CN117675566A - Resource quota allocation method and device, storage medium and electronic equipment - Google Patents

Abstract

This application discloses a resource quota allocation method, device, storage medium and electronic equipment, and relates to the field of data processing technology. The method includes: first obtaining the current historical resource quota information of the target client in the cloud platform; and then based on the historical resources Quota information determines the current first resource quota adjustment factor of the target client, and the current first resource quota adjustment factor is used to determine the resource quota of the target client; and then based on the current first resource quota adjustment factor, The current resource quota corresponding to the target client is reallocated to obtain the next resource quota corresponding to the target client. Compared with the current existing technology, this application determines the resource quota adjustment factor corresponding to the client through historical resource quota information, reallocates the client's resource quota according to the adjustment factor, and dynamically adjusts it from the perspective of the client and the cloud platform. User resource quota.

Description

A resource quota allocation method, device, storage medium and electronic equipment

Technical field

The present application relates to the field of computer technology, and in particular, to a resource quota allocation method, device, storage medium and electronic equipment.

Background technique

At present, the cloud computing service model generally adopts resource sharing and on-demand use. It needs to face the sudden dynamic resource demands of most clients. In cloud computing, the overall resources are limited, and excessive resource occupation by some clients may lead to low resource utilization and poor utilization of other clients. The client cannot use resources, which affects the fairness and quality of services.

At present, fixed resource quotas are mainly allocated to clients. Setting the resource quota of the client plays a role in limiting the number of resources occupied by a single client to a certain extent. The client can only apply for resources and use resources within the resource quota range. .

However, the use of this fixed resource quota can only be based on the maximum peak value and cannot guarantee the dynamic resource needs of the client. Under the condition of limited resources, it will lead to low resource utilization of the cloud platform, which will lead to resource waste.

Contents of the invention

In view of this, this application provides a resource quota allocation method, device, storage medium and electronic equipment. The main purpose is to improve the current existing technology that cannot guarantee the dynamic resource requirements of the client. Under the condition of limited resources, it will lead to cloud The utilization rate of platform resources is low, which leads to technical problems of resource waste.

In the first aspect, this application provides a resource quota allocation method, including:

Obtain the current historical resource quota information of the target client in the cloud platform;

Based on the historical resource quota information, determine the current first resource quota adjustment factor of the target client, where the current first resource quota adjustment factor is used to determine the resource quota of the target client;

According to the current first resource quota adjustment factor, the current resource quota corresponding to the target client is reallocated to obtain the next resource quota corresponding to the target client.

In the second aspect, this application provides a resource quota allocation device, including:

The acquisition module is configured to obtain the current historical resource quota information of the target client in the cloud platform;

A determining module configured to determine the current first resource quota adjustment factor of the target client based on the historical resource quota information, where the current first resource quota adjustment factor is used to determine the resource quota of the target client;

The allocation module is configured to reallocate the current resource quota corresponding to the target client according to the current first resource quota adjustment factor to obtain the next resource quota corresponding to the target client.

In a third aspect, this application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the resource quota allocation method of the first aspect is implemented.

In a fourth aspect, the present application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the resource quota of the first aspect is implemented. Allocation method.

With the above technical solution, this application provides a resource quota allocation method, device, storage medium and electronic device that first obtains the current historical resource quota information of the target client in the cloud platform; and then based on the historical resource quota information, Determine the current first resource quota adjustment factor of the target client, and the current first resource quota adjustment factor is used to determine the resource quota of the target client; and then based on the current first resource quota adjustment factor, adjust the target The current resource quota corresponding to the client is reallocated to obtain the next resource quota corresponding to the target client. Compared with the current existing technology, this application determines the resource quota adjustment factor corresponding to the client through historical resource quota information, reallocates the client's resource quota according to the adjustment factor, and dynamically adjusts it from the perspective of the client and the cloud platform. User resource quotas can allow the cloud platform's computing resources to be dynamically and flexibly adjusted, enhance the efficiency of resource quota allocation, avoid resource waste, and significantly improve the cloud platform's resource service capabilities.

