[go: up one dir, main page]

CN112015971B - Cloud product recommendation method and device, electronic equipment and computer readable medium - Google Patents

Cloud product recommendation method and device, electronic equipment and computer readable medium Download PDF

Info

Publication number
CN112015971B
CN112015971B CN201910463374.XA CN201910463374A CN112015971B CN 112015971 B CN112015971 B CN 112015971B CN 201910463374 A CN201910463374 A CN 201910463374A CN 112015971 B CN112015971 B CN 112015971B
Authority
CN
China
Prior art keywords
cloud
cloud product
service
user
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910463374.XA
Other languages
Chinese (zh)
Other versions
CN112015971A (en
Inventor
谢瑀
高玉嵩
张云杨
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Alibaba Cloud Computing Ltd
Original Assignee
Alibaba Cloud Computing Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Alibaba Cloud Computing Ltd filed Critical Alibaba Cloud Computing Ltd
Priority to CN201910463374.XA priority Critical patent/CN112015971B/en
Priority to PCT/CN2020/091169 priority patent/WO2020238712A1/en
Publication of CN112015971A publication Critical patent/CN112015971A/en
Application granted granted Critical
Publication of CN112015971B publication Critical patent/CN112015971B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The embodiment of the application provides a cloud product recommendation method, a cloud product recommendation device, 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 for running a cloud product; and when the service quality data is not in the target service quality interval, 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. According to the cloud product recommendation method and device, cloud products reaching target service quality can be automatically recommended, and therefore the use quality of the cloud products 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-sales operation and maintenance cost of the cloud product manufacturer is reduced, so that the purposes of guaranteeing the service level protocol of the cloud service and reasonably planning cost resources are achieved.

