Cloud platform computing resource allocation method and system, terminal and storage medium

Abstract

The application provides a cloud platform computing resource allocation method, system, terminal and storage medium, comprising: collecting idle resource rates of each host in a cloud platform cluster; converting each idle resource rate of each host into a corresponding recommended reference value by using a normalization exponential function; calculating the product of each recommended reference value of each host respectively, and accumulating the product of all hosts as a recommended coefficient; calculating a host idle degree value according to the recommended coefficient and the product of each recommended reference value of the host; and allocating a corresponding host to a virtual machine creation request according to the principle of preferentially allocating the highest idle degree value. The application converts data into relative recommended probability by analyzing the idle resource conditions of all hosts in the cluster through a softmax function, and then obtains the recommended value of the host by using DS evidence theory for information fusion, so as to allocate appropriate hosts for user requests. The application can reasonably allocate resources when planning multiple user creation of virtual machines, thereby improving the efficiency of the system.

Description

TECHNICAL FIELD

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

[0002] The cloud computing platform is also called a cloud platform, which is a service based on hardware resources and software resources, and provides computing, network and storage capabilities. The cloud computing platform can be divided into three types: a storage type cloud platform mainly for data storage, a computing type cloud platform mainly for data processing, and a comprehensive cloud computing platform for both computing and data storage processing. We can popularly understand the "cloud" in cloud computing as a collection of various types of resources existing on a cloud data center server cluster. These resources are divided into hardware resources and software resources, wherein the hardware resources include servers, memories and CPUs, and the software resources include application software and integrated development environments. A user only needs to send a request through a network to obtain resources meeting the demand from the cloud to a local computer, and all computing tasks are completed in a remote cloud data center. The user can obtain various computing services, storage services and various software resources on demand, which is due to the powerful virtualization resource pool architecture of cloud computing. The resource pool of the data center itself can be dynamically expanded, and the resources used by the user can be conveniently recycled in time. Such a service providing mode greatly increases the resource utilization rate of the cloud data center, and the cloud computing service provider can also better improve the service quality. When the cloud platform provides services for a user, a virtual machine is created for the user according to the user's cloud entry request, and the virtual machine is used to process the user's business.

[0003] The physical basis of the cloud platform is a large server cluster, so how to balance the resources inside the cluster when creating a virtual machine is crucial to the stability of the cloud platform. In the cloud computing system, a large number of heterogeneous resources are uniformly managed through the cloud platform, and when multiple users use computing resources in parallel, if the allocation is uneven, some hosts will be under excessive pressure, while the remaining hosts will be relatively idle. If this situation lasts for a long time, the high-pressure server will be down, causing business interruption and even data loss. SUMMARY

[0004] In view of the above problems of the prior art, the present application provides a cloud platform computing resource allocation method, system, terminal and storage medium to solve the above technical problems.

[0005] In a first aspect, the present application provides a cloud platform computing resource allocation method, comprising:

[0006] Collecting the idle resource rates of each host in the cloud platform cluster;

[0007] The idle resource rates of each host are converted into corresponding recommended reference values by using a normalization exponential function;

[0008] The products of the recommended reference values of each host are calculated respectively, and the products of all hosts are accumulated as a recommended coefficient;

[0009] The host idle degree value is calculated according to the recommended coefficient and the product of the recommended reference values of each host;

[0010] The corresponding host is allocated to the virtual machine creation request according to the principle of preferentially allocating the host with the highest idle degree value.

[0011] Further, the idle resource rates of each host in the cloud platform cluster are collected, including:

[0012] The resource utilization rates of each host in the cluster are collected, and the resource utilization rates include CPU utilization rates and memory utilization rates;

[0013] The idle resource rates of each host are obtained by subtracting the resource utilization rates of each host from the real number 1.

[0014] Further, the idle resource rates of each host are converted into corresponding recommended reference values by using a normalization exponential function, including:

[0015] The idle resource rates of each host are converted into corresponding recommended reference values by using the formula The idle resource rates of each host are converted into corresponding recommended reference values, wherein B is the recommended reference value, i is the i-th host, j is the j-th resource type, is the resource utilization rate of the j-th resource type of the i-th host, is the idle resource rate of the j-th resource type of the i-th host.

