Cloud service node trust evaluation method, computer equipment and program product

Abstract

The invention relates to a cloud service node trust evaluation method, computer equipment and a program product. The method comprises the following steps: for a to-be-evaluated cloud service node, performing trust evaluation on the cloud service node from a plurality of evaluation dimensions, and respectively obtaining trust values of the cloud service node under each evaluation dimension; according to the trust value of the cloud service node under each evaluation dimension, calculating an information entropy corresponding to each evaluation dimension; based on the information entropy corresponding to each evaluation dimension, determining a weight corresponding to each evaluation dimension; and determining a comprehensive trust value of the cloud service node according to the weight corresponding to each evaluation dimension and the trust value of the cloud service node under each evaluation dimension. According to the method, the trust evaluation accuracy of the cloud service node can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information security, and in particular to a cloud service node trust evaluation method, a computer device and a program product. BACKGROUND

[0002] With the explosive growth of cloud computing technology, the scale expansion and architecture complexity of cloud service nodes (CSN) show an exponential rising trend. The inherent dynamic topology, heterogeneous components and open access characteristics of CSN lead to significant limitations of traditional static trust evaluation mechanisms in dealing with new security threats.

[0003] Currently, the trust evaluation system has evolved into the core security infrastructure of the cloud computing ecosystem. Its value not only lies in breaking through the boundaries of traditional security technology, but also in building a three-dimensional decision-making framework that supports cloud environment security protection, intelligent resource scheduling and service quality assurance. The trust evaluation model not only effectively identifies and isolates untrusted behaviors or entities, but also reduces uncertainty and potential risks before decision-making, which helps to ensure that cloud services can run in a controllable and stable environment, thereby ensuring the security and reliability of the entire cloud computing system.

[0004] However, the existing trust evaluation schemes for cloud service nodes often have the problem of low evaluation accuracy. SUMMARY

[0005] Therefore, it is necessary to provide a cloud service node trust evaluation method, device, computer equipment, computer readable storage medium and computer program product capable of improving the trust evaluation accuracy of cloud service nodes.

[0006] In a first aspect, the present application provides a cloud service node trust evaluation method, comprising:

[0007] For the cloud service node to be evaluated, trust evaluation is performed on the cloud service node from multiple evaluation dimensions to obtain trust values of the cloud service node in each evaluation dimension; information entropy corresponding to each evaluation dimension is calculated respectively according to the trust values of the cloud service node in each evaluation dimension; weights corresponding to each evaluation dimension are determined respectively based on the information entropy corresponding to each evaluation dimension; and a comprehensive trust value of the cloud service node is determined according to the weights corresponding to each evaluation dimension and the trust values of the cloud service node in each evaluation dimension.

[0008] In one embodiment, the step of performing trust assessment on the cloud service node from multiple evaluation dimensions to obtain the trust value of the cloud service node under each evaluation dimension includes: when the multiple evaluation dimensions include a historical interaction behavior dimension, obtaining historical interaction data of the cloud service node in a historical time period; calculating a time decay factor for each historical interaction data, wherein the time decay factor is negatively correlated with the time difference between the interaction time of the historical interaction data and the current time; and calculating the trust value of the cloud service node under the historical interaction behavior dimension based on the interaction result indicated by each historical interaction data and the time decay factor.

[0009] In one embodiment, the step of performing trust assessment on the cloud service node from multiple evaluation dimensions to obtain the trust value of the cloud service node under each evaluation dimension includes: when the multiple evaluation dimensions include a data integrity dimension, determining the success request rate and failure request rate of the cloud service node, and calculating the initial trust value of the cloud service node under the data integrity dimension based on the success request rate and failure request rate; obtaining the resource expansion rate and load growth rate of the cloud service node; when the resource expansion rate is lower than the load growth rate, calculating the elastic failure impact factor of the initial trust value based on the resource expansion rate and load growth rate; and correcting the initial trust value based on the elastic failure impact factor to obtain the trust value of the cloud service node under the data integrity dimension.

[0010] In one embodiment, the step of performing trust assessment on the cloud service node from multiple evaluation dimensions to obtain the trust value of the cloud service node under each evaluation dimension includes: when the multiple evaluation dimensions include a time-series performance dimension, calculating the fulfillment rate of the cloud service node in executing the target task based on the ratio between the promised execution time and the actual execution time of the cloud service node in executing the target task; obtaining the resource waiting time of the cloud service node in executing the target task, and calculating the resource responsiveness of the cloud service node in executing the target task based on the resource waiting time and the actual execution time; and calculating the trust value of the cloud service node under the time-series performance dimension based on the fulfillment rate and the resource responsiveness.

[0011] In one embodiment, the step of performing trust assessment on the cloud service node from multiple evaluation dimensions to obtain the trust value of the cloud service node under each evaluation dimension includes: when the multiple evaluation dimensions include an availability dimension, obtaining the total request ratio of incoming requests to the cloud service node per unit time and the request ratio received by the cloud service node per unit time; and determining the trust value of the cloud service node under the availability dimension based on the ratio of the request ratio received by the cloud service node to the total request ratio per unit time.

[0012] In one embodiment, the method further includes: when the cloud service node performs trusted behavior within a preset time period, calculating a reward factor based on the number of successful interactions of the cloud service node within the preset time period, and increasing the overall trust value of the cloud service node based on the reward factor.