The above description is only an overview of the technical solutions of the present application. In order to have a clearer understanding of the technical means of the present application, they can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and understandable. , the specific implementation methods of the present application are specifically listed below.

Description of drawings

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

In order to more clearly explain the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to describe the embodiments or the prior art. Obviously, for those of ordinary skill in the art, It is said that other drawings can be obtained based on these drawings without exerting creative labor.

Figure 1 shows a schematic flowchart of a resource quota allocation method provided by an embodiment of the present application;

Figure 2 shows a schematic flowchart of a resource quota allocation method provided by an embodiment of the present application;

Figure 3 shows a schematic diagram of an example provided by the embodiment of the present application;

Figure 4 shows a schematic flow diagram of an example provided by the embodiment of the present application;

Figure 5 shows a schematic flow diagram of an example provided by the embodiment of the present application;

FIG. 6 shows a schematic structural diagram of a resource quota allocation device provided by an embodiment of the present application.

Detailed ways

Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. It should be noted that, as long as there is no conflict, the embodiments and features in the embodiments of this application can be combined with each other.

In order to improve the technical problem that the current existing technology cannot guarantee the dynamic resource requirements of the client, under the condition of limited resources, it will lead to low resource utilization of the cloud platform, which will lead to resource waste. This embodiment provides a resource quota allocation method, as shown in Figure 1. The method includes:

Step 101: Obtain the current historical resource quota information of the target client in the cloud platform.

In the embodiment of this application, cloud platforms can be platforms that run written programs in the "cloud" or use services provided by the "cloud". The name of the cloud platform can also be On-Demand Platform (on-demand platform), platform as a service (PaaS), etc. The name of the cloud platform is not specifically limited in the embodiment of this application.

Correspondingly, cloud computing can provide available, convenient, on-demand network access to a configurable resource sharing pool of networks, servers, storage, application software, service computing, etc., with minimal management investment. work, or with minimal interaction with the service provider, these resources can be provisioned quickly.

It should be noted that the target client can be any one of multiple clients in the cloud platform.

In some examples, historical resource quota information may include periodic acquisition, calculation of client quota changes and resource utilization records, remotely reported resource utilization records, and so on.

Step 102: Determine the current first resource quota adjustment factor of the target client based on historical resource quota information.

Wherein, the current first resource quota adjustment factor is used to determine the resource quota of the target client.

For this embodiment, the first resource quota adjustment factor is a dynamic adjustment factor, and the value of the first resource quota adjustment factor is different according to different historical resource quota information.

Step 103: Reallocate the current resource quota corresponding to the target client according to the current first resource quota adjustment factor to obtain the next resource quota corresponding to the target client.

Compared with the current existing technology, this embodiment determines the resource quota adjustment factor corresponding to the client through historical resource quota information, and reallocates the client's resource quota according to the adjustment factor. Considering the two perspectives of the client and the cloud platform, dynamic Adjusting user resource quotas can allow the computing resources of the cloud platform to be dynamically and flexibly adjusted, enhance the efficiency of resource quota allocation, avoid resource waste, and significantly improve the cloud platform's resource service capabilities.

In order to further illustrate the specific implementation process of the method in this embodiment, this embodiment provides a specific method as shown in Figure 2, which method includes:

Step 201: Obtain the current historical resource quota information of the target client in the cloud platform.

Optionally, before step 201, the method in this embodiment also includes: in response to receiving a change request for the current resource quota of the target client, analyzing the change request; accordingly, step 201 may specifically include: based on the analysis results, if it is determined If the change request is to request to increase the current resource quota, the current historical resource quota information of the target client is obtained through the collectd service.

In this embodiment of the present application, step 201 specifically includes: based on the analysis results, if it is determined that the change request is a request to reduce the current resource quota, adjusting the current resource quota according to the change request.

In some examples, step 201 specifically includes: determining that the time interval between the current and the determination time of the target client's previous resource quota reaches a preset period threshold, and obtaining the current historical resource configuration information of the target client.

It should be noted that this embodiment can obtain historical resource quota information based on the client's resource quota change request, and can also periodically obtain historical resource quota information to reallocate resource quotas.

