Intelligent cloud management based on image information

Abstract

The technical scheme of intelligent cloud management based on portrait information disclosed in the present application applies the technical idea of artificial intelligence to cloud management, and intelligently proposes cloud management suggestion information. In daily work, the use behavior of cloud resources can reflect the characteristics of cloud users or cloud tenants themselves. The technical scheme of intelligent cloud management in the present application extracts and generates portrait information reflecting the characteristics of cloud use through the abstraction of cloud use behavior data, and intelligently proposes cloud management suggestions based on the portrait information.

Description

[0001] This application is a divisional application of Chinese Patent Application No. 201710335366.8, filed on May 12, 2017, entitled "Intelligent Cloud Management Based on Profiling Information". BACKGROUND

[0002] With the development of computer technology, cloud has been widely applied in various industries. Cloud refers to a unique IT resource that can provide services including computing, data storage, information processing, etc. to remote cloud users based on a network. The party providing cloud resources is a cloud provider, and the party renting cloud resources is a cloud tenant. In practical applications, the cloud tenant is generally an institution (enterprise or other organization (such as a school, government agency, etc.)), and the cloud tenant builds its own business processing system by renting cloud resources or directly processes its own business based on the business system provided by the cloud provider. At the same time, the employees (cloud users) of the institution will process the business of the institution as cloud users of cloud resources, including business development, business processing, and maintenance of business systems based on cloud resources, etc. For example, the cloud tenant is an e-commerce company, which builds a network sales platform by renting cloud resources from a cloud provider. The cloud resources supporting the network sales platform include multiple virtual servers, cloud databases, load balancers, and other cloud resources. The employees of the e-commerce company need to develop, maintain, and process other businesses of the network sales platform built on the cloud resources. SUMMARY

[0003] The summary of the embodiments of the present application is provided to introduce some concepts that will be further described in the following detailed description in a simplified form. The summary is not intended to identify key or essential features of the claimed subject matter or to limit the scope of the claimed subject matter.

[0004] The disclosed intelligent cloud management based on profiling information applies the AI (artificial intelligence) idea to cloud management and intelligently proposes processing suggestions for cloud resource management. In daily work, the use behavior of cloud resources can reflect the characteristics of cloud users or cloud tenants themselves. The intelligent cloud management technical solution in this paper extracts and generates profiling information reflecting the use characteristics of cloud by abstracting the cloud use behavior data, and intelligently proposes cloud management suggestions based on the profiling information.

[0005] The above description is only a summary of the technical solutions of the present disclosure. In order to more clearly understand the technical means of the present disclosure, the following specific embodiments of the present disclosure can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the following specific embodiments of the present disclosure are described in detail. BRIEF DESCRIPTION OF DRAWINGS

[0006] Figure 1 System block diagram for cloud management of embodiments of the present invention;

[0007] Figure 2 Schematic diagram of a process for generating cloud user profile information of embodiments of the present invention;

[0008] Figure 3 Schematic diagram of a process for generating cloud tenant profile information of embodiments of the present invention;

[0009] Figure 4 Schematic diagram of another process for generating cloud tenant profile information of embodiments of the present invention;

[0010] Figure 5 Schematic diagram of a process for generating cloud resource health data of embodiments of the present invention;

[0011] Figure 6 Structural block diagram of a cloud management recommendation information generation module of embodiments of the present invention;

[0012] Figure 7A Another structural block diagram of a cloud management recommendation information generation module of embodiments of the present invention;

[0013] Figure 7B Still another structural block diagram of a cloud management recommendation information generation module of embodiments of the present invention;

[0014] Figure 8 Still another structural block diagram of a cloud management recommendation information generation module of embodiments of the present invention;

[0015] Figure 9 Content block diagram of a cloud management template of embodiments of the present invention;

[0016] Figure 10 Schematic diagram of an application scenario of embodiments of the present invention;

[0017] Figure 11 Schematic diagram of a processing flow of intelligent cloud management of embodiments of the present invention;

[0018] Figure 12 Block diagram of an apparatus of intelligent cloud management of embodiments of the present invention;

[0019] Figure 13 Block diagram of an electronic device of embodiments of the present invention. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings. While example embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0021] Terminology:

[0022] Cloud Provider: a party that provides cloud resources, such as Microsoft's cloud platform Microsoft Azure, Alibaba's Ali Cloud, Amazon's AWS cloud platform, and the like.

[0023] Cloud Tenant: a cloud tenant is generally an institution, such as an enterprise or other organization (e.g., a school, a government agency, etc.), which builds its own business processing system by renting cloud resources or directly uses a business processing platform based on cloud resources provided by a cloud provider to process its own business.

[0024] Cloud User: refers to a person within a cloud tenant who uses cloud resources. The use of cloud resources by a cloud user includes: development and maintenance of a business processing system based on cloud resources, processing of business using a business processing system based on cloud resources, deployment and optimization of rented cloud resources, and the like. For example, when the cloud tenant is an enterprise, the cloud user can be an employee of the enterprise, who can be an engineer responsible for system development and maintenance, a salesperson responsible for sales business of the company, or an administrative staff responsible for personnel management in the enterprise.

[0025] Cloud Resource: virtualized computer resources provided by a cloud provider to a cloud tenant, which in embodiments of the present disclosure is understood broadly and can include, for example: virtual machine resources, data storage resources, queue resources, data processing platforms provided by a cloud provider, business processing systems built by a cloud tenant based on rented virtual machines and databases, and the like.

[0026] In this document, the term "technology" can refer to, for example, system(s), method(s), computer-readable instructions, module(s), algorithm(s), hardware logic (e.g., field programmable gate array (FPGA)), application specific integrated circuit (ASIC), application specific standard product (ASSP), system on a chip (SOC), complex programmable logic device (CPLD), and / or other technology(s) as permitted under the context within this document and as permitted under the context throughout this document.

[0027] Cloud technology (also known as cloud computing technology) has been widely applied across various industries. Cloud resource providers can offer services including computing, data storage, and information processing to cloud tenants and their associated users via the network. On the cloud tenant side, to ensure the normal and efficient operation of various cloud-based services, cloud tenants need to perform various cloud management operations. In this embodiment of the invention, cloud management refers to monitoring and analyzing the usage of cloud resources, and configuring, adjusting, and maintaining these resources based on the analysis results. For example, monitoring the cloud tenant's workload and adjusting the number of virtual machines or their processing capacity based on changes in workload. Another example is providing network security protection for cloud-based business systems, monitoring for network attacks or viruses, and performing appropriate antivirus or protective measures.

[0028] Cloud management is a highly specialized and complex process. It requires a comprehensive analysis of cloud usage before a specific management plan can be developed and executed. This necessitates that cloud management personnel possess extensive cloud-related expertise, a deep understanding of cloud resource architecture and operation, and even strong cloud development experience. Only then can they develop and implement reasonable management plans based on cloud usage. Therefore, in current cloud management mechanisms, cloud management work is primarily performed by specialized technical personnel. While some auxiliary software has emerged for cloud management, these are merely tools and are inherently passive. They require user commands, configurations, and even programming to perform management operations such as monitoring, querying, and configuration. Ultimately, they still rely on user commands for passive cloud management.

