WO2020238712A1 - 云产品的推荐方法、装置、电子设备及计算机可读介质 - Google Patents

云产品的推荐方法、装置、电子设备及计算机可读介质 Download PDF

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WO2020238712A1
WO2020238712A1 PCT/CN2020/091169 CN2020091169W WO2020238712A1 WO 2020238712 A1 WO2020238712 A1 WO 2020238712A1 CN 2020091169 W CN2020091169 W CN 2020091169W WO 2020238712 A1 WO2020238712 A1 WO 2020238712A1
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cloud
cloud product
data
user
instance
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PCT/CN2020/091169
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English (en)
French (fr)
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谢瑀
高玉嵩
张云杨
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阿里巴巴集团控股有限公司
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Publication of WO2020238712A1 publication Critical patent/WO2020238712A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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

Definitions

  • the embodiments of the application relate to the field of Internet technology, and in particular to a method, device, electronic device, and computer-readable medium for recommending cloud products.
  • cloud products such as cloud computing products, cloud security products, and cloud databases.
  • cloud computing products such as cloud computing products, cloud security products, and cloud databases.
  • cloud security products such as cloud security products
  • cloud databases In the initial stage of user business, a single or a few cloud products are often used as the medium for carrying business. With the growth and changes of user business, the original cloud products can no longer meet the needs of the current business. For example, retrieval under the gradually increasing large-scale data often requires sharding to alleviate the performance bottleneck of a single cloud database and is engaged in material processing. The business gradually transitioned to a mixed transaction analysis and processing business. At this time, users are required to adjust the cloud products currently used to appropriate cloud products to better meet the current business needs.
  • the purpose of this application is to propose a method, device, electronic device, and computer readable medium for recommending cloud products to solve the problem of how to automatically recommend cloud products that achieve the target service quality in the prior art.
  • a method for recommending cloud products includes: determining service quality data corresponding to user behavior data of a cloud product instance, where the cloud product instance is a service process running the cloud product distributed on a physical machine; and determining that the service quality data is not in the target service When within the quality interval, determine the cloud product to be recommended based on the metadata of the cloud product instance in the group where the cloud product instance is located.
  • a recommending device for cloud products includes: a first determining module for determining service quality data corresponding to user behavior data of a cloud product instance, the cloud product instance being a service process running the cloud product allocated on a physical machine; and a second determining module , For determining the cloud product to be recommended based on the metadata of the cloud product instance in the group where the cloud product instance is located when it is determined that the service quality data is not within the target service quality range.
  • an electronic device including: one or more processors; a computer-readable medium configured to store one or more programs, when the one or more programs are The one or more processors execute, so that the one or more processors implement the cloud product recommendation method described in the first aspect of the foregoing embodiment.
  • a computer-readable medium having a computer program stored thereon, and when the program is executed by a processor, the cloud product recommendation method as described in the first aspect of the above-mentioned embodiment .
  • the service quality data corresponding to the user behavior data of the cloud product instance is determined.
  • the cloud product instance is the service process running the cloud product distributed on the physical machine, and the service is determined When the quality data is not within the target service quality range, the cloud product to be recommended is determined based on the metadata of the cloud product instance in the group where the cloud product instance is located.
  • the cloud product instance can be judged Whether the service quality data corresponding to the user behavior data is within the target service quality interval, and when it is determined that the service quality data is not within the target service quality interval, it can be based on the metadata of the cloud product instance in the group where the cloud product instance is located, Automatically recommend cloud products that achieve the target service quality, which can greatly improve the quality of cloud products.
  • it helps cloud product manufacturers guide users to use the right cloud products, reduce the after-sales operation and maintenance costs of cloud product manufacturers, so as to achieve the purpose of guaranteeing the service level agreement of cloud services and rationally planning cost resources.
  • FIG. 1 is a flowchart of the steps of a method for recommending cloud products in Embodiment 1 of this application;
  • FIG. 2 is a flowchart of the steps of a method for recommending a cloud database in Embodiment 2 of this application;
  • FIG. 3 is a schematic structural diagram of a recommending device for a cloud product in Embodiment 3 of this application;
  • FIG. 4 is a schematic structural diagram of a recommending device for a cloud product in Embodiment 4 of the application;
  • FIG. 5 is a schematic structural diagram of a recommending device for a cloud product in Embodiment 5 of this application;
  • FIG. 6 is a schematic diagram of the structure of the electronic device in the sixth embodiment of the application.
  • FIG. 7 is the hardware structure of the electronic device in the seventh embodiment of the application.
  • the cloud product recommendation method includes the following steps:
  • step S101 the service quality data corresponding to the user behavior data of the cloud product instance is determined.
  • the cloud products may include cloud computing products, cloud security products, and cloud databases.
  • the instance can be understood as a virtual machine or an application program allocated on a physical machine.
  • the cloud product instance can be understood as a service process for running the cloud product distributed on a physical machine.
  • an instance corresponds to a service product purchased by a user.
  • a process of running the cloud product is started on the physical machine of the back-end system.
  • This is an instance of the cloud product.
  • the user behavior data can be understood as the user's operation data for the cloud product instance, for example, an operation instruction written in SQL (Structured Query Language) language, and the type of user operation involved in the operation instruction.
  • SQL Structured Query Language
  • the service quality data includes service delay data and/or resource consumption data.
  • the service delay data may be understood as the delay response time corresponding to the user executing each operation instruction for the cloud product instance.
  • the consumable resources can be understood as consumable resources, such as CPU, memory, disk, etc.
  • the resource consumption data may include the number of read and write operations performed by the storage device per second. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • the service quality data corresponding to the user behavior data of the cloud product instance when determining the service quality data corresponding to the user behavior data of the cloud product instance, obtain the service delay data and the service delay data corresponding to the user behavior data of the cloud product instance from the time series database of the cloud product management and control system / Or resource consumption data. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • step S102 when it is determined that the service quality data is not within the target service quality range, the cloud product to be recommended is determined based on the metadata of the cloud product instance in the group where the cloud product instance is located.
  • the target service quality interval may be a pre-configured service quality target interval, or may also be a real-time determined service quality target interval.
  • the target service quality interval includes a service delay allowable interval and/or a resource consumption allowable interval.
  • the metadata includes at least one of the following: category information, version information, and specification configuration information corresponding to the cloud product instance. Specifically, the metadata of the cloud product instance is obtained from the metadata database of the cloud product management and control system. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • the method further includes: if it is determined that the service quality data corresponding to the user operation type is not in the target service Within the quality interval, it is determined that the cloud product instance does not match the user business data in the cloud product instance, wherein the target service quality interval is the service quality corresponding to the historical time period according to the user operation type
  • the characteristic data is determined.
  • the target service quality interval is determined according to the service quality characteristic data and the service quality fluctuation threshold.
  • the service quality fluctuation threshold can be set by those skilled in the art according to actual needs, which is not limited in the embodiment of the present application.
  • the service quality characteristic data includes at least one of the following: the mean, variance, median, and mode of the multiple service quality data corresponding to the user operation type in the historical time period.
  • the cloud product instance does not match the user business data in the cloud product instance, where The service delay allowable interval is determined according to the service delay characteristic data corresponding to the user operation type in the historical time period; and/or if it is determined that the resource consumption data corresponding to the user operation type is not in resource consumption Within the allowable interval, it is determined that the cloud product instance does not match the user business data in the cloud product instance, wherein the resource consumption allowable interval corresponds to the historical time period according to the user operation type
  • the resource consumption characteristic data is determined.
  • the user operation type may include an insert operation, a delete operation, an update operation, a modification operation, an empty operation, and a selection operation.
  • the service delay characteristic data includes at least one of the following: the mean, variance, median, and mode of the multiple service delay data corresponding to the user operation type in the historical time period.
  • the service delay allowable interval is determined according to the service delay characteristic data and the service delay fluctuation threshold. The sum of the service delay characteristic data and the service delay fluctuation threshold is determined as the upper limit of the service delay allowable interval, and the difference between the service delay characteristic data and the service delay fluctuation threshold is determined as the service delay allowable The lower limit of the interval.
  • the service delay fluctuation threshold can be set by those skilled in the art according to actual needs, which is not limited in the embodiment of the present application. More specifically, when the service delay characteristic data is the variance of the multiple service delay data, the user is determined based on the service delay data corresponding to the user operation type and the average value of the multiple service delay data The variance of the service delay data corresponding to the operation type; and then determine whether the variance of the service delay data corresponding to the user operation type is within the service delay allowable interval. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • the resource consumption characteristic data includes at least one of the following: the mean, variance, median, and value of multiple resource consumption data corresponding to the user operation type in the historical time period Mode.
  • the resource consumption allowable interval is determined according to the resource consumption characteristic data and the resource consumption fluctuation threshold.
  • the sum of the resource consumption characteristic data and the resource consumption fluctuation threshold is determined as the upper limit of the resource consumption allowance interval, and the difference between the resource consumption characteristic data and the resource consumption fluctuation threshold is determined as the resource consumption allowance The lower limit of the interval.
  • the resource consumption fluctuation threshold can be set by those skilled in the art according to actual needs, which is not limited in the embodiment of the present application.
  • the resource consumption characteristic data is the variance of the multiple resource consumption data
  • the user is determined based on the resource consumption data corresponding to the user operation type and the average value of the multiple resource consumption data The variance of the resource consumption data corresponding to the operation type; and then determine whether the variance of the resource consumption data corresponding to the user operation type is within the resource consumption allowable interval.
  • the method before determining that the cloud product instance does not match the user business data in the cloud product instance, the method further includes: if it is determined that the user operation type corresponds to the current moment If the service quality data is not within the target service quality range, then the event that the service quality data corresponding to the user operation type at the current moment is not within the target service quality range is recorded as an abnormal event, and based on the abnormality
  • the scoring model and the abnormal event determine the abnormal score corresponding to the user operation type; if it is determined that the abnormal score continues to increase in the current time period, it is determined that the cloud product instance does not match the user business data.
