Cloud platform resource allocation method and device, equipment, storage medium and product

Abstract

The invention discloses a cloud platform resource allocation method and device, equipment, a storage medium and a product, and relates to the technical field of cloud computing, and the cloud platform resource allocation method comprises the steps: obtaining an operation index of a cloud platform tenant; inputting the operation index into a preset decision model, and performing prediction processing on the operation index based on the preset decision model to obtain a plurality of predicted quota schemes; based on the adaptation degree between each predicted quota scheme and the cloud platform tenant and the index weight in each predicted quota scheme, a final quota scheme is obtained through calculation, and the index weight is obtained through calculation based on the importance degree of each index in the predicted quota scheme; and performing resource allocation on the cloud platform tenants according to the final quota scheme to obtain an allocation result. The resource allocation efficiency of the cloud platform is improved.

Description

Technical Field

[0001] The present application relates to the field of cloud computing technology, and in particular to a cloud platform resource allocation method, device, equipment, storage medium and product. Background Art

[0002] Cloud platform tenant quota allocation is an important management activity in the cloud computing environment. It involves the planning and control of cloud resource usage. Quota allocation ensures the rational use of resources to avoid waste of resources.

[0003] At present, the common technical solution is that the operation personnel will use different tenant historical indicators as a reference to calculate the basic quota allocation standard of resources, and then directly configure resources according to the demand work order. This method requires experienced operation personnel to spend a lot of time on process deduction, indicator quantification, etc. before they can calculate the basic quota allocation standard. The workload involved is large and time-consuming, resulting in low resource allocation efficiency.

[0004] Therefore, how to improve the resource allocation efficiency of cloud platforms is a problem that needs to be solved urgently.

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of this application is to provide a cloud platform resource allocation method, device, equipment, storage medium and product, aiming to solve the technical problem in related technologies that the basic quota allocation standard of resources is calculated based on different tenant historical indicators as a reference, and then resources are directly configured according to the demand work order, resulting in low resource allocation efficiency.

[0007] To achieve the above objectives, the present application proposes a cloud platform resource allocation method, the cloud platform resource allocation method comprising:

[0008] Obtain operational indicators for cloud platform tenants;

[0009] Inputting the operating indicators into a preset decision model, and performing forecasting processing on the operating indicators based on the preset decision model to obtain multiple forecast quota solutions;

[0010] Based on the compatibility between each of the forecast quota schemes and the cloud platform tenant and the indicator weights in each of the forecast quota schemes, a final quota scheme is calculated, wherein the indicator weight is calculated based on the importance of each indicator in the forecast quota scheme;

[0011] The final quota plan is used to allocate resources to the cloud platform tenants to obtain an allocation result.

[0012] In one embodiment, the step of calculating the final quota scheme based on the degree of compatibility between each of the predicted quota schemes and the cloud platform tenant and the indicator weights in each of the predicted quota schemes includes:

[0013] Based on the degree of compatibility between each of the predicted quota schemes and the cloud platform tenants, an indicator matrix is ​​constructed;

[0014] Based on the indicator weights, the indicator matrix is ​​refined to obtain a final quota solution.

[0015] In one embodiment, the step of performing a refinement process on the indicator matrix based on the indicator weights to obtain a final quota solution includes:

[0016] Calculate the indicator weight of each indicator according to the importance and deviation of each indicator in the forecast quota plan;

[0017] The weight of each indicator is multiplied by each indicator in the predicted quota scheme with the highest degree of adaptability to obtain a final quota scheme.

[0018] In one embodiment, the step of calculating the indicator weight of each indicator according to the importance and deviation of each indicator in the forecast quota scheme includes:

[0019] Selecting a resource value for each indicator in the indicator matrix, and calculating the importance of each indicator based on the resource value;

[0020] Based on the importance of each indicator, the deviation of each indicator is calculated;

[0021] Based on the deviation and the number of indicators in the indicator matrix, the indicator weight of each indicator is calculated.

[0022] In one embodiment, after the step of allocating resources to the cloud platform tenants according to the final quota plan and obtaining the allocation result, the step further includes:

[0023] Based on the allocation result, dynamically score the resource quota of each cloud platform tenant to obtain a dynamic resource score;

[0024] If the dynamic resource score is less than a first preset threshold, the resource quota corresponding to the cloud platform tenant is adjusted.