[0016] Further, the products of the recommended reference values of each host are calculated respectively, and the products of all hosts are accumulated as a recommended coefficient, including:

[0017] The recommended coefficient is calculated by using the formula The recommended coefficient is calculated, wherein K is the recommended coefficient, is the recommended reference value of the j-th resource type of the i-th host.

[0018] Further, the host idle degree value is calculated according to the recommended coefficient and the product of the recommended reference values of each host, including:

[0019] The host idle degree value is calculated according to the formula The host idle degree value is calculated, wherein represents the idle degree value of the host i, K is the recommended coefficient, is the recommended reference value of the j-th resource type of the i-th host, represents the product of the recommended reference values of the i-th host.

[0020] Further, the virtual machine creation request is allocated to the corresponding host according to the principle of preferentially allocating the host with the highest idle degree value, including:

[0021] The virtual machine creation requests are sorted according to the request receiving time;

[0022] The virtual machine creation request at the front of the sorted queue is selected as a target request, and the demand resource parameter of the target request is extracted;

[0023] The host with the highest idle degree value in the cluster is selected as a target host, and the target request is allocated with the corresponding resource amount from the target host according to the demand resource parameter of the target request;

[0024] After the resource allocation of the target host is completed, the idle degree values of the hosts in the cluster are updated, and the resource is allocated to the reselected target request based on the updated idle degree values.

[0025] In a second aspect, the present application provides a cloud platform computing resource allocation system, including:

[0026] A resource monitoring unit is configured to collect idle resource rates of the hosts in the cloud platform cluster;

[0027] A data conversion unit is configured to convert the idle resource rates of the hosts into corresponding recommended reference values by using a normalization exponential function;

[0028] A coefficient calculation unit is configured to calculate the product of the recommended reference values of each host, and accumulate the products of all the hosts as a recommended coefficient;

[0029] A degree calculation unit is configured to calculate the idle degree value of the host according to the recommended coefficient and the product of the recommended reference values of the host;

[0030] A resource allocation unit is configured to allocate the corresponding host to the virtual machine creation request according to the principle of preferentially allocating the host with the highest idle degree value.

[0031] Further, the resource monitoring unit is configured to:

[0032] Collect resource utilization rates of the hosts in the cluster, and the resource utilization rates include CPU utilization rates and memory utilization rates;

[0033] The idle resource rates of the hosts are obtained by subtracting the resource utilization rates of the hosts from the real number 1.

[0034] Further, the data conversion unit is configured to convert the idle resource rates of the hosts into corresponding recommended reference values by using the formula wherein B is the recommended reference value, i is the i th host, and j is the j th resource type. a resource utilization rate of a jth resource type of an ith host, a free resource rate of a jth resource type of an ith host.

[0035] Further, the coefficient calculation unit is configured to calculate the recommendation coefficient according to the formula wherein K is the recommendation coefficient, a recommendation reference value of a jth resource type of an ith host.

[0036] Further, the degree calculation unit is configured to calculate the host free degree value according to the formula wherein K is the recommendation coefficient, represents the free degree value of the host i, K is the recommendation coefficient, a recommendation reference value of a jth resource type of an ith host, represents the product of the recommendation reference values of the host i.

[0037] Further, the resource allocation unit is configured to:

[0038] sort the virtual machine creation requests according to the request receiving time;

[0039] select the earliest virtual machine creation request in the sorted queue as a target request, and extract the demand resource parameters of the target request;

[0040] select a host with the highest free degree value from the cluster as a target host, and allocate a corresponding amount of resources to the target request from the target host according to the demand resource parameters of the target request;

[0041] update the free degree values of the hosts in the cluster after the resource allocation of the target host is completed, and allocate resources to the reselected target request based on the updated free degree values.

[0042] In a third aspect, a terminal is provided, comprising:

[0043] a processor and a memory,

[0044] the memory is configured to store a computer program,

[0045] the processor is configured to call and run the computer program from the memory, so that the terminal executes the method of the terminal described above.

[0046] In a fourth aspect, a computer storage medium is provided, and the computer readable storage medium stores instructions, when the instructions are run on a computer, the computer executes the method described in the above aspects.