  • the abnormal scoring model can be constructed by forgetting function.
  • the abnormality score obtained from abnormal events in which the service quality data corresponding to the user operation type at the current moment is not within the service quality allowable interval within the current time period can automatically detect changes in user business data, thereby being accurate To determine whether the cloud product instance matches the user business data in the cloud product instance.
  • the method before determining that the cloud product instance does not match the user service data in the cloud product instance, the method further includes: if determining the service corresponding to the user operation type at the current moment If the delay data is not within the service delay allowable interval, the event that the service delay data corresponding to the user operation type at the current moment is not within the service delay allowable interval is recorded as an abnormal event, and is based on the abnormality score
  • the model and the abnormal event determine the abnormal score corresponding to the user operation type; if it is determined that the abnormal score continues to increase in the current time period, it is determined that the cloud product instance does not match the user business data.
  • the abnormal scoring model can be constructed by forgetting function.
  • the abnormality score obtained from abnormal events in which the service delay data corresponding to the user operation type at the current time is not within the service delay allowable interval during the current time period can automatically detect changes in the user's business data, so as to be accurate To determine whether the cloud product instance matches the user business data in the cloud product instance.
  • the method further includes: if determining the resource corresponding to the user operation type at the current moment If the consumption data is not within the resource consumption allowable interval, then the event that the resource consumption data corresponding to the user operation type at the current moment is not within the resource consumption allowable interval is recorded as an abnormal event, and is based on the abnormality score
  • the model and the abnormal event determine the abnormal score corresponding to the user operation type; if it is determined that the abnormal score continues to increase in the current time period, it is determined that the cloud product instance does not match the user business data.
  • the abnormal scoring model can be constructed by forgetting function.
  • the changes in the abnormal score obtained during the current time period from the abnormal events in which the resource consumption data corresponding to the user operation type at the current time are not within the resource consumption allowable interval can automatically detect the changes in the user's business data, so as to be accurate To determine whether the cloud product instance matches the user business in the cloud product instance.
  • the method further includes: if determining the resource corresponding to the user operation type at the current moment The consumption data is not within the resource consumption allowable interval, and the service delay data corresponding to the user operation type at the current moment is not within the service delay allowable interval, then the user operation type is set in the current The event that the resource consumption data corresponding to the time is not within the resource consumption allowable interval, and the service delay data corresponding to the user operation type at the current time is not within the service delay allowable interval is recorded as an abnormal event, And based on the abnormal scoring model and the abnormal event, determine the abnormal score corresponding to the user operation type; if it is determined that the abnormal score continues to increase in the current time period, it is determined that the cloud product instance and the user business data Mismatch.
  • the abnormal scoring model can be constructed by forgetting function.
  • Changes in the current time period can detect changes in user business data more accurately, and thus can more accurately determine whether the cloud product instance matches the user business data in the cloud product instance.
  • the method 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 includes: determining that the user operation type is Corresponding distribution data in the historical time period; based on the distribution data, perform a clustering operation on all cloud product instances on the cloud platform to obtain multiple groups of all cloud product instances on the cloud platform. In this way, clustering operations on all cloud product instances on the cloud platform can ensure the similarity of all cloud product instances in each group through the distribution data corresponding to the user operation type. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • the frequency of occurrence of each user operation type of the cloud product instance in the 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 type for the cloud product instance is obtained, for example, insert operation type accounts for 70%, update operation type accounts for 10%, and select operation Type accounts for 20% and so on.
  • the clustering method of K-Means can be used, based on the distribution of the user operation types of all cloud product instances on the cloud platform in the historical time period Data, perform a clustering operation on all cloud product instances on the cloud platform to obtain multiple groups of all cloud product instances on the cloud platform.
  • the distribution data of its user operation types are:
  • D A ⁇ insert operation: 0.7, update operation: 0.1, select operation: 0.2 ⁇ ;
  • the distribution data of its user operation types are:
  • the determining the cloud product to be recommended based on the metadata of the cloud product instance in the group where the cloud product instance is located includes: grouping the metadata of all cloud product instances in the group The number is determined as the metadata of the cloud product to be recommended; based on the metadata of the cloud product to be recommended, the cloud product to be recommended is determined. With this, the cloud product to be recommended can be accurately determined. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • the metadata of the cloud product instance includes the category information, version information, and specification configuration information of the cloud product instance
  • determine the first number of cloud product instances in the group that have the same category information based on The first number determines the category information of the cloud product to be recommended; determines the second number of cloud product instances in the group with the same version information; determines the version information of the cloud product to be recommended based on the second number; Determine the third number of cloud product instances in the group that have the same specification configuration information; determine the specification configuration information of the cloud product to be recommended based on the third number.
  • the category information corresponding to the maximum value of the first number is determined as the category information of the cloud product to be recommended.
  • the version information corresponding to the maximum value of the second number is determined as the version information of the cloud product to be recommended.
  • the specification configuration information corresponding to the maximum value of the third number is determined as the specification configuration information of the cloud product to be recommended.
  • the method further includes: 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 a terminal held by the user equipment.
  • the recommendation message can be sent to the terminal device held by the user in a short message or a mailbox. In this way, by sending a recommendation message to the terminal device held by the user, the user can know the metadata of the recommended cloud product, and then decide whether to accept the recommendation of the cloud product.
  • the method further includes: receiving a feedback message for the recommendation message sent by the terminal device; based on the feedback message, migrating the user service data in the cloud product instance to the Describe the cloud products to be recommended. In this way, the user business data in the cloud product instance can be migrated to the cloud product to be recommended based on the user's feedback message.
  • the user business data in the cloud product instance is not migrated to the cloud product to be recommended.
  • the user business data in the cloud product instance is migrated to the cloud product to be recommended.
  • the user business data in the cloud product instance is imported into the cloud product to be recommended through the transmission tool. After completing the migration of the user's business data, introduce the user's connection to the cloud product to be recommended.
  • the user service data in the cloud product instance is migrated to the to-be-recommended based on the feedback message
  • the cloud product includes: migrating the user service data to the cloud product to be recommended based on the migration time information.
  • the user's business data can be migrated according to the user's migration time requirements.
  • the migration time information is a time point, based on the time point, the user business data in the cloud product instance is migrated to the cloud product to be recommended.
  • the migration time information is a time period, based on the time period, the user service data in the cloud product instance is migrated to the cloud product to be recommended.
  • the service quality data corresponding to the user behavior data of the cloud product instance is determined.
  • the cloud product instance is the service process running the cloud product distributed on the physical machine, and is determined.
  • the cloud product to be recommended is determined based on the metadata of the cloud product instance in the group where the cloud product instance is located. Compared with other existing methods, the cloud product can be judged.
  • the service quality data corresponding to the user behavior data of the product instance is within the target service quality interval, and when it is determined that the service quality data is not within the target service quality interval, it can be determined according to the cloud product instance in the group where the cloud product instance is located Metadata automatically recommends cloud products that meet the target service quality, which can greatly improve the quality of cloud products. In addition, it helps cloud product manufacturers guide users to use the right cloud products, reduce the after-sales operation and maintenance costs of cloud product manufacturers, so as to achieve the purpose of guaranteeing the service level agreement of cloud services and rationally planning cost resources.
  • the method for recommending cloud products in this embodiment can be executed by any appropriate device with data processing capabilities, including but not limited to: cameras, terminals, mobile terminals, PCs, servers, in-vehicle devices, entertainment devices, advertising devices, and personal digital devices.
  • Assistant PDA
  • tablet computer notebook computer
  • handheld game console smart glasses
  • smart watch wearable device
  • virtual display device or display enhancement device such as Google Glass, Oculus Rift, Hololens, Gear VR
  • FIG. 2 there is shown a flowchart of the steps of a method for recommending a cloud database in the second embodiment of the present application.
  • the cloud database recommendation method of this embodiment includes the following steps:
  • step S201 the service quality data corresponding to the user behavior data of the cloud database instance is determined.
  • the cloud database instance can be understood as a service process running a cloud database distributed on a physical machine.
  • a cloud database instance corresponds to a cloud database service product purchased by a user.
  • the user behavior data may be understood as user operation data for the cloud database instance, for example, SQL operation instructions in the log audit data of the cloud database instance, user operation types corresponding to the SQL operation instructions, etc.
  • the log audit data includes the content of the SQL operation instruction.
  • the service quality data includes service delay data and/or resource consumption data. Wherein, the service delay data can be understood as the delay response time corresponding to the user executing each SQL operation instruction for the cloud database instance. The meaning of the resource consumption data is similar to the above, and will not be repeated here. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • the service delay data and the service delay data corresponding to the user behavior data of the cloud database instance are obtained from the time series database of the management and control system of the cloud database. / Or resource consumption data. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • step S202 when it is determined that the service quality data is not within the target service quality range, the cloud database to be recommended is determined based on the metadata of the cloud database instance in the group where the cloud database instance is located.
  • the target service quality interval may be a pre-configured service quality target interval, or may also be a real-time determined service quality target interval.
  • the target service quality interval includes an allowable service delay interval and/or an allowable resource consumption interval.
  • the metadata of the cloud database instance may include at least one of the following: type information of the 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 with time. Specifically, the metadata of the cloud database instance is obtained from the metadata database of the management and control system of the cloud database. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • the method further includes: if it is determined that the user operation type corresponds to the resource consumption data at the current moment Is not within the resource consumption allowable interval, and the service delay data corresponding to the user operation type at the current time is not within the service delay allowable interval, then the user operation type is set at the current time
  • An event in which the corresponding resource consumption data is not within the resource consumption allowable interval and the service delay data corresponding to the user operation type at the current moment is not within the service delay allowable interval is recorded as an abnormal event and is based on The abnormal score model and the abnormal event determine the abnormal score corresponding to the user operation type; if it is determined that the abnormal score continues to increase in the current time period, it is determined that the cloud database instance and the cloud database instance User business data does not match.
  • the abnormal scoring model can be constructed by forgetting function.