Furthermore, resource monitoring uses collectd and relational database management system (MySQL). collectd is a daemon process of the Linux system, which is used to collect system performance data and provide data storage methods. It integrates MySQL to realize data storage. The structure of the collectd monitoring component is shown in Figure 3. The specific process can include deploying collectd in the monitored virtual node in the cloud platform, and making it a collectd client by configuring the network node in the collectd.conf file. Deploy collectd on the monitored node, also configure the network node in the collectd.conf file, and use the monitoring end collectd as the service (server) end. The server and client communicate with each other using the socket binary protocol.

Accordingly, according to the actual situation of different virtual nodes, configure the application server plug-in that needs to be monitored and the log file storage location on the collectd client. After completing the configuration, start the collectd service. The collect client reads relevant information from the /proc file system every 10 seconds to form a time series RRB data file, which is stored in the location specified in the collectd server configuration file. collectd integrates MySQL, edits the configuration to connect to the remote MySQL host in /etc/collectd/managed_config/, and completes data storage.

Furthermore, when the client applies for a resource quota change, when the client resource quota is insufficient, a resource quota change request can be made. If the client applies to reduce the resource quota, it will be approved directly and the client resource quota will be changed. If the client's quota is insufficient and a request is made to increase the resource quota, the cloud platform calculates the user's dynamic adjustment factor based on the client's historical resource quota usage.

Step 202: Determine the current second resource quota adjustment factor of the target client and the weight information corresponding to the current second resource quota adjustment factor based on the historical resource quota information.

In this embodiment of the present application, the second resource quota adjustment factor is the initial resource quota adjustment factor obtained before performing weighted analysis.

For example, if there are n types of platform resources, including CPU, memory, hard disk, IP, router, load balancer, etc., and the resource quota is expressed as n-tuple, the user's resource quota can be determined through Formula 1. Formula 1 is specific. As follows:

w _i (t _i ,d _i ) = (w _i,1 (t _i ,d _i ),, w _i,k (t _i ,d _i ),, w _i,n (t _i ,d _i ) ) (Formula 1)

In formula 1, w _i (t _i ,d _i ) represents the i-th resource quota allocated to the user, _ti represents the starting time of the i-th quota activation, _di represents the usage time, w _i,k (t _i ,d _i ) represent the quota allocated to the k-th resource of the user for the i-th time, such as the number of CPU cores, memory size, hard disk capacity, etc.

Taking CPU resources as an example, the CPU resource quota utilization can be calculated through Formula 2. Formula 2 is as follows:

In formula 2, Ci represents the number of CPU cores used by users in a certain period of time within the quota usage period, t _i represents the period of time, C represents the total number of CPU quotas, t represents the quota usage time, and load _i represents the number of remote hosts reported by monitoring. CPU load utilization, α represents the adjustment factor.

Furthermore, if α is taken as 0.9, the actual CPU usage rate of the server is 50%, the client CPU quota is 100 cores, the quota service time is 100 days, and the client is running continuously for 15 days, 10 days, 15 days, and 25 days. The number of CPU cores used in the five time periods of days and 35 days are 24, 20, 32, 36, and 28 respectively. Then the resource quota usage rate of the CPU = 0.9*(24*15+20*10+32*15+ 36*25+28*35)/(100*100)+5*0.1*0.5=32.75%.

Assuming that the client applies for resource quota for the first time, the client determines the resource quota through Formula 3 and Formula 4 based on the weight factor. Formula 3 and Formula 4 are as follows:

w _i+1 (t _i+1 ,d _i+1 )=(wi _+1,1 (t _i+1 ,d _i+1 ),,, w _i+1,n (t _i+1 ,d _i+1 ))(Formula 3)

Δw＝wi ₊₁ (t _i+1 ,d _i+1 ) -w _i (t _i ,d _i ) (Formula 4)

Step 203: Based on the weight information, perform weighted analysis on the current second resource quota adjustment factor to obtain the current first resource quota adjustment factor.

