Resource charging method based on multi-cloud service

Abstract

The invention discloses a resource charging method based on multi-cloud service, and particularly relates to the technical field of resource management and cost optimization. The method comprises the following steps of: constructing standardized multi-cloud service interaction path data by acquiring service flow billing related data configured by a user in a multi-cloud computing architecture; the method comprises the following steps: constructing a cross-cloud traffic loopback identification matrix by extracting data forwarding directions, node switching frequencies and loopback transmission modes among different cloud service providers; analyzing repeated charging and charging asymmetry characteristics of the cross-cloud traffic loopback, and generating an accumulated premium risk assessment index; the forwarding cost difference and the reverse charging threshold value between the cloud service nodes are analyzed, and path reconstruction priority scoring data are generated; the method comprises the following steps: constructing a cross-cloud path optimization objective function for minimizing accumulated charging cost, outputting optimized suggested path data, reconstructing a cross-cloud transmission path of business service, and adjusting cross-cloud node configuration; the cross-cloud resource utilization efficiency is improved, and the charging cost is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of resource management and cost optimization technology, and more specifically, to a resource billing method based on multi-cloud services. Background Technology

[0002] With the widespread adoption of multi-cloud computing models in enterprise business architectures, more and more enterprises are choosing to use resources from multiple different cloud service providers simultaneously to improve the reliability and performance of their business systems.

[0003] Existing multi-cloud service resource billing methods fail to effectively identify and control the nonlinear cost accumulation problem caused by the differences in billing rules between different cloud service providers during multiple cross-node transmissions of cross-cloud data traffic. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a resource billing method based on multi-cloud services to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A resource billing method based on multi-cloud services includes the following steps: S1: Obtain multi-cloud service traffic billing related data configured by the user in the multi-cloud computing architecture, standardize path node identification and synchronize time-series data, and generate multi-cloud service interaction path data; S2: Based on multi-cloud service interaction path data, extract the data forwarding direction, node switching frequency and loopback transmission mode between different cloud service providers, and construct a cross-cloud traffic loopback identification matrix; S3: Based on the cross-cloud traffic loop identification matrix, analyze the characteristics of duplicate billing and billing asymmetry in cross-cloud traffic loops, and generate cumulative premium risk assessment indicators. S4: Based on the cross-cloud traffic loopback identification matrix, analyze the differences in forwarding costs and reverse billing thresholds between cloud service nodes, and generate path reconstruction priority scoring data; S5: Based on the cumulative premium risk assessment index and path reconstruction priority scoring data, construct a cross-cloud path optimization objective function that minimizes the cumulative billing cost, and output optimized suggested path data; S6: Based on the optimized suggested path data, reconstruct the cross-cloud transmission path of business services and adjust the cross-cloud node configuration to achieve data traffic distribution optimization and resource billing control under the multi-cloud service architecture.

[0006] In a preferred embodiment, S1 specifically refers to: Acquire user-configured multi-cloud business traffic billing data in a multi-cloud computing architecture, including user business service process data, cross-cloud data transmission path data, and cloud service provider inbound and outbound billing rule data; A unified node identifier encoding is applied to each cloud service node involved in the multi-cloud service traffic billing data; The multi-cloud service traffic billing data after node identifier encoding is processed with unified time series labeling to form multi-cloud service interaction path data.

[0007] In a preferred embodiment, S2 specifically refers to: Based on multi-cloud service interaction path data, extract the data traffic forwarding path between each cloud service node; Based on the data traffic forwarding path, the direction of data traffic transmission between two adjacent cloud service nodes is statistically analyzed; Based on the data traffic forwarding path, count the number of cloud service node switching events occurring per unit time. Based on the data traffic forwarding path, identify the loop path of data traffic that returns to the initial cloud service node after passing through multiple cloud service nodes. A cross-cloud traffic loopback identification matrix is ​​established based on the data traffic transmission direction, the number of cloud service node switching events per unit time, and the loopback path.

[0008] In a preferred embodiment, S3 specifically refers to: Based on the cross-cloud traffic loopback identification matrix, the data traffic loopback path is retrieved, and the number of times the same data traffic is repeatedly billed on the same cloud service node is recorded. The number of repeated billing records is accumulated and counted according to the data flow loop order to obtain the set of repeated billing counts for each node; Based on the cross-cloud traffic loopback identification matrix, the difference between the outbound billing amount and the inbound billing amount generated by the corresponding cloud service node during the forward and reverse transmission of data traffic is calculated to obtain the billing asymmetry set. The cumulative premium risk assessment index is generated by weighting and combining the set of repeated billing times of nodes with the set of billing asymmetry.