  • Changes in the current time period can more accurately detect changes in user business data, and thus can more accurately determine whether the cloud database instance matches the user business data in the cloud database instance.
  • real-time monitoring data delay and resource consumption data for a service cloud database instance I i user operation type S i if a service delay
  • the data exceeds the service delay allowable interval (Among them, ⁇ 1 is the service delay fluctuation threshold), and the resource consumption data also exceeds the resource consumption allowable interval (Where ⁇ 2 is the resource consumption fluctuation threshold), this event is recorded as an abnormal event t is the time when the abnormal event occurs. among them, Represents a delay characteristic data for the cloud database instance I i user operation type S i in a historical time period ⁇ t1 corresponding service, i.e.
  • the mean, variance, median, or mode of each service delay data It represents for resource consumption characteristics of cloud database instance I i user operation type S i in a historical time period ⁇ t1 corresponding data, i.e. for the cloud database instance I i user operation type S i in a historical time period ⁇ t1 corresponding plurality
  • the mean, variance, median or mode of each resource consumption data In particular, from the timing control database cloud database system pulls the delayed data and a plurality of resource consumption data for a plurality of services cloud database instance user operation type I i S i of the historical time period ⁇ t1.
  • the method when the user behavior data is specifically a user operation type for the cloud database instance, before the determining the cloud database to be recommended, the method further includes: determining the user operation type Corresponding distribution data in the historical time period; based on the distribution data, perform a clustering operation on all cloud database instances on the cloud platform to obtain multiple groups of all cloud database instances on the cloud platform. In this way, clustering operations on all cloud database instances on the cloud platform can ensure the similarity of all cloud database instances in each group through the distributed data corresponding to the user operation type. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • the log audit data for the cloud database instance I i in the historical time period ⁇ t1 is pulled from the time series database of the cloud database management and control system.
  • the log audit data includes user operation data of the user for the cloud database instance I i in the historical time period ⁇ t1.
  • the statistical distribution of the data the user operation type S i in a historical time period ⁇ t1 denoted Based on all online cloud database instances I i Perform clustering to obtain n groups of cloud database instances: DG 1 ,..., DG i ,..., DG n .
  • the determining the cloud database to be recommended based on the metadata of the cloud database instance in the group where the cloud database instance is located includes: grouping the metadata of all cloud database instances in the group The number is determined as the metadata of the cloud database to be recommended; based on the metadata of the cloud database to be recommended, the cloud database to be recommended is determined. With this, the cloud database to be recommended can be accurately determined. It can be understood that the above description is only exemplary, and the embodiments of the present application do not make any limitation thereto.
  • the metadata for the cloud database instance I i in the historical time period ⁇ t1 is pulled from the metadata database of the cloud database management and control system.
  • For each group DG i extract the metadata of each cloud database instance in the group, and extract the mode index based on the metadata to obtain the metadata of the cloud database to be recommended corresponding to each group DG i .
  • the method further includes: 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 a terminal held by the user equipment.
  • the recommendation message can be sent to the terminal device held by the user in a short message or a mailbox. In this way, by sending a recommendation message to the terminal device held by the user, the user can know the metadata of the recommended cloud database, and then decide whether to accept the recommendation of the cloud database.
  • the method further includes: receiving a feedback message for the recommendation message sent by the terminal device; based on the feedback message, migrating user service data in the cloud database instance to all In the cloud database to be recommended. In this way, the user business data in the cloud database instance can be migrated to the cloud database to be recommended based on the user's feedback message.
  • the user business data in the cloud database instance is not migrated to the cloud database to be recommended.
  • the user service data in the cloud database instance is migrated to the cloud database to be recommended.
  • the user business data in the cloud database instance is imported into the cloud database to be recommended through the transmission tool. After completing the migration of the user's business data, the user's connection is introduced to the cloud database to be recommended.
  • the user service data in the cloud database instance is migrated to the to-be-recommended based on the feedback message
  • the cloud database includes: migrating the user service data to the cloud database to be recommended based on the migration time information. In this way, the user's business data can be migrated according to the user's migration time requirements.
  • the migration time information is a time point, based on the time point, the user service data in the cloud database instance is migrated to the cloud database to be recommended.
  • the migration time information is a time period, based on the time period, the user service data in the cloud database instance is migrated to the cloud database to be recommended.
  • the service quality data corresponding to the user behavior data of the cloud database instance is determined.
  • the cloud database instance is the service process running the cloud database distributed on the physical machine, and is determined
  • the cloud database to be recommended is determined based on the metadata of the cloud database instance in the group where the cloud database instance is located.
  • the cloud database can be judged Whether the service quality data corresponding to the user behavior data of the database instance is within the target service quality range, and when it is determined that the service quality data is not within the target service quality range, it can be based on the cloud database instance in the group where the cloud database instance is located.
  • Metadata automatically recommends cloud databases that achieve the target service quality, which can greatly improve the quality of cloud database usage.
  • cloud database vendors guide users to use the correct cloud database and reduce the after-sales operation and maintenance costs of cloud database vendors, so as to achieve the purpose of ensuring service level agreements for cloud services and reasonably planning cost resources.
  • the cloud database recommendation method of this embodiment can be executed by any appropriate device with data processing capabilities, including but not limited to: cameras, terminals, mobile terminals, PCs, servers, vehicle-mounted devices, entertainment devices, advertising devices, personal digital Assistant (PDA), tablet computer, notebook computer, handheld game console, smart glasses, smart watch, wearable device, virtual display device or display enhancement device (such as Google Glass, Oculus Rift, Hololens, Gear VR), etc.
  • PDA personal digital Assistant
  • tablet computer notebook computer
  • handheld game console smart glasses
  • smart watch wearable device
  • virtual display device or display enhancement device such as Google Glass, Oculus Rift, Hololens, Gear VR
  • FIG. 3 it shows a schematic structural diagram of a recommending device for cloud products in Embodiment 3 of the present application.
  • the device for recommending cloud products in this embodiment includes: a first determining module 301, configured to determine service quality data corresponding to user behavior data of cloud product instances, where the cloud product instances are allocated on physical machines to run cloud products Service process; a second determining module 302, used to determine the cloud product to be recommended based on the metadata of the cloud product instance in the group where the cloud product instance is located when determining that the service quality data is not within the target service quality range .
  • the cloud product recommendation apparatus of this embodiment is used to implement the corresponding cloud product recommendation method in the foregoing multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.
  • FIG. 4 a schematic structural diagram of a recommending device for cloud products in the fourth embodiment of the present application is shown.
  • the device for recommending cloud products in this embodiment includes: a first determining module 401 for determining service quality data corresponding to user behavior data of a cloud product instance, where the cloud product instance is allocated on a physical machine to run the cloud product Service process; a second determining module 402, used to determine the cloud product to be recommended based on the metadata of the cloud product instance in the group where the cloud product instance is located when determining that the service quality data is not within the target service quality range .
  • the apparatus when the user behavior data includes a user operation type for the cloud product instance, the apparatus further includes: a first determination module 405, configured to determine the service quality data corresponding to the user operation type If it is not within the target service quality interval, it is determined that the cloud product instance does not match the user business data in the cloud product instance, wherein the target service quality interval is based on the user operation type in the historical time period. The corresponding service quality characteristic data is determined.
  • a first determination module 405 configured to determine the service quality data corresponding to the user operation type If it is not within the target service quality interval, it is determined that the cloud product instance does not match the user business data in the cloud product instance, wherein the target service quality interval is based on the user operation type in the historical time period. The corresponding service quality characteristic data is determined.
  • the device before the first determining module 405, the device further includes: a second determining module 403, configured to determine if the service quality data corresponding to the user operation type at the current moment is not in the target service quality In the interval, the event that the service quality data corresponding to the user operation type at the current moment is not within the target service quality interval is recorded as an abnormal event, and based on the abnormal scoring model and the abnormal event, all the events are determined
  • the abnormal score corresponding to the user operation type; the third determining module 404 is configured to determine that the cloud product instance does not match the user business data if it is determined that the abnormal score continues to increase in the current time period.
  • the cloud product recommendation apparatus of this embodiment is used to implement the corresponding cloud product recommendation method in the foregoing multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.
  • FIG. 5 shows a schematic structural diagram of a recommending device for cloud products in Embodiment 5 of the present application.
  • the device for recommending cloud products in this embodiment includes: a first determining module 501 for determining service quality data corresponding to user behavior data of a cloud product instance, where the cloud product instance is allocated on a physical machine to run the cloud product Service process; the second determining module 502 is used to determine the cloud product to be recommended based on the metadata of the cloud product instance in the group where the cloud product instance is located when determining that the service quality data is not within the target service quality range .
  • the apparatus further includes: a third determining module 503, configured to determine the user The distribution data corresponding to the operation type in the historical time period; the clustering module 504 is configured to perform clustering operations on all cloud product instances on the cloud platform based on the distribution data to obtain all cloud product instances on the cloud platform Multiple groupings.
  • the second determining module 502 is specifically configured to: determine the mode of the metadata of all cloud product instances in the group as the metadata of the cloud product to be recommended; The metadata of the cloud product determines the cloud product to be recommended.
  • the device further includes: 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 transfer the recommended cloud product The message is sent to the terminal device held by the user.
  • 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 transfer the recommended cloud product The message is sent to the terminal device held by the user.
  • the apparatus further includes: 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 transfer the cloud product instance based on the feedback message The user business data in is migrated to the cloud product to be recommended.
  • 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 transfer the cloud product instance based on the feedback message The user business data in is migrated to the cloud product to be recommended.
  • the migration module 508 is specifically configured to: based on the migration time information, migrate the user service data to the to-be-recommended Cloud products.
  • the cloud product recommendation apparatus of this embodiment is used to implement the corresponding cloud product recommendation method in the foregoing multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.
