DNS service quantitative evaluation method

Abstract

The invention provides a DNS (Domain Name Server) service quantitative evaluation method, which relates to the technical field of Internet, and comprises the following steps: dividing DNS service quality into a basic available layer, a performance experience layer and an access guarantee layer according to the experience progressive level of a user for DNS service; respectively determining a level score of the basic available layer, a level score of the performance experience layer and a level score of the access guarantee layer; and according to a preset weight distribution rule, performing weighted summation on the level scores of the basic available layer, the performance experience layer and the access guarantee layer to obtain a comprehensive quantitative evaluation result of the DNS service. By adopting the method, the complete appeal of the user can be truly reflected, a scientific and comprehensive decision basis is provided for the user to select a DNS service provider, and the user experience is improved.

Description

Technical Field

[0001] This invention relates to the field of Internet technology, and in particular to a method for quantitative evaluation of DNS services. Background Technology

[0002] As a core infrastructure for internet access, the Domain Name System (DNS) directly determines the user experience and business continuity when accessing websites or applications.

[0003] Currently, the industry's evaluation methods for DNS service quality often rely on isolated metrics such as resolution success rate and latency as the basis for judgment. This results in evaluation results that fail to accurately reflect the complete needs of users, making it difficult to provide users with a scientific and comprehensive basis for choosing a DNS service provider. Consequently, substandard service quality can easily lead to user churn and business losses. Summary of the Invention

[0004] This invention provides a method for quantitative evaluation of DNS services, which addresses the shortcomings of existing technologies where evaluation results cannot accurately reflect the complete needs of users, making it difficult to provide users with a scientific and comprehensive basis for choosing DNS service providers, and easily leading to user churn and business losses due to substandard service quality.

[0005] This invention provides a method for quantitative evaluation of DNS services, comprising: Based on the progressive levels of user experience with DNS services, DNS service quality is divided into the basic availability layer, the performance experience layer, and the access guarantee layer. The hierarchical scores of the basic availability layer, the performance experience layer, and the access guarantee layer are determined respectively. According to the preset weight allocation rules, the scores of the basic availability layer, the performance experience layer, and the access guarantee layer are weighted and summed to obtain a comprehensive quantitative evaluation result of the DNS service.

[0006] According to a DNS service quantitative evaluation method provided by the present invention, before the weighted summation of the hierarchical scores of the basic availability layer, the performance experience layer, and the access guarantee layer, the method further includes: Identify the type of the target business scenario, which includes at least one of e-commerce or financial business, media or gaming business, global business, and individual developer business; Based on the type of the target business scenario, determine the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer respectively; The determined weight coefficients are used as the weight allocation rules for the weighted summation.

[0007] According to the DNS service quantitative evaluation method provided by the present invention, the step of determining the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer respectively includes: From the preset mapping relationships, the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer are determined respectively. The preset mapping relationships include: If the target business scenario is e-commerce or financial business, the weight coefficient of the basic availability layer is higher than the weight coefficient of the performance experience layer, and the weight coefficient of the performance experience layer is higher than the weight coefficient of the access guarantee layer. If the target business scenario is a media or gaming business, the weight coefficient of the performance experience layer is higher than that of the basic availability layer, and the weight coefficient of the basic availability layer is higher than that of the access guarantee layer. If the target business scenario is a global business, then the weight coefficient of the access guarantee layer is higher than that of the performance experience layer, and the weight coefficient of the performance experience layer is higher than that of the basic availability layer. If the target business scenario is for individual developers, then the weight coefficients of the basic availability layer and the performance experience layer are both higher than the weight coefficient of the access guarantee layer.

[0008] According to the DNS service quantitative evaluation method provided by the present invention, the step of determining the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer respectively includes: Collect historical business operation data and corresponding service quality assessment results to build a training dataset; Using the training dataset, with the type as the input feature and the weight coefficients of the basic availability layer, performance experience layer, and access guarantee layer as the output variables, a weight allocation model is trained. Input the type of the current target business scenario into the trained weight allocation model, and output the weight coefficients corresponding to the basic availability layer, performance experience layer and access guarantee layer.

[0009] According to a DNS service quantitative evaluation method provided by the present invention, determining the tier score of the basic availability layer includes: Obtain actual availability data for the DNS service; If the actual availability is lower than the availability threshold, then the hierarchical score of the basic availability layer is set to zero; If the actual availability is not lower than the availability threshold, then the level score of the basic availability layer is determined based on the parsing success rate and the preset penalty coefficient. The penalty coefficient is used to deduct points when the success rate does not reach the ideal value.

[0010] According to a DNS service quantitative evaluation method provided by the present invention, determining the hierarchical score of the performance experience layer includes: Obtain DNS service resolution success rate data and resolution latency data, wherein the resolution latency data includes P95 latency and / or P99 latency; Based on the analyzed success rate data, a success rate score is determined; The latency score is determined based on the analyzed latency data, the preset target latency, and the attenuation scale. The success rate score and the latency score are weighted and summed to obtain the hierarchical score of the performance experience layer.

