load and transmit the modeling resources from multiple target cloud platforms, thereby effectively improving transmission efficiency.

[0087] In this embodiment of the invention, by acquiring real-time network connection data between the requesting client and each cloud platform, as well as the load data of each cloud platform, the optimal target cloud platform is dynamically selected, fundamentally solving the core contradiction of resource scheduling in a multi-cloud environment. Traditional solutions, due to their fixed selection of a single cloud platform, are prone to network congestion and load imbalance during peak access times, leading to delays in loading modeling resources. This solution, through dual-dimensional dynamic perception (network status + platform load), accurately selects the target cloud platform with the best current network quality and lowest load, ensuring that the transmission path of modeling resources is always in an optimal state. This not only significantly reduces network transmission latency but also avoids single-point overload through load-sensitive scheduling, achieving globally balanced utilization of multi-cloud resources. Ultimately, while ensuring the integrity of modeling resources, the average loading time in high-concurrency scenarios is compressed to an acceptable range, providing smooth technical support for architectural 3D modeling.

[0088] Corresponding to the above-described method for dynamic scheduling of modeling resources in a multi-cloud environment, this invention also provides a device for dynamic scheduling of modeling resources in a multi-cloud environment. This device includes a unit for executing the above-described method for dynamic scheduling of modeling resources in a multi-cloud environment, and can be configured in a desktop computer, tablet computer, laptop computer, or other terminal. Specifically, the device includes:

[0089] The acquisition unit is used to acquire, upon receiving a request from the requesting end for loading modeling resources, network connection data between the requesting end and multiple cloud platforms, and load data of multiple cloud platforms respectively.

[0090] A determining unit is configured to determine at least one target cloud platform from among the multiple cloud platforms based on the network connection data and the load data;

[0091] A transmission unit is used to load the modeling resources from at least one of the target cloud platforms and transmit them to the requesting end.

[0092] In some preferred embodiments, the step of acquiring network connection data between the requesting client and the multiple cloud platforms includes: acquiring the network connection rate and network transmission distance between the requesting client and the multiple cloud platforms respectively;

[0093] The step of acquiring load data for multiple cloud platforms includes acquiring the number of tasks and resource load rate for each cloud platform.

[0094] In some preferred embodiments, determining at least one target cloud platform from a plurality of cloud platforms based on the network connection data and the load data includes:

[0095] The network connection score of the cloud platform is determined based on the network connection data between the requesting client and the cloud platform.

[0096] The load score of the cloud platform is determined based on the load data of the cloud platform;

[0097] The overall score of the cloud platform is determined based on its network connectivity score and load score.

[0098] At least one target cloud platform is determined from the multiple cloud platforms based on the overall score.

[0099] In some preferred embodiments, determining the network connectivity score of the cloud platform based on network connectivity data between the requesting client and the cloud platform includes:

[0100] A rate score is determined based on the network connection rate between the requesting client and the multiple cloud platforms.

[0101] Distance score is determined based on the network transmission distance between the requesting end and the multiple cloud platforms;

[0102] The network connectivity score is determined based on the rate score and the distance score;

[0103] The determination of the cloud platform's load score based on the cloud platform's load data includes:

[0104] The task score is determined based on the number of tasks on the cloud platform.

[0105] Resource scores are determined based on the resource load rate of the cloud platform.

[0106] The load score is determined based on the task count score and the resource score.

[0107] In some preferred embodiments, loading the modeling resources from at least one of the target cloud platforms and transmitting them to the requesting client includes:

[0108] If there is only one target cloud platform, load the modeling resources from the target cloud platform and transmit them to the requesting client;

[0109] If there are multiple target cloud platforms, the modeling resources are divided into multiple sub-resources, and each sub-resource corresponds one-to-one with a target cloud platform.

[0110] The corresponding sub-resources are loaded from multiple target cloud platforms respectively;

[0111] The modeling resource is generated based on the sub-resources loaded by the multiple target cloud platforms and transmitted to the requesting end.

[0112] In some preferred embodiments, the multi-cloud environment modeling resource dynamic scheduling device further includes:

[0113] A priority determination unit is used to determine the priority of each loading request if there are multiple loading requests.

[0114] A sorting unit is used to process multiple loading requests sequentially in order of priority from highest to lowest.

[0115] In some preferred embodiments, the multi-cloud environment modeling resource dynamic scheduling device further includes:

[0116] The difference determination unit is used to obtain the difference between the modeling resource after the update and before the update if the modeling resource is updated.

[0117] A difference update unit is used to upload the difference portion to each of the cloud platforms, wherein the cloud platforms update the modeling resources based on the difference portion.

[0118] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned dynamic scheduling device for modeling resources in a multi-cloud environment and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0119] The aforementioned dynamic scheduling device for modeling resources in a multi-cloud environment can be implemented as a computer program, which can, for example... Figure 2 It runs on the computer device shown.