[0013] In one embodiment, the method further includes: when the cloud service node exhibits untrustworthy behavior within a preset time period, calculating a penalty factor based on the number of interaction failures of the cloud service node within the preset time period, and reducing the overall trust value of the cloud service node based on the penalty factor.

[0014] Secondly, this application also provides a cloud service node trust assessment device, comprising:

[0015] The trust value acquisition module is used to perform trust assessment on the cloud service node to be evaluated from multiple evaluation dimensions, and obtain the trust value of the cloud service node under each evaluation dimension.

[0016] The information entropy calculation module is used to calculate the information entropy corresponding to each evaluation dimension based on the trust value of the cloud service node under each evaluation dimension.

[0017] The weight calculation module is used to determine the weight of each evaluation dimension based on the information entropy of each evaluation dimension.

[0018] The comprehensive trust value determination module is used to determine the comprehensive trust value of the cloud service node based on the weights corresponding to each evaluation dimension and the trust value of the cloud service node under each evaluation dimension.

[0019] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the cloud service node trust assessment method in any of the above embodiments.

[0020] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the cloud service node trust assessment method in any of the above embodiments.

[0021] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the cloud service node trust assessment method in any of the above embodiments.

[0022] The aforementioned cloud service node trust assessment method, apparatus, computer equipment, computer-readable storage medium, and computer program product assess the trust of a cloud service node from multiple assessment dimensions, obtaining the trust value of the cloud service node under each assessment dimension. Based on the trust value of the cloud service node under each assessment dimension, the information entropy corresponding to each assessment dimension is calculated. Based on the information entropy corresponding to each assessment dimension, the weight corresponding to each assessment dimension is determined. Based on the weight corresponding to each assessment dimension and the trust value of the cloud service node under each assessment dimension, the comprehensive trust value of the cloud service node is determined. Since trust assessment of cloud service nodes can be performed from multiple assessment dimensions, it can comprehensively reflect the dynamic trust characteristics of nodes. By adaptively determining weights through the calculation of information entropy, the actual contribution of each dimension in the trust assessment system can be objectively quantified. This method abandons the subjective drawbacks of traditional experience-based weighting, and achieves scientific allocation of weights based on data distribution characteristics. It improves the objectivity and fairness of the trust assessment model from the underlying mechanism, ensuring that the assessment results can truly reflect the comprehensive trust level of the node, thereby significantly improving the accuracy of cloud service node trust assessment. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a diagram illustrating the application environment of a cloud service node trust assessment method in one embodiment.

[0025] Figure 2 This is a flowchart illustrating a cloud service node trust assessment method in one embodiment;

[0026] Figure 3 This is a schematic diagram illustrating the process of trust assessment of cloud service nodes from the perspective of historical interaction behavior in one embodiment.

[0027] Figure 4 This is a schematic diagram illustrating the process of trust assessment of cloud service nodes from the perspective of data integrity in one embodiment;

[0028] Figure 5 This is a flowchart illustrating the process of trust assessment of cloud service nodes from the perspective of time-series performance in one embodiment.

[0029] Figure 6 A structural block diagram of a cloud service node trust assessment device in one embodiment;

[0030] Figure 7 This is an internal structural diagram of a computer device in one embodiment;

[0031] Figure 8 This is a diagram of the internal structure of a computer device in another embodiment. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0033] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0034] As cloud computing technology enters a phase of explosive growth, the scale and architectural complexity of Cloud Service Nodes (CSNs) are increasing exponentially. Their inherent dynamic topology, heterogeneous components, and open access characteristics expose significant limitations in traditional static trust assessment mechanisms when addressing new security threats. Currently, trust assessment systems have evolved into core security infrastructure for the cloud computing ecosystem. Their value lies not only in breaking through the boundaries of traditional security technologies but also in building a three-dimensional decision-making framework that supports cloud environment security protection, intelligent resource scheduling, and service quality assurance. Trust assessment models can effectively identify and isolate untrusted behaviors or entities, and reduce uncertainty and potential risks before decision-making, helping to ensure that cloud services operate in a controllable and stable environment, thereby guaranteeing the security and reliability of the entire cloud computing system.

[0035] However, current trust assessment schemes for cloud service nodes still have the following limitations:

[0036] (1) Single evaluation dimension: Some previous solutions have the characteristic of single evaluation dimension, and rely too much on static historical behavior backtracking (such as historical interaction records and service performance data), which leads to trust evaluation lagging behind the actual behavior evolution of nodes;

[0037] (2) Fixed trust weights: Many existing schemes assign fixed trust weights to trust factors, and the selection of weights usually depends on experience. This approach inevitably introduces subjectivity and limits the objectivity and accuracy of the evaluation results.

[0038] To address the aforementioned issues, this application provides a cloud service node trust assessment method. This method comprehensively considers the trust value of cloud service nodes from multiple dimensions and uses the entropy weight method to adaptively calculate the trust factors, ensuring the objectivity, accuracy, and dynamic adaptability of the trust assessment results. A detailed description is provided below with reference to specific embodiments.

[0039] The cloud service node trust assessment method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0040] The cloud service node trust assessment method of this application can be performed by a computer device, which can be... Figure 1 The terminal 102 or server 104 in this application can also be a system composed of terminal 102 and server 104, in which terminal 102 and server 104 interact and cooperate to execute the cloud service node trust assessment method of this application. Taking a computer device as terminal 102 as an example, for the cloud service node to be assessed, terminal 102 can perform trust assessment on the cloud service node from multiple assessment dimensions, obtain the trust value of the cloud service node under each assessment dimension, calculate the information entropy corresponding to each assessment dimension based on the trust value of the cloud service node under each assessment dimension, determine the weight corresponding to each assessment dimension based on the information entropy corresponding to each assessment dimension, and determine the comprehensive trust value of the cloud service node based on the weight corresponding to each assessment dimension and the trust value of the cloud service node under each assessment dimension.