[0047] The cloud platform computing resource allocation method, system, terminal and storage medium provided by the application have the beneficial effects that the application analyzes the real-time CPU usage rate, logical occupied memory, real-time running memory and the like of all hosts in a cluster, converts data into relative recommended probability through a softmax function, obtains recommended values of the hosts by using DS evidence theory for information fusion, and allocates appropriate hosts for user requests.

[0048] In addition, the application has reliable design principles, simple structure and very wide application prospects. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative labor.

[0050] Figure 1 is a schematic flow chart of the method of one embodiment of the present application.

[0051] Figure 2 is a schematic flow chart of the host idle degree value acquisition process of the method of one embodiment of the present application.

[0052] Figure 3 is a schematic flow chart of the host allocation process of the method of one embodiment of the present application.

[0053] Figure 4 is a schematic block diagram of the system of one embodiment of the present application.

[0054] Figure 5 is a structural schematic diagram of a terminal provided by the embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0056] The key terms appearing in the present application will be explained below.

[0057] Softmax function, also known as normalized exponential function. It is the generalization of binary classification function sigmoid in multi-classification, the purpose is to show the result of multi-classification in the form of probability.

[0058] Evidence theory is a kind of imprecise reasoning theory first proposed by Dempster in 1967, further developed by his student Shafer in 1976, also known as Dempster / Shafer evidence theory (D-S evidence theory), belonging to the field of artificial intelligence, and applied to expert system for the first time, with the ability to process uncertain information. As an uncertain reasoning method, the main characteristics of evidence theory are: to meet the conditions weaker than Bayesian probability theory; With the ability to directly express "uncertainty" and "unknown". In the DS evidence theory, a complete set composed of mutually incompatible basic propositions (assumptions) is called a recognition framework, which represents all possible answers to a problem, but only one of them is correct. The subset of the framework is called a proposition. The trust degree assigned to each proposition is called basic probability assignment (BPA, also known as m function), m(A) is the basic belief number, which reflects the degree of belief in A. The trust function Bel(A) represents the trust degree of proposition A, and the likelihood function Pl(A) represents the trust degree of proposition A, that is, the uncertainty measure of A seems to be possible. In fact, [Bel(A), Pl(A)] represents the uncertainty interval of A, [0, Bel(A)] represents the support evidence interval of proposition A, [0, Pl(A)] represents the quasi-evidence interval of proposition A, and [Pl(A), 1] represents the rejection evidence interval of proposition A. Let m1 and m2 be the basic probability assignment functions derived from two independent evidence sources (sensors), then the Dempster combination rule can calculate the new basic probability assignment function reflecting the fusion information generated by the joint action of the two evidences.

[0059] Figure 1 is a schematic flowchart of the method of an embodiment of the present application. Wherein, Figure 1 The execution subject can be a cloud platform computing resource allocation system.

[0060] As Figure 1 shown, the method comprises:

[0061] Step 110, collecting the idle resource rates of each host in the cloud platform cluster;

[0062] Step 120, converting each item of idle resource rate of each host into the corresponding recommended reference value by using the normalized exponential function;

[0063] Step 130, calculating the product of each item of recommended reference value of each host respectively, and accumulating the product of all hosts as a recommended coefficient;

[0064] Step 140, according to the product of the recommended coefficient and the recommendation reference value of each host, the host idle degree value is calculated;

[0065] Step 150, according to the principle of preferentially allocating the highest idle degree value, the corresponding host is allocated to the virtual machine creation request.

[0066] The technical scheme of the present application mainly includes: when analyzing CPU usage, logical occupied memory, real-time running memory and other data, the softmax function is used to convert them into relative numerical values; the DS evidence theory is used to comprehensively analyze the parameters of the host, so that more reasonable data is obtained; the method can allocate host resources when a single user or multiple users operate in parallel.

[0067] The method provided by the present application can reasonably allocate the computing resources of the platform management, effectively reduce the situation that the system efficiency is reduced due to uneven resource allocation when multiple users operate in parallel, so as to fully use the computing resources and improve the system efficiency.

[0068] In order to facilitate the understanding of the present application, the principle of the cloud platform computing resource allocation method of the present application is described further in combination with the process of allocating the cloud platform computing resources in the embodiment.