Description

Cloud product recommendation method and device, 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 technology, cloud products such as cloud computing products, cloud security products, cloud databases and the like are increasing. In the early stages of user traffic, single or a few cloud products are often used as media to carry the traffic. With the growth and change of user services, original cloud products cannot adapt to the current service demands, for example, searching under the gradually growing large-scale data, and often need to be fragmented to slow down the performance bottleneck of a single cloud database, and gradually transition from an event processing service to a hybrid analysis processing service. At this time, the user is required to adjust the currently used cloud product to be a suitable cloud product to better meet the requirements of the current business. However, due to the weak perception of the service quality change of the currently used cloud product, the user cannot find out 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 examined, and then a large amount of cost is used to transfer the service data in the currently used cloud product to the cloud product with the standard service quality. Therefore, how to automatically recommend cloud products reaching the target service quality is a technical problem to be solved currently.
Disclosure of Invention
The application aims to provide a cloud product recommendation method, device, electronic equipment and computer readable medium, which are used for solving the problem of how to automatically recommend cloud products reaching target service quality in the prior art.
According to a first aspect of an embodiment of the present application, a recommendation method for cloud products 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 for running a cloud product; and when the service quality data is not in the target service quality interval, 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.
According to a second aspect of the embodiment of the application, a recommendation device for cloud products is provided. The device comprises: the cloud product 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 instance, and the cloud product instance is a service process distributed on a physical machine for running a cloud product; and the second determining module is used for determining cloud products to be recommended based on metadata of the cloud product instances in the group where the cloud product instances are located when the service quality data are not in the target service quality interval.
According to a third aspect of an embodiment 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 a method of recommending cloud products according to the first aspect of the embodiments described above.
According to a fourth aspect of the embodiments of the present application, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements the method of recommending cloud products according to the first aspect of the embodiments described above.
According to the technical scheme provided by the embodiment of the application, the service quality data corresponding to the user behavior data of the cloud product instance is determined, the cloud product instance is a service process for running the cloud product distributed on a physical machine, when the service quality data is judged not to be in a target service quality interval, 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 modes, whether the service quality data corresponding to the user behavior data of the cloud product 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 product reaching the target service quality can be automatically recommended according to the metadata of the cloud product instance in the group where the cloud product instance is located, so that the service 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-sales operation and maintenance cost of the cloud product manufacturer is reduced, so that the purposes of guaranteeing the service level protocol of the cloud service and reasonably planning cost resources are achieved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the accompanying drawings in which:
FIG. 1 is a flowchart illustrating a method for recommending cloud products according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating 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 the third embodiment of the present application;
fig. 4 is a schematic structural diagram of a recommendation device for cloud products in a fourth embodiment of the present application;
fig. 5 is a schematic structural diagram of a recommendation device for cloud products in a fifth embodiment of the present application;
Fig. 6 is a schematic structural diagram of an electronic device in a sixth embodiment of the present application;
fig. 7 is a hardware structure of an electronic device in a seventh embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely configured to illustrate the related application, and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
Referring to fig. 1, a flowchart of steps of a cloud product recommendation method according to a first embodiment of the present application is shown.
Specifically, the recommendation method of cloud products provided in the embodiment includes the following steps:
in step S101, quality of service data corresponding to user behavior data of a 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 example may be understood as a virtual machine or application distributed on a physical machine. The cloud product instance may be understood as a service process running the cloud product distributed on a physical machine. In particular, from the perspective of the business product, an instance corresponds to one of the business products purchased by the user. From the technical dimension, each time a user purchases a cloud service product, a process of running the cloud product starts on a physical machine of a background system, namely a cloud product instance. The user behavior data may be understood as operation data of a user for the cloud product instance, for example, operation instructions written in an SQL (structured query language) language, user operation types involved in the operation instructions, and the like. The quality of service data includes service delay data and/or resource consumption data. Wherein, the service delay data may be understood as a delay response time corresponding to when a user executes each operation instruction for the cloud product instance. The consumed resources may be understood as resources having a consumable nature, such as CPU, memory, disk, etc. The resource consumption data may include the number of read and write operations per second of the storage device, and the like. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some optional embodiments, when determining the service quality data corresponding to the user behavior data of the cloud product instance, 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 will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In step S102, 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 is determined based on metadata of the cloud product instance in the group in which the cloud product instance is located.
In this embodiment, the target qos interval may be a preconfigured qos target interval, and may also be a qos target interval determined in real time. The target quality of service interval includes a service delay allowed interval and/or a resource consumption allowed interval. The metadata includes at least one of: category information, version information and specification configuration information corresponding to the cloud product instance. Specifically, metadata of cloud product instances is obtained from a metadata base of a management and control system of the cloud product. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some optional embodiments, when the user behavior data includes 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 not in the target service quality interval, the cloud product instance is not matched with the user service data in the cloud product instance, wherein the target service quality interval is determined according to the service quality characteristic data corresponding to the user operation type in the historical time period. Specifically, the target quality of service interval is determined according to the quality of service characteristic data and a quality of service fluctuation threshold. The qos fluctuation threshold may be set by those skilled in the art according to actual needs, and the embodiment of the present application is not limited in any way. The quality of service characteristic data includes at least one of: and the user operation type corresponds to the mean value, variance, median and mode of a plurality of service quality data in the historical time period. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, if the service delay data corresponding to the user operation type is not in a service delay allowable interval, the cloud product instance is not matched with the user service data in the cloud product instance, wherein the service delay allowable interval is determined according to the service delay characteristic data corresponding to the user operation type in a historical time period; and/or if the resource consumption data corresponding to the user operation type is judged not to be in the 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 will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, the user operation type may include an insert operation, a delete operation, an update operation, a modify operation, a purge operation, a select operation, and the like. The service delay characteristic data includes at least one of: and the user operation type corresponds to the mean value, variance, median and mode of the service delay data in the historical time period. Specifically, the service delay permission interval is determined according to the service delay characteristic data and a service delay fluctuation threshold value. 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 section, 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 section. The service delay fluctuation threshold may be set by a person skilled in the art according to actual needs, which is not limited in any way in the embodiment of the present application. 