[0041] In one exemplary embodiment, such as Figure 2As shown, a cloud service node trust assessment method is provided. This method can be executed by a computer device, which can be... Figure 1 The terminal 102 or server 104 in the process includes the following steps:

[0042] Step 202: For the cloud service node to be evaluated, conduct a trust evaluation of the cloud service node from multiple evaluation dimensions and obtain the trust value of the cloud service node under each evaluation dimension.

[0043] The evaluation dimensions include at least two of the following: historical interaction behavior, data integrity, time-series performance, and availability. The cloud service node to be evaluated refers to the cloud service node that requires a trust assessment.

[0044] Trust assessment uses mathematical methods to comprehensively analyze data such as an entity's historical behavior, performance, and environmental conditions to generate a dynamic trust value. Essentially, it transforms the subjective concept of "trust" into a calculable, objective indicator. The trust value is a numerical score used to characterize the trustworthiness of cloud service nodes.

[0045] A cloud service node is a logical unit deployed in a cloud computing environment that provides specific service functions. It can be represented as a virtual machine instance, a containerized microservice, or a physical server cluster.

[0046] For example, for a cloud service node to be evaluated, the computer device can perform trust evaluation from four dimensions: historical interaction behavior, data integrity, timing performance, and availability, to obtain the evaluation value of the cloud service node under each evaluation dimension. The specific evaluation methods for each dimension can be referred to in the embodiments below.

[0047] Step 204: Calculate the information entropy corresponding to each evaluation dimension based on the trust value of the cloud service node under each evaluation dimension.

[0048] Information entropy is a quantitative indicator used to measure the uncertainty of information. Specifically, in this embodiment, the information entropy corresponding to each evaluation dimension can measure the uncertainty of the trust value of each evaluation dimension.

[0049] Specifically, for the trust value of a cloud service node under each evaluation dimension, the computer device can use the trust value under that evaluation dimension to calculate the information entropy and obtain the information entropy corresponding to that evaluation dimension.

[0050] For example, taking multiple evaluation dimensions including historical interaction behavior, data integrity, time-series performance, and usability, the information entropy corresponding to each evaluation dimension can be calculated using the following formula:

[0051]

[0052]

[0053]

[0054]

[0055] in, Trust value based on historical interaction behavior. The information entropy corresponding to the dimension of historical interaction behavior. The trust value under the data integrity dimension. The information entropy corresponding to the data integrity dimension. The trust value is based on the time-series performance dimension. The information entropy corresponding to the time-series performance dimension. Trust value under the usability dimension. This represents the information entropy corresponding to the usability dimension.

[0056] Step 206: Based on the information entropy corresponding to each evaluation dimension, determine the weight corresponding to each evaluation dimension.

[0057] The weights corresponding to each evaluation dimension are used to measure the importance of the trust value of each evaluation dimension. The higher the weight of an evaluation dimension, the more important the trust value of that evaluation dimension is in the final evaluation of the cloud service node.

[0058] For each evaluation dimension, the information entropy of that evaluation dimension is negatively correlated with its corresponding weight. The higher the information entropy, the higher the uncertainty / chaos of that evaluation dimension, and the lower its "usefulness" for decision-making or analysis, and therefore the lower its weight. Conversely, the lower the information entropy, the higher the discrimination of the evaluation dimension, and the higher its weight.

[0059] Specifically, after calculating the information entropy corresponding to each evaluation dimension, the computer device can determine the weight corresponding to each evaluation dimension based on the information entropy corresponding to each evaluation dimension, and then determine the final comprehensive trust value based on the calculated weights.

[0060] For example, taking multiple evaluation dimensions including historical interaction behavior, data integrity, time-series performance, and usability, the weights corresponding to each evaluation dimension can be calculated using the following formula:

[0061]

[0062]

[0063]

[0064]

[0065] in, The weights corresponding to the historical interaction behavior dimension. The weights corresponding to the data integrity dimension. The weights corresponding to the time-series performance dimension. The weights corresponding to the usability dimension.

[0066] Step 208: Determine the overall trust value of the cloud service node based on the weights of each evaluation dimension and the trust value of the cloud service node under each evaluation dimension.

[0067] The comprehensive trust value refers to the trust assessment result obtained by comprehensively assessing the trust of cloud service nodes from multiple evaluation dimensions.

[0068] Specifically, after obtaining the weights corresponding to each evaluation dimension and the trust value of the cloud service node under each evaluation dimension, the computer device can multiply the trust value of the cloud service node under each evaluation dimension by the weights corresponding to each evaluation dimension to obtain the comprehensive trust value of the cloud service node.