[0009] In a preferred embodiment, S4 specifically refers to: Based on the cross-cloud traffic loopback identification matrix, the continuous cloud service node pairs in each data traffic forwarding path are determined; For each pair of consecutive cloud service nodes, obtain the outbound billing amount generated by forward transmission and the inbound billing amount generated by reverse transmission per unit of data volume, and calculate the forward transmission cost and the reverse transmission cost. Calculate the difference in forwarding costs based on the forward transmission cost and the reverse transmission cost; Based on the tiered rules for outbound billing amount and the paginated rules for inbound billing amount of cloud service nodes, the reverse billing threshold is determined. The forwarding cost difference value and the reverse billing threshold are weighted according to the preset weight coefficients to generate path reconstruction priority score data.

[0010] In a preferred embodiment, S5 specifically refers to: Based on the cumulative premium risk assessment index and path reconstruction priority scoring data, a cross-cloud path optimization objective function is set, with the cumulative billing cost as the parameter to be minimized. Set business service process continuity constraints, cloud service node capacity constraints, data traffic inbound and outbound bandwidth constraints, and dynamic billing threshold constraints for the cross-cloud path optimization objective function. Solve the objective function for cross-cloud path optimization to obtain a set of candidate cross-cloud transmission paths; The candidate cross-cloud transmission path set is sorted in ascending order according to the cumulative billing cost, and the cross-cloud transmission path with the lowest cumulative billing cost is selected to generate optimized path suggestion data.

[0011] In a preferred embodiment, S6 specifically refers to: Based on the optimized suggested path data, obtain the cross-cloud transmission paths and corresponding cloud service node identifiers. Adjust the user's business service process configuration in the multi-cloud computing architecture according to the order of cloud service node identifiers in the cross-cloud transmission path, and update the data traffic forwarding order of the business service process; Adjust the cross-cloud node configuration in the multi-cloud computing architecture according to the cloud service node identifier in the cross-cloud transmission path, and update the cross-cloud data traffic transmission path. The updated cross-cloud data traffic transmission path is standardized with path node identification and time-series data synchronization to obtain the reconstructed cross-cloud transmission path and node configuration data for business services.

[0012] The technical effects and advantages of the resource billing method based on multi-cloud services proposed in this invention are as follows: By standardizing and synchronizing business traffic billing data in a multi-cloud environment, it is possible to accurately identify loopback transmission patterns of cross-cloud data, effectively discover and quantify the cumulative premium risks caused by duplicate billing and billing asymmetry during cross-cloud transmission. At the same time, by assessing the differences in forwarding costs between cloud nodes, the priority of path optimization is determined, thereby constructing and solving the cross-cloud path optimization objective function and outputting the lowest-cost optimized suggested path. Ultimately, proactive optimization and control of cross-cloud data traffic costs are achieved, reducing the user's overall operating costs. Attached Figure Description

[0013] Figure 1This is a schematic diagram of a resource billing method based on multi-cloud services according to the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0015] Example

[0016] Figure 1 This invention provides a resource billing method based on multi-cloud services, which includes the following steps: S1: Obtain multi-cloud service traffic billing related data configured by the user in the multi-cloud computing architecture, standardize path node identification and synchronize time-series data, and generate multi-cloud service interaction path data; S2: Based on multi-cloud service interaction path data, extract the data forwarding direction, node switching frequency and loopback transmission mode between different cloud service providers, and construct a cross-cloud traffic loopback identification matrix; S3: Based on the cross-cloud traffic loop identification matrix, analyze the characteristics of duplicate billing and billing asymmetry in cross-cloud traffic loops, and generate cumulative premium risk assessment indicators. S4: Based on the cross-cloud traffic loopback identification matrix, analyze the differences in forwarding costs and reverse billing thresholds between cloud service nodes, and generate path reconstruction priority scoring data; S5: Based on the cumulative premium risk assessment index and path reconstruction priority scoring data, construct a cross-cloud path optimization objective function that minimizes the cumulative billing cost, and output optimized suggested path data; S6: Based on the optimized suggested path data, reconstruct the cross-cloud transmission path of business services and adjust the cross-cloud node configuration to achieve data traffic distribution optimization and resource billing control under the multi-cloud service architecture.

