Resource processing method, cloud service instance processing method and device

Abstract

Embodiments of the present application provide a resource processing method, a cloud service instance processing method and device, comprising: receiving a resource application request sent by a target user equipment; the target user equipment belongs to a user equipment in a resource platform; in response to the resource application request, finding a quota corresponding to target resource description information corresponding to the target user equipment from preset quota data to obtain a target quota; the target resource is resource description information applied by the target user equipment this time, and the preset quota data includes quotas corresponding to different resource description information predicted for the user equipment in the resource platform; and according to the target quota, allocating a target resource in the resource platform to the target user equipment, the target resource including a resource described by the target resource description information. In this way, the accuracy of the preset quota data can be improved to some extent, and the resource allocation effect can be improved.

Description

Technical Field

[0001] This application relates to the field of network technology, and in particular to a resource processing method, a cloud service instance processing method, an apparatus, an electronic device, and a machine-readable medium. Background Technology

[0002] Currently, user devices can request resources from the resource platform, which then provides those resources to the user devices. Since the total amount of resources is limited, to ensure that user devices from different users can enjoy resources as much as possible, resource quotas need to be set to limit the amount of resources that user devices can use, preventing individual user devices from consuming excessive resources.

[0003] In related technologies, multiple user equipment levels are often defined, and quotas are set for each user equipment level. Accordingly, when a user equipment requests resources, resources are allocated based on the quota of the user equipment level to which the user equipment belongs.

[0004] However, the inventors discovered through research that the resource allocation method based on user device level quotas has poor allocation efficiency. Summary of the Invention

[0005] This application provides a resource processing method to solve the problem of poor allocation effect in related technologies.

[0006] Accordingly, embodiments of this application also provide a cloud service instance processing method, apparatus, electronic device, and machine-readable medium to ensure the implementation and application of the above method.

[0007] To address the aforementioned problems, this application discloses a resource processing method, including:

[0008] Receive a resource request sent by a target user equipment; the target user equipment is a user equipment in the resource platform;

[0009] In response to the resource request, the quota corresponding to the target resource description information of the target user equipment is found from the preset quota data to obtain the target quota; the target resource description information includes the resource description information requested by the target user equipment this time, and the preset quota data includes the quota corresponding to different resource description information predicted for user equipment in the resource platform;

[0010] Based on the target quota, target resources in the resource platform are allocated to the target user equipment; the target resources include the resources described by the target resource description information.

[0011] This application also discloses a resource processing apparatus, including:

[0012] The first receiving module is used to receive resource request requests sent by the target user equipment; the target user equipment belongs to the user equipment in the resource platform.

[0013] The first search module is used to respond to the resource request by searching the preset quota data for the quota corresponding to the target resource description information of the target user equipment, and obtaining the target quota; the target resource description information includes the resource description information requested by the target user equipment in this application, and the preset quota data includes the quota corresponding to different resource description information predicted for user equipment in the resource platform.

[0014] The first allocation module is used to allocate target resources in the resource platform to the target user equipment according to the target quota; the target resources include the resources described by the target resource description information.

[0015] This application also discloses a cloud service instance processing method, including:

[0016] Receive an instance creation request sent by the target user device; the target user device is a user device in the cloud service platform;

[0017] In response to the instance creation request, the quota corresponding to the target instance description information of the target user device is found from the preset quota data to obtain the target quota; the target instance description information includes the instance description information applied for by the target user device in this application, and the preset quota data includes the quota corresponding to different instance description information predicted for user devices in the cloud service platform.

[0018] Based on the target quota, a target cloud service instance in the cloud service platform is allocated to the target user device; the target cloud service instance includes the instance described by the target instance description information.

[0019] This application also discloses a cloud service instance processing device, including:

[0020] The second receiving module is used to receive an instance creation request sent by the target user equipment; the target user equipment belongs to the user equipment in the cloud service platform.

[0021] The second search module is used to respond to the instance creation request by searching the preset quota data for the quota corresponding to the target instance description information of the target user device to obtain the target quota; the target instance description information includes the instance description information applied for by the target user device this time, and the preset quota data includes the quota corresponding to different instance description information predicted for user devices in the cloud service platform.

[0022] The second allocation module is used to allocate a target cloud service instance from the cloud service platform to the target user equipment according to the target quota; the target cloud service instance includes the instance described by the target instance description information.

[0023] This application also discloses an electronic device, including: a processor; and a memory storing executable code thereon, which, when executed, causes the processor to perform one or more methods as described in this application.

[0024] This application also discloses one or more machine-readable media storing executable code thereon, which, when executed, causes a processor to perform one or more methods as described in this application.

[0025] Compared with related technologies, the embodiments of this application have the following advantages:

[0026] In this embodiment, a resource request request is received from a target user equipment (User Equipment), which is a User Equipment within a resource platform. In response to the resource request, the quota corresponding to the target resource description information for the target User Equipment is retrieved from the preset quota data to obtain the target quota. The target resource is the resource description information requested by the target User Equipment in this instance. The preset quota data includes quotas corresponding to different resource description information predicted for User Equipment within the resource platform. Based on the target quota, target resources from the resource platform are allocated to the target User Equipment. The target resources include the resources described by the target resource description information. Thus, compared to quotas set from the User Equipment level dimension, this embodiment uses the dimension of resource description information, setting quotas corresponding to different resource description information for the User Equipment itself as preset quota data. This approach can achieve higher accuracy in the preset quota data. Therefore, retrieving the target quota for the resource requested by the target User Equipment in this instance from the preset quota data and allocating resources to the target User Equipment based on the target quota can improve resource allocation efficiency to some extent. Attached Figure Description

[0027] Figure 1 This is a schematic diagram illustrating an application scenario according to an embodiment of this application;

[0028] Figure 2 This is a schematic diagram illustrating another application scenario of an embodiment of this application;

[0029] Figure 3 This is a schematic diagram illustrating another application scenario of an embodiment of this application;

[0030] Figure 4 This is a schematic diagram of an embodiment of this application;

[0031] Figure 5This is a flowchart illustrating the steps of a resource processing method according to an embodiment of this application;

[0032] Figure 6 This is a flowchart illustrating the steps of another resource processing method according to an embodiment of this application;

[0033] Figure 7 This is a schematic diagram illustrating the generation of personalized quota data according to an embodiment of this application;

[0034] Figure 8 This is a clustering diagram of an embodiment of this application;

[0035] Figure 9 This is a schematic diagram illustrating an adjustment of an embodiment of this application;

[0036] Figure 10 This is a schematic diagram illustrating a search method according to an embodiment of this application;

[0037] Figure 11 This is another architectural schematic diagram of an embodiment of this application;

[0038] Figure 12 This is a flowchart illustrating the steps of a cloud service instance processing method according to an embodiment of this application;

[0039] Figure 13 This is a structural diagram of a resource processing apparatus according to an embodiment of this application;

[0040] Figure 14 This is a structural diagram of a cloud service instance processing device according to an embodiment of this application;

[0041] Figure 15 This is a schematic diagram of the structure of a device provided in an embodiment of this application. Detailed Implementation

[0042] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] To enable those skilled in the art to better understand this application, the concepts involved in this application are explained below:

[0044] Resource platform: refers to a platform used to create resources for user devices, such as cloud computing platform, cloud service platform, etc.

[0045] Resource request: A request initiated by a user on the client to obtain computing resources from the resource pool to build an instance, so that the instance can meet its own computing needs.

[0046] Instance: In cloud computing scenarios, an instance is a carrier reflecting a user's computing power. Users can apply to build and use instances of corresponding specifications to meet their computing needs. Instance creation is based on an instance, which includes the specifications of basic computing components such as the virtual central processing unit (vCPU), memory, operating system, network, and disk. This instance is used to create virtual machines (VMs), and the cloud server can create VMs based on this instance.

[0047] Quota: Used to constrain the amount of resources a user can use. Quota can be set in units of resource measurement. For example, for instances measured in numbers, quota can also be called elastic quota. Quota represents the maximum number of instances that can be used. For memory resources measured in gigabytes (GB), quota represents the maximum number of GB that can be used.

[0048] User equipment refers to the user equipment used by users of the resource platform. User equipment in the resource platform can be user equipment that has been registered on the resource platform and has obtained resource platform certification.

[0049] Target user equipment: refers to the user equipment that needs to apply for resources from the resource platform this time. The target user equipment belongs to the user equipment in the resource platform.

[0050] Resource description information: Used to describe the availability zone where the resource is located and / or the specifications of the resource.