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 average value of the plurality of service delay data; and judging whether the variance of the service delay data corresponding to the user operation type is in the service delay permission interval or not. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, the resource consumption characteristic data includes at least one of: and the user operation type corresponds to the mean value, variance, median and mode of the plurality of resource consumption data in the historical time period. Specifically, the resource consumption allowance interval is determined according to the resource consumption characteristic 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 of 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, which is not limited in any way in the embodiment of the present application. 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 value of the plurality of resource consumption data; and judging whether the variance of the resource consumption data corresponding to the user operation type is in the resource consumption permission interval or not. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some optional embodiments, before the 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 continuously increase in the current time period, determining that the cloud product instance is not matched with the user business data. 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 permission 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 or not can be accurately judged. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, before the determining that the cloud product instance does not match the user service 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 not in the service delay allowing interval, recording an event, in which the service delay data corresponding to the user operation type at the current moment is not in the service delay allowing 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 continuously increase in the current time period, determining that the cloud product instance is not matched with the user business data. The abnormal scoring model can be constructed by a forgetting function. Therefore, the change of the user business 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 business data in the cloud product instance can be accurately judged. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, before the determining that the cloud product instance does not match the user service 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 continuously increase in the current time period, determining that the cloud product instance is not matched with the user business data. 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 or not can be accurately judged. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, before the determining that the cloud product instance does not match the user service 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, 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 event of which the service delay data corresponding to the user operation type at the current moment is not in the service delay allowable interval is recorded as an abnormal event, and an abnormal score corresponding to the user operation type is determined based on an abnormal score model and the abnormal event; and if the abnormal score is determined to continuously increase in the current time period, determining that the cloud product instance is not matched with the user business data. The abnormal scoring model can be constructed by a forgetting function. Therefore, the change of the user business 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 permission interval and the service delay data corresponding to the user operation type at the current moment is not in the service delay permission interval in the current time period, so that whether the cloud product instance is matched with the user business data in the cloud product instance or not can be judged more accurately. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
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 includes: determining distribution data corresponding to the user operation type in a historical time period; and clustering all cloud product instances on the cloud platform based on the distribution data to obtain a plurality of groups of all cloud product instances on the cloud platform. By means of the method, the device and the system, clustering operation is conducted on all cloud product instances on the cloud platform through the distributed data corresponding to the user operation types, and similarity of all cloud product instances in each group can be guaranteed. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, statistics are made of how many times each user operation type of the cloud product instance occurs over a historical period of time. Then, distribution data of the user operation types for the cloud product instance, for example, 70% of the insert operation types, 10% of the update operation types, 20% of the select operation types, and the like, is obtained based on the occurrence frequency of each user operation type in the history period. After obtaining the distribution data of the user operation types of the cloud product instances in the historical time period, a clustering method of K-Means can be utilized to perform clustering operation on all cloud product instances on a cloud platform based on the distribution data of the user operation types of all cloud product instances on the cloud platform in the historical time period so as to obtain a plurality of groups of all cloud product instances on the cloud platform. For example, for cloud product instance a, the distribution data of its user operation type is D A = { insert operation: 0.7, update operation: 0.1, select operation: 0.2}; for cloud product instance B, the distribution data of the user operation type thereof is:
d B = { insert operation 0.3, update operation 0.4, select operation 0.3}
Then it can be calculated whether they are grouped in the same group according to the clustering method of K-Means. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some optional embodiments, the determining, based on metadata of cloud product instances in the group in which the cloud product instances are located, a cloud product to be recommended includes: determining the mode of the metadata of all cloud product instances in the group as the 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. By this, the cloud product to be recommended can be accurately determined. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, when the metadata of the cloud product instance includes category information, version information, and specification configuration information of the cloud product instance, 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 cloud products 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 product to be recommended based on the third quantity. Specifically, the category information corresponding to the maximum value in the first quantity 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 will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some alternative 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. By sending the recommendation message to the terminal equipment 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. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some alternative embodiments, the method further comprises: receiving a feedback message aiming at the recommended message and sent by the terminal equipment; and based on the feedback message, migrating the user service data in the cloud product instance to the cloud product to be recommended. Therefore, the user business 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 will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
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 which is accepted by the user to be recommended, migrating the user service data in the cloud product instance to the cloud product to be recommended. Specifically, user business data in the cloud product instance are imported into the cloud product to be recommended through a transmission tool. After the migration of the user business data is completed, the connection of the user is introduced to the cloud product to be recommended. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some optional embodiments, when the feedback message includes migration time information of the user service data, the migrating, based on the feedback message, the user service data in the cloud product instance to the cloud product to be recommended includes: and based on the migration time information, migrating the user service data to the cloud product to be recommended. Therefore, the user service data can be migrated according to the migration time requirement of the user. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, when the migration time information is a time point, 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 will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
According to the recommendation method of the cloud product, the service quality data corresponding to the user behavior data of the cloud product instance is determined, the cloud product instance is a service process of running the cloud product distributed on a physical machine, when the service quality data is judged not to be in a target service quality interval, 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 modes, whether the service quality data corresponding to the user behavior data of the cloud product 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 product reaching the target service quality can be automatically recommended according to the metadata of the cloud product instance in the group where the cloud product instance is located, 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-sales operation and maintenance cost of the cloud product manufacturer is reduced, so that the purposes of guaranteeing the service level protocol of the cloud service and reasonably planning cost resources are achieved.