[0017] S1: Obtain multi-cloud service traffic billing related data configured by the user in the multi-cloud computing architecture, standardize path node identification and synchronize time-series data, and generate multi-cloud service interaction path data, including: Obtain data related to traffic billing for multi-cloud services configured by the user in a multi-cloud computing architecture; Multi-cloud service traffic billing data includes user business service process data, cross-cloud data transmission path data, and cloud service provider inbound and outbound billing rule data. User business service process data refers to the data flow sequence, logical relationships between service nodes, and invocation rules defined by the user in the business service systems built by multiple cloud service providers. For example, a user builds a disaster recovery backup system spanning multiple cloud service providers, stipulating that data is first transmitted from the storage node of cloud service provider A to the data processing node of cloud service provider B, then forwarded to the backup node of cloud service provider C, and in the event of a failure, the data is returned from the backup node to the original node. The data flow sequence, logical relationships between service nodes, and invocation rules defined above constitute the user business service process data; cross-cloud data transmission path data is... This refers to the actual path that a user takes when transmitting data between different cloud service providers. The data involved includes the names, locations, and communication order of the source node, destination node, and intermediate nodes along the path. For example, when a user's data is transmitted from the East China data center of cloud service provider A, through a specific network path, through the data processing center of cloud service provider B in the North China region, and then to the backup center of cloud service provider C in the South China region, the cross-cloud data transmission path data records the relevant information of the path nodes traversed throughout the entire data transmission process. The cloud service provider's inbound and outbound billing rules data refers to the rules established by different cloud service providers for charging for user data outbound transmission or inbound reception, including charging standards and tiered billing methods for different traffic volumes and transmission directions.

[0018] A unified node identifier encoding is applied to each cloud service node involved in the multi-cloud service traffic billing data; Unified node identifier coding refers to encoding service nodes provided by different cloud service providers one by one according to a unified data identification standard, thereby ensuring that each node can be uniquely identified. The method of unified node identifier coding is to assign a unique node identification code to each node according to a specific coding rule. First, the service nodes are classified according to the name of the cloud service provider to which they belong. Then, they are further subdivided according to the geographical location, service type, and function type of the node, and unified identifier coding is applied to each level. For example, the node identifier code can be described as the storage node identifier of a specific cloud service provider's North China data center. The coding is uniformly applied in the order of cloud service provider, geographical location, and data center function type, so that each node has a unique, definite, and non-repeating node identifier code. The unified node identifier coding process includes all service nodes involved in user business service process data and cross-cloud data transmission path data.

[0019] The multi-cloud service traffic billing data after node identifier encoding is processed with unified time series labeling to form multi-cloud service interaction path data. All acquired data types are assigned a unified time-series label. This means that for each record item of each data type, a unified time tag is recorded according to the actual physical time sequence when the data is generated or flows through the service node. For example, when the data defined in the user business service process data flows to a certain node and records a timestamp, the cross-cloud data transmission path data and the cloud service provider's inbound and outbound billing rule data record the same timestamp simultaneously, so that all data types have a unified and consistent time tag identifier. The unified time-series labeling process not only covers the start and end times of data flowing through cloud service nodes, but also covers the times when data stays, is forwarded, and is called in the node, ensuring that all data types have a complete and consistent time-series label. Finally, all processed data types are uniformly summarized to form multi-cloud service interaction path data. The multi-cloud service interaction path data formed after unified node identifier encoding and unified time-series labeling includes the data identifiers and time tags of all cross-cloud business service processes, data flow paths between nodes, and node-related billing rules.

[0020] S2: Based on multi-cloud service interaction path data, extract the data forwarding direction, node switching frequency, and loopback transmission mode between different cloud service providers, and construct a cross-cloud traffic loopback identification matrix, including: Based on multi-cloud service interaction path data, extract the data traffic forwarding path between each cloud service node; A data traffic forwarding path refers to the path data takes when executing a business process, starting from the initial cloud service node, passing through multiple cloud service nodes sequentially, and finally reaching the target cloud service node. For example, a user-deployed business system defines data as starting from the data processing node of cloud service provider A, passing through the storage node of cloud service provider B and the computing node of cloud service provider C, and finally returning to the data storage node of cloud service provider A. In this case, the data traffic forwarding path is the complete path information of cloud service provider A's data processing node, cloud service provider B's storage node, cloud service provider C's computing node, and then back to cloud service provider A's data storage node. By retrieving each multi-cloud service interaction path data, each cross-cloud data traffic forwarding path can be extracted. The extraction process of the data traffic forwarding path is carried out according to the unified node identifier encoding order and the unified time series marking order, which can ensure the completeness and accuracy of the data traffic forwarding path record.