[0025] In one embodiment, if the dynamic resource score is less than a first preset threshold, the step of adjusting the resource quota corresponding to the cloud platform tenant includes:

[0026] If the dynamic resource score is less than the first preset threshold and greater than the second preset threshold, it is determined that the current operating environment of the cloud platform tenant is at risk, and a risk warning is generated;

[0027] Send the risk warning to the display interface where the business personnel are located, so that the business personnel can communicate with the cloud platform tenants about the business and then reallocate resources;

[0028] If the dynamic resource score is less than the second preset threshold, resource allocation to the current cloud platform tenant is stopped until the cloud platform tenant completes rectification of the current operating environment, and then determines whether to allocate resources again based on the rectification result.

[0029] In addition, to achieve the above purpose, the present application also proposes a cloud platform resource allocation device, the cloud platform resource allocation device comprising:

[0030] The acquisition module is used to obtain the operational indicators of cloud platform tenants;

[0031] A processing module, used for inputting the operation index into a preset decision model, and performing forecasting processing on the operation index based on the preset decision model to obtain multiple forecast quota schemes;

[0032] A calculation module, configured to calculate a final quota scheme based on the degree of compatibility between each of the forecast quota schemes and the cloud platform tenant and the indicator weights in each of the forecast quota schemes, wherein the indicator weights are calculated based on the importance of each indicator in the forecast quota scheme;

[0033] The allocation module is used to allocate resources to the cloud platform tenants according to the final quota plan to obtain an allocation result.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a cloud platform resource allocation device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the cloud platform resource allocation method as described above.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the cloud platform resource allocation method described above are implemented.

[0036] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the cloud platform resource allocation method described above.

[0037] The present application proposes a cloud platform resource allocation method, device, equipment, storage medium and product. The present application obtains the operating indicators of the cloud platform tenants, inputs the operating indicators into a preset decision model, and predicts and processes the operating indicators based on the preset decision model to obtain multiple predicted quota schemes. The predicted quota scheme obtained at this time is not the most reasonable allocation scheme. It is also necessary to calculate the final quota scheme based on the compatibility between the predicted quota scheme and the cloud platform tenants and the indicator weights in each of the predicted quota schemes. The final quota scheme is used to allocate resources to the cloud platform tenants. The quota scheme optimization based on the neural network and the more refined processing of the indicators in the quota scheme by the indicator weights can automatically generate a better quota scheme for the tenants, reduce a lot of analysis work and experience requirements for the operating personnel, and improve the efficiency of resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A flowchart of the first embodiment of the cloud platform resource allocation method of the present application is provided;

[0041] Figure 2 A schematic diagram of a brief implementation process of the cloud platform resource allocation method of this application;

[0042] Figure 3 A flowchart of the second embodiment of the cloud platform resource allocation method of the present application is provided;

[0043] Figure 4 A detailed flow chart of the cloud platform resource allocation method of this application;

[0044] Figure 5 This is a schematic diagram of the module structure of the cloud platform resource allocation device according to an embodiment of the present application;

[0045] Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the cloud platform resource allocation method in the embodiment of the present application.

[0046] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] The main solutions of the embodiments of this application are:

[0050] Obtain operational indicators for cloud platform tenants;

[0051] Inputting the operating indicators into a preset decision model, and performing forecasting processing on the operating indicators based on the preset decision model to obtain multiple forecast quota solutions;

[0052] Based on the compatibility between each of the forecast quota schemes and the cloud platform tenant and the indicator weights in each of the forecast quota schemes, a final quota scheme is calculated, wherein the indicator weight is calculated based on the importance of each indicator in the forecast quota scheme;

[0053] The final quota plan is used to allocate resources to the cloud platform tenants to obtain an allocation result.

[0054] In the prior art, operators calculate the basic quota allocation standard of resources based on different tenant historical indicators as a reference, and then directly configure resources according to the demand work order. This method requires experienced operators to spend a lot of time on process deduction, indicator quantification, etc. before they can calculate the basic quota allocation standard. The workload involved is large and time-consuming, resulting in low resource allocation efficiency.