[0051] Availability Zones: A resource platform can cover multiple regions. Within a region, there are independent physical areas, and each physical area represents an availability zone. When users apply for resources, they can select an availability zone. Accordingly, the resources allocated to users are located within the selected availability zone. For example, servers within an availability zone act as host machines, and instances can be created for users based on host machines within the selected availability zone. Because the power and network infrastructure between availability zones are independent, availability zones can serve as disaster recovery units for cloud resources, used for fault isolation.

[0052] Flavor: A fundamental attribute of a resource. Flavor can specifically include resource model information, such as shared standard type, shared computing type, etc. Different flavors correspond to different Central Processing Unit (CPU) models, memory, bandwidth, and other performance parameters. It should be noted that, in one implementation, the resource platform in this embodiment can be considered as a server plus a resource pool. The resource pool contains multiple host devices. In response to a user device's resource request, the server allocates hardware resources from the host devices in the resource pool according to the customer's required flavor to create a virtual machine of the corresponding flavor for the customer's use.

[0053] Quota data includes quotas corresponding to different resource descriptions for different user devices within the resource platform. The quota data stores relevant user device information, resource descriptions, and corresponding quotas. For example, user device information may include the user identification number (UID). The UID and resource description are used as keys, and the corresponding quota is used as a value, stored as key-value pairs. Different user device UIDs can form various combinations with availability zone and specification information, with each combination corresponding to one resource description. One key represents one combination, and the default quota data includes the quotas for each combination. For the same user device, quotas corresponding to different resource descriptions may differ. For example, the quotas corresponding to user device 1, availability zone 1, and specification 1 may differ from those corresponding to user device 1, availability zone 1, and specification 2. For different user devices, quotas corresponding to the same resource description may differ. For example, the quotas corresponding to user device 1, availability zone 1, and specification 1 may differ from those corresponding to user device 2, availability zone 1, and specification 1. Of course, it is also possible that some different resource descriptions of the same user device correspond to the same quota, and some different user devices correspond to the same resource descriptions with the same quota.

[0054] The application scenarios involved in this application are described below.

[0055] Reference Figure 1This illustration shows an application scenario provided by an embodiment of this application. The application scenario may include a target user device 110 and a resource processing terminal 120. The resource processing terminal 120 may be a resource platform, specifically an executor within the resource platform. The target user device 110 is a user device within the resource platform. In this application scenario, the target user device 110 can send a resource request to the resource processing terminal 120. Upon receiving the resource request, the resource processing terminal 120, in response to the request, searches for the quota corresponding to the target resource description information of the target user device 110 from preset quota data to obtain the target quota. The target resource description information refers to the resource description information requested by the target user device 110 in this application. The preset quota data includes quotas corresponding to different resource description information predicted for user devices in the resource platform. The preset quota data can be stored locally on the resource processing terminal 120 or externally. Then, the target resource in the resource platform is allocated to the target user device 110 according to the found target quota. The target resource refers to the resource described by the target resource description information.

[0056] For example, assuming the target resource requested by the target user device 110 is a resource of specification 1 in availability zone 1, the resource processing terminal 120 can find the quota corresponding to the combination of the target user device 110's UID, availability zone 1, and specification 1 from the preset quota data, and use it as the target quota. Compared to the quota set from the user device level dimension, this embodiment uses the quota corresponding to different resource description information specifically set for the user device as the preset quota data from the resource description information dimension. Since the dimensions of user device and resource description information are more refined than the dimension of user device level, the precision of the preset quota data is higher to a certain extent. Accordingly, finding the target quota of the resource requested by the target user device 110 from the preset quota data and allocating resources to the target user device 110 based on the target quota can improve the resource allocation effect to a certain extent.

[0057] Taking a resource-based cloud service scenario as an example, refer to Figure 2This illustration shows another application scenario provided by the embodiments of this application. In this scenario, the resource processing terminal 220, the preset quota library 230, and the resource pool 240 can all belong to a resource platform, which can be a cloud service platform. Instances in the resource pool 240 can be generated by different host machines. The target user device 210 can send a resource request to the resource processing terminal 220. Correspondingly, in response to the resource request, the resource processing terminal 220 will search for the quota corresponding to the target resource description information of the target user device from the preset quota data of the preset quota library 230 to obtain the target quota. Then, according to the target quota, the target instance in the resource pool 240 will be allocated to the target user device 210 for use. The specifications and availability zone of the target instance are consistent with the specifications and availability zone of the resource requested by the target user device 210.

[0058] Furthermore, referring to Figure 3 This illustration shows another application scenario provided by an embodiment of this application. In this scenario, the resource processing terminal 320, in response to a resource request sent by the target user device 310, searches for a target quota in the preset quota data of the preset quota library 330, and then allocates a target instance from the resource pool 340 to the target user device 310 according to the target quota. In this scenario, the target instance requested by the target user device 310 can be specifically applied to an order processing scenario. Specifically, the target instance requested by the target user device 310 can provide order processing services to the target user device 310 and execute order processing tasks. These order processing tasks may include tasks such as generating orders based on received information, dispatching generated orders or orders submitted to the target instance, etc.

[0059] It should be noted that the target instance allocated to the target user device 310 may include pre-created instances. If the number of pre-created instances is less than the number of computing instances that need to be allocated to the target user device 310, instances can continue to be created for the target user device 310.

[0060] In this embodiment, the quotas corresponding to different resource descriptions for user devices in the resource platform at different times can be estimated in advance, and the overall quotas corresponding to different resource descriptions in different times can be calculated. Based on the overall quota, resources described by different resource descriptions are created in advance in resource pool 340. For example, during the period from 5 PM to 8 PM, if the quota for instance of user device 1, availability zone 1, and specification 1 is 100, the quota for instance of user device 2, availability zone 1, and specification 1 is 200, and the quota for instance of user device 3, availability zone 1, and specification 1 is 100, then the overall quota for the period from 5 PM to 8 PM can be calculated to be 400. The overall quota represents the maximum supply of resources for availability zone 1 and specification 1 during this period. Accordingly, for any resource description, the product of the overall quota corresponding to that resource description in that time period and the preset ratio of that resource description can be calculated to obtain the inventory of resources described by that resource description that need to be created in advance, and then created accordingly. The preset ratio can be set in advance according to actual needs. The preset ratios for different resource description information can be the same or different.

[0061] Furthermore, referring to Figure 4 This illustration shows an architecture diagram provided by an embodiment of this application. In this architecture, a quota generator in the resource platform can generate preset quota data and synchronize it to a quota database. A quota executor can find the target quota based on the preset quota data in the quota database, thereby allocating target resources to the target user equipment. Furthermore, a quota adjuster can adjust the quota in the preset quota data in response to an adjustment command when quota adjustment is needed. This allows users to adjust quotas that do not meet their needs, thereby ensuring the accuracy of the set quotas.

[0062] It should be noted that the process of obtaining the size and identifier of the target file, the address information of the memory segment, the file storage parameters, and other information, signals, or data used in the embodiments of this application is carried out in accordance with the relevant data protection laws and regulations of the country where the location is located, and with the authorization of the owner of the corresponding device.

[0063] The resource processing methods involved in this application are described in detail below.

[0064] Reference Figure 5 The diagram illustrates a flowchart of a resource processing method provided in an embodiment of this application, which may include:

[0065] Step 101: Receive the resource request sent by the target user equipment; the target user equipment belongs to the user equipment in the resource platform.

[0066] The resource request may include user-related information of the target user device, such as the target user device's UID, and resource description information including the resources required by the target user device.

[0067] Step 102: In response to the resource request, find the quota corresponding to the target resource description information of the target user equipment from the preset quota data to obtain the target quota; the target resource description information is the resource description information requested by the target user equipment this time, and the preset quota data includes the quota corresponding to different resource description information predicted for user equipment in the resource platform.

[0068] In this embodiment, since there are multiple resource description information, for a user device in the resource platform, the preset quota data can include multiple quotas corresponding to the various resource description information of that user device. For example, the resource description information can represent a combination of availability zones and specifications. Assuming there are 3 availability zones and 3 specifications, then the availability zones and specifications can form 9 combinations, and a user device can correspond to 9 quotas corresponding to each of these 9 combinations. The preset quota data can specifically include quotas corresponding to at least some user devices in the resource platform for different combinations. It is understood that the more user devices that pre-generate quota data, the wider the coverage and the more comprehensive the preset quota data. This can minimize the problem of not being able to find the target quota, leading to reduced resource allocation efficiency, and at the same time, it can meet the needs of user devices as much as possible, avoiding frequent quota requests from users.

[0069] When searching for a target quota, the UID and resource description information carried in the resource request are compared with the UID and resource description information corresponding to the quota in the preset quota data, and the quota that matches is taken as the target quota.