  • FIG. 6 is a schematic structural diagram of an electronic device in Embodiment 6 of this application; the electronic device may include:
  • the computer-readable medium 602 can be configured to store one or more programs,
  • the one or more processors When the one or more programs are executed by the one or more processors, the one or more processors implement the recommended method for cloud products as described in the first embodiment, or implement the method as described in the second embodiment above.
  • the recommended method of the cloud database When the one or more programs are executed by the one or more processors, the one or more processors implement the recommended method for cloud products as described in the first embodiment, or implement the method as described in the second embodiment above.
  • the recommended method of the cloud database When the one or more programs are executed by the one or more processors, the one or more processors implement the recommended method for cloud products as described in the first embodiment, or implement the method as described in the second embodiment above.
  • the recommended method of the cloud database When the one or more programs are executed by the one or more processors, the one or more processors implement the recommended method for cloud products as described in the first embodiment, or implement the method as described in the second embodiment above.
  • the recommended method of the cloud database When the one or more programs are executed by the one or more processors, the one or more
  • FIG. 7 is the hardware structure of the electronic device in the seventh embodiment of the 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 communicate with each other through the communication bus 704;
  • 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 determine service quality data corresponding to user behavior data of a cloud product instance, where the cloud product instance is a service process running the cloud product allocated on a physical machine; and when determining the service quality When the data is not within the target service quality range, the cloud product to be recommended is determined based on the metadata of the cloud product instance in the group where the cloud product instance is located.
  • the processor 701 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), etc. ), ready-made programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.
  • the methods, steps, and logical block diagrams disclosed in the embodiments of the present application can be implemented or executed.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the computer-readable medium 703 may be, but is not limited to, a random access storage medium (Random Access Memory, RAM), a read-only storage medium (Read Only Memory, ROM), and a programmable read-only storage medium (Programmable Read-Only Memory, PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.
  • RAM Random Access Memory
  • ROM Read Only Memory
  • PROM Programmable Read-Only Memory
  • EPROM Erasable Programmable Read-Only Memory
  • EEPROM Electrically Erasable Programmable Read-Only Memory
  • an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code configured to execute the method shown in the flowchart.
  • the computer program may be downloaded and installed from the network through the communication part, and/or installed from a removable medium.
  • CPU central processing unit
  • the above-mentioned functions defined in the method of the present application are executed.
  • the computer-readable medium described in this application may 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 electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access storage media (RAM), read-only storage media (ROM), erasable Type programmable read-only storage medium (EPROM or flash memory), optical fiber, portable compact disk read-only storage medium (CD-ROM), optical storage medium, magnetic storage medium, or any suitable combination of the above.
  • RAM random access storage media
  • ROM read-only storage media
  • EPROM or flash memory erasable Type programmable read-only storage medium
  • CD-ROM portable compact disk read-only storage medium
  • magnetic storage medium or any suitable combination of the above.
  • the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in a baseband or as a part of a carrier wave, and a computer-readable program code is carried therein. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium.
  • the computer-readable medium may send, propagate, or transmit a program configured to be used by or in combination with the instruction execution system, apparatus, or device .
  • the program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.
  • the computer program code configured to perform the operations of the present application can be written in one or more programming languages or a combination thereof.
  • the programming languages include object-oriented programming languages—such as Java, Smalltalk, C++, and also conventional Procedural programming language-such as "C" language or similar programming language.
  • the program code can be executed entirely on the user's computer, partly on the user's computer, executed as an independent software package, partly on the user's computer and partly executed on a remote computer, or entirely executed on the remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network: including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect to the Internet connection).
  • LAN local area network
  • WAN wide area network
  • each block in the flowchart or block diagram can represent a module, program segment, or part of code, and the module, program segment, or part of code contains one or more configurations to achieve the specified logical function Executable instructions.
  • sequence relationships there are specific sequence relationships, but these sequence relationships are only exemplary. In specific implementation, these steps may be fewer, more, or the execution order may be adjusted. That is, in some alternative implementations, the functions marked in the block may also occur in a different order from the order marked in the drawings.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or operations Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the modules involved in the embodiments described in the present application can be implemented in software or hardware.