[0029] Therefore, this invention introduces the technical concept of AI (artificial intelligence) into cloud management, proposes a set of cloud management technical solutions, and thereby realizes a proactive and intelligent cloud management suggestion generation mechanism.

[0030] like Figure 1 As shown, it is an example block diagram 100 of the cloud management system that implements the present disclosure. The cloud provider 101 provides cloud resources to the cloud tenant 102, and the cloud user 103 in the cloud tenant 102 uses the cloud resources for daily business processing.

[0031] The profile information processing module 104 of the cloud provider 101 acquires the cloud usage behavior data of the cloud user 103, and generates profile information 107 embodying the cloud usage characteristics of the cloud user 103 and / or the cloud tenant 102 by analyzing the cloud usage behavior data of the cloud user 103. On the other hand, the cloud resource running status data acquisition module 105 of the cloud provider 101 acquires the cloud resource running status data 108 of the cloud resources rented by the cloud tenant 102. The cloud management suggestion information generation module 106 of the cloud provider 101 generates cloud management suggestion information 109 according to the profile information 107 and the cloud resource running status data 108, and pushes the cloud management suggestion information 109 to the cloud user 103 in the cloud tenant 102 to help the cloud user 103 perform cloud management.

[0032] In the system 100 described above, the cloud provider 101 can include one or more cloud servers or cloud platforms that provide cloud resource services to the cloud tenant 102, and an operating system or a business processing system for cloud management resource management can be installed on the cloud servers or cloud platforms. The cloud user 103 can use one or more servers, computer terminals, mobile terminals, and the like to use cloud resources, and a client software that interfaces with the cloud servers or cloud platforms of the cloud provider 101 can be installed on these devices.

[0033] The above describes the system architecture of the cloud management of the embodiment of the present application as a whole, and the following will further describe the main parts of the system in detail with reference to the accompanying drawings. Figure 1

[0034] Profile information acquisition module

[0035] The profile information acquisition module 104 is used to generate the profile information 107. The profile information 107 is generated by feature extraction on the overall cloud usage behavior data of the cloud tenant 102 or the cloud usage behavior data of the multiple cloud users 103 in the cloud tenant 102, and these profile information 107 embodies the abstract forms of the cloud tenant 102 or the cloud user 103 in the cloud tenant 102 on the cloud. These abstract forms are related to the cloud tenant 102 or the cloud user 103, and can embody the characteristics of the cloud usage behavior of the cloud tenant 102 or the cloud user 103. For example, the profile information 107 can include the following aspects: Figures 2 to 4 ​As shown, in some examples, the profile information 107 can further include cloud user profile information 201 and / or cloud tenant profile information 301. After the cloud tenant 102 rents the cloud resources from the cloud provider 101, the cloud resources are used by the cloud users 101 in the cloud tenant 102, and the cloud usage behaviors herein refer to all processing behaviors or operations based on the cloud resources, such as business processing based on the business processing system of the cloud resources, system development and maintenance based on the cloud resources, cloud management, etc. The cloud user 103 in the cloud tenant 102 herein refers to a person who has the right to operate the cloud resources rented by the cloud tenant 102. For example, if the cloud tenant 102 is an enterprise, the cloud user 103 is generally an employee of the enterprise, and in some cases, can also be an authorized person outside the enterprise. In general, the cloud user 103 herein refers to a person who can access and operate the cloud resources of the cloud tenant 102.

[0036] In some examples, since the cloud resources rented by the cloud tenant 102 are ultimately used by the cloud user 103, the cloud user profile information 201 and the cloud tenant profile information 301 are both obtained based on the cloud usage behavior data 202 of the cloud user 103. The profile information 107 has a high degree of generalization, and therefore can exist in the form of label information, i.e., the profile information 107 can be one or more label information that embodies the cloud usage characteristics. The profile information 107 can embody the characteristics of the cloud tenant 102 and / or the cloud user 103 on the cloud from different feature dimensions.

[0037] The cloud provider 101 provides cloud resources to the remote cloud tenant 102 through a network. These cloud resources are not in the local computer of the cloud tenant 102 or the cloud user, and all cloud usage behaviors of the cloud user 103 in the cloud tenant 102 are implemented through the cloud resources on the side of the cloud provider 101. As the cloud provider 101, the cloud usage behavior data 202 of the cloud user 103 can be monitored and recorded, and therefore can be obtained from the log files of the cloud operating system of the cloud provider 101, or can be obtained by setting a cloud usage behavior monitoring module in the cloud server of the cloud provider 101 to monitor the cloud usage behavior.

[0038] Based on the cloud usage behavior data 202, the extraction of the profiling information 107 can be achieved by using big data analysis techniques and machine learning techniques. The extraction of the profiling information 107 can be achieved by pre-setting models of various types of label information, and then using data mining or semantic analysis techniques to extract features, and then using machine learning techniques such as classifiers to classify the label information, thereby obtaining the profiling information 107.

[0039] The following describes the cloud user profiling information 201 in detail. Figure 2 The cloud user profiling information 201 can include the following types of label information (each type of label information can be regarded as a feature dimension): Figure 2 As shown in FIG. 2, it is a schematic diagram of the generation process 200 of the cloud user profiling information 201 according to an embodiment of the present application. The cloud user profiling information 201 can include the following types of label information (each type of label information can be regarded as a feature dimension):

[0040] 1) Label information of the role 201a of the cloud user 103: For example, position label information in an enterprise (senior management, middle management, ordinary employee, etc.), and job label information in an enterprise (sales personnel, administrative personnel, R&D personnel). In some examples, the position label information can be extracted from the approval authority 202a in the business process of the enterprise, and the job label information can be extracted from the business content 202c (including business files, emails, work logs, etc.) handled by the cloud user, for example, the main business files handled by R&D personnel are technical documents (such as design drawings, program codes, product development documents, etc.), and the main business content of sales personnel is business orders or various emails about product sales, etc. In some other examples, the label information of the role 201a of the cloud user can also be obtained from the role and / or permission functions provided by the cloud platform, for example, the Microsoft Azure cloud platform provides different account roles (such as administrator, service administrator, account administrator, and collaboration administrator) and different usage permissions (such as read-write, read-only, and contributor), and the cloud user 103 can also customize the role and / or permission according to the different needs of the enterprise, such as virtual machine administrator, storage read-only personnel, and other different roles.

[0041] 2) Tag information identifying the business type 201b of the business handled by the cloud user 103: For example, the business type handled is a buy / sell order business, an overseas trade business, a technology development project, a legal service project, etc. In some examples, these tag information can be extracted from the relevant files of the business content 202c, for example, by analyzing the content of the business files or the content of the correspondence mails of the cloud user, it can be known that the business type 201b handled by the cloud user on a daily basis. In other examples, it can also be extracted from the business information provided by the cloud tenant 102 or the cloud user 103 in the order contract with the cloud provider 101 when signing the cloud resource order, for example, the name of the company is marked as an e-commerce company.