[0055] The present application proposes a cloud platform resource allocation method, device, equipment, storage medium and product. The present application obtains the operating indicators of the cloud platform tenants, inputs the operating indicators into a preset decision model, and predicts and processes the operating indicators based on the preset decision model to obtain multiple predicted quota schemes. The predicted quota scheme obtained at this time is not the most reasonable allocation scheme. It is also necessary to calculate the final quota scheme based on the compatibility between the predicted quota scheme and the cloud platform tenants and the indicator weights in each of the predicted quota schemes. The final quota scheme is used to allocate resources to the cloud platform tenants. The quota scheme optimization based on the neural network and the more refined processing of the indicators in the quota scheme by the indicator weights can automatically generate a better quota scheme for the tenants, reduce a lot of analysis work and experience requirements for the operating personnel, and improve the efficiency of resource allocation.

[0056] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a cloud platform resource allocation device, a cloud platform decision-making assistance tool, etc. The following takes the cloud platform decision-making assistance tool as an example to illustrate this embodiment and the following embodiments.

[0057] Based on this, the embodiment of the present application provides a cloud platform resource allocation method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the cloud platform resource allocation method of the present application.

[0058] In this embodiment, the cloud platform resource allocation method includes steps S10 to S40:

[0059] Step S10, obtaining the operating indicators of the cloud platform tenants;

[0060] It should be noted that the purpose of this application is to effectively manage the quotas of many tenants and resources under the cloud platform, to ensure that the operator can achieve the most efficient sale of existing resources, while taking into account the balanced business development of tenants in each region. In terms of application logic, by constructing a "cloud platform resource allocation method" component module, it is embedded in the cloud platform system, and runs through the quota management process of all computing power resources, storage power resources, PaaS (Platform as a Service) power resources, network power resources, and security authentication power resources of the platform, providing it with effective auxiliary planning and allocation solutions.

[0061] It should be noted that cloud platform tenants can be multiple users who rent cloud platform computing resources. These users execute corresponding businesses by calling corresponding cloud platform resources.

[0062] It should be noted that operational indicators can include tenant sales / quota ratio, sales trends, quota usage, safety assessment, additional purchases and other indicators.

[0063] Specifically, this application relates to a method for cloud platform quota planning and optimization allocation. The specific process is as follows: Figure 2 As shown in the figure, on the platform side, the cloud platform assists decision-making tools to assist manual resource allocation and dynamic operation scoring. On the access side, with the tenant quota decision model and operation scoring as the core, a cloud platform quota planning and execution decision device is established to specify standardized access specifications. Finally, on the tenant side, cloud platform resources are obtained through the optimal algorithm, supporting dynamic allocation according to actual business expansion without manual application submission.

[0064] The following is an explanation of the cloud platform and cloud platform decision-making support tools involved in the overall process:

[0065] Cloud platform: PaaS-level portal system, providing unified operation service capabilities for its computing resources, storage resources, network resources, private PaaS and other products. It is an integrated marketing, ordering, operation, and maintenance service management portal for IT capabilities across the entire network under the requirements of internal support capabilities and external product expansion. The cloud platform system provides users with unified portal operations, unified account authentication, unified product catalogs, unified order management and other services, realizing unified display, operation and control of IT capabilities and components.

[0066] Cloud platform decision-making support tool: This tool is built based on the user quota decision-making model. It combines the cloud platform's built-in resource allocation strategies based on different user types, as well as support for decision-making calculations and refined adjustments. It provides allocation recommendations based on the actual conditions of different users, allowing cloud platform resources to be used in the most optimal way.

[0067] Step S20, inputting the operation index into a preset decision model, and performing forecasting processing on the operation index based on the preset decision model to obtain multiple forecast quota schemes;

[0068] It should be noted that the preset decision model can be a neural network model based on deep learning. Before the preset decision model processes the operating indicators, the model has been trained. The specific training method can be to input the historical operating indicators of each tenant into the model, and then predict the operating indicators. When the loss function reaches the minimum value, the model converges. At this time, the iterative training process of the model is stopped to obtain the trained decision model.

