CN112015971A - Recommendation method and device for cloud product, electronic equipment and computer readable medium - Google Patents

Recommendation method and device for cloud product, electronic equipment and computer readable medium Download PDF

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Publication number
CN112015971A
CN112015971A CN201910463374.XA CN201910463374A CN112015971A CN 112015971 A CN112015971 A CN 112015971A CN 201910463374 A CN201910463374 A CN 201910463374A CN 112015971 A CN112015971 A CN 112015971A
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cloud product
cloud
user
data
service
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谢瑀
高玉嵩
张云杨
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Alibaba Cloud Computing Ltd
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Alibaba Group Holding Ltd
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation

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Abstract

The embodiment of the application provides a recommendation method and device for cloud products, electronic equipment and a computer readable medium, and relates to the technical field of internet. Wherein the method comprises the following steps: determining service quality data corresponding to user behavior data of a cloud product instance, wherein the cloud product instance is a service process distributed on a physical machine and used for operating a cloud product; and when the service quality data is judged not to be in the target service quality interval, determining the cloud product to be recommended based on the metadata of the cloud product instances in the cloud product instance group. According to the cloud product recommendation method and device, the cloud product reaching the target service quality can be automatically recommended, and therefore the use quality of the cloud product can be greatly improved. In addition, the cloud product manufacturer is helped to guide the user to use the correct cloud product, and the after-sale operation and maintenance cost of the cloud product manufacturer is reduced, so that the purposes of guaranteeing the service level agreement of the cloud service and reasonably planning the cost resource are achieved.

Description

Recommendation method and device for cloud product, electronic equipment and computer readable medium
Technical Field
The embodiment of the application relates to the technical field of internet, in particular to a cloud product recommendation method and device, electronic equipment and a computer readable medium.
Background
With the development of network technologies, more and more cloud products are produced, such as cloud computing products, cloud security products, cloud databases, and the like. In the initial stage of user service, a single or a few cloud products are often used as a medium for carrying the service. With the increase and change of user services, the original cloud product cannot meet the requirements of the current services, for example, when searching is performed under the condition of gradually increasing large-scale data, fragmentation is often required to alleviate the performance bottleneck of a single cloud database, and the object processing service gradually transitions to a mixed object analysis processing service. At this time, the user is required to adjust the currently used cloud product to a suitable cloud product to better meet the requirements of the current service. However, due to the weak perception of the change of the service quality of the currently used cloud product, a user cannot find that the service quality of the currently used cloud product cannot meet the requirement of the current service in time, and often after the service is damaged, the root cause is checked, and then a large amount of cost is used for migrating the service data in the currently used cloud product to the cloud product with the service quality reaching the standard. Therefore, how to automatically recommend cloud products reaching the target service quality is a technical problem to be solved urgently at present.
Disclosure of Invention
The application aims to provide a recommendation method and device of cloud products, electronic equipment and a computer readable medium, and is used for solving the problem of how to automatically recommend the cloud products reaching the target service quality in the prior art.
According to a first aspect of an embodiment of the present application, a method for recommending a cloud product is provided. The method comprises the following steps: determining service quality data corresponding to user behavior data of a cloud product instance, wherein the cloud product instance is a service process distributed on a physical machine and used for operating a cloud product; and when the service quality data is judged not to be in the target service quality interval, determining the cloud product to be recommended based on the metadata of the cloud product instances in the cloud product instance group.
According to a second aspect of the embodiments of the present application, there is provided a recommendation apparatus for cloud products. The device comprises: the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining service quality data corresponding to user behavior data of a cloud product example, and the cloud product example is a service process which is distributed on a physical machine and runs a cloud product; and the second determining module is used for determining the cloud product to be recommended based on the metadata of the cloud product instances in the cloud product instance group when the service quality data is judged not to be in the target service quality interval.
According to a third aspect of embodiments of the present application, there is provided an electronic apparatus, including: one or more processors; a computer readable medium configured to store one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method for recommending a cloud product as described in the first aspect of the embodiments above.
According to a fourth aspect of embodiments of the present application, there is provided a computer-readable medium on which a computer program is stored, the program, when executed by a processor, implementing the method for recommending a cloud product as described in the first aspect of the embodiments above.
By the technical scheme provided by the embodiment of the application, the service quality data corresponding to the user behavior data of the cloud product example is determined, the cloud product example is a service process which is distributed on a physical machine and runs a cloud product, and determining a cloud product to be recommended based on metadata of the cloud product instances in the cloud product instance group when the service quality data is determined not to be within the target service quality interval, compared with the prior other modes, the method can judge whether the service quality data corresponding to the user behavior data of the cloud product example is in the target service quality interval or not, and when the service quality data is judged not to be in the target service quality interval, the cloud product reaching the target service quality can be automatically recommended according to the metadata of the cloud product examples in the cloud product example group, so that the use quality of the cloud product can be greatly improved. In addition, the cloud product manufacturer is helped to guide the user to use the correct cloud product, and the after-sale operation and maintenance cost of the cloud product manufacturer is reduced, so that the purposes of guaranteeing the service level agreement of the cloud service and reasonably planning the cost resource are achieved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 is a flowchart illustrating steps of a method for recommending cloud products according to an embodiment of the present disclosure;
fig. 2 is a flowchart illustrating steps of a method for recommending a cloud database according to a second embodiment of the present application;
fig. 3 is a schematic structural diagram of a recommendation device for cloud products in a third embodiment of the present application;
fig. 4 is a schematic structural diagram of a cloud product recommendation device in the fourth embodiment of the present application;
fig. 5 is a schematic structural diagram of a recommendation device for cloud products in the fifth embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present application;
fig. 7 is a hardware structure of an electronic device according to a seventh embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1, a flowchart illustrating steps of a method for recommending a cloud product according to a first embodiment of the present application is shown.