[0042] 3) Tag information identifying the cloud resource type 201c used by the cloud user 103: For example, the cloud service type: cloud database service, virtual machine service, cloud storage service, mail service, and the resource composition mode of the cloud resource: public cloud, private cloud or hybrid cloud. In some examples, such tag information can be extracted from the cloud resource configuration file 202d or the cloud resource use log 202e of the cloud user 103.

[0043] 4) Tag information identifying the cloud resource use habit 201d of the cloud user 103: For example, system upgrade on rest days, daily check on the health status of the cloud resource, use frequency of the cloud resource, etc. In some examples, such tag information can be extracted from the cloud resource use log 202e.

[0044] The above exemplary introduces several dimensions of cloud user portrait information 201, it needs to be explained that the cloud user portrait information 201 embodies the characteristics of the cloud user 103 in the cloud use aspect (or said from the perspective of the cloud) embodied, these characteristics are the characteristics presented in the cloud virtual environment, and not the real characteristics of the cloud user 103 in the physical environment. For example, an employee (also a cloud user 103) of an enterprise providing legal consulting services has the highest level of control authority of cloud resources, from the perspective of the cloud, the position tag information of the employee should be senior manager, but in the actual enterprise, the position of the employee may be a system security maintenance personnel of the enterprise, in an enterprise providing legal consulting services, the system security maintenance personnel is only an ordinary employee. As can be seen, the employee in the physical environment and the cloud user 103 at the cloud level may have completely different roles. However, in the embodiment of the present application, we are interested in the characteristics information presented by the cloud user 103 in the cloud use aspect, through the analysis and processing of these characteristic information, the cloud management suggestion information 109 for this specific cloud user 103 can be made, and the cloud management processing suggestion information 109 is active and effective, and fully embodies the combination application of AI and cloud management.

[0045] The content and acquisition method of the cloud user portrait information 201 are exemplarily described above. Next, the cloud tenant portrait information 301 will be described in combination with the cloud tenant portrait information 301. Figure 3 and Figure 4 The generation process 300 of the cloud tenant portrait information 301 will be described.

[0046] The cloud tenant portrait information 301 is an abstraction of the cloud use behavior of the cloud tenant 102. The cloud use behavior of the cloud tenant 102 is different from the cloud use behavior of the cloud user 103 described above, the cloud use behavior of the cloud user 103 is an individual behavior, and the cloud use behavior of the cloud tenant 102 is from the perspective of the whole cloud tenant 102 to use the cloud resource. The cloud use behavior of the cloud tenant 102 can be regarded as being composed of the cloud use behaviors of the multiple cloud users 103 (all or part) within the cloud tenant 102, and thus the cloud use behavior of the cloud tenant 102 can be obtained by abstracting the cloud use behaviors of the multiple cloud users 103 within the cloud tenant 102. The cloud tenant portrait information 301 is a further abstraction of the cloud use behavior of the cloud tenant 102. Similar to the cloud user portrait information 201, the cloud tenant portrait information 301 can also exist in the form of tag information, and different tag information will embody the characteristics of the cloud tenant 102 presented on the cloud from different characteristic dimensions.

[0047] The cloud tenant portrait information 301 can be generated by at least two ways as follows. For example, Figure 3As shown, it is a schematic diagram of a generation process 300a of the cloud tenant portrait information 301 of an embodiment of the present application, in which Figure 3 In the generation process 300a shown, in some examples, cloud usage behavior data 202 of multiple cloud users 103 of the cloud tenant 102 is collected, and then feature extraction is performed on the multiple cloud usage behavior data 202, so as to obtain cloud tenant portrait information 301 embodying the characteristics of the overall cloud usage behavior of the cloud tenant 102. As shown in Figure 4 As shown, it is a schematic diagram of another generation process 300b of the cloud tenant portrait information 301 of an embodiment of the present application, in which feature extraction can be performed on the already generated cloud user portrait information 201, so as to obtain the cloud tenant portrait information 301.

[0048] Further, as shown in Figure 3 and Figure 4 In some examples, the cloud tenant portrait information 301 can include the following types of label information (each type of label information can be regarded as a feature dimension):

[0049] 1) Label information identifying the industry 301a of the cloud tenant 102: for example, the enterprise belongs to the e-commerce industry, the production and manufacturing industry, the consulting service industry, the financial industry, the technology research and development, the IT enterprise, etc., and different industries will set different label information.

[0050] As shown in Figure 3 In the case where the cloud tenant 102 and / or the cloud user 103 authorizes access to its business data, these industry label information can be extracted from the processed business content 202c (including business files, emails, work logs, etc.) of multiple cloud users 103 in the enterprise, for example, through analysis of the business files, it is found that most of the business files processed by most of the cloud users 103 of the enterprise are mostly related to online shopping, then it can be determined that the industry of the enterprise is the e-commerce industry, and therefore the label information of the e-commerce industry is assigned. For another example, through analysis of the business files and emails processed by the cloud users in the enterprise, it is found that most of the business files and emails of the enterprise are related to stock and investment information, then it can be determined that the enterprise belongs to the financial industry, and the label information of the financial industry is assigned.

[0051] In some other examples, the label information of the industry 301a of the cloud tenant can also be extracted from the business information provided by the cloud tenant 102 or the cloud user 103 when signing the order for cloud resources with the cloud provider 101, for example, the name of the company is marked as a financial company.

[0052] As shown in Figure 4As shown, as another way of generating industry tag information of the cloud tenant 102, the tag information in the cloud user profile information 201 of the plurality of cloud users 103 can be extracted. For example, as mentioned above in the cloud user profile information 201, the business type tag information of the business handled by the cloud user 103 is mentioned, and by analyzing the tag information of the business type 201b of the majority of the cloud users 103 in the enterprise, the industry 301a to which the enterprise belongs can be determined. For example, if the tag information of the business type 201b of the majority of the cloud users 103 in the enterprise is technology development, it can be determined that the enterprise belongs to the technology research and development industry, and the tag information of the technology research and development industry is assigned. In addition, the tag information of the cloud tenant 102 can also be extracted by the aforementioned tag information identifying the role of the cloud user 201a. For example, if the position tag information of the majority of the cloud users 103 in the enterprise is financial analyst, it can be determined that the enterprise belongs to the financial industry, and the tag information of the financial industry is assigned. In addition, the tag information identifying the cloud resource usage habit 201d of the cloud user 103 can also be used as a basis for determining the industry 301a to which the cloud tenant 102 belongs. For example, if many cloud users 103 in the enterprise are assigned the tag information of the cloud resource usage habit 201d of "system upgrade on weekends", it can be inferred that the enterprise is likely to be in the IT industry.

[0053] 2) Tag information identifying the organizational structure 301b of the cloud tenant 102 on the cloud: For example, whether the enterprise is a vertical management structure or a parallel management structure, which department in the enterprise is the core department, and the functional relationship between departments, etc.

