CN113886086A - Cloud platform computing resource allocation method, system, terminal and storage medium - Google Patents
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Abstract
The invention provides a cloud platform computing resource allocation method, a cloud platform computing resource allocation system, a terminal and a storage medium, wherein the cloud platform computing resource allocation method comprises the following steps: acquiring idle resource rate of each host in a cloud platform cluster; converting each idle resource rate of each host into a corresponding recommended reference value by utilizing a normalization index function; respectively calculating the products of all recommended reference values of all hosts, and accumulating the products of all hosts as recommended coefficients; calculating the idle degree value of the host according to the product of the recommendation coefficient and each recommendation reference value of the host; and distributing the corresponding host to the virtual machine creation request according to the principle that the assigned idle degree value is highest preferentially. According to the method, the idle resource conditions of all the hosts of the cluster are analyzed, data are converted into relative recommendation probability through a softmax function, information fusion is carried out by using a DS evidence theory to obtain the recommendation value of the hosts, and appropriate hosts are allocated for user requests. According to the invention, resources can be reasonably distributed when the cloud platform plans multiple users to create the virtual machine, so that the efficiency of the system is improved.
Description
Technical Field
The invention relates to the technical field of cloud platform management, in particular to a cloud platform computing resource allocation method, a cloud platform computing resource allocation system, a cloud platform computing resource allocation terminal and a storage medium.
Background
The cloud computing platform is also called a cloud platform, and is a service based on hardware resources and software resources, and provides computing, network and storage capabilities. Cloud computing platforms can be divided into 3 classes: the cloud computing platform comprises a storage type cloud platform taking data storage as a main part, a computing type cloud platform taking data processing as a main part and a comprehensive cloud computing platform taking computing and data storage processing into consideration. We can colloquially understand the "cloud" in cloud computing as a collection of resources of various types that exist on a cloud data center server cluster. The resources are divided into hardware resources and software resources, wherein the hardware resources comprise a server, a memory, a CPU and the like, and the software resources comprise application software, an integrated development environment and the like. A user can obtain resources meeting requirements from the cloud to a local computer only by sending a request through a network, and all computing tasks are completed in a remote cloud data center. Therefore, users can obtain various computing services, storage services and various software resources as required, and the resources can be dynamically expanded, and the resources after the users finish using can be timely and conveniently recovered. By adopting the service providing mode, the resource utilization rate of the cloud data center is greatly increased, and meanwhile, the service quality can be better improved by a cloud computing service provider. When the cloud platform provides service for the user, a virtual machine is created for the user according to the cloud access request of the user, and the virtual machine is used for processing the service of the user.
The physical basis of the cloud platform is a large server cluster, so how to balance resources inside the cluster when creating the virtual machine is important for the stability of the cloud platform. In a cloud computing system, a large number of heterogeneous resources are uniformly managed through a cloud platform, when multiple users use computing resources in parallel, if the distribution is uneven, the pressure of part of hosts is overlarge, and the rest hosts are relatively idle. If the situation lasts for a long time, the high-voltage server is down, and service interruption and even data loss are caused.
Disclosure of Invention
In view of the above deficiencies in the prior art, the present invention provides a method, a system, a terminal and a storage medium for allocating computing resources of a cloud platform, so as to solve the above technical problems.
In a first aspect, the present invention provides a cloud platform computing resource allocation method, including:
acquiring idle resource rate of each host in a cloud platform cluster;
converting each idle resource rate of each host into a corresponding recommended reference value by utilizing a normalization index function;
respectively calculating the products of all recommended reference values of all hosts, and accumulating the products of all hosts as recommended coefficients;
calculating the idle degree value of the host according to the product of the recommendation coefficient and each recommendation reference value of the host;
and distributing the corresponding host to the virtual machine creation request according to the principle that the assigned idle degree value is highest preferentially.
Further, acquiring idle resource rate of each host in the cloud platform cluster includes:
collecting the resource utilization rate of each host in the cluster, wherein the resource utilization rate comprises a CPU utilization rate and a memory utilization rate;
and obtaining the idle resource rate of each host by using the real number 1 to make a difference with the resource utilization rate of each host.
Further, the converting the idle resource rates of the hosts into the corresponding recommended reference values by using the normalized exponential function includes:
using formulas
Converting each idle resource rate of each host into a corresponding recommended reference value, wherein B is the recommended reference value, i is the ith host, j is the jth resource type,
for the resource utilization of the jth resource type of the ith host,
idle resource rate for jth resource type of ith host。
Further, calculating the product of each recommended reference value of each host, and accumulating the products of all hosts as recommended coefficients, including:
using formulas
Calculating a recommendation coefficient, wherein K is the recommendation coefficient,
the recommended reference value of the j resource type of the ith host.