[0011] According to a DNS service quantitative evaluation method provided by the present invention, determining the access protection layer level score includes: Obtain BGP coverage data and resolution capacity data for the DNS service; Based on the BGP coverage data, the number of nodes, the number of covered countries, and the number of operator interconnections are extracted and their corresponding benchmark values ​​are used to determine the BGP coverage score. Based on the parsing capacity data, the parsing capacity of the service provider and the parsing capacity required by the user are extracted. Based on the ratio of the parsing capacity of the service provider to the parsing capacity required by the user, a parsing capacity satisfaction score is determined. The access guarantee layer's hierarchical score is obtained by weighted summation of the BGP coverage score and the resolution satisfaction score.

[0012] The DNS service quantitative evaluation method provided by the present invention further includes: Using the BGP coverage score as a correction factor, the hierarchical score of the performance experience layer is discounted and adjusted to obtain the corrected hierarchical score of the performance experience layer; and / or, If the resolution satisfaction score is lower than the preset admission threshold, the access protection layer's level score will be reduced or the access protection layer's level score will be set to zero.

[0013] The DNS service quantitative evaluation method provided by the present invention further includes: Active monitoring is initiated globally through third-party detection nodes to collect and resolve success rate and latency data; and / or, Parse the user's own DNS query logs to obtain real user DNS resolution behavior data; and / or, Extract availability, BGP network, and capacity data from the SLA agreements, technical white papers, or status boards disclosed by the service provider.

[0014] A DNS service quantitative evaluation method provided by the present invention further includes a DNS resolution security index layer, and the method further includes: Obtain data on the DNS service's DDoS protection and / or anti-hijacking capabilities; Based on the anti-DDoS capability and / or anti-hijacking capability data, determine the hierarchical score of the parsing security index layer; The hierarchical scores of the security index layer are weighted and summed with the hierarchical scores of the basic availability layer, the performance experience layer, and the access protection layer to generate a comprehensive quantitative evaluation result that includes security dimensions.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the DNS service quantification evaluation method described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the DNS service quantification evaluation method as described above.

[0017] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the DNS service quantification evaluation method as described above.

[0018] This invention provides a quantitative evaluation method for DNS services. By dividing DNS service quality into three progressive layers—basic availability, performance experience, and access guarantee—it is designed entirely from the user's perspective. Starting from the user experience logic of availability, usability, and global usability, it accurately matches users' core needs for DNS services. Breaking through the evaluation model of single indicators or fragmented dimensions, it integrates multiple dimensions such as availability, performance, coverage, and capacity within a single system, thereby providing users with a scientific and comprehensive decision-making basis for selecting DNS service providers and improving the user experience. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the DNS service quantitative evaluation method provided by the present invention.

[0021] Figure 2 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] Figure 1 This is a flowchart illustrating the DNS service quantitative evaluation method provided by the present invention.

[0024] like Figure 1 As shown in the figure, the DNS service quantitative evaluation method provided in this embodiment mainly includes the following steps: 101. Based on the progressive levels of user experience with DNS services, DNS service quality is divided into the basic availability layer, the performance experience layer, and the access guarantee layer.

[0025] Specifically, based on the progressive logic of users' actual experience with DNS services, DNS service quality is divided into three layers: Basic Availability Layer, Performance Experience Layer, and Access Assurance Layer. The Basic Availability Layer focuses on addressing the core issues of DNS service availability and reliability. The core metric is continuous availability duration (SLA) percentage, with secondary metrics including failure frequency and average recovery time. The Performance Experience Layer focuses on addressing the core issues of DNS service speed and smoothness. Core metrics include resolution success rate, average resolution latency, and P95 / P99 tail latency. The Access Assurance Layer focuses on addressing the core issues of DNS service global coverage and high-volume load capacity. Core metrics include BGP service scope and resolution volume. BGP service scope includes dimensions such as the number of POPs, the number of countries / continents covered, and the number of interconnections with primary ISPs. Resolution volume includes dimensions such as peak processing capacity and historical load records.

[0026] By considering multiple indicators, a tiered evaluation system is constructed from the perspective of user perception, which aligns with users' core demands for DNS services to be available, easy to use, and globally usable, and accurately matches the progressive needs of user experience.

[0027] 102. Determine the layer scores for the basic availability layer, the performance experience layer, and the access guarantee layer, respectively.

[0028] Specifically, for the divided Basic Availability Layer, Performance Experience Layer, and Access Guarantee Layer, the DNS service quality of each layer is quantitatively calculated by combining the core indicators, auxiliary indicators, and corresponding evaluation rules of each layer, resulting in the layer scores for the Basic Availability Layer, Performance Experience Layer, and Access Guarantee Layer. Before the quantitative calculation for each layer, the indicator data is standardized, mapping the original indicator values ​​to a standardized score of 0-1. Latency indicators use a reverse mapping method, with lower latency corresponding to higher standardized scores.