Exemplarily, based on step 202, the key to the cloud computing resource quota dynamic allocation algorithm is to construct a user dynamic adjustment factor model. If there are n resource quotas used i times in history for n types of resources. The resource quota utilization rate of user i can be expressed as v ₁ (t ₁ ,d ₁ ), v ₂ (t ₂ ,d ₂ ),,, v _n (t _n ,d _n ) can be determined by formula 5. Formula Five details are as follows:

v _m (t _m ,d _m ) = (v _m,1 (t _m ,d _m ), v _m,2 (t _m,1 ,d _m ),,, v _m,k (t _m,1 ,d _m ))(Formula 5)

In Formula 5, v _m (t _m , d _m ) represents the average resource quota utilization of the m-th allocated resource, and v (t _m,1 , d _m ) represents the average resource quota of the k-th type of resource allocated for the m-th time. Utilization rate, k=1, 2, 3...n, _tm represents the starting time when the mth resource quota is allocated, and _dm represents the usage time of the mth resource quota.

Furthermore, the user quota dynamic adjustment factor is calculated before the i+1th resource quota allocation. The calculation of the i+1th adjustment factor is affected by the use of the previous i quota. The weight result of the previous i time can be used to calculate the user quota dynamic adjustment factor. i+1 resource quota adjustment factor. On the one hand, the weight is affected by the user's usage time under a certain resource quota. Under the same conditions, the longer the usage time of a certain resource quota, the higher the weight. On the other hand, taking into account the timeliness of historical data, it is related to the resource quota allocation order m. It is related to whether it is close to the i-th time. Under the same conditions, the more recent resource quota usage has a greater impact on the allocation of resource quotas, that is, the closer the allocation order m is to i, the higher the weight, and the faster the weight changes. It comes faster and faster. If linear change is adopted, this nonlinear change characteristic cannot be reflected. Therefore, the following model is used to calculate the weight of the k-th resource source quota utilization rate v _m (t _m , d _m ) after the m-th time. Formula 6 determines the initial weight. Formula 6 is as follows:

c' _i (0)＝0 (Formula 6)

After the ith change, the weight can be determined through Formula 7, which is as follows:

In Formula 7, a represents the impact factor, 0＜α＜1, and D represents the cycle time for the cloud platform to periodically dynamically adjust resource quotas, m=1,2,3...i.

It should be noted that when resource quotas are used for the same time, the weight increases as the number of resource quota allocations increases, which shows that the dynamic adjustment of the client is greater.

Step 204: Reallocate the current resource quota corresponding to the target client according to the current first resource quota adjustment factor to obtain the next resource quota corresponding to the target client.

Optionally, step 204 may specifically include: if it is determined that the first resource quota adjustment factor is greater than the preset adjustment factor threshold, determining the current resource quota as the next resource quota; if it is determined that the first resource quota adjustment factor is less than the preset adjustment factor threshold The adjustment factor threshold is processed based on the current resource quota to obtain the next resource quota.

In the embodiment of this application, after step 204, the method of this embodiment also includes: analyzing the current number of resources used by the target client, determining that the number of currently used resources is greater than the next resource quota, and then updating the next resource quota to the currently used resources. quantity.

For example, based on step 202, if the client dynamic adjustment factor Ci>1, the resource quota Δw is increased for the user, that is, the actual resource quota allocated to the user can be determined through Formula 8, which is specifically as follows:

w′ _i+1 (t _i+1 ,d _i+1 )=w _i,1 (t _i ,d _i )+Δw (Formula 8)

If the client dynamic adjustment factor Ci < 1, the resource quota will be increased according to the user dynamic adjustment factor, that is, the actual increase in the user resource quota is Δw*Ci. The actual resource quota after the increase can be determined by Formula 9, which is as follows: Show:

w′ _i+1 (t _i+1 ,d _i+1 )=q _i,1 (t _i ,d _i )+Δw*C _i (Formula 9)

For example, the user dynamic adjustment parameter C′ _i means that for all resource types, the average resource quota utilization rate of the user after i resource quota usage can be determined through Formula 10, which is used for the user to allocate a new quota for the i+1th time. dynamic adjustment reference. Formula 10 is as follows:

