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

Abstract

The application discloses a cloud platform resource allocation method and device, equipment, storage medium and product, relates to the technical field of cloud computing, and the cloud platform resource allocation method comprises the following steps: acquiring operation indexes of a cloud platform tenant; inputting the operation indexes into a preset decision model, performing prediction processing on the operation indexes based on the preset decision model, and obtaining a plurality of prediction quota schemes; and calculating a final quota scheme based on the adaptation degree between each prediction quota scheme and the cloud platform tenant and the index weight in each prediction quota scheme, wherein the index weight is calculated based on the importance of each index in the prediction quota scheme; and performing resource allocation on the cloud platform tenant by using the final quota scheme, so as to obtain an allocation result. The application improves the resource allocation efficiency of the cloud platform.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud computing, and particularly relates to a cloud platform resource allocation method and device, equipment, a storage medium and a product. BACKGROUND

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

[0003] At present, a common technical solution is that an operator calculates a basic quota allocation standard of resources according to different tenant historical indicators as a reference, and then directly configures resources according to a demand order. This method requires experienced operators to spend a lot of time on process deduction, indicator quantification, etc., to calculate the basic quota allocation standard, which involves a large amount of work, is time-consuming, and results in low resource allocation efficiency.

[0004] Therefore, how to improve the resource allocation efficiency of the cloud platform is a problem to be solved at present.

[0005] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0006] The main purpose of the present application is to provide a cloud platform resource allocation method, device, equipment, storage medium and product, which aims to solve the technical problem of low resource allocation efficiency in the related art, that is, calculating a basic quota allocation standard of resources according to different tenant historical indicators as a reference, and then directly configuring resources according to a demand order.

[0007] To achieve the above purpose, the present application provides a cloud platform resource allocation method, which comprises:

[0008] Obtaining an operation indicator of a cloud platform tenant;

[0009] Inputting the operation indicator into a preset decision model, performing prediction processing on the operation indicator based on the preset decision model, and obtaining a plurality of prediction quota schemes;

[0010] Calculating a final quota scheme based on the adaptation degree between each prediction quota scheme and the cloud platform tenant and the indicator weight in each prediction quota scheme, wherein the indicator weight is calculated based on the importance of each indicator in the prediction quota scheme;

[0011] Allocating resources to the cloud platform tenant according to the final quota scheme to obtain an allocation result.

[0012] In an embodiment, the step of calculating the final quota scheme based on the fitness between each of the predicted quota schemes and the cloud platform tenant and the index weight in each of the predicted quota schemes comprises:

[0013] constructing an index matrix based on the fitness between each of the predicted quota schemes and the cloud platform tenant;

[0014] performing fine processing on the index matrix based on the index weight to obtain the final quota scheme.

[0015] In an embodiment, the step of performing fine processing on the index matrix based on the index weight to obtain the final quota scheme comprises:

[0016] calculating the index weight of each index according to the importance and deviation of each index in the predicted quota scheme;

[0017] multiplying each of the index weights and each index in the predicted quota scheme with the highest fitness to obtain the final quota scheme.

[0018] In an embodiment, the step of calculating the index weight of each index according to the importance and deviation of each index in the predicted quota scheme comprises:

[0019] selecting a resource value of each index in the index matrix, and calculating the importance of each index based on the resource value;

[0020] calculating the deviation of each index based on the importance of each index;

[0021] calculating the index weight of each index based on the deviation and the number of indexes in the index matrix.

[0022] In an embodiment, after the step of performing resource allocation on the cloud platform tenant with the final quota scheme to obtain an allocation result, the method further comprises:

[0023] performing dynamic scoring on the resource quota of each cloud platform tenant based on the allocation result to obtain a dynamic resource score;

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

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

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

[0027] The risk prompt is sent to a display interface where a business personnel is located, so that the business personnel communicates with the cloud platform tenant and then reallocates resources;

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

[0029] In addition, to achieve the above-mentioned purpose, the present application also provides a cloud platform resource allocation device, which comprises:

[0030] An acquisition module is configured to acquire operation indexes of a cloud platform tenant;

[0031] A processing module is configured to input the operation indexes into a preset decision model, perform prediction processing on the operation indexes based on the preset decision model, and obtain a plurality of prediction quota schemes;

[0032] A calculation module is configured to calculate a final quota scheme based on an adaptation degree between each prediction quota scheme and the cloud platform tenant and an index weight in each prediction quota scheme, wherein the index weight is calculated based on an importance of each index in the prediction quota scheme;

[0033] An allocation module is configured to allocate resources to the cloud platform tenant according to the final quota scheme, and obtain an allocation result.