[0029] By accurately quantifying the service quality at each level, the influence of subjective judgment on the scoring results is avoided. At the same time, standardized processing makes the scores of different types of indicators comparable, laying the foundation for subsequent comprehensive evaluation.

[0030] 103. According to the preset weight allocation rules, the scores of the basic availability layer, performance experience layer and access guarantee layer are weighted and summed to obtain the comprehensive quantitative evaluation result of DNS service.

[0031] Specifically, the weight allocation rules for the basic availability layer, performance experience layer, and access guarantee layer are pre-set. According to these rules, the corresponding weight coefficients are assigned to the layer scores of each layer. The scores of each layer are multiplied by their corresponding weight coefficients and then summed to obtain the comprehensive quantitative evaluation result of the DNS service.

[0032] Determine the weighting coefficients of the three-tier metrics based on the business scenario. ,satisfy Calculate the scores for each level, and then obtain the comprehensive score through weighted summation. Introduce correction factors for BGP range and resolution to adjust the initial scores. For example, formula (1) is the comprehensive score formula: (1) in, Represents the weight distribution of the three layers. Represents the overall DNS score. The score represents the available base layer score. This represents the performance experience score. This represents the score for access security layer.

[0033] By integrating the tiered quantitative scores into a comprehensive evaluation result, the overall quality of DNS services can be objectively reflected. At the same time, the weight allocation can reflect the degree of influence of each tier on the overall service quality, providing an intuitive quantitative basis for DNS service provider selection.

[0034] Furthermore, based on the above embodiments, before weighted summing the scores of the basic availability layer, performance experience layer, and access guarantee layer in this embodiment, the method further includes: identifying the type of the target business scenario, which includes at least one of e-commerce or financial business, media or gaming business, global business, and individual developer business; determining the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer according to the type of the target business scenario; and using the determined weight coefficients as the weight allocation rules for weighted summation.

[0035] Specifically, based on the actual users and business attributes of the DNS service, the specific types of target business scenarios are identified. These types must include at least four categories: e-commerce or financial businesses, media or gaming businesses, global businesses, and individual developer businesses. Other business scenario types can also be expanded according to actual needs. For each identified target business scenario type, and considering the priority of the DNS service requirements at each layer, corresponding weight coefficients are determined for the basic availability layer, performance experience layer, and access guarantee layer. The values ​​of these weight coefficients ensure that the sum of the weight coefficients for all three layers is 1. These weight coefficients are then used directly as the weight allocation rule. The scores of the three layers are then weighted and summed according to this rule to obtain a comprehensive quantitative evaluation result of the DNS service under this business scenario.

[0036] By binding the weight allocation rules to the target business scenario, we can ensure that the comprehensive evaluation results highlight the core needs of the business scenario and improve the practicality of the evaluation results.

[0037] Furthermore, based on the above embodiments, this embodiment determines the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer, respectively. This includes: determining the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer from a preset mapping relationship. The preset mapping relationship includes: if the target business scenario is e-commerce or financial business, the weight coefficient of the basic availability layer is higher than the weight coefficient of the performance experience layer, and the weight coefficient of the performance experience layer is higher than the weight coefficient of the access guarantee layer; if the target business scenario is media or gaming business, the weight coefficient of the performance experience layer is higher than the weight coefficient of the basic availability layer, and the weight coefficient of the basic availability layer is higher than the weight coefficient of the access guarantee layer; if the target business scenario is global business, the weight coefficient of the access guarantee layer is higher than the weight coefficient of the performance experience layer, and the weight coefficient of the performance experience layer is higher than the weight coefficient of the basic availability layer; if the target business scenario is individual developer business, the weight coefficients of both the basic availability layer and the performance experience layer are higher than the weight coefficient of the access guarantee layer.

[0038] Specifically, based on the specific needs of different business scenarios for DNS services, a mapping relationship is established in advance between the target business scenario type and the weight coefficients of the basic availability layer, performance experience layer, and access guarantee layer. The weight coefficients of the three layers are clearly ranked from highest to lowest for each scenario, and corresponding high, medium, and low weight indicators are matched for each layer. The specific mapping relationship is as follows: (1) If the target business scenario is e-commerce or financial business, the weight coefficient of the basic availability layer is higher than that of the performance experience layer, the weight coefficient of the performance experience layer is higher than that of the access guarantee layer, the high weight indicators are stability and parsing success rate, the medium weight indicators are parsing latency and BGP range, and the low weight indicator is parsing volume.

[0039] (2) If the target business scenario is media or game business, the weight coefficient of the performance experience layer is higher than that of the basic availability layer, the weight coefficient of the basic availability layer is higher than that of the access guarantee layer, the high weight indicators are parsing latency and parsing success rate, the medium weight indicators are stability and BGP range, and the low weight indicator is parsing volume.

[0040] (3) If the target business scenario is a global business, the weight coefficient of the access guarantee layer is higher than that of the performance experience layer, the weight coefficient of the performance experience layer is higher than that of the basic availability layer, the high weight indicators are BGP range and parsing latency, the medium weight indicators are parsing success rate and stability, and the low weight indicator is parsing volume.