The user dynamic adjustment factor can be determined based on Formula 11, which is the ratio of the user dynamic adjustment parameter C′ _i to the adjustment factor baseline reference value Co. Formula 11 is as follows:

Ci＝C′ _i /Co (Formula 11)

When Ci < 1, when the user applies for resource quota A _i for the i+1th time, the actual allocated quantity can be determined by Formula 12. Formula 12 is as follows:

C _a =A _i *C _i (Formula 12)

With the rapid growth of existing cloud platform users and the constant and dynamic changes in resource demand, although the fixed quota mechanism that has been used effectively limits the number of resources used by each user and avoids resource abuse, there are also a large number of users who occupy relatively high quotas for a long time. Low utilization rates result in limited platform resource service capabilities, which poses challenges to the efficient utilization of cloud computing resources.

At present, cloud computing service models generally adopt resource sharing and on-demand use, which requires users to face sudden and dynamic resource demands. When the overall cloud computing resources are limited and the interests of both parties are maximized, excessive resource occupation by some users may cause problems. This results in low resource utilization and the inability of other users to use resources, affecting the fairness and quality of services. The setting of resource quotas plays a role in limiting the amount of resources occupied by a single user to a certain extent. Users can only apply for and use resources within the resource quota range. However, most current cloud computing platforms use fixed resource quota restrictions. The following exist question:

First, the resource demands of different users are constantly changing at different times. Fixed resource quotas can only be based on the maximum peak value and cannot guarantee the dynamic resource demands of users. Under conditions of limited resources, there is a waste of resources.

Second, some users falsely report higher resource quotas through over-application and other means, resulting in a large resource quota being occupied for a long time, but the actual resource quota utilization rate is relatively low. When the total platform resource quota is limited and service quality needs to be guaranteed, other problems arise. User resource needs cannot be responded to and met, affecting the efficiency of resource allocation.

Third, due to the heterogeneous and virtualized characteristics of cloud platform resources, it may be difficult for users to accurately calculate the actual number of resources required. Ultimately, there may be a large deviation between the applied resource quota and the actual demand, and it is also difficult for the cloud platform to effectively predict. Users have real resource needs, platform resources cannot be efficiently utilized, and platform service capabilities are difficult to effectively improve.

In summary, there are currently few studies on resource quota allocation methods. Most of the research focuses on the fairness of resource allocation, and most studies the fairness of resource allocation from the perspectives of users, computing nodes, and resource sharing. Although some studies have proposed mechanisms to establish user credit levels to limit resource usage, they are all focused on individual user resource limitations and fail to consider how to enhance the fairness of resource allocation, control and optimize resource allocation and utilization, and improve the cloud platform as a whole. The resource service capabilities of the cloud platform.

In the embodiment of this application, it is overcome that the existing fixed resource quota mechanism cannot continuously meet the dynamic resource requirements of the client. In the dynamic adjustment factor model, if a client occupies the resource quota for a long time and the utilization rate is low, the client's resource quota will be punitively allocated. If the client occupies the resource quota for a long time, that is, the usage time d ₁ ,...,d _m ,...d _i is large, then according to Equation 9, the resource weight component w _m,k (m,d _m ) is given by d Determined by _m and m, but the lower the resource utilization rate U _m (t _m , d _m ), the dynamic adjustment factor will also be smaller according to Formula 10, and fewer resources will be allocated. In the dynamic adjustment factor model, if the client's resource quota utilization continues to decrease, the client's dynamic adjustment factor Ci has an amplifying and cumulative effect. The dynamic adjustment factor model can motivate clients to release idle resource quotas after task completion.

The products collected in the embodiments of this application are the fourth phase of the first-level Internet Technology (IT) cloud management platform project. They have been developed and applied to the big cloud operation management platform to solve the problem that the resource needs of different users are constantly changing at different times. Dynamic quota changes have certain application value to avoid resource waste under conditions of limited resources.