Further, calculating the idle degree value of the host according to the product of the recommendation coefficient and each recommendation reference value of the host, including:
according to the formula
Calculating a host idle metric value, wherein
Indicating the idle degree value of the host i, K is a recommendation coefficient,
a recommended reference value of the j resource type of the i-th host,
representing the product of the recommended reference values for host i.
Further, allocating a corresponding host to the virtual machine creation request according to a principle that the priority allocation vacancy degree value is the highest includes:
sequencing the virtual machine creation requests according to the request receiving time;
selecting the top virtual machine creation request from the sorting queue as a target request, and extracting the required resource parameters of the target request;
screening out a host with the highest idle degree value from the cluster as a target host, and dividing corresponding resource quantity for the target request from the target host according to the required resource parameters of the target request;
and after the resource division of the target host is finished, updating the idle degree value of each host in the cluster, and allocating resources for the reselected target request based on the updated idle degree value.
In a second aspect, the present invention provides a cloud platform computing resource allocation system, including:
the resource monitoring unit is used for acquiring the idle resource rate of each host in the cloud platform cluster;
the data conversion unit is used for converting each idle resource rate of each host into a corresponding recommended reference value by utilizing a normalized exponential function;
the coefficient calculation unit is used for calculating the products of all recommended reference values of all the hosts respectively and accumulating the products of all the hosts as recommended coefficients;
the degree calculation unit is used for calculating the idle degree value of the host according to the product of the recommendation coefficient and each recommendation reference value of the host;
and the resource allocation unit is used for allocating the corresponding host to the virtual machine creation request according to the principle that the priority allocation vacancy degree value is the highest.
Further, the resource monitoring unit is configured to:
collecting the resource utilization rate of each host in the cluster, wherein the resource utilization rate comprises a CPU utilization rate and a memory utilization rate;
and obtaining the idle resource rate of each host by using the real number 1 to make a difference with the resource utilization rate of each host.
Further, the data conversion unit is configured to: using formulas
Converting each idle resource rate of each host into a corresponding recommended reference value, wherein B is the recommended reference value, i is the ith host, j is the jth resource type,
resources of j resource type for i hostThe utilization rate of the source is increased,
the idle resource rate of the jth resource type for the ith host.
Further, the coefficient calculation unit is configured to: using formulas
Calculating a recommendation coefficient, wherein K is the recommendation coefficient,
the recommended reference value of the j resource type of the ith host.
Further, the degree calculation unit is configured to: according to the formula
Calculating a host idle metric value, wherein
Indicating the idle degree value of the host i, K is a recommendation coefficient,
a recommended reference value of the j resource type of the i-th host,
representing the product of the recommended reference values for host i.
Further, the resource allocation unit is configured to:
sequencing the virtual machine creation requests according to the request receiving time;
selecting the top virtual machine creation request from the sorting queue as a target request, and extracting the required resource parameters of the target request;
screening out a host with the highest idle degree value from the cluster as a target host, and dividing corresponding resource quantity for the target request from the target host according to the required resource parameters of the target request;
and after the resource division of the target host is finished, updating the idle degree value of each host in the cluster, and allocating resources for the reselected target request based on the updated idle degree value.
In a third aspect, a terminal is provided, including:
a processor, a memory, wherein,
the memory is used for storing a computer program which,
the processor is used for calling and running the computer program from the memory so as to make the terminal execute the method of the terminal.
In a fourth aspect, a computer storage medium is provided having stored therein instructions that, when executed on a computer, cause the computer to perform the method of the above aspects.
The cloud platform computing resource allocation method, the cloud platform computing resource allocation system, the cloud platform computing resource allocation terminal and the storage medium have the advantages that through analyzing the real-time utilization rate, the logic occupied memory, the real-time running memory and the like of all the hosts of a cluster, data are converted into relative recommendation probability through a softmax function, information fusion is carried out through a DS evidence theory to obtain the recommendation value of the hosts, and the appropriate hosts are allocated for user requests. According to the invention, resources can be reasonably distributed when the cloud platform plans multiple users to create the virtual machine, so that the efficiency of the system is improved.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
Fig. 2 is a schematic flow chart of a host idleness level value acquisition process of a method of one embodiment of the present invention.
FIG. 3 is a schematic flow chart diagram of a host allocation process of a method of one embodiment of the invention.
FIG. 4 is a schematic block diagram of a system of one embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following explains key terms appearing in the present invention.
The softmax function is also called a normalized exponential function. The method is a popularization of a two-classification function sigmoid on multi-classification, and aims to show the multi-classification result in a probability form.