[0054] For the tag information of the organizational structure, in some examples, in the case of obtaining the authorization of the cloud tenant 102 and / or the cloud user 103 to access the business data thereof, the approval authority 202a of each cloud user 103, the cloud usage behavior data 202 such as the business process 202b of each cloud user 103, etc. can be extracted. By analyzing the different positions of each cloud user 103 in the business process 202b, the department information of each cloud user 103 can be analyzed, and based on these department information, the organizational structure 301b of the entire enterprise can be extracted.

[0055] In some examples, the cloud platform can provide the role and / or permission function of the cloud user 103, for example, the Microsoft Azure cloud platform provides different account roles (e.g., administrator, service administrator, account administrator, co-administrator) and different use permissions (e.g., read-write, read-only, contributor, etc.), and the cloud user 103 can also customize the role and / or permission according to the different needs of the enterprise, such as virtual machine administrator, storage read-only personnel, and other different roles. According to the role and / or permission of the account of the cloud user 103, the organizational structure 301b of the cloud tenant 102 can also be analyzed.

[0056] In addition, in some examples, the tag information of the organizational structure 301b can also be directly extracted through the tag information of each cloud user 103. As introduced before, each cloud user 103 is identified with the tag information of the role 201a, based on which the department information can be easily divided, and the inter-department organizational structure 301b can be generated.

[0057] 3) Tag information of cloud resource usage mode 301c of cloud tenant 102: In some examples, the cloud resource usage mode 301c can include the service type of the cloud service used by the cloud tenant 102: cloud database service, virtual machine service, cloud storage service, etc., and the resource composition mode of the cloud resource used: public cloud, private cloud, or hybrid cloud.

[0058] The tag information of the cloud resource usage mode 301c can be extracted from the cloud resource configuration file 202d or the cloud resource usage log 202e of the multiple cloud users 103. Of course, it can also be directly extracted based on the tag information of the cloud resource usage mode 301c of each cloud user 103.

[0059] The above exemplary introduces several dimensions of cloud tenant portrait information 301, it needs to be explained that the cloud tenant portrait information 301 mainly embodies the characteristics of the cloud tenant 102 in the cloud level (or from the perspective of the cloud), these characteristics are the characteristics presented in the cloud virtual environment, and not the real characteristics of the cloud tenant 102 in the physical environment. For example, a trade company in the physical environment of an enterprise, but the enterprise as a cloud tenant 102 rents a large number of cloud resources for the development of trade platform system, from the perspective of the cloud, the enterprise is an IT industry enterprise, not a trade industry.

[0060] The various label information described above are merely examples based on the technical idea of the present application, and in actual applications, various information labels can be defined flexibly according to the cloud resource characteristics of the cloud tenant 102, the cloud user 103 and the cloud provider 101, so as to generate abstract information that can more effectively reflect the characteristics of the cloud user 103 and the cloud tenant 102 in cloud use.

[0061] The exemplary content and generation manner of the cloud user portrait information 201 and the cloud tenant portrait information 301 are introduced above.

[0062] In summary, in the embodiment of the present application, the cloud user portrait information 201 and the cloud tenant portrait information 301 are both abstracted based on the cloud use behavior data 202, and based on the portrait information 107 from the cloud level (information fully reflecting the cloud use characteristics), the cloud tenant 102 and the cloud user 101 can be provided with intelligent and proactive cloud management recommendation information 109.

[0063] Cloud resource running status data acquisition module

[0064] The cloud resource running status data acquisition module 105 is configured to monitor the running status of the cloud resource, and generate cloud resource running status data 108. As shown in Figure 5 Fig. 5 is a schematic diagram of a generation process 500 of the cloud resource running status data 108 in the embodiment of the present application. The cloud resource running status data 108 refers to the running state data of the cloud resource 501 rented by the cloud tenant 102 in the process of executing business processing. In some examples, the cloud resource running status data 108 can include: cloud health status 108a, security protection status 108b of a business processing system constructed based on the cloud resource, cloud resource load condition 108c, etc. The cloud resource 502 monitored by the cloud resource running status data acquisition module 105 can include: a virtual machine 501a, a cloud storage 501b, a business processing system 501c, etc.

[0065] Since the cloud resource 501 is on the side of the cloud provider 101, the monitoring of the running status of the cloud resource 501 can be completed by the cloud provider 101. Specifically, the monitoring of the cloud resource 501 and the generation of the cloud resource running status data 108 can be completed by the cloud server of the cloud provider 101.

[0066] In addition, in some examples, as another optional manner, the monitoring of the running status of the cloud resource 501 can also be executed on the side of the cloud tenant 102, and correspondingly, the cloud resource running status data 108 can also be generated on the side of the cloud tenant 102. Specifically, the cloud resource 501 used by the cloud tenant 102 can be monitored and the cloud resource running status data 108 can be generated by the computer of the cloud tenant 102 locally.

[0067] Cloud management suggestion information generation module

[0068] The cloud management suggestion information generation module 106 is configured to generate cloud management suggestion information 109 according to the profiling information 107 and the cloud resource running status data 108. As shown in FIG. 6, it is a structural block diagram 600 of the cloud management suggestion information generation module 106 in an embodiment of the present application. Figure 6

[0069] In some examples, the cloud management suggestion information generation module 106 can utilize a Bot module 601 to generate the cloud management suggestion information 109. The Bot module 601 in the embodiment of the present application refers to an artificial intelligence module built on an artificial intelligence data platform. In many application scenarios, the Bot exists in the form of a chatbot, which can simulate human conversation with users in the form of a conversation, and answer users' questions with the support of a powerful background artificial intelligence data platform 602, and provide various suggestions to users according to the conversation with users.

[0070] After the Bot module 601 obtains the profiling information 107 and the cloud resource running status data 108, it accesses the artificial intelligence data platform to obtain cloud management processing suggestion information. That is, the profiling information 107 and the cloud resource running status data 108 are input information of the Bot module 601, and the cloud management processing suggestion information 109 is output information of the Bot module 601.

[0071] In some examples, in order to obtain more professional cloud management suggestion information 109, a third-party Bot module 702 can be utilized to generate the cloud management suggestion information 109. As shown in FIG. 7, it is another structural block diagram 700A of the cloud management suggestion information generation module 106 in an embodiment of the present application. Figure 7A

[0072] In the block diagram 700A, a main Bot module 701 is provided, which is connected to a plurality of third-party Bot modules 702. The plurality of third-party Bot modules 702 can have different functions, and each third-party Bot module 702 corresponds to a corresponding third-party artificial intelligence platform 703. In some examples, the third-party Bot module 702 can include a Bot for virtual machine monitoring analysis, a Bot for vulnerability scanning analysis, and a Bot for providing industry solutions.