The recommendation method of cloud products of the present embodiment may be performed by any suitable device having data processing capabilities, including but not limited to: cameras, terminals, mobile terminals, PCs, servers, vehicle-mounted devices, entertainment devices, advertising devices, personal Digital Assistants (PDAs), tablet computers, notebook computers, palm-top gaming machines, smart glasses, smart watches, wearable devices, virtual display devices or display enhancement devices (e.g., google Glass, oculus Rift, hololens, gear VR), and the like.
Referring to fig. 2, a flowchart of steps of a recommendation method of a cloud database according to a second embodiment of the present application is shown.
Specifically, the recommendation method of the cloud database of the embodiment includes the following steps:
In step S201, quality of service data corresponding to user behavior data of the cloud database instance is determined.
In this embodiment, the cloud database instance may be understood as a service process running a cloud database allocated on a physical machine. In particular, from the perspective of the business product, the cloud database instance corresponds to one cloud database service product purchased by the user. From the technical dimension, each time a user purchases a cloud database service product, a process of running the cloud database, namely a cloud database instance, starts on a physical machine of the background system. The user behavior data may be understood as operation data of a user for the cloud database instance, for example, an SQL operation instruction in log audit data of the cloud database instance, a user operation type corresponding to the SQL operation instruction, and the like. The log audit data comprises the content of the SQL operation instruction, the user service content in the content is completely desensitized, and only the grammar key words of the SQL operation instruction are recorded. Desensitization can be understood as not involving the specific content of the user traffic in the SQL operation instruction, for example, in the SQL operation instruction "insert into tableA (id, name) values (1, 'name 1')", id=1, name= 'name1' is the specific content of the user traffic, which is totally hidden. Convert this SQL operation instruction to "insert into tableA (id, name) values (. The quality of service data includes service delay data and/or resource consumption data. The service delay data may be understood as a delay response time corresponding to a 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 described in detail here. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some optional embodiments, when determining the service quality data corresponding to the user behavior data of the cloud database instance, 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 will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In step S202, when it is determined that the quality of service data is not within the target quality of service interval, a cloud database to be recommended is determined based on metadata of the cloud database instance in the group in which the cloud database instance is located.
In this embodiment, the target qos interval may be a preconfigured qos target interval, and may also be a qos target interval determined in real time. The target quality of service interval includes a service delay allowed interval and/or a resource consumption allowed interval. Metadata of the cloud database instance may include at least one of: 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 that does not change rapidly over time. Specifically, metadata of cloud database instances is obtained from a metadata database of a management and control system of the cloud database. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some optional embodiments, when the user behavior data is specifically 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, 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 event of which the service delay data corresponding to the user operation type at the current moment is not in the service delay allowable interval is recorded as an abnormal event, and an abnormal score corresponding to the user operation type is determined 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 business data in the cloud database instance. The abnormal scoring model can be constructed by a forgetting function. Therefore, the change of the user business 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 permission interval and the service delay data corresponding to the user operation type at the current moment is not in the service delay permission interval in the current time period, so that whether the cloud database instance is matched with the user business data in the cloud database instance can be judged more accurately. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, for the cloud database instance I i, the service delay data and the resource consumption data of the user operation type S i for the cloud database instance I i are monitored in real time by the time sequence database in the cloud database management system, if the service delay data exceeds the service delay allowable interval(Wherein Δα1 is a service delay fluctuation threshold) and the resource consumption data also exceeds the resource consumption allowance interval(Where Δα2 is a resource consumption fluctuation threshold), then this event is recorded as an abnormal eventT is the occurrence time of the abnormal event. Wherein, The service delay feature data corresponding to the user operation type S i for the cloud database instance I i in the historical time period Δt1, that is, the mean, variance, median, or mode of the plurality of service delay data corresponding to the user operation type S i for the cloud database instance I i in the historical time period Δt1 is represented.The resource consumption characteristic data corresponding to the user operation type S i for the cloud database instance I i in the historical time period Δt1, that is, the mean, variance, median, or mode of the plurality of resource consumption data corresponding to the user operation type S i for the cloud database instance I i in the historical time period Δt1 is represented. Specifically, the plurality of service delay data and the plurality of resource consumption data for the user operation type S i of the cloud database instance I i within the history period Δt1 are pulled from the time-series database of the cloud database management and control system. Then, based on the forgetting function, constructing an abnormal scoring modelContinuously monitoring abnormal scoring model in current time period delta t2And if the abnormal score is continuously increased, judging that the cloud database instance is not matched with the user business data. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some optional embodiments, 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 distribution data corresponding to the user operation type in a historical time period; and clustering all cloud database instances on the cloud platform based on the distribution data to obtain a plurality of groups of all cloud database instances on the cloud platform. By means of the method, the device and the system, clustering operation is conducted on all cloud database instances on the cloud platform through the distributed data corresponding to the user operation types, and similarity of all cloud database instances in each group can be guaranteed. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In one specific example, log audit data for cloud database instance I i over a historical time period Δt1 is pulled from a time series database of the cloud database management system. The log audit data includes user operational data for cloud database instance I i for a historical period of time Δt1. Based on the user operation data of the user for the cloud database instance I i in the history period Δt1, the distribution data of the user operation type S i in the history period Δt1 is counted asBased on all cloud database instances I i on lineClustering is carried out, and n groups of cloud database examples are obtained: DG 1,...,DGi,...,DGn. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some optional embodiments, the determining the cloud database to be recommended based on metadata of the cloud database instances in the group of cloud database instances includes: determining the mode of the metadata of all cloud database instances in the group as the 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. By this, the cloud database to be recommended can be accurately determined. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In one specific example, metadata for cloud database instance I i for a historical time period Δt1 is pulled from a metadata database of a cloud database management system. For each group DG i, extracting metadata of each cloud database instance in the group, and extracting a mode index based on the metadata, thereby obtaining metadata of the cloud database to be recommended corresponding to each group DG i. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some alternative 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. By sending the recommendation message to the terminal equipment 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. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some alternative embodiments, the method further comprises: receiving a feedback message aiming at the recommended message and sent by the terminal equipment; and based on the feedback message, migrating the user service data in the cloud database instance to the cloud database to be recommended. By the method, the user service data in the cloud database instance can be migrated to the cloud database to be recommended based on the feedback information of the user. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, when the feedback message carries information of a cloud database which is not accepted by the user, 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 business data in the cloud database instance are imported into a cloud database to be recommended through a transmission tool. After the migration of the user service data is completed, the connection of the user is introduced to the cloud database to be recommended. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In some optional embodiments, when the feedback message includes migration time information of the user service data, the migrating, based on the feedback message, the user service data in the cloud database instance to the cloud database to be recommended includes: and based on the migration time information, migrating the user service data to the cloud database to be recommended. Therefore, the user service data can be migrated according to the migration time requirement of the user. It will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