The evidence theory was first proposed by Dempster in 1967, an imprecise reasoning theory developed further by his student Shafer in 1976, also known as Dempster/Shafer evidence theory (D-S evidence theory), belongs to the field of artificial intelligence, and was applied to the expert system at the earliest with the capability of processing uncertain information. As an uncertain reasoning method, the evidence theory has the main characteristics that: satisfying a weaker condition than bayes probability theory; has the ability to express "uncertain" and "unknown" directly. In DS evidence theory, a complete set of basic propositions (hypotheses) that are mutually incompatible is called a recognition framework, representing all possible answers to a question, but only one of which is correct. A subset of this framework is called proposition. The confidence level assigned to each proposition is called the basic probability assignment (BPA, also called m-function), and m (A) is the basic confidence number and reflects the degree of confidence in A. The belief function bel (a) represents the degree of confidence in proposition a, the likelihood function pl (a) represents the degree of confidence in proposition a that is not false, i.e. the uncertainty measure that a seems likely to hold, in practice, [ bel (a), pl (a) ] represents the uncertainty interval of a, [0, bel (a) ] represents the proposition a support evidence interval, [0, pl (a) ] represents the proposition interval of proposition a, and [ pl (a),1] represents the rejection evidence interval of proposition a. Assuming that m1 and m2 are the basic probability distribution functions derived from two independent evidence sources (sensors), the Dempster combination rule can compute a new basic probability distribution function that reflects the fused information resulting from the co-action of the two evidences.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention. The execution subject in fig. 1 may be a cloud platform computing resource allocation system.
As shown in fig. 1, the method includes:
and 150, distributing the corresponding host to the virtual machine creation request according to the principle that the assigned idle degree value is highest preferentially.
The technical scheme of the invention mainly comprises the following steps: when data such as CPU utilization rate, logic occupied memory, real-time running memory and the like are analyzed, a softmax function is used for converting the data into relative values; comprehensively analyzing various parameters of the host by using a DS evidence theory so as to obtain more reasonable data; the method can allocate the host resources when a single user or a plurality of users operate in parallel.
The method provided by the invention can reasonably distribute the computing resources managed by the platform, and effectively reduce the condition of reducing the system efficiency caused by uneven resource distribution in the multi-user parallel operation, thereby fully using the computing resources and improving the system efficiency.
In order to facilitate understanding of the present invention, the cloud platform computing resource allocation method provided by the present invention is further described below with reference to the principle of the cloud platform computing resource allocation method of the present invention and in combination with the process of allocating cloud platform computing resources in the embodiments.
Referring to fig. 2 and fig. 3, in detail, the cloud platform computing resource allocation method includes:
and S1, acquiring idle resource rate of each host in the cloud platform cluster.
Collecting the resource utilization rate of each host in the cluster, wherein the resource utilization rate comprises a CPU utilization rate and a memory utilization rate; and obtaining the idle resource rate of each host by using the real number 1 to make a difference with the resource utilization rate of each host.
When the user executes the operation of increasing the virtual machine, the data of CPU utilization rate, logic occupied memory, real-time running memory and the like of each host in the current cluster are read and converted into percentage form
Where i is the host number and j is the resource type number.
And S2, converting each idle resource rate of each host into a corresponding recommended reference value by using a normalized exponential function.
Using formulas
Converting each idle resource rate of each host into a corresponding recommended reference value, wherein B is the recommended reference value, i is the ith host, j is the jth resource type,
for the resource utilization of the jth resource type of the ith host,
the idle resource rate of the jth resource type for the ith host.
For example, if the CPU occupancy rate of the host i is 50% and the memory occupancy rate is 60%, the CPU idle rate is 50% and the memory idle rate is 40%. The CPU recommendation reference value of host i
Memory recommended reference value
And S3, respectively calculating the product of each recommended reference value of each host, and accumulating the products of all hosts to be used as recommended coefficients.
Using formulas
Calculating a recommendation coefficient, wherein K is the recommendation coefficient,
the recommended reference value of the j resource type of the ith host.
And S4, calculating the idle degree value of the host according to the product of the recommendation coefficient and each recommendation reference value of the host.
According to the formula
Calculating a host idle metric value, wherein
Indicating the idle degree value of the host i, K is a recommendation coefficient,
a recommended reference value of the j resource type of the i-th host,
representing the product of the recommended reference values for host i.
And S5, distributing the corresponding host to the virtual machine creating request according to the principle that the priority distribution idle degree value is the highest.