[0034] In addition, to achieve the above-mentioned purpose, the present application also provides a cloud platform resource allocation device, which 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 as described above.

[0035] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, wherein the computer program is executed by a processor to implement the steps of the cloud platform resource allocation method as described above.

[0036] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which comprises a computer program, wherein the computer program is executed by a processor to implement the steps of the cloud platform resource allocation method as described above.

[0037] The application provides a cloud platform resource allocation method, device, equipment, storage medium and product. The application obtains operation indexes of a cloud platform tenant, inputs the operation indexes into a preset decision model, performs prediction processing on the operation indexes based on the preset decision model, obtains a plurality of prediction quota schemes, and calculates a final quota scheme based on the adaptation degree between the prediction quota schemes and the cloud platform tenant and the index weight in each prediction quota scheme. The final quota scheme is used to allocate resources to the cloud platform tenant. The quota scheme optimization based on the neural network and the more refined processing of indexes in the quota scheme through the index weight can automatically generate a better quota scheme for the tenant, reduce the analysis work and experience requirements of operation personnel, and improve the resource allocation efficiency. BRIEF DESCRIPTION OF DRAWINGS

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

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0040] Figure 1 The flowchart provided for the cloud platform resource allocation method embodiment one of the present application;

[0041] Figure 2 The brief implementation flowchart related to the cloud platform resource allocation method of the present application;

[0042] Figure 3 The flowchart provided for the cloud platform resource allocation method embodiment two of the present application;

[0043] Figure 4 The detailed flowchart related to the cloud platform resource allocation method of the present application;

[0044] Figure 5 The module structure diagram of the cloud platform resource allocation device of the present application embodiment;

[0045] Figure 6 The device structure diagram of the hardware running environment related to the cloud platform resource allocation method in the present application embodiment.

[0046] The purpose realization, functional characteristics and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are merely exemplary of the application and do not limit the application.

[0048] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the drawings of the specification and specific embodiments.

[0049] The main solution of the embodiment of the present application is:

[0050] Obtain operation indicators of a cloud platform tenant;

[0051] Input the operation indicators into a preset decision model, perform prediction processing on the operation indicators based on the preset decision model, and obtain a plurality of prediction quota schemes;

[0052] Calculate a final quota scheme based on the adaptation degree between each prediction quota scheme and the cloud platform tenant and the indicator weight in each prediction quota scheme, wherein the indicator weight is calculated based on the importance of each indicator in the prediction quota scheme;

[0053] Allocate resources to the cloud platform tenant based on the final quota scheme to obtain an allocation result.

[0054] In the prior art, an experienced operator calculates a basic quota allocation standard for resources based on different tenant historical indicators as a reference, and then directly configures resources according to a demand order. This method requires an experienced operator to spend a lot of time on process deduction, indicator quantification, etc., to calculate the basic quota allocation standard, which involves a large amount of work and takes a long time, 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 operation indicators of a cloud platform tenant, inputs the operation indicators into a preset decision model, performs prediction processing on the operation indicators based on the preset decision model, and obtains a plurality of prediction quota schemes. At this time, the prediction quota schemes obtained are not the most reasonable allocation schemes, and a final quota scheme needs to be calculated based on the adaptation degree between each prediction quota scheme and the cloud platform tenant and the indicator weight in each prediction quota scheme. The final quota scheme is used to allocate resources to the cloud platform tenant. The quota scheme optimization based on neural network and the more refined processing of indicators in the quota scheme through the indicator weight can automatically generate a better quota scheme for the tenant, reduce the analysis work and experience requirements of the operator, and improve the resource allocation efficiency.

[0056] It should be noted that the execution subject of the 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, or an electronic device capable of realizing the above functions, a cloud platform resource allocation device, a cloud platform auxiliary decision tool, etc. The cloud platform auxiliary decision tool is taken as an example to describe the embodiment and the following embodiments.

[0057] Based on this, the cloud platform resource allocation method is provided in the embodiments of the present application, with reference to Figure 1 , Figure 1 The flowchart of the first embodiment of the cloud platform resource allocation method of the present application is shown in FIG. 1.