In a specific example, when the migration time information is a time point, user service data in the cloud database instance is migrated to a 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 will be appreciated that the above description is exemplary only, and that the embodiments of the application are not limited in any way.
According to the recommendation method of the cloud database, 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 distributed on a physical machine and used for running 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 group where the cloud database instance is located, compared with other existing 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 group where the cloud database instance is located, so that 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 cost resources are achieved.
The recommendation method of the cloud database of the present embodiment may be performed by any suitable device having data processing capabilities, including but not limited to: cameras, terminals, mobile terminals, PCs, servers, vehicle-mounted devices, entertainment devices, advertising devices, personal Digital Assistants (PDAs), tablet computers, notebook computers, palm-top gaming machines, smart glasses, smart watches, wearable devices, virtual display devices or display enhancement devices (e.g., google Glass, oculus Rift, hololens, gear VR), and the like.
Referring to fig. 3, a schematic structural diagram of a recommendation device for cloud products in a third embodiment of the present application is shown.
The recommendation device for cloud products of the embodiment includes: the first determining module 301 is configured to determine quality of service data corresponding to user behavior data of a cloud product instance, where the cloud product instance is a service process allocated on a physical machine 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 recommendation device for cloud products in this embodiment is configured to implement the recommendation method for corresponding cloud products in the foregoing multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein.
Referring to fig. 4, a schematic structural diagram of a recommendation device for cloud products in a fourth embodiment of the present application is shown.
The recommendation device for cloud products 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 allocated on a physical machine for running a cloud product; a second determining module 402, 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.
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 in a target service quality interval, where the target service quality interval is determined according to 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 determining module 403, configured to record, as an abnormal event, an event for which the quality of service data corresponding to the user operation type at the current time is not in the target quality of service interval if it is determined that the quality of service data corresponding to the user operation type at the current time is not in 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; a 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 anomaly score continues to increase during the current time period.
The recommendation device for cloud products in this embodiment is configured to implement the recommendation method for corresponding cloud products in the foregoing multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein.
Referring to fig. 5, a schematic structural diagram of a recommendation device for cloud products in a fifth embodiment of the present application is shown.
The recommendation device for cloud products of the embodiment includes: the first determining module 501 is configured to determine quality of service data corresponding to user behavior data of a cloud product instance, where the cloud product instance is a service process allocated on a physical machine for running a cloud product; the second determining module 502 is 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.
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; and the clustering module 504 is configured to perform a clustering operation on all cloud product instances on the cloud platform based on the distribution data, so as to obtain multiple groupings of all cloud product instances on the cloud platform.
Optionally, the second determining module 502 is specifically configured to: determining the mode of the metadata of all cloud product instances in the group as the 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 metadata of the cloud product to be recommended; and the sending module 506 is configured to send the recommendation message to a terminal device held by the 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; and the migration module 508 is configured to migrate, based on the feedback message, the user service data in the cloud product instance to the cloud product to be recommended.
Optionally, when the feedback message includes migration time information of the user service data, the migration module 508 is specifically configured to: and based on the migration time information, migrating the user service data to the cloud product to be recommended.
The recommendation device for cloud products in this embodiment is configured to implement the recommendation method for corresponding cloud products in the foregoing multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein.
Fig. 6 is a schematic structural diagram of an electronic device in 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 cloud products according to the first embodiment, or implement the method for recommending cloud databases according to the second embodiment.
Fig. 7 is a hardware structure of an electronic device in 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;
Wherein the processor 701, the communication interface 702, and the computer readable medium 703 communicate with each other via the 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 for running a cloud product; and when the service quality data is not in the target service quality interval, 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.
The processor 701 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), and the like; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks 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 (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
In particular, according to embodiments of the present disclosure, the processes described above with reference to 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 shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU). The computer readable medium according to the present 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 electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage media element, a magnetic storage media element, or any suitable combination of the foregoing. In the context of this document, 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 the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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 of the present application may be written in 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 remote computers, the remote computers may be connected via any kind of network: including a Local Area Network (LAN) or a Wide Area Network (WAN), to connect to the user's computer, or may be connected to external computers (e.g., by way of the internet using an internet service provider).
The flowcharts 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). The specific relationships in the embodiments described above are merely exemplary, and fewer, more, or an adjusted order of execution of the steps may be possible in a specific implementation. 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 involved in the embodiments of the present application may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes a first determination module and a second determination module. The names of these modules do not constitute a limitation on the module itself in some cases, and for example, the first determining module may also be described as "a module for determining quality of service data corresponding to user behavior data of a cloud product instance".
As another aspect, the present application also provides a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the method for recommending cloud products according to the first embodiment, or implements the method for recommending cloud databases according to the second embodiment.
As another aspect, the present application also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted 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 for running a cloud product; and when the service quality data is not in the target service quality interval, 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.
The terms "first," "second," "the first," or "the second," as used in various embodiments of the present disclosure, may modify various components without regard to order and/or importance, but these terms do not limit the corresponding components. The above description is only configured for the purpose of distinguishing an element from other elements. For example, the first user device and the second user device represent different user devices, although both are user devices. 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 "coupled" (operatively 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 one element is directly connected to the other element or the one element is indirectly connected to the other element via yet another element (e.g., a third element). In contrast, it will be 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), then no element (e.g., a third element) is interposed therebetween.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the application referred to in the present application is not limited to the specific combinations of the technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the inventive concept described above. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.