Specifically, the method for recommending cloud products provided by the embodiment includes the following steps:
in step S101, quality of service data corresponding to the user behavior data of the cloud product instance is determined.
In this embodiment, the cloud product may include a cloud computing product, a cloud security product, a cloud database, and the like. The examples may be understood as virtual machines or applications distributed on physical machines. The cloud product instance may be understood as a service process distributed on a physical machine running a cloud product. Specifically, from the business product dimension, an instance corresponds to one service product purchased by a user. From the technical dimension, every time a user purchases a cloud service product, a process for running the cloud product is started on a physical machine of a background system, and the process is the cloud product instance. The user behavior data can be understood as operation data of a user for the cloud product instance, such as operation instructions written in SQL (structured query language), user operation types involved in the operation instructions, and the like. The quality of service data comprises service delay data and/or resource consumption data. The service delay data can be understood as a delay response time corresponding to the execution of each operation instruction aiming at the cloud product instance by the user. The consumable resource may be understood to be a resource that is consumable, such as a CPU, memory, disk, etc. The resource consumption data may include the number of read and write operations per second performed by the storage device, and the like. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, when the service quality data corresponding to the user behavior data of the cloud product instance is determined, service delay data and/or resource consumption data corresponding to the user behavior data of the cloud product instance are obtained from a time sequence database of a management and control system of the cloud product. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In step S102, when it is determined that the service quality data is not within the target service quality interval, a cloud product to be recommended is determined based on metadata of cloud product instances in a group in which the cloud product instances are located.
In this embodiment, the target qos interval may be a preconfigured qos target interval, or may also be a qos target interval determined in real time. The target service quality interval includes a service delay allowed interval and/or a resource consumption allowed interval. The metadata includes at least one of: and the cloud product instance corresponds to category information, version information and specification configuration information. Specifically, metadata of a cloud product instance is obtained from a metadata base of a management and control system of the cloud product. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, when the user behavior data comprises a user operation type for the cloud product instance, the method further comprises: and if the service quality data corresponding to the user operation type is judged not to be in a target service quality interval, judging that the cloud product example is not matched with the user service data in the cloud product example, wherein the target service quality interval is determined according to the service quality characteristic data corresponding to the user operation type in a historical time period. Specifically, the target qos interval is determined according to the qos feature data and a qos fluctuation threshold. The qos fluctuation threshold may be set by a person skilled in the art according to actual needs, and the embodiment of the present application is not limited in this respect. The quality of service characteristic data comprises at least one of: the user operation type is the mean, variance, median and mode of a plurality of service quality data corresponding to the historical time period. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, if it is determined that the service delay data corresponding to the user operation type is not within a service delay allowable interval, it is determined that the cloud product instance is not matched with the user service data in the cloud product instance, where the service delay allowable interval is determined according to the service delay feature data corresponding to the user operation type within a historical time period; and/or if the resource consumption data corresponding to the user operation type is judged not to be in a resource consumption allowable interval, judging that the cloud product instance is not matched with the user service data in the cloud product instance, wherein the resource consumption allowable interval is determined according to the resource consumption characteristic data corresponding to the user operation type in the historical time period. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, the user operation type may include an insert operation, a delete operation, an update operation, a modify operation, a clear operation, a select operation, and the like. The service delay characteristic data comprises at least one of: the user operation type is the mean, the variance, the median and the mode of a plurality of service delay data corresponding to the historical time period. Specifically, the service delay allowable interval is determined according to the service delay characteristic data and a service delay fluctuation threshold. And determining the sum of the service delay characteristic data and the service delay fluctuation threshold as the upper limit of the service delay allowable interval, and determining the difference of the service delay characteristic data and the service delay fluctuation threshold as the lower limit of the service delay allowable interval. The service delay fluctuation threshold may be set by a person skilled in the art according to actual needs, and the embodiment of the present application is not limited in any way. More specifically, when the service delay characteristic data is the variance of the plurality of service delay data, determining the variance of the service delay data corresponding to the user operation type based on the service delay data corresponding to the user operation type and the mean of the plurality of service delay data; and then judging whether the variance of the service delay data corresponding to the user operation type is in the service delay allowable interval or not. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, the resource consumption characteristic data includes at least one of: and the user operation type corresponds to the mean, the variance, the median and the mode of a plurality of resource consumption data in the historical time period. Specifically, the resource consumption permission interval is determined according to the resource consumption feature data and a resource consumption fluctuation threshold. And determining the sum of the resource consumption characteristic data and the resource consumption fluctuation threshold as the upper limit of the resource consumption allowable interval, and determining the difference between the resource consumption characteristic data and the resource consumption fluctuation threshold as the lower limit of the resource consumption allowable interval. The resource consumption fluctuation threshold may be set by a person skilled in the art according to actual needs, and the embodiment of the present application is not limited in any way. More specifically, when the resource consumption characteristic data is the variance of the plurality of resource consumption data, determining the variance of the resource consumption data corresponding to the user operation type based on the resource consumption data corresponding to the user operation type and the average of the plurality of resource consumption data; and then judging whether the variance of the resource consumption data corresponding to the user operation type is in the resource consumption allowable interval or not. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, before determining that the cloud product instance does not match the user traffic data in the cloud product instance, the method further comprises: if the service quality data corresponding to the user operation type at the current moment is judged not to be in the target service quality interval, recording an event that the service quality data corresponding to the user operation type at the current moment is not in the target service quality interval as an abnormal event, and determining an abnormal score corresponding to the user operation type based on an abnormal score model and the abnormal event; and if the abnormal score is determined to be continuously increased in the current time period, determining that the cloud product instance is not matched with the user business data. Wherein, the abnormal scoring model can be constructed by a forgetting function. Therefore, the change of the user service data can be automatically detected through the change of the abnormal score obtained by the abnormal event that the service quality data corresponding to the user operation type at the current moment is not in the service quality allowable interval in the current time period, so that whether the cloud product instance is matched with the user service data in the cloud product instance can be accurately judged. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, before determining that the cloud product instance does not match the user business data in the cloud product instance, the method further includes: if the service delay data corresponding to the user operation type at the current moment is judged not to be in the service delay allowable interval, recording an event that the service delay data corresponding to the user operation type at the current moment is not in the service delay allowable interval as an abnormal event, and determining an abnormal score corresponding to the user operation type based on an abnormal score model and the abnormal event; and if the abnormal score is determined to be continuously increased in the current time period, determining that the cloud product instance is not matched with the user business data. Wherein, the abnormal scoring model can be constructed by a forgetting function. Therefore, the change of the user service data can be automatically detected through the change of the abnormal score obtained by the abnormal event that the service delay data corresponding to the user operation type at the current moment is not in the service delay allowable interval in the current time period, so that whether the cloud product instance is matched with the user service data in the cloud product instance can be accurately judged. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, before determining that the cloud product instance does not match the user business data in the cloud product instance, the method further includes: if the resource consumption data corresponding to the user operation type at the current moment is judged not to be in the resource consumption allowable interval, recording an event that the resource consumption data corresponding to the user operation type at the current moment is not in the resource consumption allowable interval as an abnormal event, and determining an abnormal score corresponding to the user operation type based on an abnormal score model and the abnormal event; and if the abnormal score is determined to be continuously increased in the current time period, determining that the cloud product instance is not matched with the user business data. Wherein, the abnormal scoring model can be constructed by a forgetting function. Therefore, the change of the user service data can be automatically detected through the change of the abnormal score obtained by the abnormal event that the resource consumption data corresponding to the user operation type at the current moment is not in the resource consumption allowable interval in the current time period, so that whether the cloud product instance is matched with the user service in the cloud product instance can be accurately judged. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, before determining that the cloud product instance does not match the user business data in the cloud product instance, the method further includes: if the resource consumption data corresponding to the user operation type at the current moment is judged not to be in the resource consumption allowable interval and the service delay data corresponding to the user operation type at the current moment is judged not to be in the service delay allowable interval, recording an event that the resource consumption data corresponding to the user operation type at the current moment is not in the resource consumption allowable interval and the service delay data corresponding to the user operation type at the current moment is not in the service delay allowable interval as an abnormal event, and determining an abnormal score corresponding to the user operation type based on an abnormal score model and the abnormal event; and if the abnormal score is determined to be continuously increased in the current time period, determining that the cloud product instance is not matched with the user business data. Wherein, the abnormal scoring model can be constructed by a forgetting function. Therefore, the change of the user service data can be detected more accurately through the change of the abnormal score obtained by the abnormal event that the resource consumption data corresponding to the user operation type at the current moment is not in the resource consumption allowed interval and the service delay data corresponding to the user operation type at the current moment is not in the service delay allowed interval in the current time period, so that whether the cloud product instance is matched with the user service data in the cloud product instance can be judged more accurately. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, when the user behavior data includes a user operation type for the cloud product instance, before the determining the cloud product to be recommended, the method further comprises: determining distribution data corresponding to the user operation type in a historical time period; and based on the distribution data, clustering all cloud product instances on the cloud platform to obtain a plurality of groups of all cloud product instances on the cloud platform. Therefore, clustering operation is carried out on all cloud product instances on the cloud platform through the distribution data corresponding to the user operation type, and the similarity of all cloud product instances in each group can be ensured. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, the frequency of occurrence of each user operation type of the cloud product instance in a historical time period is counted. Then, based on the frequency of occurrence of each user operation type in the historical time period, the distribution data of the user operation types for the cloud product instance is obtained, for example, the insert operation type accounts for 70%, the update operation type accounts for 10%, the select operation type accounts for 20%, and the like. After the distribution data of the user operation types of the cloud product examples in the historical time period are obtained, clustering operation can be performed on all the cloud product examples on the cloud platform by using a K-Means clustering method based on the distribution data of the user operation types of all the cloud product examples on the cloud platform in the historical time period, so that a plurality of groups of all the cloud product examples on the cloud platform can be obtained. For example, for cloud product instance A, the distribution data of the user operation type is DA0.7 for insert operation, 0.1 for update operation, 0.2 for select operation; for the cloud product example B, the distribution data of the user operation types are as follows:
DB0.3 for insert operation, 0.4 for update operation, 0.3 for select operation