[0041] (4) If the target business scenario is for individual developers, the weight coefficients of the basic availability layer and the performance experience layer are higher than the weight coefficients of the access guarantee layer. The high weight indicators are stability and parsing success rate, the medium weight indicators are parsing latency, and the low weight indicators are BGP range and parsing volume.

[0042] As shown in Table 1, the weighting of the three-tiered metrics for different user types and business scenarios can be obtained more clearly and intuitively.

[0043] Table 1

[0044] Based on the identified target business scenario type, it is matched with the pre-defined mapping relationship. The weight coefficients of the basic availability layer, performance experience layer, and access guarantee layer under that scenario type are directly extracted, sorted by priority and their specific values. This quickly determines the weight coefficients, improving the efficiency of the evaluation process while ensuring that the weight allocation aligns with the core needs of each business scenario. The matched weight coefficients are verified to ensure that the sum of the weight coefficients across the three layers is 1. If not, normalization adjustments are made, and the adjusted coefficients are used as the final weight coefficients. This ensures that the weight coefficients meet the requirements of weighted summation calculations, avoiding distortion of subsequent comprehensive evaluation results due to incorrect coefficient values.

[0045] Furthermore, based on the above embodiments, this embodiment determines the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer, including: collecting historical business operation data and corresponding service quality evaluation results to construct a training dataset; using the training dataset, with type as the input feature and the weight coefficients of the basic availability layer, performance experience layer, and access guarantee layer as the output variables, to train a weight allocation model; inputting the type of the current target business scenario into the trained weight allocation model, and outputting the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer.

[0046] Specifically, we extensively collect historical business operation data under different business scenarios, including DNS service metrics data at each level and business operation performance data. We also collect DNS service quality assessment results for each scenario. After cleaning and labeling these data, we construct a training dataset for training the weight allocation model.

[0047] A machine learning model is constructed as the weight allocation model. The business scenario type is set as the input feature of the model, and the weight coefficients of the basic availability layer, performance experience layer, and access guarantee layer are set as the output variables of the model. The model is trained using a training dataset until the prediction accuracy of the model reaches the preset standard, and a trained weight allocation model is obtained. The machine learning algorithm automatically learns the intrinsic relationship between business scenarios and weight coefficients, replacing manual weight setting and improving the intelligence level of weight allocation.

[0048] The identified target business scenario type is input into the trained weight allocation model, which automatically outputs the weight coefficients of the basic availability layer, performance experience layer, and access guarantee layer under that scenario. This achieves automated and intelligent determination of weight coefficients, accurately matching the optimal weights according to the characteristics of the business scenario, and improving the scientificity and accuracy of the evaluation.

[0049] The weight coefficients output by the model are normalized to ensure that the sum of the weight coefficients of the three levels is 1. The normalized coefficients are used as the final weight coefficients to ensure that the weight coefficients meet the requirements of weighted summation and to ensure the accuracy of subsequent comprehensive evaluation calculations.

[0050] Furthermore, based on the above embodiments, this embodiment determines the level score of the basic availability layer by: obtaining the actual availability data of the DNS service; if the actual availability is lower than the availability threshold, then the level score of the basic availability layer is assigned to zero; if the actual availability is not lower than the availability threshold, then the level score of the basic availability layer is determined according to the resolution success rate and a preset penalty coefficient, wherein the penalty coefficient is used to deduct points when the success rate does not reach the ideal value.

[0051] Specifically, the actual availability data of the DNS service is collected. This data is the SLA availability percentage corresponding to the continuous availability duration. At the same time, auxiliary indicators such as failure frequency and mean time to repair can be collected as supplementary references to the availability data.

[0052] Availability thresholds are pre-set for the basic availability layer, such as 0.999. The actual availability data is compared with this threshold to determine whether the actual availability meets the standard. A veto rule is set: if the actual availability is lower than the availability threshold, the level score of the basic availability layer is directly assigned to zero, and the overall service evaluation of the DNS service is directly downgraded. If the actual availability is not lower than the availability threshold, the DNS service resolution success rate data is obtained, and combined with the preset success rate penalty coefficient, the level score of the basic availability layer is calculated using the exponential penalty formula. The penalty coefficient is used to exponentially deduct points for cases where the success rate does not reach the ideal value. The lower the resolution success rate, the greater the deduction. Formula (2) is the formula for calculating the level score of the basic availability layer: (2) in, Represents the actual availability percentage. Represents the availability threshold. Represents the penalty coefficient. This represents the success rate of parsing. This represents the success penalty factor.

[0053] Furthermore, based on the above embodiments, this embodiment determines the hierarchical score of the performance experience layer by: obtaining DNS service resolution success rate data and resolution latency data, wherein the resolution latency data includes P95 latency and / or P99 latency; determining a success rate score based on the resolution success rate data; determining a latency score based on the resolution latency data, a preset target latency, and a decay scale; and performing a weighted summation of the success rate score and the latency score to obtain the hierarchical score of the performance experience layer.