Compared with the current existing technology, this embodiment determines the resource quota adjustment factor corresponding to the client through historical resource quota information, and reallocates the client's resource quota according to the adjustment factor. Considering the two perspectives of the client and the cloud platform, dynamic Adjusting user resource quotas allows the computing resources of the cloud platform to be dynamically and flexibly adjusted, enhances the efficiency of resource quota allocation, and greatly improves the resource service capabilities of the cloud platform. Combining historical resource quota information with actual resource data, a dynamically changing weight model is selected to implement an algorithm for dynamic allocation of resource quotas, which not only enables users to actively initiate and passively adjust quotas, but also enables periodic active dynamic allocation. It effectively controls the number of resources used by users, meets the dynamic resource needs of more users, enhances the efficiency of resource quota allocation, and improves the resource service capabilities of the cloud platform. Based on collectd, the quota review mechanism and the resource utilization assessment mechanism are coordinated to improve the cloud Overall resource utilization of the platform.

In order to illustrate the specific implementation process of this embodiment, the following specific application examples are given, as shown in Figure 4, but are not limited to this:

Collect remote host resource usage through collectd monitoring through the monitored virtual node. The monitoring node server periodically reports resource usage data to MySQL. When the client applies for a quota change, the weight factor can be calculated according to the corresponding weight model based on the database quota usage and the resource usage reported by the remote host. Calculate the actual quota that needs to be changed based on the calculated weight factor. Eventually the quota change initiated by the client is completed. In addition to the client actively initiating quotas, dynamic allocation of resource quotas can be achieved.

As shown in the flow chart on the left side of Figure 5, the client can apply for a resource quota change. When the client applies for a resource quota change, it can make a resource quota change request when the client's resource quota is insufficient. If the client applies for a resource quota reduction, If approved, the client resource quota will be changed directly. If the client's quota is insufficient and a request is made to increase the resource quota, the cloud platform calculates the client's dynamic adjustment factor based on the client's historical resource quota usage.

If the client's dynamic adjustment factor is greater than or equal to 1, the requested quota is allocated to the client; otherwise, the client is allocated a resource quota based on the dynamic adjustment factor.

As shown in the flow chart on the right side of Figure 5, the dynamic allocation of resource quotas is based on periodic acquisition, calculation of client quota changes and resource utilization records, and remotely reported resource utilization records. After the client completes the ith resource quota usage, its dynamic adjustment factor Ci is calculated based on its historical resource quota utilization. If Ci>1, the client resource quota remains unchanged, but is allocated as a new resource quota. If the dynamic adjustment factor Ci<1, the client's resource quota after the i+1th change is calculated based on the dynamic adjustment factor. Then it is determined whether the number of resources currently used by the client is within the quota. If so, the number of resources reused by the client is adjusted as the new quota. If the quota range is exceeded, the number of resources used by the client is set to the client's new resource quota.

The cloud platform resource quota dynamic allocation algorithm can prompt clients to proactively apply for resource quota reduction after the task is completed, releasing more idle resource quotas, allowing more clients to have the opportunity to use resources, and enhancing the efficiency of resource use. .

Compared with the current existing technology, this embodiment determines the resource quota adjustment factor corresponding to the client through historical resource quota information, and reallocates the client's resource quota according to the adjustment factor. Considering the two perspectives of the client and the cloud platform, dynamic Adjusting user resource quotas allows the computing resources of the cloud platform to be dynamically and flexibly adjusted, enhances the efficiency of resource quota allocation, and greatly improves the resource service capabilities of the cloud platform. Combining historical resource quota information with actual resource data, a dynamically changing weight model is selected to implement an algorithm for dynamic allocation of resource quotas, which not only enables users to actively initiate and passively adjust quotas, but also enables periodic active dynamic allocation. It effectively controls the number of resources used by users, meets the dynamic resource needs of more users, enhances the efficiency of resource quota allocation, and improves the resource service capabilities of the cloud platform. Based on collectd, the quota review mechanism and the resource utilization assessment mechanism are coordinated to improve the cloud Overall resource utilization of the platform.