[0069] It should be noted that the training process can be the following steps:

[0070] Step 1: Use One-hot encoding for indicators such as tenant sales / quota ratio, sales trend, quota usage, additional purchase, and operation status, and use the embedding layer as the input of the model to convert the high-dimensional sparse feature vector into a dense low-dimensional feature vector.

[0071] Step 2: Process the vector through dropout (a neural network regularization technique) to randomly discard some features and generate different feature vectors as positive examples.

[0072] Step 3: The final processed features are trained through a neural network to obtain a target vector set representing the cloud platform quota plan. The execution process needs to calculate the probability value of all quota plans of the current cloud platform being selected through softmax (loss function).

[0073] Step 4: During the training process, the loss function is used to determine when the model converges and when it is appropriate to stop training. The gradient descent tends to be flat. The calculation formula is as follows:

[0074]

[0075] It should be noted that, in the actual execution process, the operation indicators can be directly predicted through the preset decision model to obtain the optimal set of all quota plans for the cloud platform, that is, the predicted quota plan.

[0076] Step S30, based on the degree of compatibility between each of the predicted quota schemes and the cloud platform tenant and the indicator weights in each of the predicted quota schemes, a final quota scheme is calculated, wherein the indicator weight is calculated based on the importance of each indicator in the predicted quota scheme;

[0077] It should be noted that the final quota plan can be the final specific implementation plan for allocating resources to tenants. The optimal allocation plan is obtained by screening multiple plans based on the compatibility between the predicted quota plan and the cloud platform tenants, as well as the indicator weights in each of the predicted quota plans.

[0078] It should be noted that the adaptability may be the degree of match between the cloud platform tenant and each solution, and the adaptability may be 60%, 80%, etc., without specific limitation.

[0079] It should be noted that the indicator weight is used to characterize the importance of each indicator. For each indicator, when the indicator weight is different, the quota result obtained when calculating the final quota plan will also be different.

[0080] In a feasible implementation manner, the step S30 of calculating the final quota scheme based on the degree of compatibility between each of the predicted quota schemes and the cloud platform tenant and the indicator weights in each of the predicted quota schemes includes:

[0081] Based on the degree of compatibility between each of the predicted quota schemes and the cloud platform tenants, an indicator matrix is ​​constructed;

[0082] It should be noted that each forecast quota plan includes multiple indicators. When constructing the matrix, the forecast quota plan is ranked according to its fitness, and arranged from top to bottom. For example, the forecast quota plan with the highest fitness is at the top, and the quota plan with the lowest fitness is at the bottom.

[0083] It should be noted that when creating the indicator matrix (also called the original quota allocation matrix), the original quota allocation matrix A is created, and the rows represent the n executable allocation schemes. Here, the allocation scheme refers to the set of built-in schemes of the cloud platform that is most suitable for the user. The columns represent the value of the mth indicator corresponding to this scheme. Here, the indicator refers to the value of the cloud platform built-in scheme corresponding to the virtual machine, network, instance, storage and other resources, and these values ​​are normalized. The value of each resource in the original quota allocation matrix cannot accurately determine the resource value that the user will eventually obtain. It is necessary to make a decision through this calculation method to obtain the final value. The original matrix example is as follows:

[0084]

[0085] Based on the indicator weights, the indicator matrix is ​​refined to obtain a final quota solution.

[0086] It should be noted that the process of refined processing can be: modifying the predicted quota plan according to the calculated indicator weights, multiplying these weight values ​​with the resource values ​​of the optimal plan respectively, and the result correctly expresses the final resource allocation recommendation for this user calculated according to the built-in plan of the cloud platform, and then obtaining the final quota plan.

[0087] In a feasible implementation manner, the step of performing a refinement process on the indicator matrix based on the indicator weights to obtain a final quota solution includes:

[0088] Calculate the indicator weight of each indicator according to the importance and deviation of each indicator in the forecast quota plan;

[0089] It should be noted that importance can be the relative importance of the current solution on a certain indicator, and deviation can be an indicator that measures the difference between individual observations in a data set and their average value, reflecting the degree of discreteness of the data. The indicator weight corresponding to each indicator can be calculated by the deviation.

[0090] The weight of each indicator is multiplied by each indicator in the predicted quota scheme with the highest degree of adaptability to obtain a final quota scheme.