Then it can be calculated whether they will be grouped in the same group according to the clustering method of K-Means. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, the determining a cloud product to be recommended based on metadata of cloud product instances in a group in which the cloud product instances are located includes: determining a mode of metadata of all cloud product instances in the group as metadata of the cloud product to be recommended; and determining the cloud product to be recommended based on the metadata of the cloud product to be recommended. Thereby, the cloud product to be recommended can be accurately determined. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, when the metadata of the cloud product instances comprises category information, version information and specification configuration information of the cloud product instances, determining a first number of cloud product instances having the same category information in the group; determining category information of cloud products to be recommended based on the first quantity; determining a second number of cloud product instances in the group having the same version information; determining version information of the cloud product to be recommended based on the second quantity; determining a third number of cloud product instances in the group having the same specification configuration information; and determining specification configuration information of the cloud products to be recommended based on the third quantity. Specifically, the category information corresponding to the maximum value in the first number is determined as the category information of the cloud product to be recommended. And determining the version information corresponding to the maximum value in the second quantity as the version information of the cloud product to be recommended. And determining the specification configuration information corresponding to the maximum value in the third quantity as the specification configuration information of the cloud product to be recommended. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, the method further comprises: generating a recommendation message of the cloud product to be recommended based on the metadata of the cloud product to be recommended; and sending the recommendation message to terminal equipment held by the user. Specifically, the recommendation message may be sent to a terminal device held by the user in a short message manner or a mailbox manner. Therefore, the user can know the metadata of the recommended cloud product by sending the recommendation message to the terminal equipment held by the user, and then whether to accept the recommendation of the cloud product is determined. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, the method further comprises: receiving a feedback message aiming at the recommendation message sent by the terminal equipment; and migrating the user service data in the cloud product instance to the cloud product to be recommended based on the feedback message. Therefore, the user service data in the cloud product instance can be migrated to the cloud product to be recommended based on the feedback message of the user. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, when the feedback message carries information that the user does not accept the recommended cloud product, the user service data in the cloud product instance is not migrated to the cloud product to be recommended. And when the feedback message carries information of the cloud product recommended by the user, migrating the user service data in the cloud product example to the cloud product to be recommended. Specifically, user business data in a cloud product instance is imported into a cloud product to be recommended through a transmission tool. And after the migration of the user business data is completed, introducing the connection of the user to the cloud product to be recommended. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, when the feedback message includes migration time information of the user service data, the migrating the user service data in the cloud product instance to the cloud product to be recommended based on the feedback message includes: and migrating the user service data to the cloud product to be recommended based on the migration time information. Therefore, the user service data can be migrated according to the migration time requirement of the user. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, when the migration time information is a time point, the user service data in the cloud product instance is migrated to the cloud product to be recommended based on the time point. And when the migration time information is a time period, migrating the user service data in the cloud product instance to the cloud product to be recommended based on the time period. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
By the recommendation method of the cloud product provided by the embodiment of the application, the service quality data corresponding to the user behavior data of the cloud product example is determined, the cloud product example is a service process distributed on a physical machine for operating the cloud product, and determining a cloud product to be recommended based on metadata of the cloud product instances in the cloud product instance group when the service quality data is determined not to be within the target service quality interval, compared with the prior other modes, the method can judge whether the service quality data corresponding to the user behavior data of the cloud product example is in the target service quality interval or not, and when the service quality data is judged not to be in the target service quality interval, the cloud product reaching the target service quality can be automatically recommended according to the metadata of the cloud product examples in the cloud product example group, so that the use quality of the cloud product can be greatly improved. In addition, the cloud product manufacturer is helped to guide the user to use the correct cloud product, and the after-sale operation and maintenance cost of the cloud product manufacturer is reduced, so that the purposes of guaranteeing the service level agreement of the cloud service and reasonably planning the cost resource are achieved.
The recommendation method of cloud products of the present embodiment may be executed by any suitable device with data processing capability, including but not limited to: cameras, terminals, mobile terminals, PCs, servers, in-vehicle devices, entertainment devices, advertising devices, Personal Digital Assistants (PDAs), tablet computers, notebook computers, handheld game consoles, smart glasses, smart watches, wearable devices, virtual display devices or display enhancement devices (such as Google Glass, Oculus rise, Hololens, Gear VR), and the like.
Referring to fig. 2, a flowchart illustrating steps of a method for recommending a cloud database according to a second embodiment of the present application is shown.
Specifically, the recommendation method for the cloud database of the embodiment includes the following steps:
in step S201, quality of service data corresponding to the user behavior data of the cloud database instance is determined.
In the present embodiment, the cloud database instance may be understood as a service process distributed on a physical machine running a cloud database. Specifically, from the service product dimension, a cloud database instance corresponds to one cloud database service product purchased by a user. From the technical dimension, each time a user purchases a cloud database service product, a process for running a cloud database is started on a physical machine of a background system, and the process is a cloud database instance. The user behavior data can be understood as operation data of a user for the cloud database instance, for example, SQL operation instructions in log audit data of the cloud database instance, user operation types corresponding to the SQL operation instructions, and the like. The log audit data comprises the content of the SQL operation instruction, the user service content in the content is desensitized, and only the grammar keywords of the SQL operation instruction are recorded. Desensitization is understood as not involving specific contents of the user traffic in the SQL operation instruction, for example, in the SQL operation instruction "insert into table a (id, name) values (1, 'name 1')", id is 1, and name is 'name 1' which is specific contents of the user traffic, and this part is all hidden. This SQL operation instruction is converted into "insert _ table a (id, name) values (. The quality of service data comprises service delay data and/or resource consumption data. The service delay data can be understood as a delay response time corresponding to the execution of each SQL operation instruction aiming at the cloud database instance by a user. The meaning of the resource consumption data is similar to the above and is not described herein again. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, when the quality of service data corresponding to the user behavior data of the cloud database instance is determined, service delay data and/or resource consumption data corresponding to the user behavior data of the cloud database instance are obtained from a time sequence database of a management and control system of the cloud database. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In step S202, when it is determined that the service quality data is not within the target service quality interval, a cloud database to be recommended is determined based on metadata of cloud database instances in a group in which the cloud database instances are located.