Further, as a specific implementation of the methods shown in Figures 1 and 2, this embodiment provides a device for allocating resource quotas. As shown in Figure 6, the device includes: an acquisition module 31, a determination module 32, and an allocation module 33. .

The acquisition module 31 is configured to obtain the current historical resource quota information of the target client in the cloud platform;

The determination module 32 is configured to determine the current first resource quota adjustment factor of the target client based on the historical resource quota information, where the current first resource quota adjustment factor is used to determine the resource quota of the target client;

The allocation module 33 is configured to reallocate the current resource quota corresponding to the target client according to the current first resource quota adjustment factor to obtain the next resource quota corresponding to the target client.

In some examples of this embodiment, the determination module 32 is specifically configured to determine the current second resource quota adjustment factor of the target client and the current second resource quota adjustment factor corresponding to the current second resource quota adjustment factor based on the historical resource quota information. weight information; based on the weight information, perform a weighted analysis on the current second resource quota adjustment factor to obtain the current first resource quota adjustment factor.

In some examples of this embodiment, the allocation module 33 is specifically configured to determine the current resource quota as the next resource quota if it is determined that the first resource quota adjustment factor is greater than the preset adjustment factor threshold; If it is determined that the first resource quota adjustment factor is less than the preset adjustment factor threshold, processing is performed based on the current resource quota to obtain the next resource quota.

In some examples of this embodiment, the allocation module 33 is specifically configured to analyze the currently used resource quantity of the target client, and determine that the currently used resource quantity is greater than the next resource quota, and then assign the next resource quota to the target client. The resource quota is updated to the currently used resource quantity.

In some examples of this embodiment, the acquisition module 31 is also configured to respond to receiving a change request for the current resource quota of the target client and analyze the change request; accordingly, the acquisition module 31 is specifically configured Based on the analysis results, if it is determined that the change request is a request to increase the current resource quota, the current historical resource quota information of the target client is obtained through the collectd service.

In some examples of this embodiment, the acquisition module 31 is specifically configured to adjust the current resource quota according to the change request if it is determined that the change request is a request to reduce the current resource quota based on the analysis results. .

In some examples of this embodiment, the acquisition module 31 is specifically configured to determine that the time interval between the current and the determination time of the previous resource quota of the target client reaches a preset period threshold, and obtain the current resource quota of the target client. Historical resource configuration information.

It should be noted that, for other corresponding descriptions of the functional units involved in the resource quota allocation device provided in this embodiment, reference can be made to the corresponding descriptions in Figures 1 and 2, which will not be described again here.

Based on the above methods shown in Figures 1 to 2, correspondingly, this embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above mentioned Figures 1 to 2 are implemented. The method shown in Figure 2.

Based on this understanding, the technical solution of this application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (can be a CD-ROM, U disk, mobile hard disk, etc.), including several Instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method of each implementation scenario of this application.

Based on the above method shown in Figures 1 to 2 and the virtual device embodiment shown in Figure 6, in order to achieve the above purpose, embodiments of the present application also provide an electronic device, such as a personal computer, a server, a notebook computer, Smart terminals such as smart phones and smart robots include a storage medium and a processor; the storage medium is used to store computer programs; and the processor is used to execute computer programs to implement the above methods as shown in Figures 1 to 2.

Optionally, the above-mentioned physical devices may also include user interfaces, network interfaces, cameras, radio frequency (Radio Frequency, RF) circuits, sensors, audio circuits, WI-FI modules, etc. The user interface may include a display screen (Display), an input unit such as a keyboard (Keyboard), etc. The optional user interface may also include a USB interface, a card reader interface, etc. Optional network interfaces may include standard wired interfaces, wireless interfaces (such as WI-FI interfaces), etc.

Those skilled in the art can understand that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or arrange different components.