[0058] In the embodiment, the cloud platform resource allocation method comprises steps S10-S40:

[0059] In step S10, the operation index of the cloud platform tenant is obtained.

[0060] It should be noted that the purpose of the present application is to effectively manage the quotas of numerous tenants and numerous resources under the cloud platform, to ensure that the operator can achieve the maximum efficiency of selling resources, while taking into account the balanced development of the business of each regional tenant. In the application logic, the "cloud platform resource allocation method" component module is constructed and embedded into the cloud platform system, and runs through the quota management process of all computing capability resources, storage capability resources, PaaS (Platform as a Service) capability resources, network capability resources, and security authentication capability resources of the platform, to provide effective auxiliary planning and allocation scheme.

[0061] It should be noted that the cloud platform tenant can be a plurality of users who rent cloud platform computing resources. These users call corresponding cloud platform resources to execute corresponding businesses.

[0062] It should be noted that the operation index can be an index such as tenant sales / quota ratio, sales trend, quota usage, security assessment, and purchase situation.

[0063] Specifically, the present application relates to a cloud platform quota planning and optimized allocation method, and the specific process is shown in FIG. 2. On the platform side, the cloud platform auxiliary decision tool assists in manually allocating resources and scoring dynamic operation. On the access side, the cloud platform quota planning and execution decision device is composed with the tenant quota decision model and operation score as the core, and the standardized access specification is specified. Finally, on the tenant side, the cloud platform resources are obtained through the optimal algorithm, which supports dynamic allocation according to the actual business expansion without manual application. Figure 2

[0064] The cloud platform and the cloud platform auxiliary decision tool involved in the overall process are explained as follows:​

[0065] Cloud platform: a PaaS level portal system, providing unified operation service capabilities for the owned computing resources, storage resources, network resources, private PaaS, etc., and is an integrated marketing, ordering, running, and operation and maintenance service management portal for the whole network under the requirements of internal support capabilities and external product expansion. The cloud platform system provides unified portal operation, unified account authentication, unified product catalog, unified order management, etc. for users, and realizes unified display, operation, and control of IT capabilities and components.

[0066] Cloud platform auxiliary decision tool: an auxiliary tool based on a user quota decision model, which combines multiple resource allocation strategies built in the cloud platform based on different user types, as well as auxiliary decision calculation and fine adjustment, to give suggestions on the allocation ratio for different user actual situations, so that the cloud platform resources can be used optimally.

[0067] Step S20, inputting the operation indicators into a preset decision model, and performing prediction processing on the operation indicators based on the preset decision model to obtain a plurality of predicted 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 operation indicators, the model has been trained. The specific training method can be to input the historical operation indicators of each tenant into the model, and then perform prediction processing on the operation indicators. When the loss function reaches the minimum value, the model is determined to converge, at which time the iteration training process of the model is stopped, and the trained decision model is obtained.

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

[0070] Step 1: using One-hot (unique heat) coding for the tenant sales / quota ratio, sales trend, quota usage, purchase increase, running condition, etc., and using an 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: processing the vector through dropout (a neural network regularization technique) to randomly throw away a part of the features and generate different feature vectors as positive examples.

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

[0073] Step 4: The training model process determines when the model converges properly to stop training through a loss function (loss), and the gradient descent tends to be flat. The calculation formula is as follows:

[0074]

[0075] It should be noted that the actual execution process can directly predict the operation indicators through the preset decision model to obtain the optimal cloud platform quota scheme set, that is, the predicted quota scheme.

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

[0077] It should be noted that the final quota scheme can be the specific implementation scheme for allocating resources to tenants, and the optimal allocation scheme is obtained from multiple schemes by the adaptation degree between the predicted quota scheme and the cloud platform tenant and the index weight in each of the predicted quota scheme.

[0078] It should be noted that the adaptation degree can be the matching degree between the cloud platform tenant and each scheme, and the adaptation degree can be 60%, 80%, etc., and is not limited.

[0079] It should be noted that the index weight is used to represent the importance of each index, and for each index, the index weight is different, and when the final quota scheme is calculated, the quota result obtained is also different.