[0069] For example, continuing with the example of multiple evaluation dimensions including historical interaction behavior, data integrity, time-series performance, and availability, the comprehensive trust value of a cloud service node... The calculation method is as follows:

[0070]

[0071] The aforementioned cloud service node trust assessment method assesses the trust of a cloud service node from multiple dimensions, obtaining its trust value for each dimension. Based on these trust values, the information entropy for each dimension is calculated, and weights are determined accordingly. Finally, the overall trust value of the cloud service node is determined based on these weights and the node's trust value across all dimensions. This method comprehensively reflects the dynamic trust characteristics of nodes by assessing trust across multiple dimensions and by adaptively determining weights through information entropy calculation, objectively quantifying the actual contribution of each dimension to the trust assessment system. This approach overcomes the subjective drawbacks of traditional experience-based weighting, scientifically allocating weights based on data distribution characteristics. This fundamentally improves the objectivity and fairness of the trust assessment model, ensuring that the assessment results accurately reflect the node's overall trust level and significantly enhancing the accuracy of cloud service node trust assessment.

[0072] The above embodiments describe the overall implementation process of the cloud service node trust assessment method of this application. In specific applications, the above-mentioned multiple assessment dimensions are historical interaction behavior, data integrity, time series performance and availability. The following embodiments explain the specific calculation method of the trust value under each assessment dimension.

[0073] In one exemplary embodiment, reference is made to Figure 3 Trust assessments of cloud service nodes are conducted from multiple evaluation dimensions to obtain trust values ​​for each cloud service node under each evaluation dimension, including:

[0074] Step 302: When multiple evaluation dimensions include historical interaction behavior dimensions, obtain the historical interaction data of cloud service nodes in historical time periods.

[0075] Step 304: Calculate the time decay factor for each historical interaction data. The time decay factor is negatively correlated with the time difference between the interaction time of the historical interaction data and the current time.

[0076] Step 306: Calculate the trust value of the cloud service node in the dimension of historical interaction behavior based on the interaction results indicated by each historical interaction data and the time decay factor.

[0077] The historical time period refers to a specific length of time preceding the current time.

[0078] Historical interaction data can indicate successful or failed interactions. A successful interaction refers to the cloud service node correctly fulfilling the client's request and meeting predefined service objectives under agreed-upon Quality of Service (QoS) constraints. Its determination requires the simultaneous fulfillment of the following conditions:

[0079] Functional success: The service returns the expected result (e.g., the calculation result is correct, the stored data is complete);

[0080] Timeliness Success: The response time does not exceed the maximum delay threshold defined in the SLA (Service Level Agreement);

[0081] Protocol success: Compliance with communication protocol specifications (such as HTTP status code 200, TCP ACK confirmation of completion);

[0082] Resource success: Resource supply meets the requested demand (e.g., allocated computing resources ≥ requested value, sufficient storage space).

[0083] Specifically, regarding the historical interaction behavior dimension, computer devices can obtain historical interaction data of cloud service nodes in historical time periods, further calculate the time decay factor of historical interaction data, and calculate the trust value of cloud service nodes in the historical interaction behavior dimension based on the interaction results indicated by each historical interaction data and the time decay factor.

[0084] Optionally, the trust value of a cloud service node based on its historical interaction behavior can be calculated using the following algorithm:

[0085]

[0086] in, As a time decay factor, more weight is given to recent historical interaction data. This is historical interaction data; when historical interaction data indicates a successful interaction, A value of 1 indicates that the interaction failed, as historical interaction data suggests. It is 0.

[0087] Optionally, the time decay factor can be calculated using the following formula:

[0088] , This represents the difference between the current time and the time of the i-th interaction. This represents the decay period, and the specific value depends on the application scenario. This marks the turning point in the decay rate; This is the decay rate control factor.

[0089] Optionally, historical time periods can be selected using a sliding window mechanism to focus only on time ranges within a given period. Historical interaction behavior can reflect changes in the environment and behavior more promptly.

[0090] In this embodiment, a dual mechanism of time decay factor and sliding time window is introduced in the calculation of historical interaction trust value: the former reduces the impact of outdated interaction data through exponential decay model to ensure that trust assessment focuses on the recent behavior of nodes; the latter realizes real-time tracking of changes in node trust value with service cycle by dynamically adjusting the assessment time range, which significantly improves the timeliness and accuracy of trust assessment.

[0091] In one exemplary embodiment, reference is made to Figure 4 Trust assessments of cloud service nodes are conducted from multiple evaluation dimensions to obtain trust values ​​for each cloud service node under each evaluation dimension, including:

[0092] Step 402: When multiple evaluation dimensions include the data integrity dimension, determine the success rate and failure rate of the cloud service node, and calculate the initial trust value of the cloud service node under the data integrity dimension based on the success rate and failure rate.

[0093] Step 404: Obtain the resource expansion rate and load growth rate of the cloud service node.

[0094] Step 406: When the resource expansion rate is lower than the load growth rate, calculate the elastic failure impact factor of the initial trust value based on the resource expansion rate and the load growth rate.

[0095] Step 408: Correct the initial trust value according to the elastic failure impact factor to obtain the trust value of the cloud service node in the data integrity dimension.

[0096] Data integrity (DI) is used to quantify the ability of cloud service nodes to prevent data loss during data transmission and processing. Its calculation requires consideration of the following factors:

[0097] Direct data loss: Data errors caused by network packet loss, verification failure, etc.

[0098] Elasticity Failure (EF): Indirect data loss caused by lag in resource scaling.

[0099] Specifically, regarding the data integrity dimension, computer devices can determine the success rate and failure rate of cloud service nodes, calculate the initial trust value of cloud service nodes under the data integrity dimension based on the success rate and failure rate, obtain the resource expansion rate and load growth rate of cloud service nodes, calculate the elastic failure impact factor of the initial trust value based on the resource expansion rate and load growth rate, and then determine the trust value of cloud service nodes under the data integrity dimension based on the initial trust value and elastic failure impact factor.