[0091] It should be noted that these weight values ​​are multiplied by the resource values ​​of the optimal solution, and the result correctly expresses the final resource allocation recommendation for this user calculated according to the built-in solution of the cloud platform, and then the final quota solution is obtained.

[0092] In a feasible implementation manner, the step of calculating the indicator weight of each indicator according to the importance and deviation of each indicator in the forecast quota scheme includes:

[0093] Selecting a resource value for each indicator in the indicator matrix, and calculating the importance of each indicator based on the resource value;

[0094] It should be noted that the calculation formula for importance can be:

[0095] Calculate the relative importance of the nth solution on the mth indicator. When the number of cloud platform tenant quota allocation solutions is t, the importance T nm The calculation method is:

[0096]

[0097] Based on the importance of each indicator, the deviation of each indicator is calculated;

[0098] It should be noted that the deviation P of each indicator is calculated m Similarly, when the number of cloud platform tenant quota allocation plans is t, the indicator deviation P m The calculation method is:

[0099]

[0100] The deviation serves as the basis for calculating the weight corresponding to each indicator in the next step.

[0101] Based on the deviation and the number of indicators in the indicator matrix, the indicator weight of each indicator is calculated.

[0102] It should be noted that the weight corresponding to each indicator m is calculated according to the deviation. When the total number of indicators is k, the weight w corresponding to each indicator m is m The calculation method is:

[0103]

[0104] The values ​​of the obtained weight matrix are used as the basis for correcting the optimal built-in solution, which is the first row of the original matrix. These weight values ​​are multiplied by the resource values ​​of the optimal solution. The result correctly expresses the final resource allocation recommendation for this user calculated according to the built-in solution of the cloud platform. The specific method is:

[0105] The indicator weights obtained above are:

[0106] W=[w1w2…w m ]

[0107] The final value of indicator a1 is a 11 ×w1, a2 finally takes the value of a 12 ×w2,a m The final value is a 1m × m , and finally a1, a2, ..., a mIt is the specific value of the resource with the most optimal allocation strategy.

[0108] Step S40: Allocate resources to the cloud platform tenants using the final quota plan to obtain an allocation result.

[0109] It should be noted that after the final quota plan is determined, resources will be allocated to users according to the above allocation suggestions, and users will apply them after receiving them. If the operator chooses to automatically allocate cloud platform resources in the first step, resources will be allocated directly according to the results calculated by the tenant quota decision-making assistance tool.

[0110] The present application proposes a cloud platform resource allocation method, device, equipment, storage medium and product. The present application obtains the operating indicators of the cloud platform tenants, inputs the operating indicators into a preset decision model, and predicts and processes the operating indicators based on the preset decision model to obtain multiple predicted quota schemes. The predicted quota scheme obtained at this time is not the most reasonable allocation scheme. It is also necessary to calculate the final quota scheme based on the compatibility between the predicted quota scheme and the cloud platform tenants and the indicator weights in each of the predicted quota schemes. The final quota scheme is used to allocate resources to the cloud platform tenants. The quota scheme optimization based on the neural network and the more refined processing of the indicators in the quota scheme by the indicator weights can automatically generate a better quota scheme for the tenants, reduce a lot of analysis work and experience requirements for the operating personnel, and improve the efficiency of resource allocation.

[0111] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , after the step S40 of allocating resources to the cloud platform tenants according to the final quota plan and obtaining the allocation result, the method further includes steps S50 to S60:

[0112] Step S50, based on the allocation result, dynamically scoring the resource quota of each cloud platform tenant to obtain a dynamic resource score;

[0113] It should be noted that the platform monitors the actual application situation and dynamically scores resources in real time according to the cloud platform quota scoring algorithm. The scoring method is as follows:

[0114] S=w1×C+w2×V+w3×T

[0115] Where: S is the quota score of the current tenant in the cloud platform operating environment, with 10 being the full score.

[0116] w is the weight value, which is defined by the program and can be adjusted according to the overall operation strategy of the cloud platform. w1++w2+w3=1, and C∈[0,10] is the comprehensive score of resource occupancy rate.