[0073] ​​The main Bot module 701 sends the acquired profiling information 107 and cloud resource status data 108 to the third-party Bot module 702, the third-party Bot module 702 analyzes the profiling information 107 and cloud resource status data 108, extracts the required information, and then generates cloud management processing suggestion information 109 by means of the related third-party artificial intelligence data platform 703, and then sends it to the main Bot module 701. After the main Bot module 701 receives the cloud management processing suggestion information 109 from the third-party Bot module 702, the main Bot module 701 provides the cloud user 103 with the cloud management processing suggestion information 109 in the form of an active conversation. The above-mentioned main Bot module 701 and each third-party Bot module 702 can be in the form of a plug-able Bot module and be arranged in the server of the cloud provider 101, or be arranged in the server of the cloud tenant 102 or the computer of the cloud user 103. It should be noted that each third-party Bot module 702 has the support of the third-party artificial intelligence data platform 703, so as to be able to call the resources of the third party to provide intelligent cloud management suggestion information 109 according to the profiling information 107 and cloud resource status data 108 sent by the main Bot module 701.

[0074] In the block diagram 700A, a unified interface rule can be designed, and each third-party Bot module 702 follows the interface rule, so as to be able to identify the information format of the profiling information 107 and cloud resource status data 108 acquired by the main Bot module 701, and be able to return cloud management suggestion information 109 that can be recognized by the main Bot module 701, and then finally push to the cloud user 103 in the form of a conversation by the main Bot module 701.

[0075] In view of the fact that there are many types of third-party Bot modules 702 at present, in some examples, the third-party Bot modules 702 can not be required to follow the unified interface rule, but the main Bot module 701 can be required to convert the interface rule. After the main Bot module 701 acquires the profiling information 107 and cloud resource status data 108, the main Bot module 701 converts the profiling information 107 and cloud resource status data 108 into a data format that can be recognized by the selected third-party Bot module 702 according to the difference of the selected third-party Bot module 702, and sends it to the third-party Bot module 702 for processing. After receiving the cloud management suggestion information 109 returned by the third-party Bot module 702, the main Bot module 701 identifies and generates processing suggestion information in the information format of the main Bot module 701, and pushes it to the cloud user 103 in the form of a conversation.

[0076] In some cases, the main Bot module 701 can also select a matching third-party Bot module 702 to provide cloud management advice 109 based on the profile information 107 and cloud resource operation status data 108. For example, if the profile information 107 obtained by the main Bot module 701 involves a cloud tenant 102 in the IT industry, whose main cloud resource is virtual machine service, and the cloud resource operation status data 108 shows that the business processing system built on virtual machines by this enterprise is frequently attacked by hackers, based on this information, the main Bot module 701 will prioritize selecting a third-party Bot module 702 that provides network security analysis to provide cloud management advice 109.

[0077] In addition, in some cases, the main Bot module 701 can also obtain feedback information through conversations with cloud users 103 to score and rank the various third-party Bot modules 702, thereby serving as the basis for selecting the third-party Bot module 702.

[0078] In some cases, it can also be used Figure 7B In the structure of block diagram 700B, the main Bot module 701 and the third-party Bot module 702 are in parallel. The main Bot module 701 can first receive profile information 107 and cloud resource operation status data 108, and then analyze and judge the profile information 107 and cloud resource operation status data 108 to determine whether the main Bot module 701 can generate cloud management suggestion information 109. If so, the main Bot module 701 directly accesses the corresponding cloud provider's artificial intelligence data platform 704 to obtain the cloud management suggestion information 109, and promotes it through a session. If, after analyzing the profile information 107 and cloud resource operation status data 108, the main Bot module 701 is found to be unable or unsuitable to generate cloud management suggestion information 109, then the profile information 107 and cloud resource operation status data 108 are sent to another third-party Bot module 702 capable of generating cloud management suggestion information 109. This third-party Bot module 702 then retrieves the cloud management suggestion information 109 from its corresponding third-party artificial intelligence data platform 703 and pushes it to the cloud user 103 via a session. The above describes the technical solution for generating cloud management suggestion information 109 using Bot module 601. Based on the above technical solution, the cloud user 103 can also pre-set a cloud management template 801 to customize or guide the content of the cloud management suggestion information 109. For example... Figure 8As shown, it is another structural block diagram 800 of the cloud management suggestion information generation module 106 of the embodiment of the present application. The block diagram 800 adds a cloud management template 801 relative to the block diagram 600, and the cloud management template 801 can record preset management matters and / or related parameters of the management matters, etc.

[0079] As shown, it is another structural block diagram 800 of the cloud management suggestion information generation module 106 of the embodiment of the present application. The block diagram 800 adds a cloud management template 801 relative to the block diagram 600, and the cloud management template 801 can record preset management matters and / or related parameters of the management matters, etc. Figure 9 As shown, it is a content block diagram 900 of the cloud management template 801 of the embodiment of the present application.

[0080] In the example of the block diagram 900, the cloud management template 801 pre-sets aspects of DDoS protection 802, cloud health monitoring 803, and maintenance plan 804, etc. Taking the DDoS protection 802 as an example, the specific setting content 805 includes: the confidence coefficient is 0.8, which means that the alarm processing is performed after the event reaches the confidence coefficient; the target range is the entire cloud tenant, which means that the cloud resources of the cloud tenant are monitored for DDoS protection; the target resource type is any type, which means that all types of resources are monitored for DDoS protection.

[0081] In the case of pre-setting the cloud management template 801, the cloud management suggestion information 109 is generated according to the portrait information 107, the cloud resource running status data 108, and the cloud management template 801. In some examples, the cloud management template 801 can be a parameter setting for the Bot module 601 (for example, the confidence coefficient of the predicted threat, only when the confidence coefficient is higher than this, the alarm processing suggestion is given), or it can be a management matter that requires the Bot module 601 to focus on (for example, focusing on cloud health).

[0082] In some examples, the cloud management template 801 can exist in the form of a program script, and can be associated with the main Bot module 701 or the third-party Bot module 702 to set, for adjusting the program running of the main Bot module 701 or other third-party Bot module 702.

[0083] The cloud management suggestion information generation module 106, after generating the cloud management suggestion information 109, will provide it to the cloud user 103 to guide the cloud user to perform cloud management operations. In some examples, the cloud management suggestion information 109 is provided to the cloud user 103 through an active session, for example, a chat interaction mode is entered by presenting a session window on the computer or mobile terminal of the cloud user 103. The session can be initiated by the robot module 601 in the block diagram 600 or the host robot module 701 in the block diagram 700A or 700B. The session mentioned here is an active session initiated based on the monitoring of the cloud resource running status, rather than passively waiting for the cloud user 103 to input instructions or in other ways to obtain cloud management suggestions. This mechanism of providing cloud management suggestion information 109 to the cloud user 103 in an active session fully embodies the technical idea of combining cloud management with AI.

[0084] In some examples, the content of the cloud management suggestion information 109 can include one or more of the following information: alarm processing suggestion information, optimization processing suggestion information, prediction processing suggestion information, etc.

[0085] Alarm processing suggestion information: based on the cloud resource running status data 108 and the portrait information 107, it is found that there are problems or risks in the use of cloud resources, therefore, processing suggestions for solving these problems or risks are provided.

[0086] Optimization processing suggestion information: based on the cloud resource running status data 108 and the portrait information 107, it is found that there are parts that can be optimized in the use of cloud resources, so that cloud resources can be used more effectively, therefore, processing suggestions for optimizing the use of cloud resources are provided.