Sequencing the virtual machine creation requests according to the request receiving time; selecting the top virtual machine creation request from the sorting queue as a target request, and extracting the required resource parameters of the target request; screening out a host with the highest idle degree value from the cluster as a target host, and dividing corresponding resource quantity for the target request from the target host according to the required resource parameters of the target request; and after the resource division of the target host is finished, updating the idle degree value of each host in the cluster, and allocating resources for the reselected target request based on the updated idle degree value.
When a single user creates a virtual machine, it will have the maximum directly
A host of values is assigned to the user; a plurality of users a1-anThe method comprises the following steps when the virtual machines are concurrently created:
simulating the situation after the user creates the virtual machine, and particularly, the situation will have the maximum
The host computer of the value is allocated to the user, the values of CPU resource occupation, logic occupation memory occupation, real-time operation memory occupation and the like of the virtual machine are updated according to the preset default parameter size of the virtual machine, and the steps S1-S4 are repeated again to obtain the value representing the idle degree of the host computer
Will be provided with
The host with the largest value is allocated to the user a2(ii) a The above steps are repeated until all users are assigned hosts.
As shown in fig. 4, the system 400 includes:
the resource monitoring unit 410 is used for acquiring idle resource rate of each host in the cloud platform cluster;
a data conversion unit 420, configured to convert each idle resource rate of each host into a corresponding recommended reference value by using a normalized exponential function;
a coefficient calculating unit 430, configured to calculate products of the recommended reference values of the hosts, and accumulate the products of all the hosts as recommended coefficients;
the degree calculation unit 440 is configured to calculate a host idle degree value according to a product of the recommendation coefficient and each recommended reference value of the host;
and the resource allocation unit 450 is configured to allocate a corresponding host to the virtual machine creation request according to the principle that the priority allocation vacancy degree value is the highest.
Optionally, as an embodiment of the present invention, the resource monitoring unit is configured to:
collecting the resource utilization rate of each host in the cluster, wherein the resource utilization rate comprises a CPU utilization rate and a memory utilization rate;
and obtaining the idle resource rate of each host by using the real number 1 to make a difference with the resource utilization rate of each host.
Optionally, as an embodiment of the present invention, the data conversion unit is configured to: using formulas
Converting each idle resource rate of each host into a corresponding recommended reference value, wherein B is the recommended reference value, i is the ith host, j is the jth resource type,
for the resource utilization of the jth resource type of the ith host,
the idle resource rate of the jth resource type for the ith host.
Optionally, as an embodiment of the present invention, the coefficient calculating unit is configured to: using formulas
Calculating a recommendation coefficient, wherein K is the recommendation coefficient,
the recommended reference value of the j resource type of the ith host.
Optionally, as an embodiment of the present invention, the degree calculating unit is configured to: according to the formula
Calculating a host idle metric value, wherein
Indicating the idle degree value of the host i, K is a recommendation coefficient,
a recommended reference value of the j resource type of the i-th host,
representing the product of the recommended reference values for host i.
Optionally, as an embodiment of the present invention, the resource allocation unit is configured to:
sequencing the virtual machine creation requests according to the request receiving time;
selecting the top virtual machine creation request from the sorting queue as a target request, and extracting the required resource parameters of the target request;
screening out a host with the highest idle degree value from the cluster as a target host, and dividing corresponding resource quantity for the target request from the target host according to the required resource parameters of the target request;
and after the resource division of the target host is finished, updating the idle degree value of each host in the cluster, and allocating resources for the reselected target request based on the updated idle degree value.
Fig. 5 is a schematic structural diagram of a terminal 500 according to an embodiment of the present invention, where the terminal 500 may be used to execute the cloud platform computing resource allocation method according to the embodiment of the present invention.
Among them, the terminal 500 may include: a processor 510, a memory 520, and a communication unit 530. The components communicate via one or more buses, and those skilled in the art will appreciate that the architecture of the servers shown in the figures is not intended to be limiting, and may be a bus architecture, a star architecture, a combination of more or less components than those shown, or a different arrangement of components.
The memory 520 may be used for storing instructions executed by the processor 510, and the memory 520 may be implemented by any type of volatile or non-volatile storage terminal or 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 memory, flash memory, magnetic disk or optical disk. The executable instructions in memory 520, when executed by processor 510, enable terminal 500 to perform some or all of the steps in the method embodiments described below.
The processor 510 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by operating or executing software programs and/or modules stored in the memory 520 and calling data stored in the memory. The processor may be composed of an Integrated Circuit (IC), for example, a single packaged IC, or a plurality of packaged ICs connected with the same or different functions. For example, processor 510 may include only a Central Processing Unit (CPU). In the embodiment of the present invention, the CPU may be a single operation core, or may include multiple operation cores.