[0054] Specifically, DNS service resolution success rate data and resolution latency data are collected. The resolution latency data includes P95 / P99 tail latency, and cold query latency and hot query latency data are distinguished. The weight of cold query latency is higher than that of hot query latency. The collected resolution success rate data is standardized from 0 to 1 to obtain a resolution success rate score. The closer the resolution success rate is to 100%, the higher the corresponding success rate score. According to the collected resolution latency data, the preset target latency and attenuation scale, the resolution latency score is calculated through the latency score function. The latency score adopts a reverse mapping method. The lower the actual latency, the higher the corresponding latency score. Preset weights are set for the resolution success rate score and the resolution latency score respectively. The preferred weights are 0.6 for success rate and 0.4 for latency. The success rate score and latency score are multiplied by their respective weights and then summed to obtain the performance experience layer level score. Formula (3) is the calculation formula for the performance experience layer level score: (3) in, + =1, Represents the delay score function. Represents the target delay. Represents the attenuation scale. L This represents real-time latency.

[0055] Furthermore, based on the above embodiments, this embodiment determines the level score of the access assurance layer by: obtaining BGP coverage data and resolution capacity data of the DNS service; based on the BGP coverage data, extracting the number of nodes, the number of covered countries, and the number of interconnected operators with their respective baseline values ​​to determine the BGP coverage score; based on the resolution capacity data, extracting the service provider's resolution capacity and the resolution capacity required by the user, and determining the resolution capacity satisfaction score according to the ratio of the service provider's resolution capacity to the resolution capacity required by the user; and performing a weighted summation of the BGP coverage score and the resolution capacity satisfaction score to obtain the level score of the access assurance layer.

[0056] Specifically, BGP coverage data and resolution capacity data for DNS services are collected. BGP coverage data includes the number of nodes (POPs), the number of covered countries, and the number of interconnected ISPs (primary ISP interconnections). Resolution capacity data includes the service provider's resolution capacity (peak QPS) and the user's required resolution capacity (estimated peak security factor). Benchmark values ​​are set for the number of nodes, the number of covered countries, and the number of interconnected ISPs. The actual collected data is compared with these benchmark values, and scores for each dimension are calculated using a quantitative formula. The scores are then weighted and summed to obtain a BGP coverage score of 0-1. The ratio of the service provider's resolution capacity to the user's required resolution capacity is calculated, with a security factor of 3 (preferably 3). A resolution satisfaction score of 0-1 is obtained using a quantitative formula; the higher the service provider's resolution capacity relative to the user's required resolution capacity, the higher the satisfaction score. Preset weights (preferably 0.7 and 0.3, adjustable according to user type) are set for the BGP coverage score and resolution satisfaction score. The two scores are multiplied by their respective weights and summed to obtain the access assurance layer's level score. Formula (4) is the formula for calculating the hierarchical score of the access protection layer: (4) in, It can be adjusted according to user type, with preferred options. It is 0.7. It is 0.3. This represents the service provider's parsing capabilities. This indicates the amount of data the user needs to parse; the security factor defaults to 3. This represents the BGP coverage score.

[0057] The formula for calculating BGP coverage score is as follows (5): (5) in, Represents the number of nodes. Represents the baseline number of nodes. Represents the number of countries covered. Represents the number of target countries. Represents the number of internet connections held by operators. This represents the number of major operators.

[0058] Substituting formulas (2)-(5) into formula (1), we can obtain the complete comprehensive scoring formula, as shown in (6): (6) Table 2 shows the description of the parameters in the formula.

[0059] Table 2

[0060] Furthermore, based on the above embodiments, this embodiment also includes: using the BGP coverage score as a correction factor to discount and adjust the hierarchical score of the performance experience layer to obtain the corrected hierarchical score of the performance experience layer; and / or, if the resolution satisfaction score is lower than the preset admission threshold, then the hierarchical score of the access guarantee layer is reduced or the hierarchical score of the access guarantee layer is set to zero.

[0061] Specifically, BGP coverage score is used as a correction factor for the performance experience layer. If the DNS service provider's BGP service coverage does not cover the user's target market, this correction factor is used to discount and adjust the performance experience layer score, resulting in a corrected performance experience layer score. The less coverage, the greater the discount. By linking global coverage capabilities with resolution performance, situations where resolution speed meets standards but the target market is inaccessible are avoided, making the evaluation results more closely reflect the user's actual access experience.

[0062] A preset admission threshold is set for the resolution capacity satisfaction score. If the resolution capacity satisfaction score is lower than this threshold, it indicates that the service provider's resolution capacity is approaching its limit. In this case, the access guarantee layer score is adjusted; the score can be lowered or set to zero. Additionally, the resolution capacity satisfaction score will also discount the reliability score of the basic availability layer. By using resolution capacity as an admission criterion for DNS services, the impact of high-volume load capacity on service stability is highlighted, preventing service crashes due to insufficient resolution capacity and improving the rigor of the evaluation results.