[0080] In one possible implementation, the step S30 of calculating the final quota scheme based on the adaptation degree between each of the predicted quota schemes and the cloud platform tenant and the index weight in each of the predicted quota schemes comprises:

[0081] Based on the adaptation degree between each of the predicted quota schemes and the cloud platform tenant, an index matrix is constructed;

[0082] It should be noted that each predicted quota scheme includes multiple indicators, and when constructing the matrix, the predicted quota schemes are arranged from top to bottom according to the size of the adaptation degree, for example, the predicted quota scheme with the highest adaptation degree is located at the top, and the predicted quota scheme with the lowest adaptation degree is located at the bottom.

[0083] It should be noted that when the index matrix (also referred to as the original quota allocation matrix) is created, the original quota allocation matrix A is created, the rows represent the n allocation schemes that can be executed, here the allocation scheme refers to the cloud platform built-in scheme set that best fits the user, and the columns represent the values of the mth index corresponding to the scheme, here the index refers to the values of virtual machines, networks, algorithms, storage, etc. Resources corresponding to the cloud platform built-in scheme, and the numerical values are normalized, the resource numerical values of each item in the original quota allocation matrix cannot accurately determine the final resource values obtained by the user, and need to be determined by the calculation method to obtain the final value. The original matrix is as follows:

[0084]

[0085] Based on the index weight, the index matrix is finely processed to obtain a final quota scheme.

[0086] It should be noted that the fine processing process can be: according to the calculated index weight, the predicted quota scheme is corrected, the weight values are multiplied by the resource values of the optimal scheme respectively, and the result correctly expresses the final resource allocation suggestion of the user calculated according to the cloud platform built-in scheme, and then the final quota scheme is obtained.

[0087] In a possible implementation, the step of finely processing the index matrix based on the index weight to obtain a final quota scheme includes:

[0088] According to the importance and deviation of each index in the predicted quota scheme, the index weight of each index is calculated;

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

[0090] Each of the index weights is multiplied by each index in the predicted quota scheme with the highest adaptation degree to obtain a final quota scheme.

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

[0092] In a possible implementation, the step of calculating the index weight of each index according to the importance and deviation of each index in the predicted quota scheme includes:

[0093] Select the resource value of each index in the index matrix, and calculate the importance of each index based on the resource value;

[0094] It should be noted that the formula for calculating the importance is:

[0095] The relative importance of the nth scheme on the mth index is calculated, and when the number of cloud platform tenant quota allocation schemes is t, the importance T nm The calculation method is:

[0096]

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

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

[0099]

[0100] The deviation is used as the basis for calculating the weight corresponding to each index in the next step.

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

[0102] It should be noted that according to the deviation, the weight corresponding to each index m is calculated, and when the total number of indexes is k, the weight w m The calculation method is:

[0103]

[0104] The weight matrix value obtained is used as the basis for modifying the optimal built-in scheme, that is, the first row of the original matrix. Multiply these weight values with the resource values of the optimal scheme, and the result correctly represents the final resource allocation suggestion for this user calculated according to the cloud platform built-in scheme. The specific method is:

[0105] The index weight obtained above is:

[0106] W=[w1w2…w m ]

[0107] The final value of index a 11 1 is a 12 ×w1, the final value of a m 2 is a 1m ×w m , and the final value of a mThe most distribution strategy is the specific value of the resource.

[0108] At step S40, the cloud platform tenants are allocated resources according to the final quota scheme, and an allocation result is obtained.

[0109] It should be noted that after the final quota scheme is determined, the resources are allocated to the user according to the above allocation suggestion, and the user applies it. If the operator selects automatic allocation of cloud platform resources in the first step, the resources will be directly allocated according to the result calculated by the tenant quota decision aid tool.

[0110] The application provides a cloud platform resource allocation method, device, equipment, storage medium and product. The application obtains the operation index of the cloud platform tenant, inputs the operation index into a preset decision model, performs prediction processing on the operation index based on the preset decision model, obtains a plurality of prediction quota schemes, and calculates a final quota scheme based on the adaptation degree between the prediction quota scheme and the cloud platform tenant and the index weight in each prediction quota scheme. The final quota scheme is used to allocate resources to the cloud platform tenant. The quota scheme optimization based on the neural network and the more refined processing of the index in the quota scheme through the index weight can automatically generate a better quota scheme for the tenant, reduce the analysis work and experience requirement of the operator, and improve the resource allocation efficiency.