  • the described module may also be provided in the processor, for example, it may be described as: a processor includes a first determining module and a second determining module. Among them, the names of these modules do not constitute a limitation on the module itself under certain circumstances.
  • the first determining module can also be described as "a module that determines the service quality data corresponding to the user behavior data of the cloud product instance" .
  • the present application also provides a computer-readable medium on which a computer program is stored.
  • the program is executed by a processor, the method for recommending cloud products as described in the first embodiment is implemented, or The recommended method for the cloud database described in the second embodiment.
  • the present application also provides a computer-readable medium, which may be included in the device described in the above-mentioned embodiments; or it may exist alone without being assembled into the device.
  • the above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the device, the device: determines the service quality data corresponding to the user behavior data of the cloud product instance, and the cloud product instance It is the service process of running cloud products allocated on physical machines; when it is determined that the service quality data is not within the target service quality range, based on the metadata of the cloud product instance in the group where the cloud product instance is located, determine the to-be-recommended Cloud products.
  • first, second, the first or “the 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 corresponding components.
  • the above expressions are only configured for the purpose of distinguishing elements from other elements.
  • the first user equipment and the second user equipment represent different user equipment, although both are user equipment.
  • the first element may be referred to as the second element, and similarly, the second element may be referred to as the first element.
  • an element for example, a first element
  • another element for example, a second element
  • an element e.g., a second element
  • an element e.g., a second element
  • the one element is directly connected to the other element or the one element passes through another element (e.g., The third element) is indirectly connected to the other element.

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Abstract

一种云产品的推荐方法、装置、电子设备及计算机可读介质，涉及互联网技术领域。所述方法包括：确定云产品实例的用户行为数据所对应的服务质量数据（S101），所述云产品实例为在物理机器上分配的运行云产品的服务进程；在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品（S102）。该方法能够自动推荐达到目标服务质量的云产品，从而能够大幅度地提高云产品的使用质量。此外，有助于云产品厂商引导用户使用正确的云产品，减少云产品厂商的售后运维成本，从而达到保障云服务的服务等级协议，并合理规划成本资源的目的。

Description

云产品的推荐方法、装置、电子设备及计算机可读介质
本申请要求2019年05月30日递交的申请号为201910463374.X、发明名称为“云产品的推荐方法、装置、电子设备及计算机可读介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请实施例涉及互联网技术领域，尤其涉及一种云产品的推荐方法、装置、电子设备及计算机可读介质。
背景技术
随着网络技术的发展，云产品越来越多，例如，云计算产品、云安全产品和云数据库等。在用户业务初期，常使用单一或者少许云产品作为承载业务的媒介。随着用户业务的增长以及变化，原先的云产品已无法适应当前业务的需求，例如，在逐渐增长的大规模数据下做检索，经常需要分片以减缓单一云数据库的性能瓶颈，从事物处理业务逐渐过渡到混合事物分析处理业务等。此时，需要用户将当前使用的云产品调整为合适的云产品，去更好地满足当前业务的需求。然而，由于对当前使用的云产品的服务质量变化的弱感知，用户无法及时发现当前使用的云产品的服务质量不能满足当前业务的需求，往往在业务出现受损后，才开始排查根因，继而动用大量的成本将当前使用的云产品中的业务数据迁移至服务质量达标的云产品中。由此可见，如何自动推荐达到目标服务质量的云产品是当前亟待解决的技术问题。
发明内容
本申请的目的在于提出一种云产品的推荐方法、装置、电子设备及计算机可读介质，用于解决现有技术中存在的如何自动推荐达到目标服务质量的云产品的问题。
根据本申请实施例的第一方面，提供了一种云产品的推荐方法。所述方法包括：确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品实例为在物理机器上分配的运行云产品的服务进程；在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品。
根据本申请实施例的第二方面，提供了一种云产品的推荐装置。所述装置包括：第一确定模块，用于确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品 实例为在物理机器上分配的运行云产品的服务进程；第二确定模块，用于在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品。
根据本申请实施例的第三方面，提供了一种电子设备，包括：一个或多个处理器；计算机可读介质，配置为存储一个或多个程序，当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如上述实施例的第一方面所述的云产品的推荐方法。
根据本申请实施例的第四方面，提供了一种计算机可读介质，其上存储有计算机程序，该程序被处理器执行时实现如上述实施例的第一方面所述的云产品的推荐方法。
通过本申请实施例提供的技术方案，确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品实例为在物理机器上分配的运行云产品的服务进程，并在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品，与现有的其它方式相比，能够判断云产品实例的用户行为数据所对应的服务质量数据是否处于目标服务质量区间内，并且在判定服务质量数据不处于目标服务质量区间内时，能够根据所述云产品实例所在分组中的云产品实例的元数据，自动推荐达到目标服务质量的云产品，从而能够大幅度地提高云产品的使用质量。此外，有助于云产品厂商引导用户使用正确的云产品，减少云产品厂商的售后运维成本，从而达到保障云服务的服务等级协议，并合理规划成本资源的目的。
附图说明
通过阅读参照以下附图所作的对非限制性实施例所作的详细描述，本申请的其它特征、目的和优点将会变得更明显：
图1为本申请实施例一中云产品的推荐方法的步骤流程图；
图2为本申请实施例二中云数据库的推荐方法的步骤流程图；
图3为本申请实施例三中云产品的推荐装置的结构示意图；
图4为本申请实施例四中云产品的推荐装置的结构示意图；
图5为本申请实施例五中云产品的推荐装置的结构示意图；
图6为本申请实施例六中电子设备的结构示意图；
图7为本申请实施例七中电子设备的硬件结构。
具体实施方式
下面结合附图和实施例对本申请作进一步的详细说明。可以理解的是，此处所描述的具体实施例仅仅配置为解释相关发明，而非对该发明的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与有关发明相关的部分。
需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互组合。下面将参考附图并结合实施例来详细说明本申请。
参照图1，示出了本申请实施例一的云产品的推荐方法的步骤流程图。
具体地，本实施例提供的云产品的推荐方法包括以下步骤：
在步骤S101中，确定云产品实例的用户行为数据所对应的服务质量数据。
在本实施例中，所述云产品可包括云计算产品、云安全产品和云数据库等。所述实例可理解为在物理机器上分配的虚拟机器或者应用程序。所述云产品实例可理解为在物理机器上分配的运行云产品的服务进程。具体地，从业务产品这个维度来看，实例对应于用户购买的一个服务产品。从技术这个维度看，每当用户购买了一个云服务产品，在后台系统的物理机器上起一个运行云产品的进程，这个就是云产品实例。所述用户行为数据可理解为用户针对所述云产品实例的操作数据，例如，使用SQL(结构化查询语言)语言编写的操作指令，所述操作指令中涉及的用户操作类型等。所述服务质量数据包括服务延迟数据和/或资源消耗数据。其中，所述服务延迟数据可理解为用户执行针对所述云产品实例的每个操作指令时所对应的延迟响应时间。所述消耗资源可理解为具有可消耗性的资源，例如，CPU、内存、磁盘等。所述资源消耗数据可包括存储设备每秒进行读写操作的次数等。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，在确定云产品实例的用户行为数据所对应的服务质量数据时，从云产品的管控系统的时序数据库中获取云产品实例的用户行为数据所对应的服务延迟数据和/或资源消耗数据。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在步骤S102中，在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品。