[0117] V∈[0,10] is the comprehensive score of security vulnerabilities of the tenant’s corresponding organization

[0118] T∈[0,10] is the comprehensive score of the application attack threat intelligence detected by the system security software.

[0119] Step S60: If the dynamic resource score is less than a first preset threshold, the resource quota corresponding to the cloud platform tenant is adjusted.

[0120] It should be noted that the first preset threshold may be 8 points, 9 points, etc., and may be changed as needed without specific limitation.

[0121] It should be noted that, taking the first preset threshold of 8 points as an example, when the real-time dynamic scoring result of the cloud platform quota scoring algorithm is greater than 8 points, it means that the cloud platform resources allocated by this user are qualified in terms of resource occupancy, security vulnerabilities, and application attack threats, and can continue to run. When it is lower than 8 points, it means that there are operating risks in the current operating environment.

[0122] In a feasible implementation manner, if the dynamic resource score is less than the first preset threshold, the step S60 of adjusting the resource quota corresponding to the cloud platform tenant includes:

[0123] If the dynamic resource score is less than the first preset threshold and greater than the second preset threshold, it is determined that the current operating environment of the cloud platform tenant is at risk, and a risk warning is generated;

[0124] It should be noted that the second preset threshold may be 6 points, 5 points, etc., without specific limitation, wherein the second preset threshold is smaller than the first preset threshold.

[0125] Send the risk warning to the display interface where the business personnel are located, so that the business personnel can communicate with the cloud platform tenants about the business and then reallocate resources;

[0126] It should be noted that when the score is lower than 8 points but higher than 6 points, it indicates that there is a risk. If manual allocation is performed in the first step of resource configuration and the configuration recommended by the tenant quota decision-making assistance tool is not fully adopted, the reallocation will be temporarily performed based on the allocation strategy recommended by the tool, and reallocation will be carried out after subsequent business and business-level communication.

[0127] If the dynamic resource score is less than the second preset threshold, resource allocation to the current cloud platform tenant is stopped until the cloud platform tenant completes rectification of the current operating environment, and then determines whether to allocate resources again based on the rectification result.

[0128] It should be noted that when the score is lower than 6 points, resource allocation will be stopped. After the cloud platform tenant has made rectifications, resource allocation will be carried out if the rectification is successful. If the rectification is unsuccessful, it means that there are still risks in the operating environment of the cloud platform tenant, and resources cannot be allocated to it.

[0129] In this embodiment, after the final allocation plan is allocated, the actual application situation is monitored on the scoring platform, and the cloud platform quota scoring algorithm is used for real-time dynamic scoring, so as to avoid risks in time according to the real-time dynamic scoring situation.

[0130] Specifically, the detailed execution flow chart involved in this application is as follows: Figure 4 As shown, the specific implementation process of this application is: the operator goes to the console to configure the cloud platform resource allocation strategy and selects manual configuration. The tenant quota decision-making assistance tool comprehensively considers the importance of indicators such as tenant sales / quota ratio, sales trend, quota usage, security assessment, and additional purchase. The cloud platform quota auxiliary decision-making model is used to calculate the n quota plans that are most suitable for this user, and the top n plans are combined into a quota optimization matrix. The quota indicators therein are refined to obtain the final quota plan, and then the final quota plan is allocated based on the mode selected by the operator, and a real-time security score is performed after allocation to avoid risks and optimize in time.

[0131] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the cloud platform resource allocation method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0132] This application also provides a cloud platform resource allocation device, please refer to Figure 5 , the cloud platform resource allocation device comprises:

[0133] An acquisition module 10 is used to obtain the operation indicators of the cloud platform tenants;

[0134] A processing module 20 is used to input the operation index into a preset decision model, and perform forecasting processing on the operation index based on the preset decision model to obtain multiple forecast quota schemes;

[0135] A calculation module 30, configured to calculate a final quota scheme based on the degree of compatibility between each of the predicted quota schemes and the cloud platform tenant and the indicator weights in each of the predicted quota schemes, wherein the indicator weights are calculated based on the importance of each indicator in the predicted quota scheme;

[0136] The allocation module 40 is used to allocate resources to the cloud platform tenants according to the final quota plan to obtain an allocation result.