[0111] Based on the first embodiment of the application, in the second embodiment of the application, the same or similar contents as the above embodiment one can refer to the above introduction, and the following will not be repeated. On this basis, please refer to Figure 3 , after the step S40 of allocating resources to the cloud platform tenant according to the final quota scheme to obtain an allocation result, the steps S50-S60 are further included:

[0112] At step S50, the resource quota of each cloud platform tenant is dynamically scored based on the allocation result, and a dynamic resource score is obtained.

[0113] It should be noted that the platform monitors the actual application situation, and dynamically scores the 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] Wherein: S is the quota score of the current tenant in the cloud platform running environment, and 10 points is 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, C∈[0,10] is the comprehensive score of resource occupancy rate

[0117] V∈[0,10] is a comprehensive score of security vulnerabilities of the corresponding institution of the tenant

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

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

[0120] It should be noted that the first preset threshold can be 8 points, 9 points, etc., which can be changed as needed, and the specific is not limited.

[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 comprehensive consideration of the user's allocated cloud platform resources in the resource occupancy rate, security vulnerabilities, and application attack threat factors is qualified, and it can continue to run. When it is lower than 8 points, it means that there is a risk in the current running environment.

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

[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 running environment of the cloud platform tenant has risks, and a risk prompt is generated;

[0124] It should be noted that the second preset threshold can be 6 points, 5 points, etc., and the specific is not limited, wherein the second preset threshold is less than the first preset threshold.

[0125] The risk prompt is sent to a display interface where the business personnel are located, so that the business personnel and the cloud platform tenant communicate business and then re-allocate resources;

[0126] It should be noted that when the score is lower than 8 points but higher than 6 points, it means that there is a risk. If the resources are manually allocated in the first step and the tenant quota decision assistance tool suggestion configuration is not completely adopted, the tool suggestion allocation strategy is temporarily executed for re-allocation, and the re-allocation is performed after subsequent business and business level communication.

[0127] If the dynamic resource score is less than the second preset threshold, the resource allocation to the current cloud platform tenant is stopped, and until the current running environment of the cloud platform tenant is rectified, according to the rectification result, it is determined whether to allocate resources again.

[0128] It should be noted that when the score is less than 6 points, the resource allocation is stopped, after the cloud platform tenant is rectified, when the rectification is successful, the resource allocation is performed again, if the rectification is not successful, it indicates that the running environment of the cloud platform tenant still has risks, and the resource allocation cannot be performed on it.

[0129] In the embodiment, after the final allocation scheme is allocated, the actual application situation is monitored on the scoring platform, real-time dynamic scoring is performed according to the cloud platform quota scoring algorithm, and risks are avoided in time according to the real-time dynamic scoring situation.

[0130] Specifically, the execution refinement flowchart of the application is as shown in Figure 4 The specific execution process of the application is that: the operation personnel goes to the console to configure the cloud platform resource allocation strategy, selects manual configuration, the tenant quota decision auxiliary tool comprehensively considers the importance of the indexes such as tenant sales / quota proportion, sales trend, quota usage, safety evaluation, and purchase situation, adopts a cloud platform quota auxiliary decision model to calculate n quota schemes most suitable for the user, combines the top n schemes to form a quota optimization matrix, performs fine processing on the quota indexes in the matrix, obtains a final quota scheme, then allocates the final quota scheme based on the mode selected by the operation personnel, and performs real-time safety scoring after the allocation to avoid risks and optimize in time.

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

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

[0133] The acquisition module 10 is configured to acquire operation indexes of a cloud platform tenant.

[0134] The processing module 20 is configured to input the operation indexes into a preset decision model, perform prediction processing on the operation indexes based on the preset decision model, and obtain a plurality of predicted quota schemes.

[0135] The calculation module 30 is configured to calculate a final quota scheme based on an adaptation degree between each predicted quota scheme and the cloud platform tenant and an index weight in each predicted quota scheme, wherein the index weight is calculated based on the importance of each index in the predicted quota scheme.

[0136] The allocation module 40 is configured to allocate resources to the cloud platform tenant in the final quota scheme to obtain an allocation result.

[0137] Optionally, the calculation module comprises:

[0138] a constructing unit, configured to construct an index matrix based on the fitness degrees between the prediction quota schemes and the cloud platform tenants;

[0139] a processing unit, configured to perform fine processing on the index matrix based on the index weights to obtain a final quota scheme.

[0140] Optionally, the processing unit comprises:

[0141] a first calculating sub-unit, configured to calculate an index weight of each index according to the importance degree and the deviation degree of each index in the prediction quota scheme;

[0142] a second calculating sub-unit, configured to multiply each index weight with each index in the prediction quota scheme with the highest fitness degree to obtain the final quota scheme.