The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical devices and supports the operation of information processing programs and other software and/or programs. The network communication module is used to realize communication between components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or can also be implemented by hardware. Compared with the current existing technology, this embodiment determines the resource quota adjustment factor corresponding to the client through historical resource quota information, and reallocates the client's resource quota according to the adjustment factor. Considering the two perspectives of the client and the cloud platform, dynamic Adjusting user resource quotas allows the computing resources of the cloud platform to be dynamically and flexibly adjusted, enhances the efficiency of resource quota allocation, and greatly improves the resource service capabilities of the cloud platform. Combining historical resource quota information with actual resource data, a dynamically changing weight model is selected to implement an algorithm for dynamic allocation of resource quotas, which not only enables users to actively initiate and passively adjust quotas, but also enables periodic active dynamic allocation. It effectively controls the number of resources used by users, meets the dynamic resource needs of more users, enhances the efficiency of resource quota allocation, and improves the resource service capabilities of the cloud platform. Based on collectd, the quota review mechanism and the resource utilization assessment mechanism are coordinated to improve the cloud Overall resource utilization of the platform.

It should be noted that in this article, relational terms such as “first” and “second” are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these Any such actual relationship or sequence exists between entities or operations. Furthermore, the terms "comprises," "comprises," or any other variations thereof are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also those not expressly listed other elements, or elements inherent to the process, method, article or equipment. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article, or apparatus that includes the stated element.

The above descriptions are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be practiced in other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments described herein but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims (10)

1. A method for allocating a resource quota, comprising:

acquiring current historical resource quota information of a target client in a cloud platform;

determining a current first resource quota adjustment factor of the target client based on the historical resource quota information, wherein the current first resource quota adjustment factor is used for determining the resource quota of the target client;

and reallocating the current resource quota corresponding to the target client according to the current first resource quota adjusting factor to obtain the next resource quota corresponding to the target client.

2. The method of claim 1, wherein the determining the current first resource quota adjustment factor for the target client based on the historical resource quota information comprises:

determining a current second resource quota adjusting factor of the target client and weight information corresponding to the current second resource quota adjusting factor according to the historical resource quota information;

and carrying out weighted analysis on the current second resource quota adjusting factor based on the weight information to obtain the current first resource quota adjusting factor.

3. The method of claim 2, wherein reallocating the current resource quota corresponding to the target client according to the current first resource quota adjustment factor to obtain a next resource quota corresponding to the target client comprises:

if the first resource quota adjusting factor is larger than a preset adjusting factor threshold, determining the current resource quota as the next resource quota;

and if the first resource quota adjusting factor is smaller than a preset adjusting factor threshold, processing based on the current resource quota to obtain the next resource quota.

4. The method of claim 3, wherein after the processing is performed based on the current resource quota if the first resource quota adjustment factor is determined to be less than a preset adjustment factor threshold, the method further comprises:

analyzing the current used resource quantity of the target client, and updating the next resource quota to the current used resource quantity if the current used resource quantity is determined to be larger than the next resource quota.

5. The method of claim 1, wherein prior to the obtaining current historical resource quota information for the target client in the cloud platform, the method further comprises:

responding to a change request of the current resource quota of the target client, and analyzing the change request;

the obtaining the current historical resource quota information of the target client in the cloud platform comprises the following steps:

based on the analysis result, if the change request is determined to be a request for increasing the current resource quota, acquiring current historical resource quota information of the target client through a collectd service.

6. The method of claim 5, wherein the method further comprises:

based on the analysis result, if the change request is determined to be the request for reducing the current resource quota, the current resource quota is adjusted according to the change request.

7. The method of claim 6, wherein the obtaining current historical resource quota information for the target client in the cloud platform further comprises:

and determining that the time interval between the current time and the determined time of one resource quota on the target client reaches a preset period threshold value, and acquiring the current historical resource configuration information of the target client.

8. A resource quota allocation apparatus, comprising:

the acquisition module is configured to acquire current historical resource quota information of the target client in the cloud platform;

a determining module configured to determine, based on the historical resource quota information, a current first resource quota adjustment factor for the target client, the current first resource quota adjustment factor being used to determine a resource quota for the target client;

the allocation module is configured to reallocate the current resource quota corresponding to the target client according to the current first resource quota adjusting factor to obtain the next resource quota corresponding to the target client.

9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1 to 7.

10. An electronic device comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, characterized in that the processor implements the method of any one of claims 1 to 7 when executing the computer program.