[0070] Step 103: Based on the target quota, allocate target resources in the resource platform to the target user equipment. The target resources include the resources described in the target resource description information.

[0071] In this embodiment of the application, the resource request may also include the amount of resources requested by the target user equipment in this instance. Accordingly, target resources in the resource platform can be allocated to the target user equipment based on the amount of resources requested and the target quota. The amount of the allocated target resources may not exceed the amount of resources requested and may not exceed the target quota.

[0072] If the requested resource amount exceeds the target quota, the resource request can be discarded and a notification can be sent to the target user device.

[0073] In summary, the resource processing method provided in this application embodiment receives a resource request sent by a target user equipment; the target user equipment belongs to the user equipment in the resource platform; in response to the resource request, it searches for the quota corresponding to the target resource description information of the target user equipment from the preset quota data to obtain the target quota; the target resource is the resource description information requested by the target user equipment this time, and the preset quota data includes the quota corresponding to different resource description information predicted for user equipment in the resource platform; according to the target quota, it allocates the target resource in the resource platform to the target user equipment, and the target resource includes the resource described by the target resource description information. Thus, compared to the quota set from the user equipment level dimension, this application embodiment uses the quota corresponding to different resource description information set for the user equipment itself as the preset quota data from the resource description information dimension, which can obtain higher accuracy of the preset quota data to a certain extent. Therefore, searching for the target quota of the resource requested by the target user equipment this time from the preset quota data and allocating resources to the target user equipment based on the target quota can improve the resource allocation effect to a certain extent.

[0074] Reference Figure 6 It illustrates a flowchart of another resource processing method provided in an embodiment of this application, which may include:

[0075] Step 201: Receive the resource request sent by the target user equipment; the target user equipment belongs to the user equipment in the resource platform.

[0076] Step 202: In response to the resource request, find the quota corresponding to the target resource description information of the target user equipment from the preset quota data to obtain the target quota; the target resource description information includes the resource description information requested by the target user equipment this time, and the preset quota data includes the quota corresponding to different resource description information predicted for user equipment in the resource platform.

[0077] Optionally, in this embodiment of the application, the aforementioned preset quota data can be obtained through the following steps:

[0078] Step 301: For each user device in the resource platform, input the user-related information and different resource description information of the user device into the preset quota prediction model. Based on the quota prediction model, predict the quota corresponding to the different resource description information of the user device to generate the quota data of the user device.

[0079] The quota prediction model can be a pre-trained model. The input of the quota prediction model can include user-related information and resource description information of the user equipment. The output of the quota prediction model can include the quota predicted for the user equipment corresponding to the input resource description information.

[0080] In this embodiment, the resource description information and the amount of resources requested by a user device can be determined in advance based on the user device's historical application records and currently held resource information in the resource platform. The user device's relevant information, historical resource description information, and historical resource amounts are used as training data to train a quota prediction model using machine learning. For example, user-related information and historical resource description information can be used as input to the model to be trained, and the historical resource amounts can be used as label values. The loss value of the model to be trained is calculated based on the output of the model and the label values. For example, the model to be trained can be a neural network model set according to actual needs. Then, the parameters of the model to be trained are optimized based on the loss value. After optimization, the above training process is repeated until the model to be trained meets a preset convergence condition. Accordingly, the converged model to be trained is used as the quota prediction model. The preset convergence condition can be set according to actual needs; for example, the preset convergence condition may include a loss value less than a preset loss value threshold or a training epoch number greater than a preset epoch number threshold. It should be noted that, in this embodiment of the application, the resource description information of the historical resources applied for by the user equipment and the amount of resources applied for are data generated within the historical period. In this embodiment of the application, a quota prediction model can be trained to predict the amount of resources required by the user equipment in the future period, which can be used as the quota.

[0081] Accordingly, when using the quota prediction model, for any resource description information, the user-related information of the user device and the resource description information can be used as inputs. The quota prediction model is then used to predict the future quota value required by the user device for the resource described by that resource description information. Specifically, the output of the quota prediction model can be obtained to get the quota for that user device for that resource description information. Here, the relevant information of a user device, a resource description information, and its corresponding quota can constitute a quota data entry. The user-related information can represent user-dimensional information. For example, the user-related information can include the aforementioned UID, or it can also include the user resource usage profile of the user device. The user resource usage profile can represent the user's resource usage habits. For example, some user devices are accustomed to using long-term usage methods such as annual or monthly subscriptions, while others are accustomed to using short-term usage methods.

[0082] Step 302: Cluster the quota data of user equipment that belong to the same category.

[0083] In this step, quota data belonging to the same category can be quota data whose similarity meets preset requirements. The criteria for classifying quota data into the same category can be set according to actual needs. For example, quota data with consistent resource description information and quota deviations within a preset deviation range can be considered as quota data belonging to the same category. Quota data belonging to the same category are then aggregated.

[0084] Because the number of user devices in a resource platform is often large, the amount of quota data obtained for these user devices is substantial. Directly using this quota data for resource allocation would lead to significant difficulties in storing and using the quota data. Therefore, in this embodiment, quota data belonging to the same category can be clustered to reduce the data volume.

[0085] Step 303: Generate preset quota data based on the quota data of each user device in the clustered resource platform.

[0086] Specifically, the quota data of each user device in the clustered resource platform can be directly determined as the preset quota data. It should be noted that steps 301-303 above, which obtain the preset quota data, can be executed by the quota generator.

[0087] In this embodiment, the quotas corresponding to different resource descriptions of user devices in the resource platform are first predicted based on a quota prediction model to generate quota data for the user devices. Next, quota data belonging to the same category within the user device quota data are clustered. Based on the clustered quota data of each user device in the resource platform, preset quota data is generated. This reduces the amount of preset quota data generated to some extent, thereby reducing storage costs and complexity. Simultaneously, it improves the query performance of the preset quota data and reduces the difficulty of using it.

[0088] Optionally, in this embodiment of the application, the quota data of the user equipment may include first quota data. Step 301 above may specifically include:

[0089] Sub-step 3011: Input the user-related information and first resource description information of the user equipment into the quota prediction model, and predict the first quota corresponding to the first resource description information of the user equipment based on the quota prediction model; the first resource description information includes the description information of resources applied for by the user equipment in the past time.

[0090] Sub-step 3012: Generate first quota data based on user-related information, first resource description information, and first quota.

[0091] Regarding sub-steps 3011-3012 above, the first resource description information can refer to the combination of availability zones and specifications that the user equipment has applied for. This first resource description information can be obtained from the user equipment's historical application records. Since the training data for the quota prediction model can only contain the amount of resources applied for by the user equipment for previously applied combinations, in this embodiment, the quota prediction model can be used to predict the first resource description information of the user equipment's historical resources, thereby ensuring the accuracy of the quota predicted by the quota prediction model to a certain extent. Of course, the quota prediction model can also be used to predict the quota corresponding to other available resource description information for the user equipment; this embodiment does not limit this.

[0092] Furthermore, user-related information, a first resource description, and the corresponding first quota can be combined into a single first quota data entry. Since user devices often request multiple combinations, multiple first quota data entries can be obtained for a single user device. The set of first quota data entries can serve as personalized quota data; for example, personalized quota data can be recorded as quota(user_v1). For example, refer to... Figure 7 This illustration shows a schematic diagram of personalized quota data generation provided by an embodiment of this application. Based on a quota prediction model, the demand of user devices is predicted according to the input user-related information and first resource description information to obtain the predicted quantity. Personalized quota data is then generated. The first resource description information can be determined based on the resources held by the user device. Personalized quota data may include resource dimensions and predicted quantities. The resource dimensions include user-related information dimension: User, availability zone dimension: AZ, and specification dimension: Flavor. The predicted quantity represents the quota.

[0093] In this embodiment, by inputting user-related information of the user equipment and the first resource description information of historical resources requested by the user equipment into the quota prediction model, the first quota corresponding to the first resource description information of the user equipment can be predicted based on the quota prediction model. Then, first quota data is generated based on the user-related information, the first resource description information, and the first quota. In this way, the quota prediction model can conveniently and accurately predict the first quota corresponding to the first resource description information requested by the user equipment, thereby improving the efficiency of generating first quota data.

[0094] Optionally, in this embodiment of the application, the quota data of the user equipment further includes second quota data. Accordingly, the operation of generating the quota data of the user equipment may further include the following steps:

[0095] Sub-step 3013: Based on the historical request volume of the user equipment for the resource described by the second resource description information, predict the second quota corresponding to the second resource description information of the user equipment.