[0143] Optionally, the first calculating sub-unit comprises:

[0144] a selecting component, configured to select a resource value of each index in the index matrix, and calculate the importance degree of each index based on the resource value;

[0145] a first calculating component, configured to calculate the deviation degree of each index based on the importance degree of each index;

[0146] a second calculating component, configured to calculate the index weight of each index based on the deviation degree and the number of indexes in the index matrix.

[0147] Optionally, the apparatus further comprises:

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

[0149] an adjusting module, configured 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 adjusting module comprises:

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

[0152] a sending unit, configured to send the risk prompt to a display interface where a business personnel is located, so that the business personnel re-performs resource allocation after business communication with the cloud platform tenant;

[0153] The allocation unit is configured to stop resource allocation to the current cloud platform tenant if the dynamic resource score is less than a second preset threshold, and determine whether to perform resource allocation again according to the rectification result after the cloud platform tenant completes rectification on the current running environment.

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

[0155] The application provides a cloud platform resource allocation device, which comprises at least one processor and a memory connected with 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 to enable the at least one processor to perform the cloud platform resource allocation method in the above-mentioned embodiment one.

[0156] Reference will be made to the following description of the drawings Figure 6 which shows a structural schematic diagram of a cloud platform resource allocation device suitable for implementing the embodiments of the application. The cloud platform resource allocation device in the embodiments of the application can include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 6 The cloud platform resource allocation device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the application.

[0157] As Figure 6As shown, the cloud platform resource allocation device can include a processing apparatus 1001 (e.g., a central processor, a graphics processor, etc.) that can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 1002 or loaded from a storage apparatus 1003 into a random access memory (RAM) 1004. In the RAM 1004, various programs and data required for operation of the cloud platform resource allocation device are also stored. The processing apparatus 1001, the ROM 1002, and the RAM 1004 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: input apparatuses 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output apparatuses 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage apparatus 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication apparatus 1009. The communication apparatus 1009 can allow the cloud platform resource allocation device to communicate with other devices wirelessly or by wire to exchange data. Although the cloud platform resource allocation device having various systems is shown in the figure, it should be understood that all the shown systems are not required to be implemented or possessed. More or less systems can be alternatively implemented or possessed.

[0158] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by a communication apparatus, or installed from the storage apparatus 1003, or installed from the ROM 1002. When the computer program is executed by the processing apparatus 1001, the above-mentioned functions defined in the methods of embodiments of the present disclosure are performed.

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

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

[0161] The above description is merely illustrative of the application and is not intended to limit the scope of the application. Any changes and modifications that can be made to the application in accordance with the principles of the application would be readily apparent to those skilled in the art and the present application is therefore not limited to the description and examples contained herein but is only limited by the claims.

[0162] The application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e., a computer program) for performing the cloud platform resource allocation method in the above-described embodiments.

[0163] The computer readable storage medium provided by the application may, for example, be a U disk, but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more conductive 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 thereof. In the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted in any suitable medium, including but not limited to electrical wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0164] The above computer readable storage medium can be included in the cloud platform resource allocation device; or can exist separately and not be assembled into the cloud platform resource allocation device.

[0165] The above computer readable storage medium carries one or more programs, which, when executed by the cloud platform resource allocation device, cause the cloud platform resource allocation device to:

[0166] An operation index of a cloud platform tenant is acquired;

[0167] The operation index is input into a preset decision model, and the operation index is processed based on the preset decision model to obtain a plurality of predicted quota schemes;

[0168] A final quota scheme is calculated based on an adaptation degree between each of the predicted quota schemes and the cloud platform tenant and an index weight in each of the predicted quota schemes, wherein the index weight is calculated based on an importance of each index in the predicted quota scheme;

[0169] The cloud platform tenant is allocated resources based on the final quota scheme to obtain an allocation result.

[0170] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can 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 the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0171] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0172] The modules described in the embodiments of the present application can be implemented in the form of software or in the form of hardware. In some cases, the names of the modules do not constitute a limitation on the modules themselves.

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

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

[0175] The computer program product provided in the present application can solve the technical problem of cloud platform resource allocation. Compared with the prior art, the computer program product provided in the present application has the same beneficial effects as the cloud platform resource allocation method provided in the above embodiments, and will not be described here.