在本实施例中，所述目标服务质量区间可为预先配置的服务质量目标区间，还可为实时确定的服务质量目标区间。所述目标服务质量区间包括服务延迟允许区间 和/或资源消耗允许区间。所述元数据包括以下中的至少一者：所述云产品实例所对应的类别信息、版本信息、规格配置信息。具体地，从云产品的管控系统的元数据库中获取云产品实例的元数据。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，当所述用户行为数据包括针对所述云产品实例的用户操作类型时，所述方法还包括：如果判定所述用户操作类型所对应的服务质量数据不处于目标服务质量区间内，则判定所述云产品实例与所述云产品实例中的用户业务数据不匹配，其中，所述目标服务质量区间是根据所述用户操作类型在历史时间段内所对应的服务质量特征数据确定得到的。具体地，根据所述服务质量特征数据和服务质量波动阈值确定所述目标服务质量区间。所述服务质量波动阈值可由本领域技术人员根据实际需要进行设定，本申请实施例对此不作任何限定。所述服务质量特征数据包括以下中的至少一者：所述用户操作类型在所述历史时间段内所对应的多个服务质量数据的均值、方差、中位数、众数。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，如果判定所述用户操作类型所对应的服务延迟数据不处于服务延迟允许区间内，则判定所述云产品实例与所述云产品实例中的用户业务数据不匹配，其中，所述服务延迟允许区间是根据所述用户操作类型在历史时间段内所对应的服务延迟特征数据确定得到的；和/或如果判定所述用户操作类型所对应的资源消耗数据不处于资源消耗允许区间内，则判定所述云产品实例与所述云产品实例中的用户业务数据不匹配，其中，所述资源消耗允许区间是根据所述用户操作类型在所述历史时间段内所对应的资源消耗特征数据确定得到的。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，所述用户操作类型可包括插入操作、删除操作、更新操作、修改操作、清空操作、选择操作等。所述服务延迟特征数据包括以下中的至少一者：所述用户操作类型在所述历史时间段内所对应的多个服务延迟数据的均值、方差、中位数、众数。具体地，根据所述服务延迟特征数据和服务延迟波动阈值确定所述服务延迟允许区间。将所述服务延迟特征数据与所述服务延迟波动阈值的和确定为所述服务延迟允许区间的上限，将所述服务延迟特征数据与所述服务延迟波动阈值的差确定为所述服务延迟允许区间的下限。其中，所述服务延迟波动阈值可由本领域技术人员根据实际需要进行设定，本申请实施例对此不做任何限定。更具 体地，当所述服务延迟特征数据为所述多个服务延迟数据的方差时，基于所述用户操作类型所对应的服务延迟数据和所述多个服务延迟数据的均值，确定所述用户操作类型所对应的服务延迟数据的方差；再判断所述用户操作类型所对应的服务延迟数据的方差是否处于所述服务延迟允许区间。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，所述资源消耗特征数据包括以下中的至少一者：所述用户操作类型在所述历史时间段内所对应的多个资源消耗数据的均值、方差、中位数、众数。具体地，根据所述资源消耗特征数据和资源消耗波动阈值确定所述资源消耗允许区间。将所述资源消耗特征数据与所述资源消耗波动阈值的和确定为所述资源消耗允许区间的上限，将所述资源消耗特征数据与所述资源消耗波动阈值的差确定为所述资源消耗允许区间的下限。其中，所述资源消耗波动阈值可由本领域技术人员根据实际需要进行设定，本申请实施例对此不做任何限定。更具体地，当所述资源消耗特征数据为所述多个资源消耗数据的方差时，基于所述用户操作类型所对应的资源消耗数据和所述多个资源消耗数据的均值，确定所述用户操作类型所对应的资源消耗数据的方差；再判断所述用户操作类型所对应的资源消耗数据的方差是否处于所述资源消耗允许区间。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，所述判定所述云产品实例与所述云产品实例中的用户业务数据不匹配之前，所述方法还包括：如果判定所述用户操作类型在当前时刻所对应的服务质量数据不处于所述目标服务质量区间内，则将所述用户操作类型在所述当前时刻所对应的服务质量数据不处于所述目标服务质量区间内的事件记录为异常事件，并基于异常评分模型和所述异常事件，确定所述用户操作类型所对应的异常评分；如果判定所述异常评分在当前时间段内持续增高，则判定所述云产品实例与所述用户业务数据不匹配。其中，所述异常评分模型可由遗忘函数构建得到。籍此，通过用户操作类型在当前时刻所对应的服务质量数据不处于服务质量允许区间内的异常事件得到的异常评分在当前时间段内的变化，能够自动探测用户业务数据的变化，从而能够准确地判断云产品实例与云产品实例中的用户业务数据是否匹配。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，所述判定所述云产品实例与所述云产品实例中的用户业务数据不匹配之前，所述方法还包括：如果判定所述用户操作类型在当前时刻所对 应的服务延迟数据不处于所述服务延迟允许区间内，则将所述用户操作类型在所述当前时刻所对应的服务延迟数据不处于所述服务延迟允许区间内的事件记录为异常事件，并基于异常评分模型和所述异常事件，确定所述用户操作类型所对应的异常评分；如果判定所述异常评分在当前时间段内持续增高，则判定所述云产品实例与所述用户业务数据不匹配。其中，所述异常评分模型可由遗忘函数构建得到。籍此，通过用户操作类型在当前时刻所对应的服务延迟数据不处于服务延迟允许区间内的异常事件得到的异常评分在当前时间段内的变化，能够自动探测用户业务数据的变化，从而能够准确地判断云产品实例与云产品实例中的用户业务数据是否匹配。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，所述判定所述云产品实例与所述云产品实例中的用户业务数据不匹配之前，所述方法还包括：如果判定所述用户操作类型在当前时刻所对应的资源消耗数据不处于所述资源消耗允许区间内，则将所述用户操作类型在所述当前时刻所对应的资源消耗数据不处于所述资源消耗允许区间内的事件记录为异常事件，并基于异常评分模型和所述异常事件，确定所述用户操作类型所对应的异常评分；如果判定所述异常评分在当前时间段内持续增高，则判定所述云产品实例与所述用户业务数据不匹配。其中，所述异常评分模型可由遗忘函数构建得到。籍此，通过用户操作类型在当前时刻所对应的资源消耗数据不处于资源消耗允许区间内的异常事件得到的异常评分在当前时间段内的变化，能够自动探测用户业务数据的变化，从而能够准确地判断云产品实例与云产品实例中的用户业务是否匹配。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，所述判定所述云产品实例与所述云产品实例中的用户业务数据不匹配之前，所述方法还包括：如果判定所述用户操作类型在当前时刻所对应的资源消耗数据不处于所述资源消耗允许区间内，且所述用户操作类型在所述当前时刻所对应的服务延迟数据不处于所述服务延迟允许区间内，则将所述用户操作类型在所述当前时刻所对应的资源消耗数据不处于所述资源消耗允许区间内，且所述用户操作类型在所述当前时刻所对应的服务延迟数据不处于所述服务延迟允许区间内的事件记录为异常事件，并基于异常评分模型和所述异常事件，确定所述用户操作类型所对应的异常评分；如果判定所述异常评分在当前时间段内持续增高，则判定所述云产品实例与所述用户业务数据不匹配。其中，所述异常评分模型可由遗忘函数构建得到。籍此，通过用户操作类型在当前时刻所对应的资源消耗数据不处 于资源消耗允许区间内且用户操作类型在当前时刻所对应的服务延迟数据不处于服务延迟允许区间内的异常事件得到的异常评分在当前时间段内的变化，能够更加精准地探测用户业务数据的变化，从而能够更加准确地判断云产品实例与云产品实例中的用户业务数据是否匹配。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，当所述用户行为数据包括针对所述云产品实例的用户操作类型时，所述确定待推荐的云产品之前，所述方法还包括：确定所述用户操作类型在历史时间段内所对应的分布数据；基于所述分布数据，对云平台上所有云产品实例进行聚类操作，以获得所述云平台上所有云产品实例的多个分组。籍此，通过用户操作类型所对应的分布数据，对云平台上所有云产品实例进行聚类操作，能够确保每个分组中所有云产品实例的相似性。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，统计所述云产品实例的每种用户操作类型在历史时间段内出现频数。然后，基于每种用户操作类型在历史时间段内的出现频数，获得针对所述云产品实例的用户操作类型的分布数据，例如，插入操作类型占70％、更新操作类型占10％、选择操作类型占20％等。在获得所述云产品实例的用户操作类型在历史时间段内的分布数据之后，可利用K-Means的聚类方法，基于云平台上所有云产品实例的用户操作类型在历史时间段内的分布数据，对云平台上所有云产品实例进行聚类操作，以获得所述云平台上所有云产品实例的多个分组。例如，对于云产品实例A，其用户操作类型的分布数据为：
D A＝{插入操作：0.7，更新操作：0.1，选择操作：0.2}；
对于云产品实例B，其用户操作类型的分布数据为：
D B＝{插入操作：0.3，更新操作：0.4，选择操作：0.3}
那么根据K-Means的聚类方法，则可以计算出它们是否会分在同一个分组中。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，所述基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品，包括：将所述分组中所有云产品实例的元数据的众数确定为所述待推荐的云产品的元数据；基于所述待推荐的云产品的元数据，确定所述待推荐的云产品。籍此，能够准确地确定待推荐的云产品。可以理解的是，以上 描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，当云产品实例的元数据包括云产品实例的类别信息、版本信息和规格配置信息时，确定所述分组中具有相同的类别信息的云产品实例的第一数量；基于所述第一数量确定待推荐的云产品的类别信息；确定所述分组中具有相同的版本信息的云产品实例的第二数量；基于所述第二数量确定待推荐的云产品的版本信息；确定所述分组中具有相同的规格配置信息的云产品实例的第三数量；基于所述第三数量确定待推荐的云产品的规格配置信息。具体地，将所述第一数量中的最大值对应的类别信息确定为所述待推荐的云产品的类别信息。将所述第二数量中的最大值对应的版本信息确定为所述待推荐的云产品的版本信息。将所述第三数量中的最大值对应的规格配置信息确定为所述待推荐的云产品的规格配置信息。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，所述方法还包括：基于所述待推荐的云产品的元数据，生成所述待推荐的云产品的推荐消息；将所述推荐消息发送至用户持有的终端设备。具体地，可以短信的方式或者邮箱的方式将所述推荐消息发送至用户持有的终端设备。籍此，通过向用户持有的终端设备发送推荐消息，能够让用户知晓推荐的云产品的元数据，进而决定是否接受所述云产品的推荐。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，所述方法还包括：接收所述终端设备发送的针对所述推荐消息的反馈消息；基于所述反馈消息，将所述云产品实例中的用户业务数据迁移至所述待推荐的云产品中。籍此，能够基于用户的反馈消息，将云产品实例中的用户业务数据迁移至待推荐的云产品中。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，当所述反馈消息携带有用户不接受推荐的云产品的信息时，不将所述云产品实例中的用户业务数据迁移至所述待推荐的云产品中。当所述反馈消息携带有用户接受推荐的云产品的信息时，将所述云产品实例中的用户业务数据迁移至所述待推荐的云产品中。具体地，将云产品实例中的用户业务数据通过传输工具导入到待推荐的云产品中。在完成用户业务数据的迁移之后，将用户的连接引入到待推荐的云产品上。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，当所述反馈消息包括所述用户业务数据的迁移时间信息 时，所述基于所述反馈消息，将所述云产品实例中的用户业务数据迁移至所述待推荐的云产品中，包括：基于所述迁移时间信息，将所述用户业务数据迁移至所述待推荐的云产品中。籍此，能够按照用户的迁移时间要求，对用户业务数据进行迁移。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，当所述迁移时间信息为时间点时，基于所述时间点，将所述云产品实例中的用户业务数据迁移至所述待推荐的云产品中。当所述迁移时间信息为时间段时，基于所述时间段，将所述云产品实例中的用户业务数据迁移至所述待推荐的云产品中。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
通过本申请实施例提供的云产品的推荐方法，确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品实例为在物理机器上分配的运行云产品的服务进程，并在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品，与现有的其它方式相比，能够判断云产品实例的用户行为数据所对应的服务质量数据是否处于目标服务质量区间内，并且在判定服务质量数据不处于目标服务质量区间内时，能够根据所述云产品实例所在分组中的云产品实例的元数据，自动推荐达到目标服务质量的云产品，从而能够大幅度地提高云产品的使用质量。此外，有助于云产品厂商引导用户使用正确的云产品，减少云产品厂商的售后运维成本，从而达到保障云服务的服务等级协议，并合理规划成本资源的目的。
本实施例的云产品的推荐方法可以由任意适当的具有数据处理能力的设备执行，包括但不限于：摄像头、终端、移动终端、PC机、服务器、车载设备、娱乐设备、广告设备、个人数码助理(PDA)、平板电脑、笔记本电脑、掌上游戏机、智能眼镜、智能手表、可穿戴设备、虚拟显示设备或显示增强设备(如Google Glass、Oculus Rift、Hololens、Gear VR)等。
参照图2，示出了本申请实施例二的云数据库的推荐方法的步骤流程图。
具体地，本实施例的云数据库的推荐方法包括以下步骤：
在步骤S201中，确定云数据库实例的用户行为数据所对应的服务质量数据。