[0096] Sub-step 3014: Generate second quota data based on user-related information, second resource description information, and second quota; the resources described in the second resource description information include resources that the user device has not applied for.

[0097] Regarding sub-steps 3013-3014 above, the second resource description information can refer to a combination of availability zones and specifications that the user equipment has not applied for. Resource description information other than the first resource description information can be considered as the second resource description information. User equipment may contain resource description information that has never been applied for. For example, for a user equipment of a newly registered user on the resource platform, all resource description information can be considered second resource description information. For a user equipment of an existing user on the resource platform, resource description information that the user has not applied for is considered second resource description information. For example, resource description information consisting of newly launched availability zones and newly launched specifications can be considered as second resource description information that has not been applied for.

[0098] In this embodiment, the quota corresponding to the second resource description information can be predicted based on the historical application volume of the reference user equipment for the resource described by the second resource description information, thus obtaining the second quota. Specifically, for any second resource description information, the reference user equipment can be a user equipment in the resource platform that has previously applied for the resource described by the second resource description information, and the historical application volume can refer to the application volume of the reference user equipment for the resource described by the second resource description information in the past. Alternatively, the reference user equipment can also be a user equipment that has previously applied for the resource described by the second resource description information and whose similarity to the user equipment is greater than a preset similarity threshold; this embodiment does not impose any restrictions on this.

[0099] The initial request volume of reference user equipment for the resource described in the second resource description information can be statistically analyzed, and the second quota can be determined based on the initial request volume. For example, the average of the statistically analyzed initial request volumes can be calculated as the second quota corresponding to the second resource description information, or the largest initial request volume can be used as the second quota corresponding to the second resource description information. Alternatively, the magnitude corresponding to the initial request volume of the reference user equipment can be determined, and the second quota corresponding to the second resource description information can be determined based on the magnitude. For example, assuming that user equipment A has not requested a resource located in availability zone 1 with specification X, and the initial request volume of reference user equipment for the resource located in availability zone 1 with specification X is less than 10, then a value within 10 can be generated for user equipment A as the second quota corresponding to availability zone 1 with specification X.

[0100] It should be noted that, in this embodiment, other methods can also be used to generate the second quota, such as receiving a manually input quota value as the second quota. Alternatively, it can be generated through a preset flexible quota rule engine; this embodiment does not limit this approach.

[0101] Furthermore, user-related information, a second resource description, and the corresponding second quota can be combined into a single second quota data entry. The set of second quota data can serve as baseline quota data; for example, the baseline quota data can be recorded as quota(baseline).

[0102] In this embodiment, based on the historical request volume of the user equipment for the resource described by the second resource description information that the user equipment has not requested before, the second quota corresponding to the second resource description information of the user equipment is predicted, and second quota data is generated based on the second quota. This ensures the comprehensiveness of the quota data and avoids the problem of no quota when the user equipment subsequently requests the resource described by the second resource description information. At the same time, predicting the second quota based on the actual historical request volume of the reference user equipment for the resource described by the second resource description information can, to some extent, ensure the accuracy of the second quota data.

[0103] Optionally, the above-mentioned clustering operation of quota data belonging to the same category in the quota data of user equipment may specifically include:

[0104] Sub-step 3021: Based on the deviation of the first quota of each first quota data and the similarity of the first resource description information of each first quota data, determine the first quota data belonging to the same category; wherein, the deviation of the first quota of the first quota data belonging to the same category is within a preset deviation range, and the similarity of the first resource description information of the first quota data belonging to the same category is greater than a first similarity threshold.

[0105] Sub-step 3022: Aggregate the first quota data belonging to the same category to obtain the third quota data.

[0106] Regarding sub-steps 3021-3022, since the volume of the first quota data is often large and the volume of the second quota data is often small in actual application scenarios, this embodiment can specifically cluster the first quota data in the quota data of the user equipment, thereby reducing the processing volume of the clustering operation to a certain extent. Of course, it is also possible to cluster both the first and second quota data, and this embodiment does not limit this. Specifically, the implementation method of clustering the quota data of the entire user equipment is the same as the implementation method of clustering the first quota data, and will not be repeated here.

[0107] Specifically, the preset deviation range can be set according to actual needs; for example, the preset deviation range can be 0-5%. If the deviation of the first quota is within the preset deviation range, it can be determined that the first quotas in the first quota data are relatively similar. First quota data that are identical in all dimensions except the user dimension and whose first quota deviation is within the preset deviation range can be defined as data with high similarity, that is, quota data whose similarity meets the preset requirements. Therefore, first quota data among all first quota data whose first resource description information similarity is greater than the first similarity threshold and whose first quota deviation is within the preset deviation range can be determined as first quota data belonging to the same category.

[0108] In this embodiment, the similarity between various first resource description information can be calculated, for example, using Euclidean distance, or other methods can be used. Optionally, first quota data with identical first resource description information and whose first quota deviation is within a preset deviation range can be identified as first quota data belonging to the same category.

[0109] Furthermore, multiple first resource descriptions belonging to the same category can be aggregated into a single first resource description to obtain third quota data. This third quota data can also be called category quota data, which can be recorded as `quota(category_v1)`. Since the amount of first resource description information that needs to be stored is compressed, the storage dimension can be reduced, thereby reducing storage volume. Optionally, the first quotas in multiple first quota data belonging to the same category can be normalized to the average of the first quotas in that category. In this way, for multiple first quota data belonging to that category, only multiple user device-related data, one first resource description, and one first quota need to be stored, further reducing storage volume.

[0110] It should be noted that the first quota data may contain quota data that cannot be aggregated. Therefore, after clustering the first quota data to obtain the third quota data, the quota data other than the third quota data in the first quota data can be used as the fourth quota data. The fourth quota data can represent the remaining personalized quota data and can be recorded as quota(user_v2). For example, referring to 8, a clustering diagram provided by an embodiment of this application is shown. After clustering, category quota data quota(category_v1) and the remaining personalized quota data quota(user_v2) can be obtained. The category in the category quota data can represent a class. An aggregated first resource description information in the category quota data can be used as a class, and the first resource description information, the corresponding class identifier, and the corresponding quota are stored accordingly. The class identifier can correspond to user-related information of user devices in the first quota data belonging to that class; this embodiment of the application does not limit this.

[0111] Of course, if there is no quota data that cannot be aggregated in the first quota data, the first quota data is completely converted into the third quota data. In this case, there is no fourth quota data.

[0112] Optionally, in this embodiment of the application, after obtaining the third quota data, the following steps may also be performed:

[0113] Sub-step 401: Determine the second quota corresponding to the second resource description information that is consistent with the resource description information in the third quota data, and use it as the reference quota.

[0114] Sub-step 402: Adjust the quota to be corrected in the third quota data according to the reference quota; the similarity between the resource description information corresponding to the quota to be corrected and the second resource description information corresponding to the reference quota is greater than the second similarity threshold.

[0115] Regarding sub-steps 401-402 above, the third quota data is obtained through classification and aggregation. The amount of third quota data is relatively small. In this embodiment, the quota in the third quota data is corrected only based on the reference quota in the second quota data, which reduces the processing load of the correction operation to some extent. Of course, the entire first quota data can also be corrected directly. For example, the same correction method can be used to further correct the fourth quota data to ensure the comprehensiveness of the correction operation. This embodiment does not limit this approach. The baseline quota data can be recorded as quota(baseline). For example, referring to... Figure 9This illustration shows an adjustment diagram provided by an embodiment of this application. After adjusting the category quota data quota(category_v1) based on the baseline quota data quota(baseline), the adjusted third quota data, category quota data quota(category_v2), can be obtained. It should be noted that for a given second resource description information, the reference user equipment that has applied for that second resource description information is fixed. Therefore, the second quota corresponding to the same second resource description information for different user equipment can be the same. Accordingly, the second quota data can be aggregated to obtain category data. Alternatively, a second resource description information can be directly used as a type of baseline quota, and the second resource description information, the corresponding category identifier, and the corresponding second quota can be stored accordingly to save storage costs and reduce storage difficulty. The Baseline in the baseline quota data can represent a category identifier, which can correspond to user-related information of user equipment that has not applied for that second resource description information, or it can not correspond; this embodiment of the application does not impose any restrictions on this. In the scenario where resources are instances, quota(category_v2) and quota(user_v2) in this application embodiment can represent the quota corresponding to an instance that has existed, and quota(baseline) can represent the quota corresponding to an instance that has not existed.

[0116] In this embodiment, the similarity between the resource description information corresponding to the quota to be corrected and the second resource description information corresponding to the reference quota can be calculated, and the above adjustment process is performed on the quota to be corrected if the similarity is greater than the second similarity threshold. Optionally, the resource description information corresponding to the quota to be corrected and the second resource description information corresponding to the reference quota can be the same.