[0100] Optionally, the trust level of a cloud service node in terms of data integrity can be evaluated using the following formula:

[0101]

[0102] in, To improve the success rate of requests, For data failure request rate, As an influencing factor of elastic failure, For resource expansion rate, For the load growth rate, This is the elastic failure weighting coefficient. When the resource expansion rate is lower than the load growth rate, the data integrity trust value is reduced proportionally. The load growth rate measures how quickly the system load changes, while the resource expansion rate reflects the speed at which system resources expand.

[0103] For example, the computing device first identifies key metrics reflecting system load, such as concurrent users, requests per second (QPS), CPU utilization, or memory usage, or any one or more of these. Within a preset monitoring period, the device continuously collects values ​​for these load metrics at fine-grained time intervals, forming discrete time-series data. To accurately capture load surges that could lead to elasticity failure, the computing device uses a sliding window or differential calculation method to derive the load change slope within adjacent sampling points or small windows in the sequence, and selects the maximum positive change value as the final load growth rate. This process ensures that the evaluation model can cover the worst-case scenario when the system faces instantaneous traffic surges.

[0104] For example, the computing device determines the types of resources that need to be expanded, such as the number of servers, container instances, CPU cores, or memory capacity, and pre-determines the load-bearing capacity corresponding to each unit of resource. The system records the time difference between issuing the resource expansion command (trigger time) and the resource completing initialization and actually being added to the service pool (ready time), i.e., the expansion response latency. After converting the amount of resources successfully added during this period into equivalent load-bearing capacity, dividing it by the expansion response latency yields the resource expansion rate. This rate reflects the effective computing power growth rate that the system can actually deliver per unit time when facing expansion demands.

[0105] In the above embodiments, since the elastic failure impact factor of the initial trust value is calculated based on the resource expansion rate and the load growth rate when the resource expansion rate is lower than the load growth rate, and the initial trust value is corrected based on the elastic failure impact factor, the trust assessment of cloud service nodes can be accurately performed in the data integrity dimension, thereby improving the accuracy of the trust value of cloud service nodes in the data integrity dimension.

[0106] In one exemplary embodiment, reference is made to Figure 5 Trust assessments of cloud service nodes are conducted from multiple evaluation dimensions to obtain trust values ​​for each cloud service node under each evaluation dimension, including:

[0107] Step 502: When multiple evaluation dimensions include the time-series performance dimension, calculate the fulfillment rate of the cloud service node in executing the target task based on the ratio between the promised execution time and the actual execution time of the cloud service node in executing the target task.

[0108] Step 504: Obtain the resource waiting time when the cloud service node executes the target task, and calculate the resource responsiveness of the cloud service node in executing the target task based on the resource waiting time and the actual execution time.

[0109] Step 506: Calculate the trust value of the cloud service node in the time-series performance dimension based on the fulfillment rate and resource responsiveness.

[0110] The evaluation of cloud service node temporal efficiency (TE) primarily considers the efficiency of the node in executing tasks. The target task can be any task executed by the cloud service node before the current time.

[0111] Specifically, regarding the time-series performance dimension, computer devices can calculate the fulfillment rate of a cloud service node in executing a target task based on the ratio between the promised execution time and the actual execution time. They can further obtain the resource waiting time of the cloud service node when executing the target task, calculate the resource responsiveness of the cloud service node in executing the target task based on the resource waiting time and the actual execution time, and determine the trust value of the cloud service node in the time-series performance dimension based on the product of the fulfillment rate and the resource responsiveness.

[0112] For example, the trust value of a cloud service node in the time-series performance dimension can be calculated using the following formula:

[0113]

[0114] in, For the contract fulfillment rate, This indicates the promised execution time for the cloud service node to perform the target task (i.e., the maximum task processing time agreed upon by the cloud service node in the SLA). This indicates the actual execution time of the target task by the cloud service node; For resource responsiveness, This indicates the resource waiting time.

[0115] For example, a computer device can calculate the fulfillment rate and resource responsiveness corresponding to multiple target tasks. For each target task, the product of the fulfillment rate and resource responsiveness corresponding to that target task is calculated. The average of the products corresponding to each target task is then calculated to obtain the trust value of the cloud service node in that time-series performance dimension.

[0116] In the above embodiments, since the fulfillment rate and resource responsiveness of the cloud service node in executing the target task can be calculated, the trust value of the cloud service node in the time-series performance dimension can be calculated based on the fulfillment rate and resource responsiveness, thereby improving the accuracy of the trust value calculation in the time-series performance dimension.

[0117] In one exemplary embodiment, a trust assessment of a cloud service node is performed from multiple evaluation dimensions to obtain the trust value of the cloud service node under each evaluation dimension. This includes: when the multiple evaluation dimensions include an availability dimension, obtaining the total ratio of requests entering the cloud service node per unit time and the ratio of requests received by the cloud service node per unit time; and determining the trust value of the cloud service node under the availability dimension based on the ratio of the ratio of requests received by the cloud service node to the total ratio of requests per unit time.

[0118] Among them, the Availability Trust Value (AV) of a cloud service node in the availability dimension can reflect its ability to process requests within a specified time.

[0119] Specifically, regarding the availability dimension, computer devices can obtain the ratio of total requests received by cloud service nodes per unit time and the ratio of requests accepted by cloud service nodes per unit time, and calculate the ratio of the ratio of requests accepted by cloud service nodes to the ratio of total requests per unit time, thereby obtaining the trust value of cloud service nodes in the availability dimension.