[0120] Please see Figure 2 , Figure 2 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a terminal or a server. The terminal can be an electronic device with communication functions, such as a smartphone, tablet, laptop, desktop computer, personal digital assistant, or wearable device. The server can be a standalone server or a server cluster composed of multiple servers.

[0121] The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0122] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it enables the processor 502 to execute a dynamic scheduling method for modeling resources in a multi-cloud environment.

[0123] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0124] The internal memory 504 provides an environment for the execution of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a dynamic scheduling method for modeling resources in a multi-cloud environment.

[0125] The network interface 505 is used for network communication with other devices. Those skilled in the art will understand that the above structure is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. A specific computer device 500 may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements.

[0126] The processor 502 is used to run a computer program 5032 stored in a memory to implement the steps of a dynamic scheduling method for modeling resources in a multi-cloud environment provided in any of the above method embodiments.

[0127] It should be understood that, in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0128] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0129] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program. When executed by a processor, the computer program causes the processor to perform the steps of the method for dynamic scheduling of modeling resources in a multi-cloud environment provided in any of the above-described method embodiments.

[0130] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk, or any other physical storage medium capable of storing program code. The computer-readable storage medium can be non-volatile or volatile.

[0131] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0132] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0133] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0135] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0136] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Since these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0137] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamic scheduling of modeling resources in a multi-cloud environment, characterized in that, The method involves storing modeling resources on multiple cloud platforms. If a request to load modeling resources is received from the requesting end, network connection data between the requesting end and multiple cloud platforms is obtained, and load data of multiple cloud platforms is obtained respectively. Based on the network connection data and the load data, at least one target cloud platform is determined from the plurality of cloud platforms; The modeling resources are loaded from at least one of the target cloud platforms and transmitted to the requesting client.

2. The method for dynamic scheduling of modeling resources in a multi-cloud environment according to claim 1, characterized in that, The step of acquiring network connection data between the requesting client and the multiple cloud platforms includes: acquiring the network connection rate and network transmission distance between the requesting client and the multiple cloud platforms respectively; The step of acquiring load data for multiple cloud platforms includes acquiring the number of tasks and resource load rate for each cloud platform.

3. The method for dynamic scheduling of modeling resources in a multi-cloud environment according to claim 2, characterized in that, The step of determining at least one target cloud platform from a plurality of cloud platforms based on the network connection data and the load data includes: The network connection score of the cloud platform is determined based on the network connection data between the requesting client and the cloud platform. The load score of the cloud platform is determined based on the load data of the cloud platform; The overall score of the cloud platform is determined based on its network connectivity score and load score. At least one target cloud platform is determined from the multiple cloud platforms based on the overall score.

4. The method for dynamic scheduling of modeling resources in a multi-cloud environment according to claim 3, characterized in that, The process of determining the network connection score of the cloud platform based on the network connection data between the requesting client and the cloud platform includes: A rate score is determined based on the network connection rate between the requesting client and the multiple cloud platforms. Distance score is determined based on the network transmission distance between the requesting end and the multiple cloud platforms; The network connectivity score is determined based on the rate score and the distance score; The determination of the cloud platform's load score based on the cloud platform's load data includes: The task score is determined based on the number of tasks on the cloud platform. Resource scores are determined based on the resource load rate of the cloud platform. The load score is determined based on the task count score and the resource score.

5. The method for dynamic scheduling of modeling resources in a multi-cloud environment according to claim 3, characterized in that, Loading the modeling resources from at least one of the target cloud platforms and transmitting them to the requesting client includes: If there is only one target cloud platform, load the modeling resources from the target cloud platform and transmit them to the requesting client; If there are multiple target cloud platforms, the modeling resources are divided into multiple sub-resources, and each sub-resource corresponds one-to-one with a target cloud platform. The corresponding sub-resources are loaded from multiple target cloud platforms respectively; The modeling resource is generated based on the sub-resources loaded by the multiple target cloud platforms and transmitted to the requesting end.

6. The method for dynamic scheduling of modeling resources in a multi-cloud environment according to claim 1, characterized in that, The method further includes: If there are multiple loading requests, the priority of each loading request shall be determined. Multiple loading requests are processed sequentially in descending order of priority.

7. The method for dynamic scheduling of modeling resources in a multi-cloud environment according to claim 1, characterized in that, The method further includes: If the modeling resource is updated, obtain the difference between the modeling resource before and after the update. The differences are uploaded to the respective cloud platforms, and the cloud platforms update the modeling resources based on the differences.

8. A dynamic scheduling device for modeling resources in a multi-cloud environment, characterized in that, Includes a unit for performing the method as described in any one of claims 1-7.

9. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, can implement the method as described in any one of claims 1-7.

Images