Claims (19)

1. A method for recommending cloud products, the method comprising:
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 for running a cloud product;
When the service quality data is not in the target service quality interval, determining a cloud product to be recommended based on metadata of the cloud product instance in the group where the cloud product instance is located; the target service quality interval comprises a service delay allowing interval and a resource consumption allowing interval;
When the user behavior data includes a user operation type for the cloud product instance, the method further includes: if the service quality data corresponding to the user operation type is not in the target service quality interval, the cloud product instance is not matched with the user service data in the cloud product instance, 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;
Before the determining that the cloud product instance does not match the user service 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, 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 event of which the service delay data corresponding to the user operation type at the current moment is not in the service delay allowable interval is recorded as an abnormal event, and an abnormal score corresponding to the user operation type is determined based on an abnormal score model and the abnormal event; and if the abnormal score is determined to continuously increase in the current time period, determining that the cloud product instance is not matched with the user business data.
2. The method of claim 1, wherein the quality of service characteristic data comprises at least one of:
And the user operation type corresponds to the mean value, variance, median and mode of a plurality of service quality data in the historical time period.
3. The method of claim 1, wherein the target quality of service interval is determined from the quality of service characteristic data and a quality of service fluctuation threshold.
4. A method according to any of claims 1-3, characterized in that the quality of service data comprises service delay data and resource consumption data.
5. The method of claim 1, wherein when the user behavior data comprises a user operation type 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 clustering all cloud product instances on the cloud platform based on the distribution data to obtain a plurality of groups of all cloud product instances on the cloud platform.
6. The method of claim 1, wherein the determining the cloud product to be recommended based on metadata of the cloud product instance in the group in which the cloud product instance is located comprises:
determining the mode of the metadata of all cloud product instances in the group as the 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.
7. The method of claim 6, wherein 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.
8. The method of claim 7, wherein the method further comprises:
Receiving a feedback message aiming at the recommended message and sent by the terminal equipment;
And based on the feedback message, migrating the user service data in the cloud product instance to the cloud product to be recommended.
9. The method of claim 8, wherein when the feedback message includes migration time information of the user traffic 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 based on the migration time information, migrating the user service data to the cloud product to be recommended.
10. The method according to any of claims 6-9, wherein the metadata of the cloud product to be recommended comprises at least one of: and the class information, version information and specification configuration information of the cloud product to be recommended.
11. A recommendation device for cloud products, the device comprising:
The cloud product 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 instance, and the cloud product instance is a service process distributed on a physical machine for running a cloud product; the target service quality interval comprises a service delay allowing interval and a resource consumption allowing interval;
The second determining module is used for determining cloud products to be recommended based on metadata of cloud product instances in a group where the cloud product instances are located when the service quality data are not in a target service quality interval;
When the user behavior data includes a user operation type for the cloud product instance, the apparatus further includes: the first judging module is used for judging that the cloud product instance is not matched with the user service data in the cloud product instance 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 obtained by determining according to the service quality characteristic data corresponding to the user operation type in a historical time period;
Before the first determination module, the apparatus further includes: a second determining module, configured to, if it is determined that the resource consumption data corresponding to the user operation type at the current time is not in the resource consumption allowable interval and the service delay data corresponding to the user operation type at the current time is not in the service delay allowable interval, record, as an abnormal event, an event in which the resource consumption data corresponding to the user operation type at the current time is not in the resource consumption allowable interval and the service delay data corresponding to the user operation type at the current time is not in the service delay allowable 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 business data if the abnormal score is judged to be continuously increased in the current time period.
12. The apparatus of claim 11, wherein when the user behavior data comprises a user operation type 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 carrying out clustering operation on all cloud product instances on the cloud platform based on the distribution data so as to obtain a plurality of groups of all cloud product instances on the cloud platform.
13. The apparatus according to claim 11, wherein the second determining module is specifically configured to:
determining the mode of the metadata of all cloud product instances in the group as the 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.
14. The apparatus of claim 13, wherein the apparatus further comprises:
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.
15. The apparatus of claim 14, wherein the apparatus further comprises:
The receiving module is used for receiving a feedback message aiming at the recommended message and sent by the terminal equipment;
and the migration module is used for migrating the user service data in the cloud product instance to the cloud product to be recommended based on the feedback message.
16. The apparatus of claim 15, wherein when the feedback message includes migration time information of the user traffic data,
The migration module is specifically configured to:
and based on the migration time information, migrating the user service data to the cloud product to be recommended.
17. An electronic device, comprising:
One or more processors;
a computer readable medium configured to store one or more programs,
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the recommendation method of cloud products of any of claims 1-10.
18. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a recommendation method for a cloud product according to any of claims 1-10.
19. A computer program product comprising a computer program, characterized in that the program, when executed by a processor, implements a method of recommending cloud products according to any of claims 1-10.
CN201910463374.XA 2019-05-30 2019-05-30 Cloud product recommendation method and device, electronic equipment and computer readable medium Active CN112015971B (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN201910463374.XA CN112015971B (en) 2019-05-30 2019-05-30 Cloud product recommendation method and device, electronic equipment and computer readable medium
PCT/CN2020/091169 WO2020238712A1 (en) 2019-05-30 2020-05-20 Cloud product recommendation method and apparatus, electronic device, and computer readable medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910463374.XA CN112015971B (en) 2019-05-30 2019-05-30 Cloud product recommendation method and device, electronic equipment and computer readable medium