在本实施例中，所述云数据库实例可理解为在物理机器上分配的运行云数据库的服务进程。具体地，从业务产品这个维度来看，云数据库实例对应于用户购买的 一个云数据库服务产品。从技术这个维度看，每当用户购买了一个云数据库服务产品，在后台系统的物理机器上起一个运行云数据库的进程，这个就是云数据库实例。所述用户行为数据可理解为用户针对所述云数据库实例的操作数据，例如，云数据库实例的日志审计数据中的SQL操作指令、SQL操作指令对应的用户操作类型等。其中，日志审计数据包括SQL操作指令的内容，该内容中的用户业务内容全部脱敏，只记录SQL操作指令的语法关键字。脱敏可理解为不涉及SQL操作指令中用户业务的具体内容，例如，在SQL操作指令“insert into tableA(id,name)values(1,‘name1’)”中，id＝1，name＝‘name1’是用户业务的具体内容，这部分会全部隐除。将这条SQL操作指令转换成“insert into tableA(id,name)values(？,？)”进行记录。所述服务质量数据包括服务延迟数据和/或资源消耗数据。其中，所述服务延迟数据可理解为用户执行针对所述云数据库实例的每个SQL操作指令时所对应的延迟响应时间。所述资源消耗数据的含义与上文类似，在此不再赘述。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，在确定云数据库实例的用户行为数据所对应的服务质量数据时，从云数据库的管控系统的时序数据库中获取云数据库实例的用户行为数据所对应的服务延迟数据和/或资源消耗数据。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在步骤S202中，在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云数据库实例所在分组中的云数据库实例的元数据，确定待推荐的云数据库。
在本实施例中，所述目标服务质量区间可为预先配置的服务质量目标区间，还可为实时确定的服务质量目标区间。所述目标服务质量区间包括服务延迟允许区间和/或资源消耗允许区间。所述云数据库实例的元数据可包括以下中的至少一者：云数据库引擎的类型信息、云数据库的版本信息、云数据库的规格配置信息。所述元数据为稳态数据，不随时间变化而发生迅速变化。具体地，从云数据库的管控系统的元数据库中获取云数据库实例的元数据。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，当所述用户行为数据具体为针对所述云数据库实例的用户操作类型时，所述方法还包括：如果判定所述用户操作类型在当前时刻所对应的资源消耗数据不处于所述资源消耗允许区间内，且所述用户操作类型在所述当前时刻所对应的服务延迟数据不处于所述服务延迟允许区间内，则将所述用户操作类型 在所述当前时刻所对应的资源消耗数据不处于所述资源消耗允许区间内，且所述用户操作类型在所述当前时刻所对应的服务延迟数据不处于所述服务延迟允许区间内的事件记录为异常事件，并基于异常评分模型和所述异常事件，确定所述用户操作类型所对应的异常评分；如果判定所述异常评分在当前时间段内持续增高，则判定所述云数据库实例与所述云数据库实例中的用户业务数据不匹配。其中，所述异常评分模型可由遗忘函数构建得到。籍此，通过用户操作类型在当前时刻所对应的资源消耗数据不处于资源消耗允许区间内且用户操作类型在当前时刻所对应的服务延迟数据不处于服务延迟允许区间内的异常事件得到的异常评分在当前时间段内的变化，能够更加精准地探测用户业务数据的变化，从而能够更加准确地判断云数据库实例与云数据库实例中的用户业务数据是否匹配。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，对于云数据库实例I i，通过云数据库管控系统中的时序数据库，实时监控针对云数据库实例I i的用户操作类型S i的服务延迟数据和资源消耗数据，若服务延迟数据超出服务延迟允许区间
Figure PCTCN2020091169-appb-000001
(其中，Δα1为服务延迟波动阈值)，且资源消耗数据也超出资源消耗允许区间
Figure PCTCN2020091169-appb-000002
(其中，Δα2为资源消耗波动阈值)，则此事件记录为异常事件
Figure PCTCN2020091169-appb-000003
t为表示异常事件发生时刻。其中，
Figure PCTCN2020091169-appb-000004
表示针对云数据库实例I i的用户操作类型S i在历史时间段Δt1内所对应的服务延迟特征数据，即针对云数据库实例I i的用户操作类型S i在历史时间段Δt1内所对应的多个服务延迟数据的均值、方差、中位数或者众数。
Figure PCTCN2020091169-appb-000005
表示针对云数据库实例I i的用户操作类型S i在历史时间段Δt1内所对应的资源消耗特征数据，即针对云数据库实例I i的用户操作类型S i在历史时间段Δt1内所对应的多个资源消耗数据的均值、方差、中位数或者众数。具体地，从云数据库管控系统的时序数据库中拉取历史时间段Δt1内针对云数据库实例I i的用户操作类型S i的多个服务延迟数据和多个资源消耗数据。然后，基于遗忘函数，构建异常评分模型
Figure PCTCN2020091169-appb-000006
在当前时间段Δt2内，持续监控异常评分模型
Figure PCTCN2020091169-appb-000007
若异常评分持续增高，则判定所述云数据库实例与所述用户业务数据不匹配。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，当所述用户行为数据具体为针对所述云数据库实例的用 户操作类型时，所述确定待推荐的云数据库之前，所述方法还包括：确定所述用户操作类型在历史时间段内所对应的分布数据；基于所述分布数据，对云平台上所有云数据库实例进行聚类操作，以获得所述云平台上所有云数据库实例的多个分组。籍此，通过用户操作类型所对应的分布数据，对云平台上所有云数据库实例进行聚类操作，能够确保每个分组中所有云数据库实例的相似性。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，从云数据库管控系统的时序数据库中拉取历史时间段Δt1内针对云数据库实例I i的日志审计数据。所述日志审计数据包括用户在历史时间段Δt1内针对云数据库实例I i的用户操作数据。基于用户在历史时间段Δt1内针对云数据库实例I i的用户操作数据，统计用户操作类型S i在历史时间段Δt1内的分布数据，记为
Figure PCTCN2020091169-appb-000008
基于线上所有云数据库实例I i的
Figure PCTCN2020091169-appb-000009
进行聚类，得到云数据库实例的n个分组：DG 1，...，DG i，...，DG n。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，所述基于所述云数据库实例所在分组中的云数据库实例的元数据，确定待推荐的云数据库，包括：将所述分组中所有云数据库实例的元数据的众数确定为所述待推荐的云数据库的元数据；基于所述待推荐的云数据库的元数据，确定所述待推荐的云数据库。籍此，能够准确地确定待推荐的云数据库。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，从云数据库管控系统的元数据库中拉取历史时间段Δt1内针对云数据库实例I i的元数据。对于每个分组DG i，提取组内每个云数据库实例的元数据，并基于元数据提取众数指标，得到与每个分组DG i对应的待推荐的云数据库的元数据。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，所述方法还包括：基于所述待推荐的云数据库的元数据，生成所述待推荐的云数据库的推荐消息；将所述推荐消息发送至用户持有的终端设备。具体地，可以短信的方式或者邮箱的方式将所述推荐消息发送至用户持有的终端设备。籍此，通过向用户持有的终端设备发送推荐消息，能够让用户知晓推荐的云数据库的元数据，进而决定是否接受所述云数据库的推荐。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，所述方法还包括：接收所述终端设备发送的针对所述推 荐消息的反馈消息；基于所述反馈消息，将所述云数据库实例中的用户业务数据迁移至所述待推荐的云数据库中。籍此，能够基于用户的反馈消息，将云数据库实例中的用户业务数据迁移至待推荐的云数据库中。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，当所述反馈消息携带有用户不接受推荐的云数据库的信息时，不将所述云数据库实例中的用户业务数据迁移至待推荐的云数据库中。当所述反馈消息携带有用户接受待推荐的云数据库的信息时，将所述云数据库实例中的用户业务数据迁移至待推荐的云数据库中。具体地，将云数据库实例中的用户业务数据通过传输工具导入到待推荐的云数据库中。在完成用户业务数据的迁移之后，将用户的连接引入到待推荐的云数据库上。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一些可选实施例中，当所述反馈消息包括所述用户业务数据的迁移时间信息时，所述基于所述反馈消息，将所述云数据库实例中的用户业务数据迁移至所述待推荐的云数据库中，包括：基于所述迁移时间信息，将所述用户业务数据迁移至所述待推荐的云数据库中。籍此，能够按照用户的迁移时间要求，对用户业务数据进行迁移。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
在一个具体的例子中，当所述迁移时间信息为时间点时，基于所述时间点，将所述云数据库实例中的用户业务数据迁移至待推荐的云数据库中。当所述迁移时间信息为时间段时，基于所述时间段，将所述云数据库实例中的用户业务数据迁移至待推荐的云数据库中。可以理解的是，以上描述仅为示例性的，本申请实施例对此不做任何限定。
通过本申请实施例提供的云数据库的推荐方法，确定云数据库实例的用户行为数据所对应的服务质量数据，所述云数据库实例为在物理机器上分配的运行云数据库的服务进程，并在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云数据库实例所在分组中的云数据库实例的元数据，确定待推荐的云数据库，与现有的其它方式相比，能够判断云数据库实例的用户行为数据所对应的服务质量数据是否处于目标服务质量区间内，并且在判定服务质量数据不处于目标服务质量区间内时，能够根据所述云数据库实例所在分组中的云数据库实例的元数据，自动推荐达到目标服务质量的云数据库，从而能够大幅度地提高云数据库的使用质量。此外，有助于云数据库厂商引导用户使用正确的云数据库，减少云数据库厂商的售后 运维成本，从而达到保障云服务的服务等级协议，并合理规划成本资源的目的。
本实施例的云数据库的推荐方法可以由任意适当的具有数据处理能力的设备执行，包括但不限于：摄像头、终端、移动终端、PC机、服务器、车载设备、娱乐设备、广告设备、个人数码助理(PDA)、平板电脑、笔记本电脑、掌上游戏机、智能眼镜、智能手表、可穿戴设备、虚拟显示设备或显示增强设备(如Google Glass、Oculus Rift、Hololens、Gear VR)等。
参照图3，示出了本申请实施例三中云产品的推荐装置的结构示意图。
本实施例的云产品的推荐装置包括：第一确定模块301，用于确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品实例为在物理机器上分配的运行云产品的服务进程；第二确定模块302，用于在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品。
本实施例的云产品的推荐装置用于实现前述多个方法实施例中相应的云产品的推荐方法，并具有相应的方法实施例的有益效果，在此不再赘述。
参照图4，示出了本申请实施例四中云产品的推荐装置的结构示意图。
本实施例的云产品的推荐装置包括：第一确定模块401，用于确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品实例为在物理机器上分配的运行云产品的服务进程；第二确定模块402，用于在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品。
可选地，当所述用户行为数据包括针对所述云产品实例的用户操作类型时，所述装置还包括：第一判定模块405，用于如果判定所述用户操作类型所对应的服务质量数据不处于目标服务质量区间内，则判定所述云产品实例与所述云产品实例中的用户业务数据不匹配，其中，所述目标服务质量区间是根据所述用户操作类型在历史时间段内所对应的服务质量特征数据确定得到的。
可选地，所述第一判定模块405之前，所述装置还包括：第二判定模块403，用于如果判定所述用户操作类型在当前时刻所对应的服务质量数据不处于所述目标服务质量区间内，则将所述用户操作类型在所述当前时刻所对应的服务质量数据不 处于所述目标服务质量区间内的事件记录为异常事件，并基于异常评分模型和所述异常事件，确定所述用户操作类型所对应的异常评分；第三判定模块404，用于如果判定所述异常评分在当前时间段内持续增高，则判定所述云产品实例与所述用户业务数据不匹配。
本实施例的云产品的推荐装置用于实现前述多个方法实施例中相应的云产品的推荐方法，并具有相应的方法实施例的有益效果，在此不再赘述。
参照图5，示出了本申请实施例五中云产品的推荐装置的结构示意图。
本实施例的云产品的推荐装置包括：第一确定模块501，用于确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品实例为在物理机器上分配的运行云产品的服务进程；第二确定模块502，用于在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品。
可选地，当所述用户行为数据包括针对所述云产品实例的用户操作类型时，所述第二确定模块502之前，所述装置还包括：第三确定模块503，用于确定所述用户操作类型在历史时间段内所对应的分布数据；聚类模块504，用于基于所述分布数据，对云平台上所有云产品实例进行聚类操作，以获得所述云平台上所有云产品实例的多个分组。
可选地，所述第二确定模块502，具体用于：将所述分组中所有云产品实例的元数据的众数确定为所述待推荐的云产品的元数据；基于所述待推荐的云产品的元数据，确定所述待推荐的云产品。
可选地，所述装置还包括：生成模块505，用于基于所述待推荐的云产品的元数据，生成所述待推荐的云产品的推荐消息；发送模块506，用于将所述推荐消息发送至用户持有的终端设备。
可选地，所述装置还包括：接收模块507，用于接收所述终端设备发送的针对所述推荐消息的反馈消息；迁移模块508，用于基于所述反馈消息，将所述云产品实例中的用户业务数据迁移至所述待推荐的云产品中。
可选地，当所述反馈消息包括所述用户业务数据的迁移时间信息时，所述迁移模块508，具体用于：基于所述迁移时间信息，将所述用户业务数据迁移至所述待推荐的云产品中。
本实施例的云产品的推荐装置用于实现前述多个方法实施例中相应的云产品的推荐方法，并具有相应的方法实施例的有益效果，在此不再赘述。
图6为本申请实施例六中电子设备的结构示意图；该电子设备可以包括：