[0069] Please refer to Figure 2 and Figure 3 , specifically, the cloud platform computing resource allocation method comprises:

[0070] S1, the idle resource rate of each host in the cloud platform cluster is collected.

[0071] The resource utilization rate of each host in the cluster is collected, and the resource utilization rate includes CPU utilization rate and memory utilization rate; the idle resource rate of each host is obtained by subtracting the resource utilization rate of each host from the real number 1.

[0072] When the user performs the operation of increasing the virtual machine, the CPU usage rate, logical occupied memory, real-time running memory and other data of each host in the current cluster are read and converted into percentage form Wherein i is the host number, and j is the resource type number.

[0073] S2, the normalization exponential function is used to convert the idle resource rate of each host into the corresponding recommended reference value.

[0074] The formula is used to convert the idle resource rate of each host into the corresponding recommended reference value, wherein B is the recommended reference value, i is the i th host, j is the j th resource type, is the resource utilization rate of the j th resource type of the i th host, The idle resource rate of the jth resource type of the ith host.

[0075] For example, the CPU occupancy rate of host i is 50%, and the memory occupancy rate is 60%, so the CPU idle rate is 50%, and the memory idle rate is 40%. The CPU recommended reference value of host i is The memory recommended reference value is

[0076] S3, the product of each recommended reference value of each host is calculated respectively, and the product of all hosts is accumulated as the recommended coefficient.

[0077] The recommended coefficient is calculated by the formula K is the recommended coefficient, The recommended reference value of the jth resource type of the ith host.

[0078] S4, the host idle degree value is calculated according to the product of the recommended coefficient and the recommended reference value of each host.

[0079] The host idle degree value is calculated according to the formula K is the recommended coefficient, The host idle degree value of host i is K, The recommended reference value of the jth resource type of the ith host, The product of the recommended reference value of host i.

[0080] S5, according to the principle of preferentially allocating the highest idle degree value, the corresponding host is allocated to the virtual machine creation request.

[0081] The virtual machine creation request is sorted according to the request receiving time; the frontmost virtual machine creation request is selected as the target request from the sorted queue, and the demand resource parameter of the target request is extracted; the host with the highest idle degree value is selected from the cluster as the target host, and the corresponding resource amount is allocated to the target request from the target host according to the demand resource parameter of the target request; after the resource allocation of the target host is completed, the idle degree value of each host in the cluster is updated, and the target request is allocated resources based on the updated idle degree value.

[0082] When a single user creates a virtual machine, the host with the maximum value is directly allocated to the user; when multiple users a1-a n concurrently create virtual machines, the following steps are taken:

[0083] The situation after the user creates a virtual machine is simulated, and the host with the maximum The host is assigned to the user, and based on the pre-set default parameters of the virtual machine, the values ​​of CPU resources used by the virtual machine, logical memory usage, and real-time running memory are updated. Steps S1-S4 are then repeated to obtain values ​​representing the host's idle level.

[0084] Will The host with the highest value is assigned to user a2; repeat the above steps until all users are assigned hosts.

[0085] like Figure 4 As shown, the system 400 includes:

[0086] Resource monitoring unit 410 is used to collect the idle resource rate of each host in the cloud platform cluster;

[0087] The data conversion unit 420 is used to convert the idle resource rates of each host into corresponding recommended reference values ​​using a normalized exponential function;

[0088] The coefficient calculation unit 430 is used to calculate the product of the recommended reference values ​​of each host separately, and to sum the products of all hosts as the recommendation coefficient.

[0089] The degree calculation unit 440 is used to calculate the host idle degree value based on the product of the recommendation coefficient and the host's various recommended reference values;

[0090] Resource allocation unit 450 is used to allocate the corresponding host to the virtual machine creation request according to the principle of prioritizing the allocation of the highest idle value.

[0091] Optionally, as an embodiment of the present invention, the resource monitoring unit is used for:

[0092] Collect the resource utilization of each host in the cluster, including CPU utilization and memory utilization;

[0093] The idle resource rate of each host is obtained by subtracting the real number 1 from the resource utilization rate of each host.

[0094] Optionally, as an embodiment of the present invention, the data conversion unit is used to: utilize formula Convert the idle resource rates of each host into corresponding recommended reference values, where B is the recommended reference value, i is the i-th host, and j is the j-th resource type. Let i be the resource utilization rate of the j-th resource type on the i-th host. Let be the idle resource rate of the j-th resource type on the i-th host.