[0120] For example, the formula for calculating the trust value of a cloud service node in the availability dimension can be:

[0121]

[0122] in: This represents the percentage of requests accepted by a cloud service node per unit of time. The condition for accepting a request is that the request must be processed within the time threshold; otherwise, it is considered a timeout and rejected. This represents the percentage of total requests sent to cloud service nodes per unit of time.

[0123] In the above embodiments, the trust value of a cloud service node in the availability dimension is determined based on the ratio of the number of requests received by the cloud service node per unit time to the total number of requests, which can improve the accuracy of trust value calculation in the availability dimension.

[0124] In an exemplary embodiment, the cloud service node trust assessment method of this application further includes: when a cloud service node performs trusted behavior within a preset time period, calculating a reward factor based on the number of successful interactions of the cloud service node within the preset time period, and improving the overall trust value of the cloud service node based on the reward factor.

[0125] Optionally, if the cloud service node does not exhibit any interaction failure behavior within a preset time period, it can be determined that the cloud service node has performed trustworthy behavior within the preset time period; otherwise, it can be determined that the cloud service node has engaged in untrustworthy behavior within the preset time period.

[0126] For example, when the number of successful interactions of a cloud service node within a preset time period is greater than a preset threshold, it can be determined that the cloud service node is performing trustworthy behavior within the preset time period; when the number of successful interactions of a cloud service node within a preset time period is less than a preset threshold, it can be determined that the cloud service node is performing untrustworthy behavior within the preset time period.

[0127] In an exemplary embodiment, the cloud service node trust assessment method of this application further includes: when a cloud service node exhibits untrustworthy behavior within a preset time period, calculating a penalty factor based on the number of interaction failures of the cloud service node within the preset time period, and reducing the overall trust value of the cloud service node based on the penalty factor.

[0128] In one specific embodiment, to more efficiently detect malicious nodes, a reward and punishment mechanism is introduced when calculating historical interaction trust values. This mechanism dynamically adjusts node trust values ​​to achieve intelligent management of node behavior. When a node performs trustworthy behavior, the trust model increases its trust value by introducing a reward factor. This positive feedback mechanism not only incentivizes healthy interaction and cooperation among nodes but also encourages nodes to proactively maintain a high level of trust. Conversely, when a node exhibits untrustworthy behavior, the trust model reduces its trust value by implementing a penalty factor, thereby quickly identifying and isolating abnormal nodes and effectively curbing resource abuse and potential security threats.

[0129] Finally, the formula for evaluating the overall trust value after introducing the reward and punishment mechanism is as follows:

[0130]

[0131] in: As a reward and punishment factor, As the reward coefficient, The penalty coefficient is... Used to measure The changes in the node's negative behavior within a given time period. This indicates that the node has exhibited unexpected behavior recently. express Number of successful interactions within a given time period express Number of failed interactions within a given time period.

[0132] In the above embodiments, through the designed reward and punishment feedback mechanism, trust value rewards can be implemented for expected trustworthy behaviors, and abnormal behaviors can be punished and downgraded, forming a two-way governance system of "incentivizing positive interaction and curbing malicious behavior", which enhances the system's ability to identify abnormal node behaviors and its response efficiency.

[0133] In one specific embodiment, this application provides a cloud service node trust assessment method, including the following steps:

[0134] 1. For the cloud service node to be evaluated, conduct a trust assessment of the cloud service node from multiple evaluation dimensions, and obtain the trust value of the cloud service node under each evaluation dimension.

[0135] The evaluation dimensions include four aspects: historical interaction behavior, data integrity, time-series performance, and availability. The specific trust evaluation methods under each evaluation dimension are as follows:

[0136] 1) Historical interaction behavior

[0137] Obtain historical interaction data of cloud service nodes within a historical time period; calculate the time decay factor for each historical interaction data point, which is negatively correlated with the time difference between the interaction time of the historical data point and the current time; and calculate the trust value of the cloud service node in the dimension of historical interaction behavior based on the interaction results indicated by each historical interaction data point and the time decay factor.

[0138] 2) Data integrity

[0139] Determine the success rate and failure rate of the cloud service node, and calculate the initial trust value of the cloud service node in the data integrity dimension based on the success rate and failure rate; obtain the resource expansion rate and load growth rate of the cloud service node; when the resource expansion rate is lower than the load growth rate, calculate the elastic failure impact factor of the initial trust value based on the resource expansion rate and load growth rate; correct the initial trust value based on the elastic failure impact factor to obtain the trust value of the cloud service node in the data integrity dimension.

[0140] 3) Timing performance

[0141] The fulfillment rate of a cloud service node in executing a target task is calculated based on the ratio between the promised execution time and the actual execution time. The resource waiting time of the cloud service node in executing the target task is obtained, and the resource responsiveness of the cloud service node in executing the target task is calculated based on the resource waiting time and the actual execution time. The trust value of the cloud service node in the time-series performance dimension is calculated based on the fulfillment rate and the resource responsiveness.

[0142] 4) Availability

[0143] Obtain the ratio of total requests received by the cloud service node per unit time and the ratio of requests accepted by the cloud service node per unit time; determine the trust value of the cloud service node in the availability dimension based on the ratio of requests accepted by the cloud service node to the total request ratio per unit time.

[0144] 2. Based on the trust value of the cloud service node under each evaluation dimension, calculate the information entropy corresponding to each evaluation dimension.