一个或多个处理器601；
计算机可读介质602，可以配置为存储一个或多个程序，
当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如上述实施例一所述的云产品的推荐方法，或者实现如上述实施例二所述的云数据库的推荐方法。
图7为本申请实施例七中电子设备的硬件结构；如图7所示，该电子设备的硬件结构可以包括：处理器701，通信接口702，计算机可读介质703和通信总线704；
其中，处理器701、通信接口702、计算机可读介质703通过通信总线704完成相互间的通信；
可选地，通信接口702可以为通信模块的接口，如GSM模块的接口；
其中，处理器701具体可以配置为：确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品实例为在物理机器上分配的运行云产品的服务进程；在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品。
处理器701可以是通用处理器，包括中央处理器(Central Processing Unit，简称CPU)、网络处理器(Network Processor，简称NP)等；还可以是数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其它可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
计算机可读介质703可以是，但不限于，随机存取存储介质(Random Access Memory，RAM)，只读存储介质(Read Only Memory，ROM)，可编程只读存储介质(Programmable Read-Only Memory，PROM)，可擦除只读存储介质(Erasable Programmable Read-Only Memory，EPROM)，电可擦除只读存储介质(Electric Erasable Programmable Read-Only Memory，EEPROM)等。
特别地，根据本公开的实施例，上文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含配置为执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信部分从网络上被下载和安装，和/或从可拆卸介质被安装。在该计算机程序被中央处理单元(CPU)执行时，执行本申请的方法中限定的上述功能。需要说明的是，本申请所述的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读介质例如可以但不限于是电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储介质(RAM)、只读存储介质(ROM)、可擦式可编程只读存储介质(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储介质(CD-ROM)、光存储介质件、磁存储介质件、或者上述的任意合适的组合。在本申请中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。而在本申请中，计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输配置为由指令执行系统、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：无线、电线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写配置为执行本申请的操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如”C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络：包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
附图中的流程图和框图，图示了按照本申请各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含一个或多个配置为实现规定的逻辑功能的可执行指令。上述具体实施例中有特定先后关系，但这些先后关系只是示例性的，在具体实现的时候，这些步骤可能会更少、更多或执行顺序有调整。即在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本申请实施例中所涉及到的模块可以通过软件的方式实现，也可以通过硬件的方式来实现。所描述的模块也可以设置在处理器中，例如，可以描述为：一种处理器包括第一确定模块和第二确定模块。其中，这些模块的名称在某种情况下并不构成对该模块本身的限定，例如，第一确定模块还可以被描述为“确定云产品实例的用户行为数据所对应的服务质量数据的模块”。
作为另一方面，本申请还提供了一种计算机可读介质，其上存储有计算机程序，该程序被处理器执行时实现如上述实施例一所述的云产品的推荐方法，或者实现如上述实施例二所述的云数据库的推荐方法。
作为另一方面，本申请还提供了一种计算机可读介质，该计算机可读介质可以是上述实施例中描述的装置中所包含的；也可以是单独存在，而未装配入该装置中。上述计算机可读介质承载有一个或者多个程序，当上述一个或者多个程序被该装置执行时，使得该装置：确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品实例为在物理机器上分配的运行云产品的服务进程；在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品。
在本公开的各种实施方式中所使用的表述“第一”、“第二”、“所述第一”或“所述第二”可修饰各种部件而与顺序和/或重要性无关，但是这些表述不限制相应部件。以上表述仅配置为将元件与其它元件区分开的目的。例如，第一用户设备和第二用户设备表示不同的用户设备，虽然两者均是用户设备。例如，在不背离本 公开的范围的前提下，第一元件可称作第二元件，类似地，第二元件可称作第一元件。
当一个元件(例如，第一元件)称为与另一元件(例如，第二元件)“(可操作地或可通信地)联接”或“(可操作地或可通信地)联接至”另一元件(例如，第二元件)或“连接至”另一元件(例如，第二元件)时，应理解为该一个元件直接连接至该另一元件或者该一个元件经由又一个元件(例如，第三元件)间接连接至该另一个元件。相反，可理解，当元件(例如，第一元件)称为“直接连接”或“直接联接”至另一元件(第二元件)时，则没有元件(例如，第三元件)插入在这两者之间。
以上描述仅为本申请的较佳实施例以及对所运用技术原理的说明。本领域技术人员应当理解，本申请中所涉及的发明范围，并不限于上述技术特征的特定组合而成的技术方案，同时也应涵盖在不脱离上述发明构思的情况下，由上述技术特征或其等同特征进行任意组合而形成的其它技术方案。例如上述特征与本申请中公开的(但不限于)具有类似功能的技术特征进行互相替换而形成的技术方案。

Claims (22)

  1. 一种云产品的推荐方法，其特征在于，所述方法包括：
    确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品实例为在物理机器上分配的运行云产品的服务进程；
    在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品。
  2. 根据权利要求1所述的方法，其特征在于，当所述用户行为数据包括针对所述云产品实例的用户操作类型时，所述方法还包括：
    如果判定所述用户操作类型所对应的服务质量数据不处于目标服务质量区间内，则判定所述云产品实例与所述云产品实例中的用户业务数据不匹配，其中，所述目标服务质量区间是根据所述用户操作类型在历史时间段内所对应的服务质量特征数据确定得到的。
  3. 根据权利要求2所述的方法，其特征在于，所述判定所述云产品实例与所述云产品实例中的用户业务数据不匹配之前，所述方法还包括：
    如果判定所述用户操作类型在当前时刻所对应的服务质量数据不处于所述目标服务质量区间内，则将所述用户操作类型在所述当前时刻所对应的服务质量数据不处于所述目标服务质量区间内的事件记录为异常事件，并基于异常评分模型和所述异常事件，确定所述用户操作类型所对应的异常评分；
    如果判定所述异常评分在当前时间段内持续增高，则判定所述云产品实例与所述用户业务数据不匹配。
  4. 根据权利要求2所述的方法，其特征在于，所述服务质量特征数据包括以下中的至少一者：
    所述用户操作类型在所述历史时间段内所对应的多个服务质量数据的均值、方差、中位数、众数。
  5. 根据权利要求2所述的方法，其特征在于，根据所述服务质量特征数据和服务质量波动阈值确定所述目标服务质量区间。
  6. 根据权利要求1-5中任意一项权利要求所述的方法，其特征在于，所述服务质量数据包括服务延迟数据和/或资源消耗数据，
    对应地，所述目标服务质量区间包括服务延迟允许区间和/或资源消耗允许区间。
  7. 根据权利要求1所述的方法，其特征在于，当所述用户行为数据包括针对所述 云产品实例的用户操作类型时，
    所述确定待推荐的云产品之前，所述方法还包括：
    确定所述用户操作类型在历史时间段内所对应的分布数据；
    基于所述分布数据，对云平台上所有云产品实例进行聚类操作，以获得所述云平台上所有云产品实例的多个分组。
  8. 根据权利要求1所述的方法，其特征在于，所述基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品，包括：
    将所述分组中所有云产品实例的元数据的众数确定为所述待推荐的云产品的元数据；
    基于所述待推荐的云产品的元数据，确定所述待推荐的云产品。
  9. 根据权利要求8所述的方法，其特征在于，所述方法还包括：
    基于所述待推荐的云产品的元数据，生成所述待推荐的云产品的推荐消息；
    将所述推荐消息发送至用户持有的终端设备。
  10. 根据权利要求9所述的方法，其特征在于，所述方法还包括：
    接收所述终端设备发送的针对所述推荐消息的反馈消息；
    基于所述反馈消息，将所述云产品实例中的用户业务数据迁移至所述待推荐的云产品中。
  11. 根据权利要求10所述的方法，其特征在于，当所述反馈消息包括所述用户业务数据的迁移时间信息时，
    所述基于所述反馈消息，将所述云产品实例中的用户业务数据迁移至所述待推荐的云产品中，包括：
    基于所述迁移时间信息，将所述用户业务数据迁移至所述待推荐的云产品中。
  12. 根据权利要求8-11中任意一项权利要求所述的方法，其特征在于，所述待推荐的云产品的元数据包括以下中的至少一者：所述待推荐的云产品的类别信息、版本信息、规格配置信息。
  13. 一种云产品的推荐装置，其特征在于，所述装置包括：
    第一确定模块，用于确定云产品实例的用户行为数据所对应的服务质量数据，所述云产品实例为在物理机器上分配的运行云产品的服务进程；
    第二确定模块，用于在判定所述服务质量数据不处于目标服务质量区间内时，基于所述云产品实例所在分组中的云产品实例的元数据，确定待推荐的云产品。
  14. 根据权利要求13所述的装置，其特征在于，当所述用户行为数据包括针对所述云产品实例的用户操作类型时，所述装置还包括：
    第一判定模块，用于如果判定所述用户操作类型所对应的服务质量数据不处于目标服务质量区间内，则判定所述云产品实例与所述云产品实例中的用户业务数据不匹配，其中，所述目标服务质量区间是根据所述用户操作类型在历史时间段内所对应的服务质量特征数据确定得到的。
  15. 根据权利要求14所述的装置，其特征在于，所述第一判定模块之前，所述装置还包括：
    第二判定模块，用于如果判定所述用户操作类型在当前时刻所对应的服务质量数据不处于所述目标服务质量区间内，则将所述用户操作类型在所述当前时刻所对应的服务质量数据不处于所述目标服务质量区间内的事件记录为异常事件，并基于异常评分模型和所述异常事件，确定所述用户操作类型所对应的异常评分；
    第三判定模块，用于如果判定所述异常评分在当前时间段内持续增高，则判定所述云产品实例与所述用户业务数据不匹配。
  16. 根据权利要求13所述的装置，其特征在于，当所述用户行为数据包括针对所述云产品实例的用户操作类型时，
    所述第二确定模块之前，所述装置还包括：
    第三确定模块，用于确定所述用户操作类型在历史时间段内所对应的分布数据；
    聚类模块，用于基于所述分布数据，对云平台上所有云产品实例进行聚类操作，以获得所述云平台上所有云产品实例的多个分组。
  17. 根据权利要求13所述的装置，其特征在于，所述第二确定模块，具体用于：
    将所述分组中所有云产品实例的元数据的众数确定为所述待推荐的云产品的元数据；
    基于所述待推荐的云产品的元数据，确定所述待推荐的云产品。
  18. 根据权利要求17所述的装置，其特征在于，所述装置还包括：
    生成模块，用于基于所述待推荐的云产品的元数据，生成所述待推荐的云产品的推荐消息；
    发送模块，用于将所述推荐消息发送至用户持有的终端设备。
  19. 根据权利要求18所述的装置，其特征在于，所述装置还包括：
    接收模块，用于接收所述终端设备发送的针对所述推荐消息的反馈消息；
    迁移模块，用于基于所述反馈消息，将所述云产品实例中的用户业务数据迁移至所述待推荐的云产品中。
  20. 根据权利要求19所述的装置，其特征在于，当所述反馈消息包括所述用户业务数据的迁移时间信息时，
    所述迁移模块，具体用于：
    基于所述迁移时间信息，将所述用户业务数据迁移至所述待推荐的云产品中。
  21. 一种电子设备，包括：
    一个或多个处理器；
    计算机可读介质，配置为存储一个或多个程序，
    当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如权利要求1-12中任意一项权利要求所述的云产品的推荐方法。
  22. 一种计算机可读介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如权利要求1-12中任意一项权利要求所述的云产品的推荐方法。
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