In this embodiment, the target qos interval may be a preconfigured qos target interval, or may also be a qos target interval determined in real time. The target service quality interval includes a service delay allowed interval and/or a resource consumption allowed interval. The metadata of the cloud database instance may include at least one of: the cloud database engine management method comprises the following steps of type information of a cloud database engine, version information of the cloud database and specification configuration information of the cloud database. The metadata is steady-state data and does not change rapidly along with time. Specifically, metadata of the cloud database instance is obtained from a metadata database of a management and control system of the cloud database. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, when the user behavior data is specific to a user operation type for the cloud database instance, the method further comprises: if the resource consumption data corresponding to the user operation type at the current moment is judged not to be in the resource consumption allowable interval and the service delay data corresponding to the user operation type at the current moment is judged not to be in the service delay allowable interval, recording an event that the resource consumption data corresponding to the user operation type at the current moment is not in the resource consumption allowable interval and the service delay data corresponding to the user operation type at the current moment is not in the service delay allowable interval as an abnormal event, and determining an abnormal score corresponding to the user operation type based on an abnormal score model and the abnormal event; and if the abnormal score is judged to be continuously increased in the current time period, judging that the cloud database instance is not matched with the user service data in the cloud database instance. Wherein, the abnormal scoring model can be constructed by a forgetting function. Therefore, the change of the user service data can be detected more accurately through the change of the abnormal score obtained by the abnormal event that the resource consumption data corresponding to the user operation type at the current moment is not in the resource consumption allowed interval and the service delay data corresponding to the user operation type at the current moment is not in the service delay allowed interval in the current time period, so that whether the cloud database instance is matched with the user service data in the cloud database instance can be judged more accurately. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In one specific example, for cloud database instance IiMonitoring the cloud database instance I in real time through a time sequence database in the cloud database management and control systemiType S of user operationiIf the service delay data exceeds the service delay allowable interval
Figure BDA0002078718900000131
(where Δ α 1 is a service delay fluctuation threshold), and the resource consumption data also exceeds the resource consumption allowable interval
Figure BDA0002078718900000132
(where Δ α 2 is the threshold of fluctuation of resource consumption), then this event is recorded as an abnormal event
Figure BDA0002078718900000133
t represents the occurrence time of an abnormal event. Wherein the content of the first and second substances,
Figure BDA0002078718900000134
representing instances for cloud database IiType S of user operationiService delay characteristic data corresponding to historical time period delta t1, namely for cloud database instance IiType S of user operationiA mean, a variance, a median, or a mode of the plurality of service delay data corresponding to the history time period Δ t 1.
Figure BDA0002078718900000135
Representing instances for cloud database IiType S of user operationiCorresponding resource consumption characteristic data in historical time period delta t1, namely aiming at cloud database instance IiType S of user operationiThe mean, variance, median, or mode of the plurality of resource consumption data corresponding to the historical time period Δ t 1. Specifically, an example I of the cloud database in the historical time period delta t1 is pulled from a time sequence database of the cloud database management and control systemiType S of user operationiA plurality of service delay data and a plurality of resource consumption data. Then, based on the forgetting function, an abnormal scoring model is constructed
Figure BDA0002078718900000141
Continuously monitoring the abnormal scoring model in the current time period delta t2
Figure BDA0002078718900000142
And if the abnormal score is continuously increased, judging that the cloud database instance is not matched with the user service data. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, when the user behavior data is specific to a user operation type for the cloud database instance, before the determining the cloud database to be recommended, the method further includes: determining distribution data corresponding to the user operation type in a historical time period; and based on the distribution data, clustering all cloud database instances on the cloud platform to obtain a plurality of groups of all cloud database instances on the cloud platform. Therefore, clustering operation is carried out on all cloud database instances on the cloud platform through the distribution data corresponding to the user operation type, and the similarity of all cloud database instances in each group can be ensured. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, the historical time period Δ t1 corresponding to the cloud database instance I in the time sequence database of the cloud database management and control system is pullediLog audit data. The log audit data comprises the historical time period delta t of the user1-in-cloud database instance IiThe user operation data. Based on user aiming at cloud database instance I in historical time period delta t1iUser operation data of (2), statistics of user operation type SiDistribution data in the historical time period delta t1, recorded as
Figure BDA0002078718900000143
All cloud database instances I on lineiIs/are as follows
Figure BDA0002078718900000144
Clustering is carried out to obtain n groups of cloud database instances: DG1，...，DGi，...，DGn. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, the determining, based on the metadata of the cloud database instances in the group of cloud database instances, a cloud database to be recommended includes: determining a mode of metadata of all cloud database instances in the group as metadata of the cloud database to be recommended; and determining the cloud database to be recommended based on the metadata of the cloud database to be recommended. Therefore, the cloud database to be recommended can be accurately determined. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, the metadata database of the cloud database management system is used for pulling the cloud database instance I in the historical time period delta t1iThe metadata of (1). DG for each packetiExtracting metadata of each cloud database instance in the group, and extracting mode indexes based on the metadata to obtain DGs of each groupiAnd corresponding metadata of the cloud database to be recommended. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, the method further comprises: generating a recommendation message of the cloud database to be recommended based on the metadata of the cloud database to be recommended; and sending the recommendation message to terminal equipment held by the user. Specifically, the recommendation message may be sent to a terminal device held by the user in a short message manner or a mailbox manner. Therefore, the recommendation message is sent to the terminal device held by the user, the user can know the recommended metadata of the cloud database, and then whether to accept the recommendation of the cloud database is determined. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, the method further comprises: receiving a feedback message aiming at the recommendation message sent by the terminal equipment; and migrating the user service data in the cloud database instance to the cloud database to be recommended based on the feedback message. Therefore, the user service data in the cloud database instance can be migrated to the cloud database to be recommended based on the feedback message of the user. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, when the feedback message carries information that the user does not accept the recommended cloud database, the user service data in the cloud database instance is not migrated to the cloud database to be recommended. And when the feedback message carries information of the cloud database to be recommended accepted by the user, migrating the user service data in the cloud database instance to the cloud database to be recommended. Specifically, user service data in the cloud database instance is imported into the cloud database to be recommended through a transmission tool. And after the migration of the user business data is completed, introducing the connection of the user to a cloud database to be recommended. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In some optional embodiments, when the feedback message includes migration time information of the user service data, the migrating the user service data in the cloud database instance to the cloud database to be recommended based on the feedback message includes: and migrating the user service data to the cloud database to be recommended based on the migration time information. Therefore, the user service data can be migrated according to the migration time requirement of the user. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
In a specific example, when the migration time information is a time point, the user service data in the cloud database instance is migrated to the cloud database to be recommended based on the time point. And when the migration time information is a time period, migrating the user service data in the cloud database instance to a cloud database to be recommended based on the time period. It should be understood that the above description is only exemplary, and the embodiments of the present application are not limited in this respect.
By the recommendation method of the cloud database provided by the embodiment of the application, the service quality data corresponding to the user behavior data of the cloud database instance is determined, the cloud database instance is a service process which is distributed on a physical machine and runs the cloud database, when the service quality data is judged not to be in a target service quality interval, the cloud database to be recommended is determined based on the metadata of the cloud database instance in the cloud database instance group, compared with the existing other modes, whether the service quality data corresponding to the user behavior data of the cloud database instance is in the target service quality interval can be judged, and when the service quality data is judged not to be in the target service quality interval, the cloud database reaching the target service quality can be automatically recommended according to the metadata of the cloud database instance in the cloud database instance group, therefore, the use quality of the cloud database can be greatly improved. In addition, the cloud database manufacturer is helped to guide the user to use the correct cloud database, and the after-sale operation and maintenance cost of the cloud database manufacturer is reduced, so that the purposes of guaranteeing the service level agreement of the cloud service and reasonably planning the cost resource are achieved.
The recommendation method of the cloud database of the present embodiment may be executed by any suitable device with data processing capability, including but not limited to: cameras, terminals, mobile terminals, PCs, servers, in-vehicle devices, entertainment devices, advertising devices, Personal Digital Assistants (PDAs), tablet computers, notebook computers, handheld game consoles, smart glasses, smart watches, wearable devices, virtual display devices or display enhancement devices (such as Google Glass, Oculus rise, Hololens, Gear VR), and the like.
Referring to fig. 3, a schematic structural diagram of a recommendation device for cloud products in the third embodiment of the present application is shown.
The cloud product recommendation device of the embodiment includes: a first determining module 301, configured to determine service quality data corresponding to user behavior data of a cloud product instance, where the cloud product instance is a service process distributed on a physical machine and used for running a cloud product; a second determining module 302, configured to determine, when it is determined that the quality of service data is not within the target quality of service interval, a cloud product to be recommended based on metadata of a cloud product instance in a group in which the cloud product instance is located.
The cloud product recommendation device of this embodiment is used to implement the corresponding cloud product recommendation method in the foregoing method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again.
Referring to fig. 4, a schematic structural diagram of a recommendation device for cloud products in the fourth embodiment of the present application is shown.
The cloud product recommendation device of the embodiment includes: a first determining module 401, configured to determine service quality data corresponding to user behavior data of a cloud product instance, where the cloud product instance is a service process distributed on a physical machine and used for running a cloud product; a second determining module 402, configured to determine, when it is determined that the service quality data is not within the target service quality interval, a cloud product to be recommended based on metadata of a cloud product instance in a group in which the cloud product instance is located.
Optionally, when the user behavior data includes a user operation type for the cloud product instance, the apparatus further includes: a first determining module 405, configured to determine that the cloud product instance is not matched with the user service data in the cloud product instance if it is determined that the service quality data corresponding to the user operation type is not within a target service quality interval, where the target service quality interval is determined according to the service quality feature data corresponding to the user operation type in a historical time period.
Optionally, before the first determining module 405, the apparatus further includes: a second determination module 403, configured to record, as an abnormal event, an event that the quality of service data corresponding to the user operation type at the current time is not within the target quality of service interval, and determine an abnormal score corresponding to the user operation type based on an abnormal score model and the abnormal event, if it is determined that the quality of service data corresponding to the user operation type at the current time is not within the target quality of service interval; a third determining module 404, configured to determine that the cloud product instance does not match the user service data if it is determined that the anomaly score continues to increase within the current time period.
The cloud product recommendation device of this embodiment is used to implement the corresponding cloud product recommendation method in the foregoing method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again.
Referring to fig. 5, a schematic structural diagram of a recommendation device for cloud products in the fifth embodiment of the present application is shown.