[0145] 3. Based on the information entropy corresponding to each evaluation dimension, determine the weight corresponding to each evaluation dimension.

[0146] 4. Determine the overall trust value of the cloud service node based on the weights of each evaluation dimension and the trust value of the cloud service node under each evaluation dimension.

[0147] 5. When a cloud service node performs trusted behavior within a preset time period, a reward factor is calculated based on the number of successful interactions of the cloud service node within the preset time period, and the overall trust value of the cloud service node is improved based on the reward factor.

[0148] 6. When a cloud service node exhibits untrustworthy behavior within a preset time period, a penalty factor is calculated based on the number of failed interactions of the cloud service node within the preset time period, and the overall trust value of the cloud service node is reduced based on the penalty factor.

[0149] In this embodiment, the explanation of each step can be found in the above embodiment, and will not be repeated here.

[0150] The solution in this embodiment has the following beneficial effects:

[0151] First, an innovative adaptive multidimensional trust assessment model is constructed. By integrating multiple trust factors such as the historical interaction behavior of cloud service nodes, service availability, data integrity, and time-series performance, a fine-grained trust measurement system is established. Unlike the limitations of traditional solutions that rely on single indicators or coarse-grained assessments, this model achieves a comprehensive evaluation of node trust status through quantitative analysis of detailed dimensions. This effectively solves the problem of existing technologies having a single trust assessment standard that cannot fully reflect the dynamic trust characteristics of nodes.

[0152] Second, an objective weight optimization method based on information entropy technology. This method uses entropy weighting to dynamically calibrate the weights of multi-dimensional trust indicators, objectively quantifying the actual contribution of each dimension to the trust assessment system. This approach avoids the subjective drawbacks of traditional experience-based weighting, scientifically allocating weights based on data distribution characteristics. It improves the objectivity and fairness of the trust assessment model from the underlying mechanism, ensuring that the assessment results truly reflect the comprehensive trustworthiness level of the nodes.

[0153] Third, the accompanying reward and punishment feedback mechanism can reward trust value for expected trustworthy behavior and punish abnormal behavior by downgrading it, forming a two-way governance system of "incentivizing positive interaction and curbing malicious behavior", which enhances the system's ability to identify abnormal node behavior and its response efficiency.

[0154] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0155] Based on the same inventive concept, this application also provides a cloud service node trust assessment device for implementing the cloud service node trust assessment method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more cloud service node trust assessment device embodiments provided below can be found in the limitations of the cloud service node trust assessment method described above, and will not be repeated here.

[0156] In one exemplary embodiment, such as Figure 6 As shown, a cloud service node trust assessment device 600 is provided, comprising:

[0157] The trust value acquisition module 602 is used to perform trust assessment on the cloud service node to be evaluated from multiple evaluation dimensions, and obtain the trust value of the cloud service node under each evaluation dimension.

[0158] The information entropy calculation module 604 is used to calculate the information entropy corresponding to each evaluation dimension based on the trust value of the cloud service node under each evaluation dimension.

[0159] The weight calculation module 606 is used to determine the weight of each evaluation dimension based on the information entropy of each evaluation dimension.

[0160] The comprehensive trust value determination module 608 is used to determine the comprehensive trust value of the cloud service node based on the weights corresponding to each evaluation dimension and the trust value of the cloud service node under each evaluation dimension.

[0161] The aforementioned cloud service node trust assessment device assesses the trust of a cloud service node from multiple dimensions, obtaining a trust value for each dimension. Based on these trust values, it calculates the information entropy for each dimension and determines the weight for each dimension. Finally, it determines the overall trust value of the cloud service node based on the weights and trust values ​​for each dimension. Because it assesses trust from multiple dimensions, it comprehensively reflects the dynamic trust characteristics of the node. By adaptively determining weights through information entropy calculation, it objectively quantifies the actual contribution of each dimension to the trust assessment system. This method overcomes the subjective drawbacks of traditional experience-based weighting, scientifically allocating weights based on data distribution characteristics. This fundamentally improves the objectivity and fairness of the trust assessment model, ensuring that the assessment results truly reflect the overall trust level of the node, thus significantly improving the accuracy of cloud service node trust assessment.

[0162] In one embodiment, the trust value acquisition module is further configured to: when multiple evaluation dimensions include a historical interaction behavior dimension, acquire historical interaction data of the cloud service node in a historical time period; calculate a time decay factor for each historical interaction data, wherein the time decay factor is negatively correlated with the time difference between the interaction time of the historical interaction data and the current time; and calculate the trust value of the cloud service node under the historical interaction behavior dimension based on the interaction result indicated by each historical interaction data and the time decay factor.

[0163] In one embodiment, the trust value acquisition module is further configured to: when multiple evaluation dimensions include a data integrity dimension, determine the success request rate and failure request rate of the cloud service node, and calculate the initial trust value of the cloud service node under the data integrity dimension based on the success request rate and failure request rate; obtain the resource expansion rate and load growth rate of the cloud service node; when the resource expansion rate is lower than the load growth rate, calculate the elastic failure impact factor of the initial trust value based on the resource expansion rate and load growth rate; and correct the initial trust value based on the elastic failure impact factor to obtain the trust value of the cloud service node under the data integrity dimension.