[0063] The revised performance experience layer score and the adjusted access assurance layer score will replace the original scores and be weighted and summed to participate in the calculation of the comprehensive quantitative evaluation result of DNS services. This will create a relationship of mutual correlation and correction between the scores of each layer, making the comprehensive evaluation result more objective.

[0064] Furthermore, based on the above embodiments, this embodiment also includes: initiating proactive monitoring globally through third-party detection nodes to collect resolution success rate and latency data; and / or parsing the user's own DNS query logs to obtain real user resolution behavior data; and / or extracting availability, BGP network, and capacity data from the SLA agreement, technical white paper, or status board disclosed by the service provider.

[0065] Specifically, leveraging third-party testing services, DNS query requests are initiated across multiple nodes globally to collect dynamic metrics such as DNS service resolution success rate and resolution latency (including P95 / P99 tail latency and cold / hot query latency) in real time. This objectively reflects the actual performance of the service in different regions, avoiding the bias of data from a single node. The DNS query logs of the DNS service users themselves are analyzed to extract real user resolution behavior data, including actual resolution success rate, accessed regions, and resolution latency. This data supplements proactive monitoring data, obtaining metrics that closely reflect real user scenarios, making the data more aligned with actual user experience and improving the practicality of the evaluation results. Official documents released by DNS service providers, such as SLA agreements, technical white papers, and public status boards, are reviewed to extract static metrics such as service stability (SLA availability percentage), BGP network (number of nodes and number of covered countries), and resolution capacity (peak processing capacity). These are cross-validated with the dynamic data from proactive monitoring and log analysis to ensure the authenticity and comprehensiveness of the data.

[0066] The system integrates multi-source data obtained through proactive monitoring, log analysis, and service provider disclosure. Abnormal and duplicate data are cleaned and removed to obtain the final indicator data used for score calculation at each level. This ensures that the indicator data used for evaluation is authentic, comprehensive, and effective, thereby improving the objectivity of the evaluation results from the data source.

[0067] Furthermore, based on the above embodiments, this embodiment also includes a DNS security index layer, and the method further includes: obtaining DDoS protection capability and / or anti-hijacking capability data of the DNS service; determining the level score of the DNS security index layer based on the DDoS protection capability and / or anti-hijacking capability data; and weighting and summing the level score of the DNS security index layer with the level scores of the basic availability layer, performance experience layer and access guarantee layer to generate a comprehensive quantitative evaluation result including security dimensions.

[0068] Specifically, in addition to the basic availability layer, performance experience layer, and access guarantee layer, a DNS resolution security indicator layer is added. This layer can exist independently as a fourth layer or be incorporated into the performance experience layer as an auxiliary indicator. The core indicators of the DNS resolution security indicator layer are the DNS service's anti-DDoS and anti-hijacking capabilities, and other security indicators can be expanded according to actual needs. The DNS resolution security indicator layer enriches the evaluation dimensions of DNS services, meeting the evaluation needs of business scenarios with extremely high security requirements, such as finance and government, making the evaluation system more comprehensive.

[0069] Data on DNS service providers' DDoS protection capabilities, such as peak DDoS protection levels and anti-hijacking capabilities, such as DNS record anti-tampering capabilities and domain hijacking monitoring capabilities, can be collected through third-party security testing and official disclosures by service providers. This data supports the calculation of the DNS security indicator layer, ensuring that the security dimension assessment is based on actual security protection capabilities.

[0070] The collected security indicator data is standardized from 0 to 1. The scores are then weighted and summed according to the weights of each security indicator to obtain the hierarchical scores of the security indicator layers. The stronger the security protection capability, the higher the corresponding hierarchical score.

[0071] The weighted summation yields a comprehensive evaluation result that includes the security dimension. Corresponding weight coefficients are set for the DNS security indicator layer. The score of this layer is multiplied by the scores of the basic availability layer, performance experience layer, and access guarantee layer, respectively, and then summed to generate a comprehensive quantitative evaluation result of DNS service that includes the security dimension.

[0072] By incorporating security dimensions into the overall comprehensive evaluation, the evaluation results can fully reflect the availability, performance, access assurance, and security capabilities of DNS services, providing a more accurate basis for decision-making in selecting DNS service providers for business scenarios with high security requirements.

[0073] The method of the present invention will be illustrated by the following two scenarios: Example 1: Evaluation of DNS service providers in e-commerce business scenarios (1) Scene parameter settings: weight coefficients (Basic Available Layer) (Performance Experience Layer) (Access assurance layer); minimum requirement threshold, i.e., availability threshold Success rate penalty coefficient Target delay attenuation scale .

[0074] (2) Data collection: The availability percentage of service provider A is 99.98%, the resolution success rate is 99.99%, and the P95 latency is 18ms. BGP covers 100 countries and is interconnected with 80 first-tier operators. The service provider's resolution capacity is 10 million QPS, and the resolution capacity required for e-commerce business is 3 million QPS.