[0095] Optionally, as an embodiment of the present invention, the coefficient calculation unit is used to: utilize the formula Calculate the recommendation coefficient, where K is the recommendation coefficient. the recommended reference value of the jth resource type of the ith host.

[0096] Optionally, as one embodiment of the present application, the degree calculation unit is configured to calculate the idle degree value of the host according to the formula wherein, the idle degree value of the host i, K is the recommended coefficient, the recommended reference value of the jth resource type of the ith host, the product of the recommended reference values of the host i.

[0097] Optionally, as one embodiment of the present application, the resource allocation unit is configured to:

[0098] sort the virtual machine creation requests according to the request receiving time;

[0099] select the virtual machine creation request at the top of the sorted queue as a target request, and extract the demand resource parameter of the target request;

[0100] select the host with the highest idle degree value from the cluster as a target host, and allocate the corresponding resource amount for the target request from the target host according to the demand resource parameter of the target request;

[0101] update the idle degree values of the hosts in the cluster after the resource allocation of the target host is completed, and allocate resources for the reselected target request based on the updated idle degree values.

[0102] Figure 5 A structural schematic diagram of a terminal 500 is provided in an embodiment of the present application, and the terminal 500 can be used to execute the cloud platform computing resource allocation method provided in the embodiment of the present application.

[0103] The terminal 500 can include a processor 510, a memory 520 and a communication unit 530. These components communicate through one or more buses, and those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation on the present application, which can be a bus structure or a star structure, and can include more or fewer components than shown in the figure, or combine some components, or different component arrangements.

[0104] ​The memory 520 can be used to store the execution instructions of the processor 510, and the memory 520 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 520 are executed by the processor 510, the terminal 500 can execute part or all of the steps in the following method embodiments.

[0105] The processor 510 is the control center of the storage terminal, connects all parts of the electronic terminal through various interfaces and lines, executes the software programs and / or modules stored in the memory 520, and calls the data stored in the memory, so as to execute various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, can be composed of a single packaged IC, or can be composed of multiple packaged ICs connected together. For example, the processor 510 can only include a central processing unit (CPU). In the embodiments of the application, the CPU can be a single operation core or can include multiple operation cores.

[0106] The communication unit 530 is used to establish a communication channel, so that the storage terminal can communicate with other terminals. Receive user data sent by other terminals or send user data to other terminals.

[0107] The application also provides a computer storage medium, wherein the computer storage medium can store a program, and the program can include part or all of the steps in the embodiments provided by the application when executed. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0108] Therefore, the application converts the data into relative recommended probability through the softmax function by analyzing the CPU real-time usage rate, logical occupied memory, real-time running memory, etc. of all hosts in the cluster, and then obtains the recommended value of the host through information fusion of the DS evidence theory, and allocates appropriate hosts for the user request. The application can reasonably allocate resources when planning the creation of virtual machines by multiple users, so as to improve the efficiency of the system. The technical effects that can be achieved by the present embodiment can be referred to the description in the above, which will not be described here.

[0109] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present application can be implemented by means of software plus necessary universal hardware platforms. Based on such an understanding, the technical solutions in the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and the like, and includes a plurality of instructions for causing a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, or the like) to execute all or part of the steps of the methods described in the embodiments of the present application.

[0110] The same or similar parts among the various embodiments in the specification can be referred to each other. Especially, for the terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0111] In the several embodiments of the present application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are merely schematic, for example, the division of the units is merely a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, system or unit, and can be electrical, mechanical or other forms.

[0112] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0113] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0114] Although the present application has been described in detail with reference to the preferred embodiments, it should be understood that the application is not limited to those preferred embodiments. Various equivalent modifications or changes in the application can be made all of which fall within the scope of the present application. Any modifications or changes in the application should be construed as falling within the scope of the present application. The scope of the application should be determined by the appended claims.