[0176] The above only describes some embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation, direct / indirect application in other related technical fields based on the technical concept of the present application, and the contents of the present application and the accompanying drawings 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: obtaining operation indexes of a cloud platform tenant; inputting the operation indexes into a preset decision model, performing prediction processing on the operation indexes based on the preset decision model, and obtaining a plurality of predicted quota schemes; based on the adaptation degree between each of the predicted quota schemes and the cloud platform tenant and the index weight in each of the predicted quota schemes, a final quota scheme is calculated; resource allocation is performed on the cloud platform tenant based on the final quota scheme, and an allocation result is obtained; the step of calculating the final quota scheme based on the adaptation degree between each of the predicted quota schemes and the cloud platform tenant and the index weight in each of the predicted quota schemes comprises: based on the adaptation degree between each of the predicted quota schemes and the cloud platform tenant, an index matrix is constructed; based on the index weight, the index matrix is finely processed to obtain the final quota scheme; the step of finely processing the index matrix based on the index weight to obtain the final quota scheme comprises: according to the importance and deviation of each index in the predicted quota scheme, the index weight of each index is calculated; each index weight is multiplied by each index in the predicted quota scheme with the highest adaptation degree to obtain the final quota scheme; the step of calculating the index weight of each index according to the importance and deviation of each index in the predicted quota scheme comprises: selecting the resource value of each index in the index matrix, and calculating the importance of each index based on the resource value; based on the importance of each index, the deviation of each index is calculated; based on the deviation and the number of indexes in the index matrix, the index weight of each index is calculated.

2. The method of claim 1, wherein, after the step of performing resource allocation on the cloud platform tenant based on the final quota scheme to obtain an allocation result, the method further comprises: based on the allocation result, dynamically scoring 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, adjusting the resource quota corresponding to the cloud platform tenant.

3. The method of claim 2, wherein, the step of adjusting the resource quota corresponding to the cloud platform tenant if the dynamic resource score is less than the first preset threshold comprises: if the dynamic resource score is less than the first preset threshold and greater than a second preset threshold, it is determined that the current running environment of the cloud platform tenant has risks, and a risk prompt is generated; the risk prompt is sent to a display interface where a business personnel is located, so that the business personnel communicates with the cloud platform tenant and re-performs resource allocation; if the dynamic resource score is less than the second preset threshold, stopping resource allocation on the current cloud platform tenant until the cloud platform tenant completes rectification of the current running environment, and then determining whether to perform resource allocation again according to the rectification result.

4. A cloud platform resource allocation apparatus, characterized by, the cloud platform resource allocation device comprises: an acquisition module configured to obtain operation indexes of a cloud platform tenant; The processing module is configured to input the operation index into a preset decision model, perform prediction processing on the operation index based on the preset decision model, and obtain a plurality of predicted quota schemes. The computing module is configured to calculate a final quota scheme based on an adaptation degree between each of the predicted quota schemes and the cloud platform tenant and an index weight in each of the predicted quota schemes, wherein the index weight is calculated based on an importance of each index in the predicted quota scheme. The allocation module is configured to allocate resources to the cloud platform tenant based on the final quota scheme, and obtain an allocation result. The computing module comprises: The constructing unit is configured to construct an index matrix based on the adaptation degree between each of the predicted quota schemes and the cloud platform tenant. The processing unit is configured to perform fine processing on the index matrix based on the index weight, and obtain the final quota scheme. The processing unit comprises: The first calculating sub-unit is configured to calculate the index weight of each index based on an importance and a deviation degree of each index in the predicted quota scheme. The second calculating sub-unit is configured to multiply each of the index weights and each index in the predicted quota scheme with the highest adaptation degree, and obtain the final quota scheme. The first calculating sub-unit comprises: The selecting component is configured to select a resource value of each index in the index matrix, and calculate the importance of each index based on the resource value. The first calculating component is configured to calculate the deviation degree of each index based on the importance of each index. The second calculating component is configured to calculate the index weight of each index based on the deviation degree and a number of indexes in the index matrix.

5. A cloud platform resource allocation device, characterized by, The device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the cloud platform resource allocation method according to any one of claims 1 to 3.

6. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the cloud platform resource allocation method according to any one of claims 1 to 3.

7. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to implement the steps of the cloud platform resource allocation method according to any one of claims 1 to 3.

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