Publications (2)

Publication Number Publication Date
CN112015971A CN112015971A (en) 2020-12-01
CN112015971B true CN112015971B (en) 2024-09-06

Family

ID=73501497

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910463374.XA Active CN112015971B (en) 2019-05-30 2019-05-30 Cloud product recommendation method and device, electronic equipment and computer readable medium

Country Status (2)

Country Link
CN (1) CN112015971B (en)
WO (1) WO2020238712A1 (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112887129B (en) * 2021-01-15 2023-07-25 杭州安恒信息技术股份有限公司 Specification configuration method, system and related device of cloud security product
CN113434289B (en) * 2021-06-16 2024-07-12 北京达佳互联信息技术有限公司 Cloud host allocation method and device, electronic equipment and storage medium
CN113569137B (en) * 2021-07-06 2024-06-18 北京汇钧科技有限公司 Recommendation method and device for cloud host specification, storage medium and electronic device
CN114610476A (en) * 2021-08-06 2022-06-10 湖南亚信软件有限公司 A method, apparatus, device and storage medium for optimizing cloud service cost
CN114844798B (en) * 2022-07-04 2022-10-14 海马云（天津）信息技术有限公司 Cloud application service abnormity detection method and device
CN117290602A (en) * 2023-10-10 2023-12-26 中国建设银行股份有限公司 Public cloud product recommendation method, public cloud product recommendation device, public cloud product recommendation equipment, public cloud product recommendation medium and public cloud product recommendation computer program