[0164] In one embodiment, the trust value acquisition module is further configured to: when multiple evaluation dimensions include a time-series performance dimension, calculate the fulfillment rate of the cloud service node in executing the target task based on the ratio between the promised execution time and the actual execution time of the cloud service node in executing the target task; obtain the resource waiting time of the cloud service node in executing the target task, and calculate the resource responsiveness of the cloud service node in executing the target task based on the resource waiting time and the actual execution time; and calculate the trust value of the cloud service node in the time-series performance dimension based on the fulfillment rate and the resource responsiveness.

[0165] In one embodiment, the trust value acquisition module is further configured to: when multiple evaluation dimensions include an availability dimension, obtain the total request ratio of incoming requests to the cloud service node within a unit time and the request ratio received by the cloud service node within a unit time; and determine the trust value of the cloud service node under the availability dimension based on the ratio of the request ratio received by the cloud service node to the total request ratio within a unit time.

[0166] In one embodiment, the cloud service node trust assessment device of this application further includes a trust value adjustment module, which is used to: calculate a reward factor based on the number of successful interactions of the cloud service node within the preset time period when the cloud service node performs trusted behavior within the preset time period, and improve the overall trust value of the cloud service node based on the reward factor.

[0167] In one embodiment, the cloud service node trust assessment device of this application further includes a trust value adjustment module, which is used to: calculate a penalty factor based on the number of interaction failures of the cloud service node within the preset time period when the cloud service node has untrustworthy behavior within the preset time period, and reduce the overall trust value of the cloud service node based on the penalty factor.

[0168] Each module in the aforementioned cloud service node trust assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can invoke and execute the corresponding operations of each module.

[0169] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a cloud service node trust assessment method.

[0170] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a cloud service node trust assessment method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0171] Those skilled in the art will understand that Figure 7 and Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0172] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the cloud service node trust assessment method in any of the above embodiments.

[0173] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the cloud service node trust assessment method in any of the above embodiments.

[0174] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the cloud service node trust assessment method in any of the above embodiments.

[0175] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0176] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0177] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0178] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A cloud service node trust assessment method, characterized in that, The method includes: For the cloud service node to be evaluated, a trust assessment is performed on the cloud service node from multiple assessment dimensions to obtain the trust value of the cloud service node under each assessment dimension. Based on the trust value of the cloud service node under each evaluation dimension, calculate the information entropy corresponding to each evaluation dimension. Based on the information entropy corresponding to each evaluation dimension, the weights corresponding to each evaluation dimension are determined respectively. The overall trust value of the cloud service node is determined based on the weights corresponding to each evaluation dimension and the trust value of the cloud service node under each evaluation dimension.

2. The method according to claim 1, characterized in that, The process of performing trust assessments on the cloud service node from multiple evaluation dimensions, and obtaining the trust value of the cloud service node under each evaluation dimension, includes: When the multiple evaluation dimensions include historical interaction behavior dimensions, obtain the historical interaction data of the cloud service node in the historical time period; Calculate the time decay factor for each historical interaction data point, whereby the time decay factor is negatively correlated with the time difference between the interaction time of the historical interaction data and the current time. Based on the interaction results indicated by each historical interaction data and the time decay factor, the trust value of the cloud service node in the dimension of historical interaction behavior is calculated.

3. The method according to claim 1, characterized in that, The process of performing trust assessments on the cloud service node from multiple evaluation dimensions, and obtaining the trust value of the cloud service node under each evaluation dimension, includes: When the multiple evaluation dimensions include the data integrity dimension, the success request rate and failure request rate of the cloud service node are determined, and the initial trust value of the cloud service node under the data integrity dimension is calculated based on the success request rate and failure request rate. Obtain the resource expansion rate and load growth rate of the cloud service node; When the resource expansion rate is lower than the load growth rate, the elastic failure impact factor of the initial trust value is calculated based on the resource expansion rate and the load growth rate. The initial trust value is corrected based on the elastic failure impact factor to obtain the trust value of the cloud service node under the data integrity dimension.

4. The method according to claim 1, characterized in that, The process of performing trust assessments on the cloud service node from multiple evaluation dimensions, and obtaining the trust value of the cloud service node under each evaluation dimension, includes: When the multiple evaluation dimensions include the time-series performance dimension, the fulfillment rate of the cloud service node in executing the target task is calculated based on the ratio between the promised execution time and the actual execution time of the target task. Obtain the resource waiting time when the cloud service node executes the target task, and calculate the resource responsiveness of the cloud service node when executing the target task based on the resource waiting time and the actual execution time; Based on the fulfillment rate and the resource responsiveness, calculate the trust value of the cloud service node in the time-series performance dimension.

5. The method according to claim 1, characterized in that, The process of performing trust assessments on the cloud service node from multiple evaluation dimensions, and obtaining the trust value of the cloud service node under each evaluation dimension, includes: When the multiple evaluation dimensions include the availability dimension, obtain the total request ratio of the cloud service node per unit time and the request ratio of the cloud service node per unit time. The trust value of the cloud service node in terms of availability is determined by the ratio of the percentage of requests received by the cloud service node per unit time to the percentage of total requests.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: When the cloud service node performs trusted behavior within a preset time period, a reward factor is calculated based on the number of successful interactions of the cloud service node within the preset time period, and the overall trust value of the cloud service node is improved based on the reward factor.

7. The method according to any one of claims 1 to 5, characterized in that, The method further includes: When the cloud service node exhibits untrustworthy behavior within a preset time period, a penalty factor is calculated based on the number of failed interactions of the cloud service node within the preset time period, and the overall trust value of the cloud service node is reduced based on the penalty factor.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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