[0137] Optionally, the calculation module includes:

[0138] A construction unit, configured to construct an indicator matrix based on the degree of compatibility between each of the predicted quota schemes and the cloud platform tenants;

[0139] The processing unit is used to perform fine processing on the indicator matrix based on the indicator weight to obtain a final quota solution.

[0140] Optionally, the processing unit includes:

[0141] A first calculation subunit is used to calculate the indicator weight of each indicator according to the importance and deviation of each indicator in the forecast quota scheme;

[0142] The second calculation subunit is used to multiply each indicator weight by each indicator in the predicted quota solution with the highest adaptability to obtain a final quota solution.

[0143] Optionally, the first computing subunit includes:

[0144] A selection component is used to select a resource value of each indicator in the indicator matrix, and calculate the importance of each indicator based on the resource value;

[0145] A first calculation component is used to calculate the deviation of each indicator based on the importance of each indicator;

[0146] The second calculation component is used to calculate the indicator weight of each indicator based on the deviation and the number of indicators in the indicator matrix.

[0147] Optionally, the device further comprises:

[0148] A scoring module, used to dynamically score the resource quota of each cloud platform tenant based on the allocation result to obtain a dynamic resource score;

[0149] The adjustment module is used to adjust the resource quota corresponding to the cloud platform tenant if the dynamic resource score is less than a first preset threshold.

[0150] Optionally, the adjustment module includes:

[0151] a determination unit, configured to determine that a current operating environment of the cloud platform tenant is at risk and generate a risk warning if the dynamic resource score is less than a first preset threshold and greater than a second preset threshold;

[0152] A sending unit, used to send the risk warning to the display interface where the business personnel are located, so that the business personnel can reallocate resources after business communication with the cloud platform tenants;

[0153] The allocation unit is used to stop allocating resources to the current cloud platform tenant if the dynamic resource score is less than a second preset threshold value, until the cloud platform tenant completes the rectification of the current operating environment, and then determine whether to allocate resources again based on the rectification result.

[0154] The cloud platform resource allocation device provided by the present application adopts the cloud platform resource allocation method in the above embodiment, which can solve the technical problem of cloud platform resource allocation. Compared with the prior art, the beneficial effects of the cloud platform resource allocation device provided by the present application are the same as the beneficial effects of the cloud platform resource allocation method provided by the above embodiment, and other technical features in the cloud platform resource allocation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0155] The present application provides a cloud platform resource allocation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the cloud platform resource allocation method in the above-mentioned embodiment one.

[0156] Reference below Figure 6 , which shows a schematic diagram of the structure of a cloud platform resource allocation device suitable for implementing the embodiment of the present application. The cloud platform resource allocation device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The cloud platform resource allocation device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0157] like Figure 6As shown, the cloud platform resource allocation device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the cloud platform resource allocation device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the cloud platform resource allocation device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a cloud platform resource allocation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0158] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0159] The cloud platform resource allocation device provided by the present application adopts the cloud platform resource allocation method in the above embodiment to solve the technical problem of cloud platform resource allocation. Compared with the prior art, the beneficial effects of the cloud platform resource allocation device provided by the present application are the same as the beneficial effects of the cloud platform resource allocation method provided by the above embodiment, and the other technical features in the cloud platform resource allocation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0160] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0161] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0162] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the cloud platform resource allocation method in the above-mentioned embodiment.

[0163] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0164] The above-mentioned computer-readable storage medium may be included in the cloud platform resource allocation device; or it may exist independently without being assembled into the cloud platform resource allocation device.

[0165] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the cloud platform resource allocation device, the cloud platform resource allocation device:

[0166] Obtain operational indicators for cloud platform tenants;

[0167] Inputting the operating indicators into a preset decision model, and performing forecasting processing on the operating indicators based on the preset decision model to obtain multiple forecast quota solutions;

[0168] Based on the compatibility between each of the forecast quota schemes and the cloud platform tenant and the indicator weights in each of the forecast quota schemes, a final quota scheme is calculated, wherein the indicator weight is calculated based on the importance of each indicator in the forecast quota scheme;

[0169] The final quota plan is used to allocate resources to the cloud platform tenants to obtain an allocation result.