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103002005A (en) * 2011-09-07 2013-03-27 埃森哲环球服务有限公司 Cloud Service Monitoring System
CN103649914A (en) * 2011-06-06 2014-03-19 国际商业机器公司 Automated recommendations for cloud-computing options

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101399707A (en) * 2008-11-20 2009-04-01 北京邮电大学 Method and device for selecting internet service based on credit
US9800477B2 (en) * 2014-07-31 2017-10-24 International Business Machines Corporation Generating a service cost model using discovered attributes of provisioned virtual machines
KR102029849B1 (en) * 2015-01-27 2019-10-08 노키아 솔루션스 앤드 네트웍스 오와이 Traffic flow monitoring
CN106100883A (en) * 2016-03-12 2016-11-09 浙江工商大学 A kind of cloud service evaluation method and device
CN107018024B (en) * 2017-05-10 2020-10-23 广东工业大学 Method and device for recommending cloud services

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103649914A (en) * 2011-06-06 2014-03-19 国际商业机器公司 Automated recommendations for cloud-computing options
CN103002005A (en) * 2011-09-07 2013-03-27 埃森哲环球服务有限公司 Cloud Service Monitoring System

Also Published As

Publication number Publication date
CN112015971A (en) 2020-12-01
WO2020238712A1 (en) 2020-12-03

Similar Documents

Publication Publication Date Title
CN112015971B (en) Cloud product recommendation method and device, electronic equipment and computer readable medium
CN110309142B (en) Method and device for rule management
CN111371858A (en) Group control equipment identification method, device, medium and electronic equipment
CN113641633A (en) File processing method, file processing device, electronic equipment, medium and computer program
CN115587575A (en) Data table creation method, target data query method, device and equipment
CN107819745B (en) Method and device for defending against abnormal traffic
US10929447B2 (en) Systems and methods for customized data parsing and paraphrasing
US20140149409A1 (en) Massive rule-based classification engine
CN110895761B (en) After-sales service application information processing method and device
CN113052509A (en) Model evaluation method, model evaluation apparatus, electronic device, and storage medium
WO2019056496A1 (en) Method for generating picture review probability interval and method for picture review determination
CN107256244B (en) Data processing method and system
CN119106750A (en) Task processing method based on large model, device, equipment and medium
CN113609168A (en) Data export method, device, terminal and readable storage medium
CN119046465A (en) Log classification method, device, equipment, storage medium and computer program product
CN112633955A (en) Advertisement conversion abnormity detection method and system and computer readable storage medium
CN113269600A (en) Information sending method and device
CN117910850A (en) Index data analysis engine, index data calculation device and calculation method
CN116304352A (en) Message pushing method, device, equipment and storage medium
CN115563942A (en) Contract generation method and device, electronic equipment and computer readable medium
CN110703119B (en) Method and device for evaluating battery health status
CN111507734A (en) Cheating request identification method and device, electronic equipment and computer storage medium
US20200192945A1 (en) Content validation
US20150186974A1 (en) Techniques For Searching Using Market-Adjusted Statistics
CN113626068A (en) Page management method and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20210907

Address after: Room 508, floor 5, building 4, No. 699, Wangshang Road, Changhe street, Binjiang District, Hangzhou City, Zhejiang Province

Applicant after: Alibaba (China) Co.,Ltd.

Address before: A four-storey 847 mailbox in Grand Cayman Capital Building, British Cayman Islands

Applicant before: ALIBABA GROUP HOLDING Ltd.

TA01 Transfer of patent application right
TA01 Transfer of patent application right

Effective date of registration: 20211013

Address after: Room 508, floor 5, building 4, No. 699, Wangshang Road, Changhe street, Binjiang District, Hangzhou City, Zhejiang Province

Applicant after: Alibaba (China) Co.,Ltd.

Address before: A four-storey 847 mailbox in Grand Cayman Capital Building, British Cayman Islands

Applicant before: ALIBABA GROUP HOLDING Ltd.

TA01 Transfer of patent application right
TA01 Transfer of patent application right

Effective date of registration: 20211130

Address after: 310000 No. 12, Zhuantang science and technology economic block, Xihu District, Hangzhou City, Zhejiang Province

Applicant after: Aliyun Computing Co.,Ltd.

Address before: Room 508, 5 / F, building 4, No. 699, Wangshang Road, Changhe street, Binjiang District, Hangzhou City, Zhejiang Province

Applicant before: Alibaba (China) Co.,Ltd.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant