Cloud resource benefit measurement model and measurement method thereof

Abstract

The invention discloses a cloud resource benefit measurement model and a measurement method thereof, and relates to the technical field of cloud resources, the cloud resource benefit measurement model comprises a benefit factor design module, a factor grading requirement module, a factor weight acquisition method module and a resource benefit calculation method module; the benefit factor design module is used for dividing cloud resource benefit indexes into three first-level factors, namely a cost benefit factor, a performance benefit factor and a safety benefit factor, and the safety benefit factor is composed of two second-level factors, namely a safety guarantee factor and a safety operation factor. According to the cloud resource benefit measurement model and the measurement method thereof, enterprise cloud resource benefit quantification unified reference is provided for cloud platform constructors, operators and users, the cloud resource use effect is helped to be understood, the platform and service cloud resource optimization direction is pointed out, enterprises in all industries are helped to carry out high-efficiency and high-quality cloud treatment, the cloud use effect on the enterprises is optimized, and the cloud management efficiency is improved. And cloud computing is promoted to become a core engine for digital and low-carbon development of enterprises in various industries.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud resources, and specifically provides a cloud resource benefit measurement model and a measurement method thereof. Background Technique

[0002] With the continuous introduction of digital transformation and dual-carbon policies in recent years, cloud computing, as a new service form of IT resources, has become a necessary choice for enterprises to carry out digital transformation and carbon reduction. The use of cloud by enterprises has shifted from peripheral systems to core systems. As the process of enterprises migrating to the cloud deepens, the demand for cloud use by enterprises has extended from stable operation in the initial stage to the stage of fully leveraging the advantages of cloud computing to empower business. Whether cloud resources can be used well is crucial for enterprises to fully utilize the advantages and conveniences of the cloud to improve business efficiency. However, problems such as waste of enterprise cloud resource costs and unmet usage benefits are becoming increasingly apparent. The demand for optimizing the use of enterprise cloud resources is constantly increasing. As a key factor in measuring the effectiveness of enterprises migrating to and using the cloud, cloud resource benefits have become an important quantitative indicator for enterprises to understand the effects of migrating to and using the cloud for business and a key reference basis for planning resource use optimization strategies. However, there is still no professional and reliable means for measuring the benefits of enterprise cloud resources in the current industry. Therefore, it is urgent to develop a measurement model for enterprise cloud resource benefits to provide a quantitative reference for the current situation of enterprise cloud resource benefits and indicate the direction of business cloud resource optimization.

[0003] Therefore, in view of this, research and improvement are carried out on the existing structure and deficiencies, and a cloud resource benefit measurement model and a measurement method thereof are proposed. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a cloud resource benefit measurement model and a measurement method thereof, which solve the problems raised in the above background technique.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A cloud resource benefit measurement model, the cloud resource benefit measurement model includes a benefit factor design module, a factor grading requirement module, a factor weight acquisition method module, and a resource benefit calculation method module;

[0006] The benefit factor design module is used to classify cloud resource benefit indicators into three categories of first-level factors: cost-benefit factors, performance-benefit factors, and security-benefit factors. The cost-benefit factor consists of two second-level factors: cost controllability and resource efficiency. The performance-benefit factor consists of three second-level factors: stability, agility, and adaptability. The security-benefit factor consists of two second-level factors: security guarantee and security operation;

[0007] The factor grading requirement module is used to set grading requirements for the third-level factors. The third-level factors are divided into quantitative indicators and ability indicators, and each third-level factor is divided into five levels according to the level of the index and ability;

[0008] The factor weight acquisition method module is used to assign values to benefit factors through the analytic hierarchy process, and the factor weights are generated by combining the general model weights and the project adaptability weights;

[0009] In the factor weight acquisition method module, the factor weights are generated by combining the general model weights and the project adaptability weights. The determination of the factor adaptability weights of the cloud resource benefit measurement model is obtained through an AHP questionnaire survey. Among them, relevant personnel are required to compare the relative importance of factors A and B in each row of the questionnaire. The questionnaire uses a 1-9 scale method, and a "√" is marked on the corresponding digital scale in each row according to experience;

[0010] The general model weights use the entropy weight method. In order to make the weights more objective, certain characteristics of the original data are used to determine the weights, such as information entropy. Information entropy is a basic concept in information theory, used to describe the uncertainty of the occurrence of various possible events of an information source. Its mathematical expression is where p(xi) represents the probability of the occurrence of event xi. When the occurrence probabilities of all events are equal, the information entropy reaches the maximum value, indicating that the system is completely uncertain. For a deterministic event, that is, the probability of a certain event is 1 and there is no uncertainty, the information entropy is 0. In the entropy weight method, if the information entropy of a certain index is smaller, it indicates that the uncertainty of the index value is smaller, the amount of information provided is more, and the role it can play in comprehensive evaluation is also greater, and its weight is also greater;

[0011] Among them, the difference between the general model weights and the project adaptability weights is that the general model weights start from the objective data of multiple companies and determine the importance weights that should be assigned to each index generally recognized by multiple companies from the overall perspective of the industry, while the project adaptability weights are more unique and proprietary, which are the importance weights of different indexes of the cloud resources benefits of a single company using the model;

[0012] The resource benefit calculation method module is used to calculate by grading scores upward starting from the third-level factors.

[0013] Furthermore, the cost controllability consists of three third-level factors: cost traceability, cost planning, and cost operation.

[0014] Furthermore, the resource efficiency consists of three third-level factors: resource usage, resource procurement, and resource optimization.

[0015] Furthermore, the stability consists of three third-level factors: service availability, fault management, and change control.

[0016] Furthermore, the agility consists of two third-level factors: resource pool operation and service performance.

[0017] Furthermore, the adaptability consists of three third-level factors: production efficiency, business quality, and user experience.

[0018] Furthermore, the security guarantee consists of two aspects: security warning and security protection, and the security operation consists of two third-level factors: security mechanism and security control.

[0019] Furthermore, the evaluation of each index in the third-level factors is divided into five levels, corresponding to scores of 20, 40, 60, 80, and 100 from low to high respectively.

[0020] Furthermore, the resource benefit score calculation formula of the resource benefit calculation method module is as follows:

[0021] ΣPi = P1 * (P1a * i1 + P1b * i2) + P2 * (P2a * i3 + P2b * i4) + P3 * (P3a * i5 + P3b * i6)

[0022] Where ΣPi is the business resource benefit, i1 to i6 are scores, and P1 to P3 are the weights of the first-level factors.

[0023] A measurement method of a cloud resource benefit measurement model, which applies the above-mentioned cloud resource benefit measurement model. The measurement method of the cloud resource benefit measurement model includes the following steps:

[0024] Step 1: Collect basic information of enterprise business or projects, determine the test factors of the evaluation object, and at the same time conduct a survey on the importance degree of internal factors of the project;

[0025] Step 2: Conduct an evaluation according to the specific benefit measurement model factors and grading requirements, and at the same time integrate the data of the factor importance degree survey into the project adaptability weight. Among them, the enterprise selects one or more of the three benefit modules of cost, performance, and security for evaluation according to its own needs;

[0026] Step 3: Use the scores of the three aspects of factors and the project evaluation weight to measure the resource benefit. According to the quantified score of the cloud resource benefit, the rating results are divided into five levels from low to high, combine the cloud platform name or characteristics to produce the evaluation result, and output a benefit optimization report according to the evaluation result to help the enterprise better understand its own cloud resource benefit situation.

[0027] The present invention provides a cloud resource benefit measurement model and its measurement method, which have the following beneficial effects:

[0028] The cloud resource benefit measurement model and its measurement method provide a unified quantitative reference for the construction parties, operation parties, and usage parties of cloud platforms, helping to understand the effectiveness of cloud resource usage, indicating the optimization direction of platform and business cloud resources, assisting enterprises in various industries to conduct efficient and high-quality cloud governance, optimizing the effectiveness of enterprises' adoption and utilization of cloud services, and promoting cloud computing to become the core engine for the digital and low-carbon development of enterprises in various industries. Description of the Drawings

[0029] Figure 1 Schematic diagram of the cloud resource benefit measurement process for a cloud resource benefit measurement model and its measurement method of the present invention;

[0030] Figure 2 Schematic diagram of the measurement factor grading requirements for cost insight and root cause analysis of a cloud resource benefit measurement model and its measurement method of the present invention;

[0031] Figure 3 Schematic diagram of the measurement factor grading requirements for budget management and quota management of a cloud resource benefit measurement model and its measurement method of the present invention;

[0032] Figure 4 Schematic diagram of the measurement factor grading requirements for cost saving ratio and abnormal expenses of a cloud resource benefit measurement model and its measurement method of the present invention;

[0033] Figure 5 Schematic diagram of the measurement factor grading requirements for average utilization rate, inefficient resources, and idle resources of a cloud resource benefit measurement model and its measurement method of the present invention;

[0034] Figure 6 Schematic diagram of the measurement factor grading requirements for resource procurement of a cloud resource benefit measurement model and its measurement method of the present invention;

[0035] Figure 7 Schematic diagram of the measurement factor grading requirements for resource optimization of a cloud resource benefit measurement model and its measurement method of the present invention;

[0036] Figure 8 Schematic diagram of the measurement factor grading requirements for business availability of a cloud resource benefit measurement model and its measurement method of the present invention;

[0037] Figure 9 Schematic diagram of the measurement factor grading requirements for fault classification, fault discovery, fault handling, and review and improvement of a cloud resource benefit measurement model and its measurement method of the present invention;

[0038] Figure 10 Schematic diagram of the measurement factor grading requirements for personnel setup and change process of a cloud resource benefit measurement model and its measurement method of the present invention;

[0039] Figure 11 Schematic diagram of the grading requirements for measurement factors of level division, scheduling allocation, and resource expansion of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0040] Figure 12 Schematic diagram of the grading requirements for measurement factors of business performance of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0041] Figure 13 Schematic diagram of the grading requirements for measurement factors of resource delivery time, deployment or go - live time, and production cycle improvement of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0042] Figure 14 Schematic diagram of the grading requirements for measurement factors of business innovation, technology upgrade, and process optimization of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0043] Figure 15 Schematic diagram of the grading requirements for measurement factors of resource operation and maintenance experience and end - customer experience of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0044] Figure 16 Schematic diagram of the grading requirements for measurement factors of risk assessment, continuous monitoring, and threat warning of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0045] Figure 17 Schematic diagram of the grading requirements for measurement factors of system security, platform security, data security, and network security of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0046] Figure 18 Schematic diagram of the grading requirements for measurement factors of emergency response, disaster recovery, and security inspection of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0047] Figure 19 Schematic diagram of the grading requirements for measurement factors of audit management and permission management of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0048] Figure 20 Schematic diagram of the business resource benefit calculation process of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0049] Figure 21 Schematic diagram of the design of the general model weight and project adaptability weight of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0050] Figure 22 Basic information collection form of the AHP questionnaire for a cloud resource benefit measurement model and its measurement method according to the present invention;

[0051] Figure 23 Checklist for comparing the relative importance of factors of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0052] Figure 24 Table for comparing the importance of first-level factors of cloud resource benefits of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0053] Figure 25 Table for comparing the importance of second-level factors of cloud resource benefits of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0054] Figure 26 Schematic diagram of the format of the index scoring table for each company of a cloud resource benefit measurement model and its measurement method according to the present invention;

[0055] Figure 27 Schematic diagram of the format of the information entropy filling table of a cloud resource benefit measurement model and its measurement method according to the present invention. Detailed implementation manners

[0056] The following further describes in detail the implementation manners of the present invention in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0057] As Figures 1 - 25 shown, the present invention provides a technical solution: A cloud resource benefit measurement model. The cloud resource benefit measurement model includes a benefit factor design module, a factor classification requirement module, a factor weight acquisition method module, and a resource benefit calculation method module;

[0058] The benefit factor design module is used to classify cloud resource benefit indicators into three categories of first-level factors: cost-benefit factors, performance-benefit factors, and security-benefit factors. The cost-benefit factor consists of two second-level factors: cost controllability and resource efficiency. The performance-benefit factor consists of three second-level factors: stability, agility, and adaptability. The security-benefit factor consists of two second-level factors: security guarantee and security operation;

[0059] Among them, cost controllability consists of three third-level factors: cost traceability, cost planning, and cost operation. Resource efficiency consists of three third-level factors: resource usage, resource procurement, and resource optimization. Stability consists of three third-level factors: service availability, fault management, and change control. Agility consists of two third-level factors: resource pool operation and service performance. Adaptability consists of three third-level factors: production efficiency, service quality, and user experience. Security guarantee consists of two aspects: security warning and security protection. Security operation consists of two third-level factors: security mechanism and security control;

[0060] Cost tracing is the ability to observe and analyze cloud cost usage within the business cycle. Cost tracing consists of cost insight and root cause analysis, and the meanings of each part are as follows:

[0061] Cost insight: The ability to monitor and gain insights into the costs of business cloud resources, such as cost collection, bill aggregation, cost visualization, etc.;

[0062] Root cause analysis: The ability to analyze and evaluate the usage status of business cloud resource costs, such as cost reports, cost anomaly analysis, etc.;

[0063] The measurement factor grading requirements for cost insight and root cause analysis are as Figure 2 shown;

[0064] Cost planning is the ability to plan cloud cost quotas within the business cycle. Cost management consists of budget management and quota management, and the meanings of each factor are as follows:

[0065] Budget management: The ability to control costs for the usage budget of business cloud resources, such as budget setting, budget approval, budget tracking, budget analysis, etc.;

[0066] Quota management: The ability to allocate and manage the usage quotas of business cloud resources, such as voluntary applications, quota setting, quota usage reports, etc.;

[0067] The measurement factor grading requirements for budget management and quota management are as Figure 3 shown;

[0068] Cost operation is the statistical information of cloud cost operation data at the end node of the business cycle. Cost operation consists of cost savings ratio and abnormal expenses, and the meanings of each factor are as follows:

[0069] Meaning of cost savings ratio: The percentage of the value of the resource cost after using cloud resources being less than the cost before use in the cost value before use;

[0070] Meaning of abnormal expenses: The frequency and deviation value of abnormal expenses of cloud resources during the business cycle. Abnormal situations include cost surges, large deviations between actual costs and predicted costs, and excessively high restricted resource costs, etc.;

[0071] The measurement factor grading requirements for cost savings ratio and abnormal expenses are as Figure 4 shown;

[0072] Resource usage is the statistical information of cloud resource usage data at the end node of the business cycle. Resource usage consists of average utilization rate, high-efficiency resources, low-efficiency resources, and idle resources;

[0073] Meaning of average utilization rate: The average value of the sampled data of the CPU and memory resource usage rates in the cloud platform during the business cycle;

[0074] Meaning of inefficient resources: The percentage of CPU and memory resources (excluding resources during application deployment) with a utilization rate lower than 5% in the cloud platform within the business cycle among the total resources;

[0075] Meaning of idle resources: The percentage of CPU and memory resources that are not powered on or are powered on but not in use (low network traffic) in the cloud platform within the business cycle among the total resources;

[0076] The grading requirements for the measurement factors of average utilization rate, inefficient resources, and idle resources are as Figure 5 shown;

[0077] Resource procurement is a process of reasonably procuring cloud resources within the business cycle to reduce resource waste;

[0078] Meaning of resource procurement: The process method of reasonably procuring resources for the business, including the resource usage cycle, resource procurement type planning, etc. The grading requirements for its measurement factors are as Figure 6 shown;

[0079] Resource optimization is a means of optimizing the utilization rate of cloud resources within the business cycle. Meaning of resource optimization: The ability of the business to optimize cloud resources and reduce resource waste, including elastic scheduling, etc. The grading requirements for its measurement factors are as Figure 7 shown;

[0080] In terms of stability, business availability is the ratio of the duration during which the business system remains available within the business cycle to the total business duration. The calculation method of business availability is as follows:

[0081] Business availability = MTBF / (MTBF + MTTR);

[0082] MTBF: Business downtime (planned downtime is not included), MTTR: Fault recovery time;

[0083] The unavailable time caused by the business's own problems (such as the business architecture not having high availability and emergency disaster recovery capabilities, as well as its own defects or potential problems) is not included in MTTR;

[0084] The grading requirements for the measurement factors of business availability are as Figure 8 shown;

[0085] Fault management is the ability to respond to and manage the entire process before, during, and after the occurrence of faults that affect stability within the business cycle. Faults only include faults at the cloud resource layer and do not include faults caused by the business's own problems;

[0086] Fault management consists of fault classification, fault discovery, and fault handling. The meanings of each part are as follows:

[0087] Meaning of fault classification: The ability to classify and integrate the types of faults that occur and plan response strategies;

[0088] Meaning of fault discovery: The ability to continuously monitor the business stability situation and promptly discover faults;

[0089] Meaning of fault handling: The ability to analyze and resolve faults that affect business stability;

[0090] Meaning of review and improvement: The ability to conduct a review and analysis of faults after fault repair and handle, and improve and track problems;

[0091] The measurement factor grading requirements for fault classification, fault discovery, fault handling, and review and improvement are as Figure 9 shown;

[0092] Change control is the ability to internally control business stability problems that are likely to occur when making changes and alterations to systems or resources due to external or internal reasons during the business cycle;

[0093] Change control consists of two parts: personnel setup and change process. The meanings of each part are as follows:

[0094] Meaning of personnel setup: The ability to configure relevant review and operation personnel for system or resource change operations;

[0095] Meaning of change process: The ability to plan and manage the system or resource change process;

[0096] The measurement factor grading requirements for personnel setup and change process are as Figure 10 shown;

[0097] In agility, resource pool operation is the ability to reasonably plan and operate the resource pool to improve the agility of business resources during the business cycle;

[0098] Resource pool planning consists of level division, scheduling and allocation, and resource expansion. The meanings of each part are as follows:

[0099] Meaning of level division: The ability to divide the importance levels of existing businesses and systems and allocate resources;

[0100] Meaning of scheduling and allocation: The ability to agilely adjust, allocate, and distribute resources in the resource pool according to the corresponding business demand loads;

[0101] Meaning of resource expansion: The agile scalability ability of various resources in the resource pool to cope with changes in the internal and external business environment

[0102] The measurement factor grading requirements for level division, scheduling and allocation, and resource expansion are as Figure 11 shown;

[0103] Business performance is the statistical information on the impact on business performance after adopting cloud computing, which is counted at the end node of the business cycle;

[0104] Meaning of business performance: The impact on business performance by using cloud resources, including increased access volume, increased transaction volume, business response speed, etc. The grading requirements for its measurement factors are as Figure 12 shown;

[0105] Production efficiency is the statistical information on the data of the improvement of system production efficiency after adopting cloud computing, which is counted at the end node of the business cycle;

[0106] Production efficiency consists of resource delivery time, deployment or go-live time, and production cycle improvement;

[0107] Meaning of resource delivery time: Resource delivery time refers to the average time length from when the resource requester initiates resource evaluation to when the resource is correctly delivered;

[0108] Meaning of deployment time: Deployment or go-live time refers to the average time length from when the business software starts to be deployed to when the business is officially up and running;

[0109] Meaning of production cycle improvement: After using cloud resources, the change in the business production cycle. According to the actual business situation, the production cycle can be defined as product production and application go-live;

[0110] The grading requirements for the measurement factors of resource delivery time, deployment or go-live time, and production cycle improvement are as Figure 13 shown;

[0111] Business quality is the statistical information on the data of the improvement of business quality after adopting cloud computing, which is counted at the end node of the business cycle;

[0112] Business quality consists of business innovation, technology upgrade, and process optimization;

[0113] Meaning of business innovation: After using cloud resources, the change in the innovation ability of the business, including improved agility, innovative development models, etc.;

[0114] Meaning of technology upgrade: After using cloud resources, the technology improvement obtained by the business itself through cloud computing, including application upgrade, etc.;

[0115] Meaning of process optimization: After using cloud resources, the optimization of the relevant internal operation processes of the business, including approval processes, change processes, etc.;

[0116] The grading requirements for the measurement factors of business innovation, technology upgrade, and process optimization are as Figure 14 shown;

[0117] Customer experience refers to the statistical information of the overall customer experience data of the business system after adopting cloud computing by the business operation and maintenance personnel at the end node of the business cycle. (Consider implementing it through a questionnaire.)

[0118] Customer experience consists of resource operation and maintenance experience and end - customer experience. The meanings of each part are as follows:

[0119] Meaning of resource operation and maintenance experience: After using cloud resources, the satisfaction of internal business personnel with resource operation and maintenance experience, including resource delivery experience, change experience, professionalism of operation and maintenance personnel, etc. (Implemented through a questionnaire survey.)

[0120] Meaning of end - customer experience: After using cloud resources, the satisfaction of business customers with business experience, including product (application) availability, product (application) usability, response efficiency, etc.;

[0121] The grading requirements for the measurement factors of resource operation and maintenance experience and end - customer experience are as Figure 15 shown;

[0122] Security warning refers to the ability to continuously evaluate, detect, and give early warnings about internal and external security threats and risks during the business operation cycle;

[0123] Security warning consists of risk assessment, continuous monitoring, and threat warning. The meanings of each part are as follows:

[0124] Meaning of risk assessment: The ability to conduct early assessment and prediction of potential security risks during business operation;

[0125] Meaning of continuous monitoring: The ability to continuously monitor the security situation during business operation, such as log aggregation, data analysis, etc.;

[0126] Meaning of threat warning: The ability to give timely warnings about risks and threats monitored or predicted during business operation;

[0127] The grading requirements for the measurement factors of risk assessment, continuous monitoring, and threat warning are as Figure 16 shown;

[0128] Security protection refers to the ability to defend against and avoid external security threats to application systems, cloud platforms, and infrastructure during the business cycle;

[0129] Security risks consist of system security, platform security, data security, and network security. The meanings of each part are as follows:

[0130] Meaning of system security: The ability to provide security protection for business system applications, such as Web firewalls, vulnerability protection, etc.;

[0131] Meaning of platform security: The ability to perform security protection and compliance construction for the business cloud platform, such as physical environment security compliance, security perception ability, etc.;

[0132] Meaning of data security: The ability to perform security protection against behaviors such as business data loss, theft, and damage, such as data encryption, database auditing, etc.;

[0133] Meaning of network security: The ability to perform security protection for business network resources, such as cloud firewalls, DDOS defense, etc.;

[0134] The grading requirements for the measurement factors of system security, platform security, data security, and network security are as Figure 17 shown;

[0135] A security mechanism is the ability to set up a security guarantee mechanism within the business cycle to further strengthen the business's response to internal and external security threats;

[0136] The security mechanism consists of emergency response, disaster recovery, and security inspection. The meanings of each part are as follows:

[0137] Meaning of emergency response: The ability to formulate and implement response measures to reduce business losses when the business faces sudden security problems;

[0138] Meaning of disaster recovery: The ability to maintain business security and quickly recover in the face of disasters that the business may face, such as natural disasters, equipment failures, and human sabotage;

[0139] Meaning of security inspection: The ability to regularly conduct inspection and testing measures on various aspects of business security tools, services, technologies, etc. to ensure the security situation of the business;

[0140] The grading requirements for the measurement factors of emergency response, disaster recovery, and security inspection are as Figure 18 shown;

[0141] Security management is the ability to manage the relevant process configurations of internal security guarantee work within the business cycle;

[0142] Security management consists of audit management and permission management. The meanings of each part are as follows:

[0143] Meaning of audit management: The ability to set up and control the audit process for business security-related decisions or changes;

[0144] Meaning of permission management: The ability to allocate and manage the access, modification, and other permissions of internal business personnel;

[0145] The grading requirements for the measurement factors of audit management and permission management are as Figure 19 shown;

[0146] The factor grading requirement module is used to set grading requirements for the third-level factors. The third-level factors are divided into quantitative indicators and ability indicators, and each third-level factor is divided into five levels according to the level of the index and ability;

[0147] The factor weight acquisition method module is used to assign values to the benefit factors through the analytic hierarchy process, and the factor weights are generated by combining the general model weights and the project adaptability weights. For specific reference, see Figure 21 , and the determination of the factor adaptability weights of the cloud resource benefit measurement model is obtained through an AHP questionnaire survey. The specific content of the AHP questionnaire is as shown in Figures 22 - 25 . Among them, Figure 22 is the basic information collection form, Figure 23 is the tick table for comparing the relative importance of factors. Among them, relevant personnel are required to compare the relative importance of factors A and B in each row of the questionnaire. The questionnaire uses a 1-9 scale method, and a "√" is marked on the corresponding digital scale in each row according to experience. Figure 24 is the importance comparison table of the first-level factors of cloud resource benefits, Figure 25 is the importance comparison table of the second-level factors of cloud resource benefits;

[0148] The general model weight uses the entropy weight method. The definition of the entropy weight method in the general model: In order to make the weight more objective, certain characteristics of the original data are used to determine the weight, such as information entropy. Information entropy is a basic concept in information theory, used to describe the uncertainty of the occurrence of various possible events of the information source. Its mathematical expression is where p(xi) represents the probability of the occurrence of event xi. When the occurrence probabilities of all events are equal, the information entropy reaches the maximum value, indicating that the system is completely uncertain. For a deterministic event (that is, the probability of a certain event is 1), there is no uncertainty, and the information entropy is 0. In the entropy weight method, if the information entropy of a certain index is smaller, it indicates that the uncertainty of the index value is smaller, the amount of information provided is more, and the role it can play in the comprehensive evaluation is also greater, and its weight is also greater;

[0149] Implementation case: As shown in Figure 26 , there are five companies in this figure, and each company has 12 indicators. The indicator scores are obtained through manual scoring. Using the definition of the entropy weight method, first standardize each indicator, and the formula is as follows:

[0150] X11’=(60 - 20) / (60 - 20)=1;

[0151] Among them, X11’ is the standardized result of the first row in the first column of the figure, X12’ is the second row in the first column, and so on. The first (60 - 20) is the first row in the first column minus the minimum index score in that row, and the second (60 - 20) is the maximum index score in that row minus the minimum index score in that row. When standardizing the second index, the formula becomes X12’ = (40 - 20) / (60 - 20) = 1, where (40 - 20) is the second row in the first column minus the minimum index score in that row, and (60 - 20) is the maximum index score in that row minus the minimum index score in that row. By analogy, the standardized results in each grid can be obtained;

[0152] Then calculate the ratio of each index. The specific formula is: y11 = x11’ / (x11’ + x21’ +... + x51’), where y11 is the ratio of the first row in the first column. Then substitute the ratio of each index into the following formula: Among them, e1 is the information entropy of each index. Then calculate the weight based on the information entropy. The weight formula is: W1 = (1 - e1) / (12 - e1 - e2 -... - e12), where W1 is the first index, W2 is the second index, and so on to obtain the weights of all indexes;

[0153] The difference between the general model weight and the project adaptability weight is that the general model weight starts from the objective data of multiple companies and determines the importance weights that should be assigned to each index generally recognized by multiple companies from the perspective of the industry as a whole. The project adaptability weight is more unique and proprietary, which is the importance weight of different indexes of the cloud resource benefits of a single company using the model;

[0154] The resource benefit calculation method module is used to calculate by grading scores upward from the third-level factors. Each index in the third-level factors is evaluated in five grades, corresponding to scores 20, 40, 60, 80, and 100 from low to high. The resource benefit score calculation formula of the resource benefit calculation method module is as follows:

[0155] ΣPi = P1*(P1a*i1 + P1b*i2) + P2*(P2a*i3 + P2b*i4) + P3*(P3a*i5 + P3b*i6)

[0156] Among them, ΣPi is the business resource benefit, i1 to i6 are scores, and P1 to P3 are the weights of the first-level factors.

[0157] A measurement method of a cloud resource benefit measurement model, which applies a cloud resource benefit measurement model as described above. The measurement method of the cloud resource benefit measurement model includes the following steps:

[0158] Step 1: Collect the basic information of the enterprise business or project, determine the evaluation object test factors, and at the same time conduct a survey on the importance degree of the internal factors of the project;

[0159] Step 2: Evaluate according to specific benefit measurement model factors and grading requirements. At the same time, integrate the data of factor importance surveys into project adaptability weights. Among them, the enterprise selects one or more of the three benefit modules of cost, performance, and security for evaluation according to its own needs;

[0160] Step 3: Use the scores of the three aspects of factors and the project evaluation weights to measure the resource benefits. According to the quantitative scores of cloud resource benefits, divide the rating results into five levels from low to high. Combine the cloud platform name or characteristics to generate the evaluation results, and produce a benefit optimization report based on the evaluation results to help the enterprise better understand its own cloud resource benefit situation;

[0161] Among them, according to the results of the enterprise project cloud resource benefit measurement scores, divide the project resource benefit levels into five levels.

[0162] —— Basic level ★: Poor cloud resource benefits;

[0163] —— Enhanced level ★★: Average cloud resource benefits;

[0164] —— Excellent level ★★★: Good cloud resource benefits;

[0165] —— Advanced level ★★★★: Excellent cloud resource benefits;

[0166] —— Leading level ★★★★★: Leading cloud resource benefits.

[0167] Based on the above description, the present invention provides a unified reference for quantifying the benefits of enterprise cloud resources for cloud platform constructors, operators, and users (cloud resources are a general term for project use of cloud computing-related services), helps to understand the effectiveness of cloud resource use, points out the optimization direction of platform and business cloud resources, assists enterprises in various industries to conduct efficient and high-quality cloud governance, optimize the effectiveness of enterprises' cloud adoption and use, and promote cloud computing to become the core engine for the digital and low-carbon development of enterprises in various industries.

[0168] The embodiments of the present invention are given for purposes of illustration and description, and are not exhaustive or limit the invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are chosen and described in order to better illustrate the principles of the invention and its practical application, and to enable those of ordinary skill in the art to understand the invention and design various embodiments with various modifications suitable for specific purposes.

Claims

1. A cloud resource efficiency measurement model, characterized by: The cloud resource benefit measurement model includes a benefit factor design module, a factor classification requirement module, a factor weight acquisition method module and a resource benefit calculation method module; The benefit factor design module is used to divide the cloud resource benefit indicators into three primary factors: cost benefit factor, performance benefit factor and security benefit factor. The cost benefit factor is composed of two secondary factors: cost controllability and resource efficiency. The performance benefit factor is composed of three secondary factors: stability, agility and adaptability. The security benefit factor is composed of two secondary factors: security assurance and secure operation. The factor grading requirement module is used to set grading requirements for the three-level factors. The three-level factors are divided into quantitative indicators and capability indicators. Each three-level factor is divided into five levels according to the index and capability. The factor weight acquisition method module is used to assign values ​​to benefit factors through the hierarchical analysis method, and the factor weight is generated by combining the general model weight with the project adaptability weight; In the factor weight acquisition method module, the factor weight is generated by combining the general model weight with the project adaptability weight, and the determination of the cloud resource benefit measurement model factor adaptability weight is obtained by surveying using the AHP questionnaire, in which relevant personnel are required to compare the relative importance of factors A and B in each row of the questionnaire. The questionnaire uses a 1-9 scale method, and "√" is marked on the corresponding digital scale in each row according to experience; The general model weight adopts the entropy weight method. In order to make the weight more objective, some characteristics of the original data are used to determine the weight, such as information entropy. Information entropy is a basic concept in information theory, which is used to describe the uncertainty of possible events in the information source. Its mathematical expression is: Where p(xi) represents the probability of event xi. When the probability of all events is equal, the information entropy reaches its maximum value, indicating that the system is completely uncertain. For a deterministic event, that is, the probability of a certain event is 1, there is no uncertainty, and the information entropy is 0. In the entropy weight method, if the information entropy of an indicator is smaller, it means that the uncertainty of the indicator value is smaller, the amount of information provided is more, the role it can play in the comprehensive evaluation is greater, and its weight is greater; The difference between the general model weight and the project adaptability weight is that the general model weight is based on the objective data of multiple companies and determines the importance weights that should be given to various indicators that are generally recognized by multiple companies from the perspective of the industry as a whole, while the project adaptability weight is more unique and proprietary, and is the importance weight of different indicators of cloud resource benefits for a single company using the model; The resource benefit calculation method module is used to calculate the scores from the third-level factors upward step by step.

2. A cloud resource efficiency measurement model according to claim 1, characterized in that: The cost controllability is composed of three three-level factors: cost tracing, cost planning, and cost operation.

3. The cloud resource efficiency measurement model according to claim 1, characterized in that: The resource efficiency is composed of three third-level factors: resource usage, resource procurement, and resource optimization.

4. The cloud resource efficiency measurement model according to claim 1, characterized in that: The stability is composed of three three-level factors: business availability, fault management, and change control.

5. The cloud resource efficiency measurement model according to claim 1, characterized in that: The agility is composed of two third-level factors: resource pool operation and business performance.

6. The cloud resource efficiency measurement model according to claim 1, characterized in that: The adaptability is composed of three third-level factors: production efficiency, service quality, and user experience.

7. The cloud resource efficiency measurement model according to claim 1, characterized in that: The security assurance is composed of two aspects: security warning and security protection. The security operation is composed of two three-level factors: security mechanism and security control.

8. The cloud resource efficiency measurement model according to claim 1, characterized in that: The evaluation of each indicator in the three-level factor is divided into five levels, corresponding to scores of 20, 40, 60, 80, and 100 from low to high.

9. The cloud resource efficiency measurement model according to claim 1, characterized in that: The resource benefit score calculation formula of the resource benefit calculation method module is as follows: ΣPi=P1*(P1a*i1+P1b*i2)+P2*(P2a*i3+P2b*i4)+P3*(P3a*i5+P3b*i6) Where ΣPi is the business resource benefit, i1 to i6 are scores, and P1 to P3 are the weights of the first-level factors.

10. A cloud resource benefit measurement model measurement method, which is applied with a cloud resource benefit measurement model according to any one of claims 1 to 9, characterized in that: The measurement method of the cloud resource benefit measurement model includes the following steps: Step 1: Collect basic information about the enterprise’s business or project, determine the test factors of the evaluation object, and conduct a survey on the importance of internal factors of the project; Step 2: Evaluate according to the specific benefit measurement model factors and classification requirements, and integrate the factor importance survey data into the project adaptability weight. Among them, the enterprise chooses one or more of the three benefit modules of cost, performance and safety for evaluation according to its own needs; Step 3: Use the three-factor scores and project evaluation weights to measure resource benefits. According to the quantified scores of cloud resource benefits, the rating results are divided into five levels from low to high. The evaluation results are produced in combination with the cloud platform name or characteristics. Based on the evaluation results, a benefit optimization report is produced to help enterprises gain a deeper understanding of their own cloud resource benefits.

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