[0170] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0171] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0172] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0173] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned cloud platform resource allocation method, and can solve the technical problem of cloud platform resource allocation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the cloud platform resource allocation method provided in the above-mentioned embodiment, and will not be repeated here.

[0174] The present application also provides a computer program product, including a computer program, which implements the steps of the cloud platform resource allocation method as described above when executed by a processor.

[0175] The computer program product provided in this application can solve the technical problem of cloud platform resource allocation. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the cloud platform resource allocation method provided in the above embodiment, which will not be repeated here.

[0176] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A cloud platform resource allocation method, characterized in that: The method comprises: Obtain operational indicators for cloud platform tenants; Inputting the operating indicators into a preset decision model, and performing forecasting processing on the operating indicators based on the preset decision model to obtain multiple forecast quota solutions; Based on the compatibility between each of the forecast quota schemes and the cloud platform tenant and the indicator weights in each of the forecast quota schemes, a final quota scheme is calculated, wherein the indicator weight is calculated based on the importance of each indicator in the forecast quota scheme; The final quota plan is used to allocate resources to the cloud platform tenants to obtain an allocation result.

2. The method according to claim 1, characterized in that The step of calculating the final quota scheme based on the degree of compatibility between each of the predicted quota schemes and the cloud platform tenant and the indicator weights in each of the predicted quota schemes comprises: Based on the degree of compatibility between each of the predicted quota schemes and the cloud platform tenants, an indicator matrix is ​​constructed; Based on the indicator weights, the indicator matrix is ​​refined to obtain a final quota solution.

3. The method according to claim 2, characterized in that The step of performing a refined processing on the indicator matrix based on the indicator weights to obtain a final quota solution includes: Calculate the indicator weight of each indicator according to the importance and deviation of each indicator in the forecast quota plan; The weight of each indicator is multiplied by each indicator in the predicted quota scheme with the highest degree of adaptability to obtain a final quota scheme.

4. The method according to claim 3, characterized in that The step of calculating the indicator weight of each indicator according to the importance and deviation of each indicator in the forecast quota scheme includes: Selecting a resource value for each indicator in the indicator matrix, and calculating the importance of each indicator based on the resource value; Based on the importance of each indicator, the deviation of each indicator is calculated; Based on the deviation and the number of indicators in the indicator matrix, the indicator weight of each indicator is calculated.

5. The method according to claim 1, characterized in that After the step of allocating resources to the cloud platform tenants according to the final quota plan and obtaining the allocation result, the method further includes: Based on the allocation result, dynamically score the resource quota of each cloud platform tenant to obtain a dynamic resource score; If the dynamic resource score is less than a first preset threshold, the resource quota corresponding to the cloud platform tenant is adjusted.

6. The method according to claim 5, characterized in that If the dynamic resource score is less than a first preset threshold, the step of adjusting the resource quota corresponding to the cloud platform tenant includes: If the dynamic resource score is less than the first preset threshold and greater than the second preset threshold, it is determined that the current operating environment of the cloud platform tenant is at risk, and a risk warning is generated; Send the risk warning to the display interface where the business personnel are located, so that the business personnel can communicate with the cloud platform tenants about the business and then reallocate resources; If the dynamic resource score is less than the second preset threshold, resource allocation to the current cloud platform tenant is stopped until the cloud platform tenant completes rectification of the current operating environment, and then determines whether to allocate resources again based on the rectification result.

7. A cloud platform resource allocation device, characterized in that: The cloud platform resource allocation device comprises: The acquisition module is used to obtain the operational indicators of cloud platform tenants; A processing module, used for inputting the operation index into a preset decision model, and performing forecasting processing on the operation index based on the preset decision model to obtain multiple forecast quota schemes; A calculation module, configured to calculate a final quota scheme based on the degree of compatibility between each of the forecast quota schemes and the cloud platform tenant and the indicator weights in each of the forecast quota schemes, wherein the indicator weights are calculated based on the importance of each indicator in the forecast quota scheme; The allocation module is used to allocate resources to the cloud platform tenants according to the final quota plan to obtain an allocation result.

8. A cloud platform resource allocation device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the cloud platform resource allocation method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the cloud platform resource allocation method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the cloud platform resource allocation method according to any one of claims 1 to 6 are implemented.

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