[0075] (3) Quantitative calculation: Basic Availability Layer Score: Availability meets the standard, calculated based on success rate. .

[0076] Performance experience layer score: parsing success rate 99.99%, latency score 0.95, weighted score 0.9799.

[0077] Access protection layer score: BGP coverage 90%, weighted score is 0.9689 Overall Score: Service Provider A has a high overall score, and its core indicators, such as stability and success rate, meet the requirements of e-commerce business.

[0078] Example 2: Evaluation of DNS service providers for global business scenarios (1) Scene parameter settings: weight coefficients (Basic Available Layer) (Performance Experience Layer) (Access protection layer); minimum threshold requirements Success rate penalty coefficient Target delay Attenuation scale The baseline number of nodes is 80, the target number of countries is 150, and the number of major operators is selected from the global Top 100.

[0079] (2) Data collection: Service provider B has an availability of 99.95%, a resolution success rate of 99.98%, and a global average P95 latency of 22ms; the service provider's resolution capacity is 15 million QPS; the user's required resolution capacity is 4 million QPS; the actual number of nodes is 95, the actual number of countries covered is 140, and the actual number of first-level operators interconnected is 90.

[0080] (3) Quantitative calculation: Basic available layer score: Performance experience score: Access protection layer score: Overall Score: .

[0081] Service Provider B scored approximately 0.9865 points overall, demonstrating excellent performance in core high-weight indicators (BGP coverage and resolution latency). Its basic availability layer stability and resolution success rate met the standards, and its global coverage and massive resolution capabilities of the access assurance layer fully matched the peak demand of global business, thus meeting the core requirements of global business scenarios for DNS services.

[0082] Compared with the prior art, the present invention has the following beneficial effects: (1) Aligning with user perception: The three-layer evaluation architecture is designed entirely from the user's perspective, starting from the experience logic of usability, ease of use and global usability, and accurately matching the user's core demands for DNS services; (2) Adapt to different needs: The scenario-based weight allocation rules can be dynamically adjusted according to the business type, which solves the drawback of the traditional evaluation method of "one-size-fits-all" and meets the evaluation needs of different scenarios such as e-commerce, games, and global business. (3) The evaluation results are objective and accurate: Through standardized quantitative processes and normalization, combined with threshold protection and index penalty mechanisms, subjective judgment bias is avoided, while the barrel effect is reflected, highlighting the impact of the indicator shortcomings on the overall experience. (4) Highly practical: Users can directly implement the application and make horizontal comparisons and scientific selections of different DNS service providers.

[0083] Figure 2 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0084] like Figure 2 As shown, the electronic device may include a processor 210, a communications interface 220, a memory 230, and a communication bus 240, wherein the processor 210, communications interface 220, and memory 230 communicate with each other via the communication bus 240. The processor 210 can invoke logical instructions in the memory 230 to execute a DNS service quantification evaluation method. This method includes: dividing DNS service quality into a basic availability layer, a performance experience layer, and an access guarantee layer based on the user's progressive experience of the DNS service; determining the level scores of the basic availability layer, the performance experience layer, and the access guarantee layer, respectively; and weighting and summing the level scores of the basic availability layer, the performance experience layer, and the access guarantee layer according to a preset weight allocation rule to obtain a comprehensive quantification evaluation result of the DNS service.

[0085] Furthermore, the logical instructions in the aforementioned memory 230 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the DNS service quantitative evaluation method provided by the above methods. The method includes: dividing the DNS service quality into a basic availability layer, a performance experience layer, and an access guarantee layer according to the progressive levels of user experience with the DNS service; determining the level score of the basic availability layer, the level score of the performance experience layer, and the level score of the access guarantee layer, respectively; and weighting and summing the level scores of the basic availability layer, the performance experience layer, and the access guarantee layer according to a preset weight allocation rule to obtain a comprehensive quantitative evaluation result of the DNS service.

[0087] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a DNS service quantification evaluation method provided by the methods described above. The method includes: dividing DNS service quality into a basic availability layer, a performance experience layer, and an access guarantee layer based on the progressive levels of user experience with DNS services; determining the level scores of the basic availability layer, the performance experience layer, and the access guarantee layer, respectively; and performing a weighted summation of the level scores of the basic availability layer, the performance experience layer, and the access guarantee layer according to a preset weight allocation rule to obtain a comprehensive quantification evaluation result of the DNS service.

[0088] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0089] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for quantitatively evaluating DNS services, characterized in that, include: Based on the progressive levels of user experience with DNS services, DNS service quality is divided into the basic availability layer, the performance experience layer, and the access guarantee layer. The hierarchical scores of the basic availability layer, the performance experience layer, and the access guarantee layer are determined respectively. According to the preset weight allocation rules, the scores of the basic availability layer, the performance experience layer, and the access guarantee layer are weighted and summed to obtain a comprehensive quantitative evaluation result of the DNS service.