A communication unit 530 for establishing a communication channel so that the storage terminal can communicate with other terminals. And receiving user data sent by other terminals or sending the user data to other terminals.
The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and the program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Therefore, the invention analyzes the real-time utilization rate, the logic occupied memory, the real-time running memory and the like of the CPUs of all the hosts of the cluster, converts the data into the relative recommendation probability through the softmax function, then uses the DS evidence theory to perform information fusion to obtain the recommendation value of the host, and allocates the appropriate host to the user request. According to the invention, resources can be reasonably allocated when the cloud platform plans multiple users to create the virtual machine, so that the efficiency of the system is improved.
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a software product, where the computer software product is stored in a storage medium, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, and the storage medium can store program codes, and includes instructions for enabling a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, and the like) to perform all or part of the steps of the method in the embodiments of the present invention.
The same and similar parts in the various embodiments in this specification may be referred to each other. Especially, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the description in the method embodiment.
In the embodiments provided in the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A cloud platform computing resource allocation method is characterized by comprising the following steps:
acquiring idle resource rate of each host in a cloud platform cluster;
converting each idle resource rate of each host into a corresponding recommended reference value by utilizing a normalization index function;
respectively calculating the products of all recommended reference values of all hosts, and accumulating the products of all hosts as recommended coefficients;
calculating the idle degree value of the host according to the product of the recommendation coefficient and each recommendation reference value of the host;
and distributing the corresponding host to the virtual machine creation request according to the principle that the assigned idle degree value is highest preferentially.
2. The method of claim 1, wherein collecting idle resource rates of hosts in a cloud platform cluster comprises:
collecting the resource utilization rate of each host in the cluster, wherein the resource utilization rate comprises a CPU utilization rate and a memory utilization rate;
and obtaining the idle resource rate of each host by using the real number 1 to make a difference with the resource utilization rate of each host.
3. The method of claim 2, wherein converting the idle resource rates of the hosts into the recommended reference values using a normalized exponential function comprises:
using formulas
Converting each idle resource rate of each host into a corresponding recommended reference value, wherein B is the recommended reference value, i is the ith host, j is the jth resource type,
for the resource utilization of the jth resource type of the ith host,
the idle resource rate of the jth resource type for the ith host.
4. The method of claim 3, wherein calculating the product of recommended reference values of each host and accumulating the product of all hosts as a recommended coefficient comprises:
using formulas
Calculating a recommendation coefficient, wherein K is the recommendation coefficient,
the recommended reference value of the j resource type of the ith host.
5. The method of claim 4, wherein calculating the host idle metric value based on a product of the recommendation coefficient and the recommendation reference values of the host comprises:
according to the formula
Calculating a host idle metric value, wherein
Indicating the idle degree value of the host i, K is a recommendation coefficient,
a recommended reference value of the j resource type of the i-th host,
representing the product of the recommended reference values for host i.
6. The method of claim 1, wherein assigning the corresponding host to the virtual machine creation request according to the principle that the assigned priority value is highest comprises:
sequencing the virtual machine creation requests according to the request receiving time;
selecting the top virtual machine creation request from the sorting queue as a target request, and extracting the required resource parameters of the target request;
screening out a host with the highest idle degree value from the cluster as a target host, and dividing corresponding resource quantity for the target request from the target host according to the required resource parameters of the target request;
and after the resource division of the target host is finished, updating the idle degree value of each host in the cluster, and allocating resources for the reselected target request based on the updated idle degree value.
7. A cloud platform computing resource allocation system, comprising:
the resource monitoring unit is used for acquiring the idle resource rate of each host in the cloud platform cluster;
the data conversion unit is used for converting each idle resource rate of each host into a corresponding recommended reference value by utilizing a normalized exponential function;
the coefficient calculation unit is used for calculating the products of all recommended reference values of all the hosts respectively and accumulating the products of all the hosts as recommended coefficients;
the degree calculation unit is used for calculating the idle degree value of the host according to the product of the recommendation coefficient and each recommendation reference value of the host;
and the resource allocation unit is used for allocating the corresponding host to the virtual machine creation request according to the principle that the priority allocation vacancy degree value is the highest.
8. The system of claim 7, wherein the resource monitoring unit is configured to:
collecting the resource utilization rate of each host in the cluster, wherein the resource utilization rate comprises a CPU utilization rate and a memory utilization rate;
and obtaining the idle resource rate of each host by using the real number 1 to make a difference with the resource utilization rate of each host.
9. A terminal, comprising:
a processor;
a memory for storing instructions for execution by the processor;
wherein the processor is configured to perform the method of any one of claims 1-6.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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