[0087] Prediction processing suggestion information: based on the cloud resource running status data 108 and the portrait information 107, it is predicted that a situation may occur in the future, therefore, processing suggestions for how to adjust the use of cloud resources in response to the situation that may occur in the future are given.

[0088] In some examples, the conversation initiated by the main Bot module 701 can also be dynamically adjusted according to the input information of the cloud user 103. After the main Bot module 701 provides the cloud management suggestion information 109 to the cloud user in the form of a conversation, the main Bot module can further obtain the input information of the cloud user 109 in the process of the subsequent conversation, obtain new cloud management suggestion information 109 according to the portrait information 107, cloud resource running status data 108, and input information of the conversation. In the process of obtaining the new cloud management suggestion information 109, the main Bot module 701 adjusts the third-party Bot module 702 selected according to the input information of the cloud user 103, so as to provide further processing suggestion information that is more in line with the needs of the cloud user 103.

[0089] In the above conversation process, the input information of the conversation can include any one or more of the context information of the conversation, the voice information of the cloud user 103, the emotion information of the cloud user 103, the environment information of the cloud user 103, and the time information. That is, in the conversation process, it is not limited to obtaining only the context information input in the form of text, but can also include information that can be obtained by the terminal of the cloud user 103 or other sensor devices, so as to more effectively judge the needs of the cloud user 103 and provide more effective cloud management suggestion information 109.

[0090] Embodiment of application scenario

[0091] The functions and implementation methods of each part of the intelligent cloud management are introduced above, and the technical solution of the intelligent cloud management will be further introduced below through an embodiment of a specific application scenario.

[0092] An embodiment will be further described below to illustrate the technical solution of the embodiment of the present application. As shown in Figure 10 Fig. 1 is a schematic diagram of an application scenario 1000 of the embodiment of the present application. Taking an employee (cloud user 103) named “John” 1070 as an example, John works for an e-commerce company (cloud tenant 102) located in Shenzhen, and his main work is development and operation and maintenance, and the cloud resource he uses is a virtual machine, and he has a high requirement for the performance of the virtual machine. For such a cloud user 103 as John, the cloud management suggestion information 109 is generated through the following four aspects.

[0093] 1) Portrait information 107

[0094] As shown in the figure, six label information are abstracted from John's cloud usage behavior data as the cloud user portrait information 201: "e-commerce" 1071 (label information reflecting industry type), "Shenzhen" 1072 (label information reflecting work location), "development and operation and maintenance" 1073 (label information reflecting work type), "virtual machine" 1074 (label information reflecting specific cloud resources used), "SQL Azure" 1075 (label information reflecting cloud service type used), and "performance" (label information reflecting requirements for cloud resources) 1076. SQL Azure is a relational database service running on cloud computing based on the Windows Azure cloud operating system. Among the above label information of the portrait information 107, some information labels are also label information of the cloud tenant portrait information 301 of the company (as a cloud tenant) where John is located, such as "e-commerce" 1071 and "Shenzhen" 1072. In this embodiment, it can also be considered that the cloud management suggestion information 109 is made based on the cloud user portrait information 201 and the cloud tenant portrait information 301.

[0095] 2) Bot module 601

[0096] According to John's portrait information (for example, "e-commerce" 1071, "development and operation and maintenance" 1073, "performance" 1076, and "SQL Azure" 1075), the main Bot module 701 selects to use the following several types of third-party Bot modules 702:

[0097] Monitoring virtual machine and measuring SQL database 702a: used to monitor the virtual machine and measure the performance indicators of the SQL database according to the cloud resource running state data (such as the load state of the virtual machine, the read-write speed of the SQL database, etc.), and give configuration management suggestions for the virtual machine and the SQL database in combination with John's portrait information (for example, comparing "performance" 1076 and the work type "development and operation and maintenance" 1073);

[0098] DDoS (Distributed Denial of Service) analysis 702b: used to analyze the attack situation of DDoS according to the cloud resource running state data (records of attacks blocked by the firewall), and give suggestions for the protection strategy of e-commerce in combination with John's portrait information 107 (for example, the industry label information of "e-commerce" 1071);

[0099] Machine learning based on cloud usage 702c: Machine learning is performed on cloud resource health data (cloud resource health data for various time periods in the past year) and John's profile information 107 (for example, industry tag information of "e-commerce" 1071 and tag information of the used cloud service "SQL Azure" 1075), prediction data of cloud resource health in the future period is generated, and management suggestions for adjusting the cloud resource are generated according to the prediction data. On the other hand, it can also be a third-party Bot module 702 that performs big data analysis on the cloud usage behavior data 202 of some industries, thereby forming a rich knowledge base for the industry.

[0100] 3) Cloud management template 801

[0101] John also customized the cloud management template 801 according to his own needs, which mainly includes the following two aspects of the template:

[0102] DDoS protection 802: The protection level of DDoS protection, the program to be monitored, etc. can be defined;

[0103] Cloud health detection 803: The time and frequency of cloud health detection and the area to be monitored can be defined.

[0104] 4) Cloud resource health data 108 obtained by monitoring

[0105] The cloud resource health data 108 can include: virtual machine load rate 1081, SQL Azure metric data 1082, firewall interception record 1083, and other aspects of log records. These cloud resource health data 108, combined with profile information 107 and cloud management template 801, will serve as the basis for the Bot module 601 to generate cloud management suggestion information.

[0106] Based on the above four aspects of technical content, the following aspects of cloud management suggestions 109 are formed and pushed to John in the form of actively initiating a session:

[0107] 1) Alert suggestion 1091: The third-party Bot module 702 extracts the SQL Azure metric data through the analysis of the cloud resource health data, and finds through the analysis of the metric data that there are some requests blocked by the firewall in the past few hours. Therefore, a session is actively initiated to suggest John to immediately check whether the request blocked by the firewall is an incorrect operation or an intrusion threat.

[0108] 2) Optimization suggestion 1092: The third-party Bot module 702 analyzes the data of the load status of the virtual machine, finds that the CPU of the virtual machine is always at a high load rate of 70%-80% on weekends, and therefore initiates a conversation to suggest John to create a script program to automatically increase the load capacity of the virtual machine on weekends and automatically reduce the load capacity of the virtual machine after the weekend.

[0109] 3) Prediction suggestion 1093: The third-party Bot module 702 analyzes that the company where John works is assigned an industry label information of "e-commerce" 1071, and based on the big data analysis of the third-party Bot module 702 on the "e-commerce" 1071 industry, it is predicted that there will be an order peak from November to December, and therefore suggests John to keep the current load capacity of the virtual machine and increase the performance level of SQLAzure in November to cope with the potential bottleneck of processing performance.

[0110] After obtaining the above three suggestions, John can effectively manage the cloud resources he uses, can specifically solve the problems and risks existing in the cloud resources, can optimize the configuration of the cloud resources, and can make early deployment of the cloud resources for possible future situations. These processing suggestions are actively proposed to John, and John does not need to have a deep understanding of the running situation of the cloud resources. John only needs to operate according to the cloud management suggestions given in the conversation, which greatly reduces the difficulty of cloud management.