The cloud product recommendation device of the embodiment includes: a first determining module 501, configured to determine service quality data corresponding to user behavior data of a cloud product instance, where the cloud product instance is a service process distributed on a physical machine and used for running a cloud product; a second determining module 502, configured to determine, when it is determined that the service quality data is not within the target service quality interval, a cloud product to be recommended based on metadata of a cloud product instance in a group in which the cloud product instance is located.
Optionally, when the user behavior data includes a user operation type for the cloud product instance, before the second determining module 502, the apparatus further includes: a third determining module 503, configured to determine distribution data corresponding to the user operation type in a historical time period; a clustering module 504, configured to perform clustering operation on all cloud product instances on a cloud platform based on the distribution data to obtain multiple groups of all cloud product instances on the cloud platform.
Optionally, the second determining module 502 is specifically configured to: determining a mode of metadata of all cloud product instances in the group as metadata of the cloud product to be recommended; and determining the cloud product to be recommended based on the metadata of the cloud product to be recommended.
Optionally, the apparatus further comprises: a generating module 505, configured to generate a recommendation message of the cloud product to be recommended based on the metadata of the cloud product to be recommended; a sending module 506, configured to send the recommendation message to a terminal device owned by a user.
Optionally, the apparatus further comprises: a receiving module 507, configured to receive a feedback message for the recommendation message sent by the terminal device; a migration module 508, configured to migrate the user service data in the cloud product instance to the cloud product to be recommended based on the feedback message.
Optionally, when the feedback message includes migration time information of the user service data, the migration module 508 is specifically configured to: and migrating the user service data to the cloud product to be recommended based on the migration time information.
The cloud product recommendation device of this embodiment is used to implement the corresponding cloud product recommendation method in the foregoing method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again.
Fig. 6 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present application; the electronic device may include:
one or more processors 601;
a computer-readable medium 602, which may be configured to store one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for recommending a cloud product according to the first embodiment or the method for recommending a cloud database according to the second embodiment.
Fig. 7 is a hardware structure of an electronic device according to a seventh embodiment of the present application; as shown in fig. 7, the hardware structure of the electronic device may include: a processor 701, a communication interface 702, a computer-readable medium 703 and a communication bus 704;
the processor 701, the communication interface 702, and the computer-readable medium 703 are configured to perform communication with each other through a communication bus 704;
alternatively, the communication interface 702 may be an interface of a communication module, such as an interface of a GSM module;
the processor 701 may be specifically configured to: determining service quality data corresponding to user behavior data of a cloud product instance, wherein the cloud product instance is a service process distributed on a physical machine and used for operating a cloud product; and when the service quality data is judged not to be in the target service quality interval, determining the cloud product to be recommended based on the metadata of the cloud product instances in the cloud product instance group.
The Processor 701 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The computer-readable medium 703 may be, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code configured to perform the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. The computer program, when executed by a Central Processing Unit (CPU), performs the above-described functions defined in the method of the present application. It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access storage media (RAM), a read-only storage media (ROM), an erasable programmable read-only storage media (EPROM or flash memory), an optical fiber, a portable compact disc read-only storage media (CD-ROM), an optical storage media piece, a magnetic storage media piece, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code configured to carry out operations for the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may operate over any of a variety of networks: including a Local Area Network (LAN) or a Wide Area Network (WAN) -to the user's computer, or alternatively, to an external computer (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions configured to implement the specified logical function(s). In the above embodiments, specific precedence relationships are provided, but these precedence relationships are only exemplary, and in particular implementations, the steps may be fewer, more, or the execution order may be modified. That is, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present application may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a first determination module and a second determination module. The names of the modules do not form a limitation on the modules themselves in some cases, for example, the first determination module may also be described as a "module that determines quality of service data corresponding to user behavior data of a cloud product instance".
As another aspect, the present application further provides a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the method for recommending a cloud product as described in the first embodiment or implements the method for recommending a cloud database as described in the second embodiment.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: determining service quality data corresponding to user behavior data of a cloud product instance, wherein the cloud product instance is a service process distributed on a physical machine and used for operating a cloud product; and when the service quality data is judged not to be in the target service quality interval, determining the cloud product to be recommended based on the metadata of the cloud product instances in the cloud product instance group.
The expressions "first", "second", "said first" or "said second" used in various embodiments of the present disclosure may modify various components regardless of order and/or importance, but these expressions do not limit the respective components. The above description is only configured for the purpose of distinguishing elements from other elements. For example, the first user equipment and the second user equipment represent different user equipment, although both are user equipment. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure.
When an element (e.g., a first element) is referred to as being "operably or communicatively coupled" or "connected" (operably or communicatively) to "another element (e.g., a second element) or" connected "to another element (e.g., a second element), it is understood that the element is directly connected to the other element or the element is indirectly connected to the other element via yet another element (e.g., a third element). In contrast, it is understood that when an element (e.g., a first element) is referred to as being "directly connected" or "directly coupled" to another element (a second element), no element (e.g., a third element) is interposed therebetween.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims (22)

1. A recommendation method for cloud products is characterized by comprising the following steps:
determining service quality data corresponding to user behavior data of a cloud product instance, wherein the cloud product instance is a service process distributed on a physical machine and used for operating a cloud product;
and when the service quality data is judged not to be in the target service quality interval, determining the cloud product to be recommended based on the metadata of the cloud product instances in the cloud product instance group.
2. The method of claim 1, wherein when the user behavior data comprises a user operation type for the cloud product instance, the method further comprises:
and if the service quality data corresponding to the user operation type is judged not to be in a target service quality interval, judging that the cloud product example is not matched with the user service data in the cloud product example, wherein the target service quality interval is determined according to the service quality characteristic data corresponding to the user operation type in a historical time period.