Claims

1. A cloud platform computing resource allocation method, characterized in that, The method comprises the following steps: collecting idle resource rates of each host in a cloud platform cluster; converting each idle resource rate of each host into a corresponding recommended reference value by using a normalized exponential function; calculating the product of each recommended reference value of each host respectively, and accumulating the product of all hosts as a recommended coefficient; calculating a host idle degree value according to the recommended coefficient and the product of each recommended reference value of each host; allocating a corresponding host to a virtual machine creation request according to the principle of preferentially allocating the host with the highest idle degree value; The method for collecting idle resource rates of each host in a cloud platform cluster comprises the following steps: collecting resource utilization rates of each host in the cluster, wherein the resource utilization rates comprise CPU utilization rates and memory utilization rates; obtaining idle resource rates of each host by subtracting the resource utilization rates of each host from the real number 1; converting each idle resource rate of each host into a corresponding recommended reference value by using a normalized exponential function, comprising: Using the formula converting each idle resource rate of each host into a corresponding recommended reference value, wherein B is the recommended reference value, i is the i-th host, and j is the j-th resource type, is the resource utilization rate of the j-th resource type of the i-th host, 1- is the idle resource rate of the j-th resource type of the i-th host; calculating the product of each recommended reference value of each host respectively, and accumulating the product of all hosts as a recommended coefficient, comprising: The recommendation coefficient is calculated by the formula wherein K is the recommendation coefficient, is the recommended reference value of the jth resource type of the ith host. calculating a host idle degree value according to the recommended coefficient and the product of each recommended reference value of each host, comprising: According to the formula The host idle degree value is calculated, wherein The idle degree value of the host i, K is the recommended coefficient, The recommended reference value of the jth resource type of the ith host, The product of the recommended reference values of the host i.

2. The method of claim 1, wherein, allocating a corresponding host to a virtual machine creation request according to the principle of preferentially allocating the host with the highest idle degree value, comprising: sorting virtual machine creation requests according to request receiving time; selecting the earliest virtual machine creation request from the sorted queue as a target request, and extracting the demand resource parameters of the target request; selecting a host with the highest idle degree value from the cluster as a target host, and dividing a corresponding resource amount for the target request from the target host according to the demand resource parameters of the target request; updating the idle degree values of each host in the cluster after the target host completes resource division, and allocating resources to a newly selected target request based on the updated idle degree values.

3. A cloud platform computing resource allocation system, characterized by, The method comprises the following steps: a resource monitoring unit configured to collect idle resource rates of each host in a cloud platform cluster; a data conversion unit configured to convert each idle resource rate of each host into a corresponding recommended reference value by using a normalized exponential function; a coefficient calculation unit configured to calculate the product of each recommended reference value of each host respectively, and accumulate the product of all hosts as a recommended coefficient; a degree calculation unit configured to calculate a host idle degree value according to the recommended coefficient and the product of each recommended reference value of each host; a resource allocation unit configured to allocate a corresponding host to a virtual machine creation request according to the principle of preferentially allocating the host with the highest idle degree value; The resource monitoring unit is configured to: collect resource utilization rates of each host in the cluster, wherein the resource utilization rates comprise CPU utilization rates and memory utilization rates; obtain idle resource rates of each host by subtracting the resource utilization rates of each host from the real number 1; convert each idle resource rate of each host into a corresponding recommended reference value by using a normalized exponential function, comprising: Using the formula converting each idle resource rate of each host into a corresponding recommended reference value, wherein B is the recommended reference value, i is the i-th host, and j is the j-th resource type, is the resource utilization rate of the j-th resource type of the i-th host, 1- is the idle resource rate of the j-th resource type of the i-th host; calculate the product of each recommended reference value of each host respectively, and accumulate the product of all hosts as a recommended coefficient, comprising: The recommendation coefficient is calculated by the formula wherein K is the recommendation coefficient, is the recommended reference value of the jth resource type of the ith host. calculate a host idle degree value according to the recommended coefficient and the product of each recommended reference value of each host, comprising: According to the formula The host idle degree value is calculated, wherein The idle degree value of the host i, K is the recommended coefficient, The recommended reference value of the jth resource type of the ith host, The product of each recommended reference value of the host i is represented.

4. A terminal, characterized by comprising: The method comprises the following steps: a processor; a memory configured to store execution instructions of the processor; wherein the processor is configured to execute the method of any one of claims 1-2.

5. A computer readable storage medium storing a computer program, characterized in that, The program, when executed by the processor, implements the method of any one of claims 1-2.

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