[0111] Example process

[0112] The technical solution of intelligent cloud management is described in detail above through the application scenario 1000. Next, the processing flow 1100 of intelligent cloud management of the embodiment of the present application will be introduced, as shown in Figure 11 The processing flow 1100 of intelligent cloud management of the embodiment of the present application is a schematic diagram of the processing flow 1100 of intelligent cloud management of the embodiment of the present application, and the processing flow 1100 includes:

[0113] S101: Obtain the portrait information 107 based on the cloud usage behavior data 202. As mentioned above, the portrait information 107 can be the cloud user portrait information 201, can be the cloud tenant portrait information 301, or can be a combination of the two.

[0114] In some examples, the cloud user portrait information 201 can be generated by: obtaining the cloud usage behavior data 202 of the cloud user 103, and generating the cloud user portrait information 201 according to the cloud usage behavior data 202 of the cloud user 103.

[0115] In some examples, the cloud tenant portrait information 301 can be generated by obtaining cloud usage behavior data 202 of a plurality of cloud users 103, and generating the cloud tenant portrait information 301 according to the cloud usage behavior data 202 of the plurality of cloud users 103.

[0116] In some examples, the cloud tenant portrait information 301 can also be generated by obtaining cloud user portrait information 201 of a plurality of cloud users 103, and generating the cloud tenant portrait information 301 according to the cloud user portrait information 201 of the plurality of cloud users 103.

[0117] S102: Obtain cloud resource running status data 108. Specifically, the cloud resource running status data 108 can be obtained by monitoring the cloud resource running status.

[0118] It should be noted that although the above S101 and S102 are schematically represented in the form of a flowchart, there is no sequence between the above S101 and S102, and they can be executed in parallel or in sequence.

[0119] S103: Generate cloud management suggestion information 109 according to the portrait information 107 and the cloud resource running status data 108. In some examples, the cloud management suggestion information 109 can include one or more of alarm suggestion information, optimization suggestion information, and prediction suggestion information for cloud management.

[0120] In some examples, this step can be completed by using a third-party robot module 702, which can specifically include: sending the portrait information 107 and the cloud resource running status data 108 to the third-party robot module 702, and obtaining the cloud management suggestion information 109 returned by the third-party robot module 702.

[0121] In addition, in some examples, in the process of generating the cloud management suggestion information 109 using the third-party robot module 702, it can also include: selecting a matched third-party robot module 702 according to the portrait information 107 and the cloud resource running status data 108, and sending it to the third-party robot module 702.

[0122] In addition, the processing flow 1100 can also include: receiving feedback information of the cloud user 103, and ranking the plurality of third-party robot modules 702 according to the feedback information.

[0123] In some examples, the above-described processing flow 1100 may further include: obtaining a predetermined cloud management template 801, which is pre-set by the cloud user 103. Accordingly, the above-described step S103 may further include: generating cloud management suggestion information 109 based on the profile information 107, cloud resource operation status data 108, and the predetermined cloud management template 801.

[0124] In some examples, the pre-defined cloud management template 801 may record preset management items and / or related parameters of the management items.

[0125] S104: Push cloud management advice information 109 to cloud users 103 via a conversation.

[0126] In some examples, during the above-mentioned session, the input information of the cloud user 103 can also be obtained, and new cloud management suggestion information 109 can be obtained based on the profile information 107, cloud resource operation status data 108 and the input information of the cloud user 103, and then the new cloud management suggestion information 109 can be pushed to the cloud user 103.

[0127] The input information of cloud user 103 may include one or more of the following: text information entered by cloud user 103, voice information entered by cloud user 103, emotional information of cloud user 103, information about the environment in which cloud user 103 is located, and time information of cloud user 103's response to the session.

[0128] The specific implementation of each processing operation in the above steps has been explained in detail above, and the same applies to each of the above steps.

[0129] Specific implementation example

[0130] like Figure 12 As shown, it is a block diagram 1200 of the intelligent cloud management device according to an embodiment of the present invention, including: a profile information acquisition module 104, a cloud resource operation status data acquisition module 105, and a cloud management suggestion information generation module 106.

[0131] The cloud profile information acquisition module 104 is used to acquire profile information based on cloud usage behavior data; the cloud resource operation status data acquisition module 105 is used to acquire cloud resource operation status data 108; and the cloud management suggestion information generation module 106 is used to generate cloud management suggestion information 109 based at least on the profile information 107 and the cloud resource operation status data 108.

[0132] The aforementioned intelligent cloud management device can be installed on the server of the cloud provider 101, or on the server of the cloud tenant or the computer of the cloud user 103.

[0133] Furthermore, in some examples, the above Figures 1 to 10 as well as Figure 12 The various components or modules involved include, for example, the profile information acquisition module 104, the cloud resource operation status data acquisition module 105, the cloud management suggestion information generation module 106, the robot module 601, the main robot module 701, the third-party robot module 702, etc., and Figure 11 One or more steps in the flowchart shown can be implemented by software programs, hardware circuits, or a combination of both. For example, the various components or modules and one or more steps described above can be implemented in a System-on-a-Chip (SoC). An SoC may include an integrated circuit chip that includes one or more of the following: processing units (such as a central processing unit (CPU), microcontroller, microprocessor unit, digital signal processing unit (DSP), etc.), memory, one or more communication interfaces, and / or further circuitry for performing its functions and optionally embedded firmware.

[0134] like Figure 13 The diagram shown is a structural block diagram of an electronic device 1300 according to an embodiment of the invention. The electronic device 1300 includes a memory 1301 and a processor 1302.

[0135] Memory 1301 is used to store programs. In addition to the programs described above, memory 1301 may also be configured to store various other data to support operation on electronic device 1300. Examples of such data include instructions for any application or method operating on electronic device 1300, contact data, phonebook data, messages, pictures, videos, etc.

[0136] The memory 1301 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0137] The memory 1301 is coupled to the processor 1302 and contains instructions stored thereon that, when executed by the processor 1302, cause the electronic device to perform actions including: obtaining the portrait information 107 based on the cloud usage behavior data 202; obtaining the cloud resource running status data 108; and generating the cloud management suggestion information 109 according to at least the portrait information 107 and the cloud resource running status data 108. In addition, the above-mentioned actions can further include pushing the cloud management suggestion information 109 to the cloud user 103 in a conversational manner. In addition, in the process of the conversation, input information of the cloud user 103 can be obtained; new cloud management suggestion information 109 can be obtained according to the portrait information 107, the cloud resource running status data 108 and the input information of the cloud user 103; and the new cloud management suggestion information 109 can be provided to the cloud user 103.

[0138] In some examples, the portrait information 107 can include the cloud user portrait information 201, and obtaining the portrait information based on the cloud usage behavior data 202 can include: obtaining the cloud usage behavior data 202 of the cloud user 103; and generating the cloud user portrait information 201 according to the cloud usage behavior data 202 of the cloud user 103.