[0117] Specifically, the intersection of the resource description information in the third quota data and the resource description information in the second quota data can be taken. The quota corresponding to the resource description information in the third quota data that falls into this intersection is taken as the quota to be corrected, and the quota corresponding to the resource description information in the second quota data that falls into this intersection is taken as the reference quota. The corresponding quota to be corrected is then adjusted based on the reference quota. Since in real-world applications, new users often have resource description information that they have not applied for before, the quotas in the second quota data often include quotas generated for new users.

[0118] In this embodiment, based on the second quota corresponding to the second resource description information in the second quota data and the resource description information in the third quota data with a similarity greater than a second similarity threshold, the corresponding quota in the third quota data is adjusted. This avoids the problem of large differences in quotas for the same resource description information between new users and old users. In one implementation, the quota to be corrected can be the maximum value (max) of the two. Specifically, if the reference quota is greater than the quota to be corrected, the quota to be corrected is adjusted to the reference quota. This avoids the quota for new users exceeding the quota for old users with the same resource description information, thereby ensuring the rationality of the set quotas.

[0119] Optionally, when searching for the quota corresponding to the target resource description information of the target user equipment from the preset quota data, the fourth quota data, the third quota data, and the second quota data can be used as the quota data to be queried in a preset order, and the following operations can be performed on the quota data to be queried:

[0120] Sub-step 2021: Find the quota corresponding to the target resource description information of the target user device from the quota data to be queried.

[0121] Sub-step 2022: If the search is successful, the found quota will be set as the target quota.

[0122] Sub-step 2023: If the search is unsuccessful, continue searching for the next quota data to be queried until the target quota is found; wherein, the fourth quota data includes the quota data in the first quota data excluding the third quota data.

[0123] For the sub-steps 2021-2023 mentioned above, the preset order can be pre-set according to actual needs. For example, the preset order can be fourth quota data - third quota data - second quota data. Accordingly, the fourth quota data can be used as the quota data to be queried first. The quota with user-related information that matches the user-related information of the target user device and the resource description information that matches the target resource description information can be found in the fourth quota data. If found, the search is considered successful, and the found quota is determined as the target quota, ending the search process. Otherwise, the search can be performed from the next quota data to be queried: the third quota data. Specifically, the quota with user-related information that matches the user-related information of the target user device and the resource description information that matches the target resource description information can be found from the third quota data. If found, the search is considered successful. If the search is successful in the third quota data, the found quota is determined as the target quota, ending the search process. Otherwise, the search can continue from the next quota data to be queried: the second quota data. Specifically, the system searches for quotas in the second quota data that match the user information of the target user device and whose resource description information matches the target resource description information. If found, the search is considered successful. Alternatively, in one implementation, since different user devices may have the same second quota corresponding to the same second resource description information, the system can also search for quotas in the second quota data whose second resource description information matches the target resource description information. If found, the search is considered successful. Accordingly, if the search is successful in the second quota data, the found quota is designated as the target quota, and the search process ends. Otherwise, it is determined that no valid quota exists, and the search process ends. In this embodiment, by searching for the target quota one by one from the fourth quota data, the third quota data, and the second quota data, the scope of each query can be narrowed, thereby improving query efficiency to a certain extent.

[0124] In this embodiment of the application, the quota executor can perform the lookup based on the elastic quota query and verification engine. For example, refer to... Figure 10This illustration shows a search diagram provided by an embodiment of this application. It first queries personalized quota data, and if a target quota is found in the personalized quota data, the target quota is returned. Otherwise, it further queries categorized quota data, and if a target quota is found in the categorized quota data, the target quota is returned. Otherwise, it further queries baseline quota data, and if a target quota is found in the baseline quota data, the target quota is returned. Otherwise, a failure message is returned. In this embodiment, by prioritizing the search of personalized quota data, then searching categorized quota data if no target quota is found in the personalized quota data, and finally searching baseline quota data if no target quota is found in the categorized quota data, the quota specifically generated for the user device can be returned first, thereby improving resource allocation efficiency. Furthermore, using categorized quota data as the second priority for searching ensures query efficiency to a certain extent due to the higher query performance of categorized quota data.

[0125] Step 203: If the target resource quantity corresponding to the target user equipment does not exceed the target quota, allocate the corresponding amount of target resources to the target user equipment according to the resource quantity applied for by the target user equipment this time; the target resource quantity includes the sum of the target resources already occupied by the target user equipment and the resource quantity applied for this time.

[0126] Step 204: If the target resource quantity exceeds the target quota, discard the resource request and return a prompt message indicating insufficient quota to the target user equipment.

[0127] Regarding steps 203-204 above, in this embodiment, the amount of target resources already occupied by the target user equipment refers to the amount of resources described in the target resource description information currently allocated to the target user equipment. This amount can be the amount of resources the target user equipment holds in the target resource description information. First, the amount of resources currently occupied by the target user equipment can be obtained, and the sum of the occupied target resources and the requested resources can be calculated as the target resource amount corresponding to the target user equipment. If the target resource amount does not exceed the target quota, the resource request can be responded to normally, and the requested target resources can be allocated to the target user equipment. Conversely, if the target resource amount exceeds the target quota, risk control can be triggered, the target resources will not be allocated, the resource request will be discarded, and a prompt message will be returned to the target user equipment to indicate that the quota is insufficient and allocation is not possible. Alternatively, in one implementation, if the requested resource amount exceeds the target quota, the target user equipment can be allocated a target resource corresponding to the requested resource amount. If the resource request amount does not exceed the target quota, the resource request can be discarded, and a prompt message indicating insufficient quota can be returned to the target user equipment.

[0128] In this embodiment, a quota is pre-predicted for the user device. If the target resource amount exceeds the target quota, it indicates that the user device may be experiencing an anomaly, such as account theft leading to abnormal behavior. Accordingly, by directly returning a prompt indicating insufficient quota to the target user device when the target quota is exceeded, the problem of allocating unnecessary target resources to the target user device can be avoided, thereby saving resource costs. Optionally, in this embodiment, the resource description information may include availability zone information and / or specification information. The resources described by the resource description information may include resources whose specifications match the specification information, and / or resources located in the availability zone indicated by the availability zone information. Compared to setting quotas based on user device level, this embodiment, by increasing the precision of the quota to the granularity of user device, availability zone, and specification, can significantly improve the accuracy of the quota, thereby enhancing the flexibility of risk control.

[0129] Of course, exceeding the target quota for target resources can also be caused by changes in the needs of the target user devices, making the quota insufficient to meet those needs. Accordingly, returning a notification message indicating insufficient quota to the target user devices allows users to promptly request a quota increase. Simultaneously, the quotas corresponding to different resource descriptions can serve as reference information, enabling users to choose which resource description to request based on the available quotas.

[0130] It should be noted that in this embodiment, after generating quota data for the user device, the quota data can be returned to the user device for easy viewing and adjustment as needed. For example, if the quota is insufficient or excessive, the user device user can initiate a quota adjustment request through a preset console or application. After the quota adjustment request is approved by the background, a quota adjustment request can be initiated to the quota adjuster. Accordingly, in response to the quota adjustment request, the quota indicated in the quota adjustment request can be modified to the target value indicated in the quota adjustment request. Of course, the operator can also directly initiate quota adjustments, and the operator can also initiate quota rule adjustments to adjust the classification of quota data for specific user devices or adjust the quota of user devices, thereby avoiding unnecessary quota occupation or ensuring that user devices have sufficient quota. One quota data point can correspond to one quota rule.

[0131] In one implementation, adjustments for individual user devices will be directly synchronized to the personalized quota data corresponding to that user device. When a user device requests a quota adjustment or the operator initiates an adjustment, the custom quota may be smaller than the default quota generated by the quota generator for the user device. For example, if the generated quota data shows a quota of 100 for the user device, in response to the quota adjustment request, the quota of 100 may be adjusted to 80. Furthermore, the operation information for adjustments to categorized quota data or baseline quota data can be recorded in the elastic quota rule engine. Correspondingly, the elastic quota calculation engine in the quota generator can refer to the operation information recorded in the elastic quota rule engine. For example, it can use this operation information to optimize the quota prediction model used by the elastic quota calculation engine, thereby ensuring the consistency of the overall data logic.

[0132] For example, refer to Figure 11 This illustration shows another architectural diagram provided by an embodiment of this application. The quota executor may include an elastic quota query and verification engine, which can be used to query target quotas from the quota database. The quota generator may include a resource prediction engine and an elastic quota calculation engine. The resource prediction engine can call the elastic quota calculation engine to generate preset quota data and synchronize it to the quota database. For example, synchronization can be performed asynchronously. The quota adjuster may include an elastic quota rule engine and an elastic quota operation and maintenance engine. Operation information for adjusting categorized quota data or baseline quota data can be recorded in the elastic quota rule engine, and the elastic quota rule engine can synchronize with the elastic quota operation and maintenance engine in real time to adjust the corresponding quotas in the quota database through the elastic quota operation and maintenance engine. The elastic quota operation and maintenance engine can also respond to quota adjustments initiated by user devices or operators and adjust the corresponding quotas in the quota database accordingly.

[0133] It should be noted that, in this embodiment, the resource demand of the user equipment in different periods can also be determined based on the user equipment's resource usage behavior. The user equipment's quota can then be adjusted according to the resource demand in different periods. For example, if the user equipment's resource demand increases in a specific month, the user equipment's quota can be increased in that month to better meet the user equipment's needs, thereby reducing the quota adjustment operations required by the user equipment.

[0134] Optionally, the quota prediction model in this application embodiment can be optimized through the following steps:

[0135] Step 501: Generate model optimization information based on the operation information of quota adjustment operations in the preset quota data.

[0136] The adjustment operation may include the adjustment operation performed in response to a quota adjustment request sent by a user equipment.

[0137] Step 502: Optimize the quota prediction model based on the model optimization information.

[0138] Regarding steps 501-502 above, firstly, the recorded adjustment operation information is read. This adjustment operation information can be recorded after the adjustment operation is performed and may include the adjusted quota, as well as the corresponding user-related information and resource description information. The adjusted quota is used as the label value, and the corresponding user-related information and resource description information are used as the model input. The training pair consisting of the model input and the label value is used as model optimization information. Next, the quota prediction model can be trained again based on these training pairs to optimize the model parameters. For example, the optimization operation can end when the number of training rounds meets a preset requirement. Specifically, the training method can be referred to the aforementioned description and will not be repeated here.

[0139] In this embodiment, model optimization information is generated using the operation information of quota adjustment operations in the preset quota data. The quota prediction model is then optimized based on this optimization information. This further improves the accuracy of the quota prediction model, thereby enhancing the accuracy of the subsequently generated quotas.

[0140] Reference Figure 12 The document illustrates a flowchart of a cloud service instance processing method provided in an embodiment of this application. The method may include:

[0141] Step 601: Receive the instance creation request sent by the target user device; the target user device belongs to the user device in the cloud service platform.

[0142] The resource request may include user-related information of the target user device, such as the target user device's UID, and instance description information including the instance required by the target user device this time.

[0143] Step 602: In response to the instance creation request, find the quota corresponding to the target instance description information of the target user device from the preset quota data to obtain the target quota; the target instance description information includes the instance description information applied for by the target user device this time, and the preset quota data includes the quota corresponding to different instance description information predicted for user devices in the cloud service platform.

[0144] In this embodiment, the cloud service platform can be the aforementioned resource platform. The specific implementation of this step can be found in the foregoing description.

[0145] Step 603: Based on the target quota, allocate target cloud service instances in the cloud service platform to the target user devices; the target cloud service instances include the instances described in the target instance description information.

[0146] For example, a cloud service instance can be an elastic storage instance, an elastic scaling instance, an elastic service instance, etc. The specific implementation of this step can be found in the foregoing descriptions.

[0147] In summary, the cloud service instance processing method provided in this application receives an instance creation request sent by a target user device, which is a user device within a cloud service platform. In response to the instance creation request, the method searches for the quota corresponding to the target instance description information of the target user device from preset quota data to obtain the target quota. The target instance description information includes the instance description information requested by the target user device in this instance request, and the preset quota data includes the quotas corresponding to different instance description information predicted for user devices within the cloud service platform. Based on the target quota, a target cloud service instance from the cloud service platform is allocated to the target user device; the target cloud service instance includes the instance described by the target instance description information. Thus, compared to quotas set from the user device level dimension, this application uses the quotas corresponding to different instance description information set for the user device itself as preset quota data from the dimension of instance description information. This approach can achieve higher accuracy in the preset quota data. Therefore, searching for the target quota of the instance requested by the target user device in this instance request from the preset quota data and allocating cloud service instances to the target user device based on the target quota can improve the cloud service instance allocation effect to some extent.

[0148] Reference Figure 13 The diagram illustrates a structural diagram of a resource processing apparatus provided in an embodiment of this application. The apparatus may include:

[0149] The first receiving module 701 is used to receive resource request requests sent by the target user equipment; the target user equipment is a user equipment in the resource platform.

[0150] The first search module 702 is used to respond to a resource request by searching the preset quota data for the quota corresponding to the target resource description information of the target user equipment to obtain the target quota. The target resource description information includes the resource description information requested by the target user equipment in this application, and the preset quota data includes the quota corresponding to different resource description information predicted for user equipment in the resource platform.

[0151] The first allocation module 703 is used to allocate target resources in the resource platform to target user equipment according to the target quota; the target resources include the resources described by the target resource description information.

[0152] Optionally, the preset quota data is obtained through the following module:

[0153] The prediction module is used to input user-related information and different resource description information of each user device in the resource platform into a preset quota prediction model, and predict the quota corresponding to different resource description information of the user device based on the quota prediction model, so as to generate the quota data of the user device.

[0154] The clustering module is used to cluster quota data belonging to the same category in the quota data of user devices;

[0155] The first generation module is used to generate preset quota data based on the quota data of each user device in the clustered resource platform.

[0156] Optionally, the user equipment quota data includes first quota data; the prediction module is specifically used for:

[0157] The user-related information and first resource description information of the user equipment are input into the quota prediction model, and the first quota corresponding to the first resource description information of the user equipment is predicted based on the quota prediction model; the first resource description information includes the description information of the resources that the user equipment has applied for in the past time.

[0158] Generate the first quota data based on user-related information, first resource description information, and first quota.

[0159] Optionally, the user equipment quota data also includes second quota data; the prediction module is further specifically used for:

[0160] Based on the historical request volume of the user equipment for the resource described in the second resource description information, predict the second quota corresponding to the second resource description information of the user equipment;

[0161] The second quota data is generated based on user-related information, second resource description information, and second quota; the resources described in the second resource description information include resources that the user device has not applied for.

[0162] Optionally, the clustering module is specifically used for:

[0163] Based on the deviation of the first quota of each first quota data and the similarity of the first resource description information of each first quota data, first quota data belonging to the same category are determined; wherein, the deviation of the first quota of the first quota data belonging to the same category is within a preset deviation range, and the similarity of the first resource description information of the first quota data belonging to the same category is greater than a first similarity threshold.

[0164] The first quota data belonging to the same category are aggregated to obtain the third quota data.

[0165] Optionally, the device further includes:

[0166] The determination module is used to determine the second quota corresponding to the second resource description information that is consistent with the resource description information in the third quota data, and use it as the reference quota;

[0167] The adjustment module is used to adjust the quota to be corrected in the third quota data according to the reference quota; the similarity between the resource description information corresponding to the quota to be corrected and the second resource description information corresponding to the reference quota is greater than the second similarity threshold.

[0168] Optionally, the first allocation module 703 is specifically used for:

[0169] If the target resource quantity corresponding to the target user equipment does not exceed the target quota, the target resource quantity shall be allocated to the target user equipment according to the resource quantity requested by the target user equipment this time; the target resource quantity includes the sum of the target resource quantity already occupied by the target user equipment and the resource quantity requested this time; or

[0170] If the target resource quantity exceeds the target quota, discard the resource request and return a prompt message indicating insufficient quota to the target user equipment.

[0171] Optionally, the first lookup module 702 is specifically used for:

[0172] The fourth quota data, the third quota data, and the second quota data are selected as the quota data to be queried in a preset order, and the following operations are performed on the quota data to be queried:

[0173] Find the quota corresponding to the target resource description information of the target user device from the quota data to be queried;

[0174] If the search is successful, the found quota will be set as the target quota;

[0175] If the search is unsuccessful, continue searching for the next quota data to be queried until the target quota is found;

[0176] The fourth quota data includes the quota data in the first quota data excluding the third quota data.

[0177] Optionally, the resource description information includes availability zone information and / or specification information. The resources described in the resource description information include resources whose specifications match the specification information, and / or resources located in the availability zone indicated by the availability zone information.

[0178] Optionally, the quota prediction model can be optimized through the following modules:

[0179] The second generation module is used to generate model optimization information based on the operation information of quota adjustment operations in the preset quota data;

[0180] The optimization module is used to optimize the quota prediction model based on model optimization information.

[0181] In summary, the resource processing apparatus provided in this application embodiment receives a resource request sent by a target user equipment (User Equipment). The target User Equipment belongs to the User Equipment in the resource platform. In response to the resource request, it searches for the quota corresponding to the target resource description information of the target User Equipment from the preset quota data to obtain the target quota. The target resource is the resource description information requested by the target User Equipment in this application. The preset quota data includes the quotas corresponding to different resource description information predicted for the User Equipment in the resource platform. Based on the target quota, it allocates the target resource in the resource platform to the target User Equipment. The target resource includes the resource described by the target resource description information. Thus, compared to the quota set from the user equipment level dimension, this application embodiment uses the quotas corresponding to different resource description information set for the User Equipment itself as the preset quota data from the resource description information dimension. To a certain extent, the accuracy of the preset quota data can be improved. Therefore, searching for the target quota of the resource requested by the target User Equipment in this application from the preset quota data and allocating resources to the target User Equipment based on the target quota can improve the resource allocation effect to a certain extent.

[0182] Reference Figure 14 The diagram illustrates a structural diagram of a cloud service instance processing apparatus provided in an embodiment of this application. The apparatus may include:

[0183] The second receiving module 801 is used to receive an instance creation request sent by the target user equipment; the target user equipment belongs to the user equipment in the cloud service platform.

[0184] The second search module 802 is used to respond to the instance creation request by searching the preset quota data for the quota corresponding to the target instance description information of the target user device to obtain the target quota; the target instance description information includes the instance description information applied for by the target user device this time, and the preset quota data includes the quota corresponding to different instance description information predicted for user devices in the cloud service platform;

[0185] The second allocation module 803 is used to allocate target cloud service instances in the cloud service platform to target user devices according to the target quota; the target cloud service instances include the instances described by the target instance description information.

[0186] In summary, the cloud service instance processing apparatus provided in this application embodiment receives an instance creation request sent by a target user device, which is a user device within the cloud service platform. In response to the instance creation request, it searches for the quota corresponding to the target instance description information of the target user device from the preset quota data to obtain the target quota. The target instance description information includes the instance description information requested by the target user device in this instance request, and the preset quota data includes the quotas corresponding to different instance description information predicted for user devices within the cloud service platform. Based on the target quota, a target cloud service instance from the cloud service platform is allocated to the target user device; the target cloud service instance includes the instance described by the target instance description information. Thus, compared to quotas set from the user device level dimension, this application embodiment uses the quotas corresponding to different instance description information set for the user device itself as the preset quota data from the dimension of instance description information. This can result in higher accuracy of the preset quota data. Therefore, searching for the target quota of the instance requested by the target user device in this instance request from the preset quota data and allocating cloud service instances to the target user device based on the target quota can improve the cloud service instance allocation effect to a certain extent.

[0187] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0188] This application also provides a non-volatile readable storage medium storing one or more modules (programs). When these modules are applied to a device, they enable the device to execute the instructions for the method steps in this application.

[0189] This application provides one or more machine-readable media storing instructions that, when executed by one or more processors, cause an electronic device to perform one or more methods as described in the above embodiments. In this application, the electronic device includes various types of devices such as terminal devices and servers (clusters).

[0190] The embodiments of this disclosure can be implemented as an apparatus configured as desired using any suitable hardware, firmware, software, or any combination thereof, including electronic devices such as terminal devices, servers (clusters), etc. Figure 15 An exemplary apparatus 1000 is schematically shown that can be used to implement various embodiments of the present application.

[0191] In one embodiment, Figure 15 An exemplary device 1000 is shown, which includes one or more processors 1002, a control module (chipset) 1004 coupled to at least one of the processors 1002, a memory 1006 coupled to the control module 1004, a non-volatile memory (NVM) / storage device 1008 coupled to the control module 1004, one or more input / output devices 1010 coupled to the control module 1004, and a network interface 1012 coupled to the control module 1004.

[0192] Processor 1002 may include one or more single-core or multi-core processors, and processor 1002 may include any combination of general-purpose processors or special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In some embodiments, device 1000 can serve as a terminal device, server (cluster), or other device in the embodiments of this application.

[0193] In some embodiments, apparatus 1000 may include one or more computer-readable media (e.g., memory 1006 or NVM / storage device 1008) having instructions 1014 and one or more processors 1002 that are combined with the one or more computer-readable media and configured to execute the instructions 1014 to implement the module and thus perform the actions in this disclosure.

[0194] In one embodiment, the control module 1004 may include any suitable interface controller to provide any suitable interface to at least one of the processors 1002 and / or any suitable device or component communicating with the control module 1004.

[0195] The control module 1004 may include a memory controller module to provide an interface to the memory 1006. The memory controller module may be a hardware module, a software module, and / or a firmware module.

[0196] Memory 1006 may be used, for example, to load and store data and / or instructions 1014 for device 1000. In one embodiment, memory 1006 may include any suitable volatile memory, such as suitable dynamic random access memory (DRAM). In some embodiments, memory 1006 may include dual data rate type quad synchronous dynamic random access memory (DDR4 SDRAM).

[0197] In one embodiment, the control module 1004 may include one or more input / output controllers to provide interfaces to the NVM / storage device 1008 and (one or more) input / output devices 1010.

[0198] For example, NVM / storage device 1008 may be used to store data and / or instructions 1014. NVM / storage device 1008 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (one or more) non-volatile storage devices (e.g., one or more hard disk drives (HDDs), one or more compact disc (CD) drives, and / or one or more digital video disc (DVD) drives).

[0199] NVM / storage device 1008 may include storage resources that are physically part of a device on which device 1000 is mounted, or that are accessible to the device but do not necessarily have to be part of the device. For example, NVM / storage device 1008 may be accessed via a network via one or more input / output devices 1010.

[0200] One or more input / output devices 1010 may provide an interface for device 1000 to communicate with any other suitable device. Input / output devices 1010 may include communication components, audio components, sensor components, etc. Network interface 1012 may provide an interface for device 1000 to communicate via one or more networks. Device 1000 may wirelessly communicate with one or more components of a wireless network according to any of one or more wireless network standards and / or protocols, such as accessing wireless networks based on communication standards, such as Wireless Fidelity (WiFi) networks, Second Generation Mobile Networks (2G) networks, Third Generation Mobile Networks (3G) networks, Fourth Generation Mobile Networks (4G) networks, Fifth Generation Mobile Networks (5G) networks, etc., or combinations thereof.

[0201] In one embodiment, at least one of the processors 1002 may be logically packaged with one or more controllers (e.g., memory controller modules) of the control module 1004. In one embodiment, at least one of the processors 1002 may be logically packaged with one or more controllers of the control module 1004 to form a system-in-a-package (SiP). In one embodiment, at least one of the processors 1002 may be integrated with the logic of one or more controllers of the control module 1004 on the same die. In one embodiment, at least one of the processors 1002 may be integrated with the logic of one or more controllers of the control module 1004 on the same die to form a system-on-chip (SoC).

[0202] In various embodiments, device 1000 may be, but is not limited to, a server, desktop computing device, or mobile computing device (e.g., laptop computing device, handheld computing device, tablet computer, netbook, etc.). In various embodiments, device 1000 may have more or fewer components and / or different architectures. For example, in some embodiments, device 1000 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touchscreen display), a non-volatile memory port, multiple antennas, a graphics chip, an application-specific integrated circuit (ASIC), and a speaker.

[0203] The detection device can use a main control chip as a processor or control module, and sensor data, position information, etc. can be stored in a memory or NVM / storage device. The sensor group can be used as an input / output device, and the communication interface can include a network interface.

[0204] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0205] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0206] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0207] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more blocks of a block diagram.

[0208] These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable terminal equipment, provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks of a block diagram.

[0209] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0210] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the element.

[0211] The resource processing method, cloud service instance processing method, apparatus, electronic device, and machine-readable medium provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A resource processing method, characterized by, The method comprises: receiving a resource application request sent by a target user equipment; the target user equipment belongs to user equipment in a resource platform; in response to the resource application request, finding a quota corresponding to target resource description information corresponding to the target user equipment from preset quota data, to obtain a target quota; the target resource description information comprises resource description information applied by the target user equipment this time; the resource description information comprises available zone information and / or specification information; the specification comprises model information of the resource; the model information comprises a shared standard type and a shared computing type; the resource described by the resource description information comprises a resource with a specification matched with the specification information and / or a resource located in an available zone indicated by the available zone information; the preset quota data comprises quotas corresponding to different resource description information predicted for user equipment in the resource platform; allocating target resources in the resource platform to the target user equipment according to the target quota; the target resources comprise resources described by the target resource description information; wherein the preset quota data is obtained by: for each user equipment in the resource platform, inputting user-related information of the user equipment and different resource description information into a preset quota prediction model, and predicting quotas corresponding to different resource description information corresponding to the user equipment based on the quota prediction model to generate quota data of the user equipment; clustering quota data belonging to the same category in the quota data of the user equipment; generating the preset quota data based on the quota data of each user equipment in the resource platform after clustering; in response to the resource application request, finding a quota corresponding to target resource description information corresponding to the target user equipment from preset quota data, to obtain a target quota, comprising: comparing user-related information of the target user equipment carried in the resource application request and resource description information of resources required by the target user equipment this time with user-related information corresponding to the quota and resource description information in the preset quota data, and taking the quota consistent with the comparison as the target quota.

2. The method of claim 1, wherein, The quota data of the user equipment comprises first quota data; inputting the user-related information of the user equipment and different resource description information into a preset quota prediction model, and predicting quotas corresponding to different resource description information corresponding to the user equipment based on the quota prediction model to generate the quota data of the user equipment, comprising: inputting the user-related information of the user equipment and first resource description information into the quota prediction model, and predicting a first quota corresponding to the first resource description information corresponding to the user equipment based on the quota prediction model; the first resource description information comprises description information of resources applied by the user equipment at a historical time; generating the first quota data according to the user-related information, the first resource description information, and the first quota.

3. The method of claim 2, wherein, The quota data of the user equipment further comprises second quota data; the method further comprises: predicting, according to a historical application amount of a resource described by second resource description information by a reference user equipment, a second quota corresponding to the second resource description information corresponding to the user equipment; generating the second quota data according to the user-related information, the second resource description information, and the second quota; the resource described by the second resource description information including a resource that has not been applied for by the user equipment.

4. The method of claim 3, wherein, The clustering of the quota data in the quota data of the user equipment that belongs to the same category includes: determining first quota data that belongs to the same category according to a deviation of a first quota of each first quota data and a similarity of first resource description information of each first quota data; wherein the deviation of the first quota of the first quota data that belongs to the same category is within a preset deviation range, and the similarity of the first resource description information of the first quota data that belongs to the same category is greater than a first similarity threshold; aggregating the first quota data that belongs to the same category to obtain third quota data.

5. The method of claim 4, wherein, The method further includes: determining a second quota corresponding to second resource description information consistent with resource description information in the third quota data as a reference quota; adjusting a to-be-corrected quota in the third quota data according to the reference quota; the similarity between resource description information corresponding to the to-be-corrected quota and second resource description information corresponding to the reference quota is greater than a second similarity threshold.

6. The method according to any one of claims 1 to 5, characterized in that, The allocating of a target resource in the resource platform to the target user equipment according to the target quota includes: in a case where a target resource amount corresponding to the target user equipment does not exceed the target quota, allocating a corresponding amount of target resources to the target user equipment according to a resource amount applied for by the target user equipment this time; the target resource amount including a sum of a resource amount of target resources already occupied by the target user equipment and the resource amount applied for this time; or in a case where the target resource amount exceeds the target quota, discarding the resource application request, and returning prompt information for indicating quota shortage to the target user equipment.

7. The method according to any one of claims 1 to 5, characterized in that, The quota prediction model is optimized by the following manner: generating model optimization information according to operation information of an adjustment operation of a quota in the preset quota data; optimizing the quota prediction model according to the model optimization information.

8. A cloud service instance processing method, comprising: The method includes: receiving an instance creation request sent by a target user equipment; the target user equipment belonging to user equipment in a cloud service platform; in response to the instance creation request, searching, from preset quota data, for a quota corresponding to target instance description information corresponding to the target user equipment, to obtain a target quota; the target instance description information includes instance description information applied for this time by the target user equipment, the instance description information includes availability zone information and / or specification information, the specification includes model information of a resource, the model information includes a shared standard type and a shared computing type, the resource described in the instance description information includes a resource with a specification matched with the specification information and / or a resource located in an availability zone indicated by the availability zone information; the preset quota data includes quotas corresponding to different instance description information predicted for user equipment in the cloud service platform; allocating, according to the target quota, a target cloud service instance in the cloud service platform for the target user equipment; the target cloud service instance includes an instance described by the target instance description information; wherein, the preset quota data is obtained by the following way: for each user equipment in a resource platform, inputting user-related information of the user equipment and different resource description information into a preset quota prediction model, and predicting, based on the quota prediction model, quotas corresponding to different resource description information corresponding to the user equipment, to generate quota data of the user equipment; clustering quota data belonging to the same category in the quota data of the user equipment; generating the preset quota data based on the quota data of each user equipment in the resource platform after clustering; the response to the instance creation request, searching, from preset quota data, for a quota corresponding to target instance description information corresponding to the target user equipment, to obtain a target quota, includes: comparing user-related information of the target user equipment carried in the instance creation request and resource description information of resources required by the target user equipment this time with user-related information corresponding to the quota in the preset quota data and resource description information, and taking the quota consistent with the comparison as the target quota.

9. A resource processing device, characterized by the apparatus includes: a first receiving module configured to receive a resource application request sent by a target user equipment; the target user equipment belongs to user equipment in a resource platform; a first searching module configured to search, from preset quota data, for a quota corresponding to target resource description information corresponding to the target user equipment in response to the resource application request, to obtain a target quota; the target resource description information includes resource description information applied for this time by the target user equipment, the resource description information includes availability zone information and / or specification information, the specification includes model information of a resource, the model information includes a shared standard type and a shared computing type, the resource described in the resource description information includes a resource with a specification matched with the specification information and / or a resource located in an availability zone indicated by the availability zone information; the preset quota data includes quotas corresponding to different resource description information predicted for user equipment in the resource platform; The first distribution module is configured to distribute target resources in the resource platform to the target user equipment according to the target quota; the target resources include resources described by the target resource description information; The preset quota data is obtained by the following modules: The prediction module is configured to input user-related information of each user equipment in the resource platform and different resource description information into a preset quota prediction model, and predict quotas corresponding to different resource description information corresponding to the user equipment based on the quota prediction model, to generate quota data of the user equipment; The clustering module is configured to cluster quota data belonging to the same category in the quota data of the user equipment; The first generation module is configured to generate preset quota data based on the quota data of each user equipment in the resource platform after clustering; The first finding module is specifically configured to: The user-related information of the target user equipment and the resource description information of the resources required by the target user equipment this time carried in the resource application request are compared with user-related information corresponding to the quota and resource description information in the preset quota data, and the quota consistent with the comparison is taken as the target quota.

10. A cloud service instance processing apparatus, characterized by comprising: The device comprises: The second receiving module is configured to receive an instance creation request sent by a target user equipment; the target user equipment belongs to user equipment in a cloud service platform; The second finding module is configured to find, in response to the instance creation request, a quota corresponding to target instance description information corresponding to the target user equipment from preset quota data, to obtain a target quota; the target instance description information includes instance description information applied by the target user equipment this time, the instance description information includes availability zone information and / or specification information, the specification includes model information of resources, the model information includes shared standard type and shared computing type, resources described by the instance description information include resources with a specification matched with the specification information and / or resources located in an availability zone indicated by the availability zone information; the preset quota data includes quotas corresponding to different instance description information predicted for user equipment in the cloud service platform; The second distribution module is configured to distribute a target cloud service instance in the cloud service platform to the target user equipment according to the target quota; the target cloud service instance includes an instance described by the target instance description information; The preset quota data is obtained by the following modules: The prediction module is configured to input user-related information of each user equipment in the resource platform and different resource description information into a preset quota prediction model, and predict quotas corresponding to different resource description information corresponding to the user equipment based on the quota prediction model, to generate quota data of the user equipment; The clustering module is configured to cluster quota data belonging to the same category in the quota data of the user equipment; The first generation module is configured to generate preset quota data based on the quota data of each user equipment in the resource platform after clustering; The second finding module is specifically configured to: The user-related information of the target user equipment carried in the instance creation request and the resource description information of the required resource of the target user equipment are compared with the user-related information corresponding to the quota in the preset quota data and the resource description information, and the quota consistent with the comparison is taken as the target quota.

11. An electronic device, comprising: Comprise: a processor; a memory having stored thereon executable code that, when executed, causes the processor to perform the method of any one of claims 1 to 8.

12. One or more machine readable media having stored thereon executable code that, when executed, causes a processor to perform the method of any one of claims 1 to 8.

Images