3. The method of claim 2, wherein prior to determining that the cloud product instance does not match the user traffic data in the cloud product instance, the method further comprises:
if the service quality data corresponding to the user operation type at the current moment is judged not to be in the target service quality interval, recording an event that the service quality data corresponding to the user operation type at the current moment is not in the target service quality interval as an abnormal event, and determining an abnormal score corresponding to the user operation type based on an abnormal score model and the abnormal event;
and if the abnormal score is determined to be continuously increased in the current time period, determining that the cloud product instance is not matched with the user business data.
4. The method of claim 2, wherein the quality of service characterization data comprises at least one of:
the user operation type is the mean, variance, median and mode of a plurality of service quality data corresponding to the historical time period.
5. The method of claim 2, wherein the target quality of service interval is determined based on the quality of service characterization data and a quality of service fluctuation threshold.
6. The method according to any of claims 1-5, wherein the quality of service data comprises service delay data and/or resource consumption data,
correspondingly, the target service quality interval comprises a service delay allowable interval and/or a resource consumption allowable interval.
7. The method of claim 1, wherein when the user behavior data comprises a type of user operation for the cloud product instance,
before the determining the cloud product to be recommended, the method further includes:
determining distribution data corresponding to the user operation type in a historical time period;
and based on the distribution data, clustering all cloud product instances on the cloud platform to obtain a plurality of groups of all cloud product instances on the cloud platform.
8. The method of claim 1, wherein determining the cloud product to be recommended based on the metadata of the cloud product instances in the group of cloud product instances comprises:
determining a mode of metadata of all cloud product instances in the group as metadata of the cloud product to be recommended;
and determining the cloud product to be recommended based on the metadata of the cloud product to be recommended.
9. The method of claim 8, further comprising:
generating a recommendation message of the cloud product to be recommended based on the metadata of the cloud product to be recommended;
and sending the recommendation message to terminal equipment held by the user.
10. The method of claim 9, further comprising:
receiving a feedback message aiming at the recommendation message sent by the terminal equipment;
and migrating the user service data in the cloud product instance to the cloud product to be recommended based on the feedback message.
11. The method of claim 10, wherein when the feedback message includes migration time information of the user service data,
the migrating the user service data in the cloud product instance to the cloud product to be recommended based on the feedback message includes:
and migrating the user service data to the cloud product to be recommended based on the migration time information.
12. The method according to any one of claims 8 to 11, wherein the metadata of the cloud product to be recommended comprises at least one of: and the type information, the version information and the specification configuration information of the cloud product to be recommended.
13. An apparatus for recommending cloud products, the apparatus comprising:
the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining service quality data corresponding to user behavior data of a cloud product example, and the cloud product example is a service process which is distributed on a physical machine and runs a cloud product;
and the second determining module is used for determining the cloud product to be recommended based on the metadata of the cloud product instances in the cloud product instance group when the service quality data is judged not to be in the target service quality interval.
14. The apparatus of claim 13, wherein when the user behavior data comprises a type of user operation for the cloud product instance, the apparatus further comprises:
the first judging module is used for judging that the cloud product example is not matched with the user service data in the cloud product example if the service quality data corresponding to the user operation type is judged not to be in a target service quality interval, wherein the target service quality interval is determined according to the service quality characteristic data corresponding to the user operation type in a historical time period.
15. The apparatus of claim 14, wherein the first determining module is preceded by:
a second determination module, configured to record, as an abnormal event, an event that the service quality data corresponding to the user operation type at the current time is not within the target service quality interval if it is determined that the service quality data corresponding to the user operation type at the current time is not within the target service quality interval, and determine an abnormal score corresponding to the user operation type based on an abnormal score model and the abnormal event;
and the third judging module is used for judging that the cloud product instance is not matched with the user service data if judging that the abnormal score is continuously increased in the current time period.
16. The apparatus of claim 13, wherein when the user behavior data comprises a type of user operation for the cloud product instance,
before the second determining module, the apparatus further includes:
a third determining module, configured to determine distribution data corresponding to the user operation type in a historical time period;
and the clustering module is used for clustering all cloud product examples on the cloud platform based on the distribution data so as to obtain a plurality of groups of all cloud product examples on the cloud platform.
17. The apparatus of claim 13, wherein the second determining module is specifically configured to:
determining a mode of metadata of all cloud product instances in the group as metadata of the cloud product to be recommended;
and determining the cloud product to be recommended based on the metadata of the cloud product to be recommended.
18. The apparatus of claim 17, further comprising:
the generation module is used for generating recommendation information of the cloud product to be recommended based on the metadata of the cloud product to be recommended;
and the sending module is used for sending the recommendation message to terminal equipment held by the user.
19. The apparatus of claim 18, further comprising:
a receiving module, configured to receive a feedback message for the recommendation message sent by the terminal device;
and the migration module is used for migrating the user service data in the cloud product example to the cloud product to be recommended based on the feedback message.
20. The apparatus of claim 19, wherein when the feedback message includes migration time information of the user service data,
the migration module is specifically configured to:
and migrating the user service data to the cloud product to be recommended based on the migration time information.
21. An electronic device, comprising:
one or more processors;
a computer readable medium configured to store one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method for recommending cloud products according to any of claims 1-12.
22. A computer-readable medium on which a computer program is stored, the program, when executed by a processor, implementing the method for recommending a cloud product according to any of claims 1-12.
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