[0139] In other examples, the portrait information 107 can include the cloud tenant portrait information 301, and obtaining the portrait information based on the cloud usage behavior data 202 can include: obtaining the cloud user portrait information 201 of a plurality of cloud users 103; and generating the cloud tenant portrait information 301 according to the cloud user portrait information 201 of the plurality of cloud users 103.

[0140] Further, in some examples, generating the cloud management suggestion information 109 according to the portrait information 107 and the cloud resource running status data 108 can include: sending the portrait information 107 and the cloud resource running status data 108 to the third-party robot module 702, and obtaining the cloud management suggestion information 109 returned by the third-party robot module 702.

[0141] For the above-mentioned processing operations, the details of which have been described in the foregoing method and device embodiments, the details of the above-mentioned processing operations are also applicable to the electronic device 1300, i.e., the specific processing operations mentioned in the foregoing embodiments can be written in the memory 1301 in the form of a program and executed by the processor 1302.

[0142] Further, as shown in Figure 12 The electronic device 1300 can further include a communication component 1303, a power supply component 1304, an audio component 1305, a display 1306, a chip set 107 and other components. Figure 13The electronic device 1300 can not include all the components shown in FIG. 13. Also, the electronic device 1300 can include other components instead of or in addition to those shown in FIG. 13. Figure 13 The components shown in FIG. 13 can be implemented as software modules or hardware modules.

[0143] The communication component 1303 is configured to facilitate wired or wireless communication between the electronic device 1300 and other devices. The electronic device can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an example embodiment, the communication component 1303 receives a broadcast signal or broadcast related information from an external broadcasting management system via a broadcast channel. In an example embodiment, the communication component 1303 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technology.

[0144] The power component 1304 supplies power to the various components of the electronic device. The power component 1304 can include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device.

[0145] The audio component 1305 is configured to output and / or input audio signals. For example, the audio component 1305 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 1301 or transmitted via the communication component 1303. In some embodiments, the audio component 1305 also includes a speaker for outputting audio signals.

[0146] The display 1306 includes a screen, which can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense a touch, a slide, and a gesture on the touch panel. The touch sensor can not only sense a boundary of a touching or a sliding action, but also detect duration and pressure related to the touching or sliding action.

[0147] The above-described memory 1301, processor 1302, communication component 1303, power component 1304, audio component 1305, and display 1306 can be connected with a chipset 1307. The chipset 1307 can provide an interface between the processor 1302 and the remaining components of the electronic device 1300. In addition, the chipset 1307 can provide an access interface for each component of the electronic device 1300 to the memory 1301 and a communication interface for mutual access between the components.

[0148] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The foregoing program can be stored in a computer readable storage medium. The program executes to perform the steps of the above-mentioned method embodiments; and the foregoing storage medium includes ROM, RAM, magnetic disk or optical disk and various storage media that can store program codes.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent substitutions for part or all of the technical features; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method comprising: Access to behavioral data of at least one user using the cloud operating system; The behavioral data is used to generate profile information, which reflects the cloud usage characteristics of the at least one user or a cloud tenant associated with the at least one user. Obtain operational status data of cloud resources; The image information and the operational status data of the cloud resources are sent to the robot module; as well as In response to the sending, cloud management recommendation information is obtained from the robot module to adjust one or more cloud resources based on a predicted future condition, which is predicted based at least on the profile information and the operational status data of the cloud resources.

2. The method according to claim 1, wherein the robot module is an AI module built on an artificial intelligence AI data platform within a module for generating cloud management recommendation information.

3. The method according to claim 1, wherein the robot module is a third-party robot module running on a third-party AI data platform.

4. The method of claim 1, wherein accessing the behavioral data includes accessing the log files of the cloud operating system.

5. The method of claim 1, wherein accessing the behavior data includes accessing the behavior data to a monitoring module.

6. The method according to claim 1, wherein the profile information includes user profile information, and generating the profile information includes: Access the behavioral data of the at least one user using one or more cloud resources; as well as The user profile information is generated based on the user's behavior data regarding the use of one or more cloud resources.

7. The method according to claim 1, wherein the profile information includes cloud tenant profile information, and generating the profile information includes: Access at least one user profile of multiple users of the tenant; as well as The cloud tenant profile information is generated based on the user profile information of at least one of the multiple users.

8. The method of claim 1, wherein the sending comprises: Based on the profile information and the operational status data of the cloud resources, the robot module is selected from multiple robot modules; as well as The image information and the operational status data of the cloud resources are sent to the selected robot module.

9. The method according to claim 8, further comprising: Receive feedback from users; as well as The multiple robot modules are ranked based on the feedback information.

10. An apparatus comprising: Processing unit; as well as A memory, coupled to the processing unit and containing instructions stored thereon, which, when executed by the processing unit, cause the device to perform actions, including: Access to behavioral data of at least one user using the cloud operating system; The behavioral data is used to generate profile information, which reflects the cloud usage characteristics of the at least one user or a cloud tenant associated with the at least one user. Obtain operational status data of cloud resources; The image information and the operational status data of the cloud resources are sent to the robot module; and In response to the sending, cloud management recommendation information is obtained from the robot module to adjust one or more cloud resources based on a predicted future condition, which is predicted based at least on the profile information and the operational status data of the cloud resources.

11. The device of claim 10, wherein the robot module is an AI module built on an artificial intelligence (AI) data platform within a module for generating cloud management recommendation information.

12. The device according to claim 10, wherein the robot module is a third-party robot module running on a third-party AI data platform.

13. The device of claim 10, wherein accessing the behavioral data includes accessing the log files of the cloud operating system.

14. The device of claim 10, wherein accessing the behavior data includes accessing the behavior data to a monitoring module.

15. The device of claim 10, wherein the predicted future condition is based on a predicted problem, the predicted problem being determined based on the operational status data of the cloud resources.

16. The device of claim 10, wherein the robot module uses the profile information and data analysis of the virtual machine load capacity to determine the predicted future conditions.

17. A non-volatile storage medium storing a set of instructions, said set of instructions, when executed by one or more hardware processors, causing the one or more hardware processors to perform actions, said actions including: Access to behavioral data of at least one user using the cloud operating system; The behavioral data is used to generate profile information, which reflects the cloud usage characteristics of the at least one user or a cloud tenant associated with the at least one user. Obtain operational status data of cloud resources; The image information and the operational status data of the cloud resources are sent to the robot module; as well as In response to the sending, cloud management recommendation information is obtained from the robot module to adjust one or more cloud resources based on a predicted future condition, which is predicted based at least on the profile information and the operational status data of the cloud resources.

18. The non-volatile storage medium of claim 17, wherein the robot module is an AI module built on an artificial intelligence (AI) data platform within a module for generating cloud management recommendation information.

19. The non-volatile storage medium of claim 17, wherein accessing the behavioral data includes accessing the behavioral data to a monitoring module.

20. The non-volatile storage medium of claim 17, wherein the robot module uses the profile information and data analysis of virtual machine load capacity to determine the predicted future conditions.

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