2. The DNS service quantitative evaluation method according to claim 1, characterized in that, Before performing a weighted summation of the hierarchical scores for the basic availability layer, the performance experience layer, and the access guarantee layer, the method further includes: Identify the type of the target business scenario, which includes at least one of e-commerce or financial business, media or gaming business, global business, and individual developer business; Based on the type of the target business scenario, determine the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer respectively; The determined weight coefficients are used as the weight allocation rules for the weighted summation.

3. The DNS service quantitative evaluation method according to claim 2, characterized in that, The determination of the weight coefficients for the basic availability layer, performance experience layer, and access guarantee layer includes: From the preset mapping relationships, the weight coefficients corresponding to the basic availability layer, performance experience layer, and access guarantee layer are determined respectively. The preset mapping relationships include: If the target business scenario is e-commerce or financial business, the weight coefficient of the basic availability layer is higher than the weight coefficient of the performance experience layer, and the weight coefficient of the performance experience layer is higher than the weight coefficient of the access guarantee layer. If the target business scenario is a media or gaming business, the weight coefficient of the performance experience layer is higher than that of the basic availability layer, and the weight coefficient of the basic availability layer is higher than that of the access guarantee layer. If the target business scenario is a global business, then the weight coefficient of the access guarantee layer is higher than that of the performance experience layer, and the weight coefficient of the performance experience layer is higher than that of the basic availability layer. If the target business scenario is for individual developers, then the weight coefficients of the basic availability layer and the performance experience layer are both higher than the weight coefficient of the access guarantee layer.

4. The DNS service quantitative evaluation method according to claim 2, characterized in that, The determination of the weight coefficients for the basic availability layer, performance experience layer, and access guarantee layer includes: Collect historical business operation data and corresponding service quality assessment results to build a training dataset; Using the training dataset, with the type as the input feature and the weight coefficients of the basic availability layer, performance experience layer, and access guarantee layer as the output variables, a weight allocation model is trained. Input the type of the current target business scenario into the trained weight allocation model, and output the weight coefficients corresponding to the basic availability layer, performance experience layer and access guarantee layer.

5. The DNS service quantitative evaluation method according to claim 1, characterized in that, The determination of the hierarchical score of the basic available layer includes: Obtain actual availability data for the DNS service; If the actual availability is lower than the availability threshold, then the hierarchical score of the basic availability layer is set to zero; If the actual availability is not lower than the availability threshold, then the level score of the basic availability layer is determined based on the parsing success rate and the preset penalty coefficient. The penalty coefficient is used to deduct points when the success rate does not reach the ideal value.

6. The DNS service quantitative evaluation method according to claim 1, characterized in that, The determination of the performance experience layer level score includes: Obtain DNS service resolution success rate data and resolution latency data, wherein the resolution latency data includes P95 latency and / or P99 latency; Based on the analyzed success rate data, a success rate score is determined; The latency score is determined based on the analyzed latency data, the preset target latency, and the attenuation scale. The success rate score and the latency score are weighted and summed to obtain the hierarchical score of the performance experience layer.

7. The DNS service quantitative evaluation method according to claim 1, characterized in that, The determination of the access protection layer level score includes: Obtain BGP coverage data and resolution capacity data for the DNS service; Based on the BGP coverage data, the number of nodes, the number of covered countries, and the number of operator interconnections are extracted and their corresponding benchmark values ​​are used to determine the BGP coverage score. Based on the parsing capacity data, the parsing capacity of the service provider and the parsing capacity required by the user are extracted. Based on the ratio of the parsing capacity of the service provider to the parsing capacity required by the user, a parsing capacity satisfaction score is determined. The access guarantee layer's hierarchical score is obtained by weighted summation of the BGP coverage score and the resolution satisfaction score.

8. The DNS service quantitative evaluation method according to claim 7, characterized in that, Also includes: Using the BGP coverage score as a correction factor, the hierarchical score of the performance experience layer is discounted and adjusted to obtain the corrected hierarchical score of the performance experience layer. And / or, If the resolution satisfaction score is lower than the preset admission threshold, the access protection layer's level score will be reduced or the access protection layer's level score will be set to zero.

9. The DNS service quantitative evaluation method according to claim 1, characterized in that, Also includes: Active monitoring is initiated globally through third-party detection nodes to collect and analyze success rate and latency data. And / or, Analyze the user's own DNS query logs to obtain real user DNS resolution behavior data; And / or, Extract availability, BGP network, and capacity data from the SLA agreements, technical white papers, or status boards disclosed by the service provider.

10. The DNS service quantitative evaluation method according to any one of claims 1-9, characterized in that, The method also includes parsing a security metrics layer, and further includes: Obtain data on the DNS service's DDoS protection and / or anti-hijacking capabilities; Based on the anti-DDoS capability and / or anti-hijacking capability data, determine the hierarchical score of the parsing security index layer; The hierarchical scores of the security index layer are weighted and summed with the hierarchical scores of the basic availability layer, the performance experience layer, and the access protection layer to generate a comprehensive quantitative evaluation result that includes security dimensions.

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