[0021] Based on the data traffic forwarding path, the direction of data traffic transmission between two adjacent cloud service nodes is statistically analyzed; Data traffic transmission direction refers to the direction in which data flows between two adjacent cloud service nodes, i.e., the path sequence from one node to another. For example, if data is transmitted from the data processing node of cloud service provider A to the storage node of cloud service provider B, then when statistically analyzing data traffic transmission direction, it is necessary to record the number of traffic flows from the data processing node of cloud service provider A to the storage node of cloud service provider B. If data is then transmitted from the storage node of cloud service provider B to the computing node of cloud service provider C, the number of traffic flows from the storage node of cloud service provider B to the computing node of cloud service provider C must also be recorded. By statistically analyzing the transmission direction and corresponding number of transmissions between every two adjacent nodes in the statistical data traffic forwarding path, a complete record of data traffic transmission direction can be obtained, forming a statistical set of data traffic transmission direction between adjacent cloud service nodes.

[0022] Based on the data traffic forwarding path, count the number of cloud service node switching events occurring per unit time. The number of cloud service node switches occurring per unit time refers to the total number of times data switches from one cloud service node to another in a business service process within a set fixed time period, such as in hours or minutes. For example, within a one-hour statistical period, the user's business service system data travels from the data node of cloud service provider A to the data node of cloud service provider B, and then from the data node of cloud service provider B to the data node of cloud service provider C, resulting in two cloud service node switches. When calculating the number of cloud service node switches occurring per unit time, it is necessary to count the number of node switches for each data traffic forwarding path recorded in the multi-cloud service interaction path data, and finally obtain the statistical result set of the number of cloud service node switches occurring per unit time. The statistical result set of the number of node switches can reflect the frequency of data traffic switching nodes in the cross-cloud service architecture.

[0023] Based on the data traffic forwarding path, identify the loop path of data traffic that returns to the initial cloud service node after passing through multiple cloud service nodes. A loopback path refers to a data transmission path where data originates from a cloud service node, passes through a series of different cloud service nodes, and then returns to the initial originating node, forming a circular transmission path. For example, data from a user's business system initially originates from the data storage node of cloud service provider A, passes through the data processing node of cloud service provider B, the data backup node of cloud service provider C, and finally returns to the data storage node of cloud service provider A, thus forming a data loopback path. The process of identifying loopback paths involves tracing each node of each data traffic forwarding path to determine whether the data traffic ultimately returns to the initial node after passing through different nodes. If the data traffic returns to the initial node during forwarding, the complete sequence of data nodes is identified and recorded as a loopback path, forming a set of loopback paths.

[0024] A cross-cloud traffic loop identification matrix is ​​established based on the data traffic transmission direction, the number of cloud service node switching events per unit time, and the loopback path. The rows and columns of the cross-cloud traffic loopback identification matrix represent different cloud service nodes, while the data elements within the matrix represent the data traffic transmission relationship between each cloud service node, the node switching frequency, and whether a loopback path exists; for example:

[0025] The cross-cloud traffic loopback identification matrix can comprehensively reflect the specific forwarding path, node switching frequency, and loopback status of data traffic in a multi-cloud service environment.

[0026] S3: Based on the cross-cloud traffic loopback identification matrix, analyze the characteristics of duplicate billing and billing asymmetry in cross-cloud traffic loopbacks, and generate cumulative premium risk assessment indicators, including: Based on the cross-cloud traffic loopback identification matrix, the data traffic loopback path is retrieved, and the number of times the same data traffic is repeatedly billed on the same cloud service node is recorded. A data traffic loop path refers to the sequence of data traffic originating from a cloud service node, passing through multiple other cloud service nodes, and then returning to the original cloud service node. These data traffic paths constitute a data traffic loop path. The cross-cloud traffic loop identification matrix records the transmission from cloud service provider A's data storage node to cloud service provider B's data processing node, from cloud service provider B's data processing node to cloud service provider C's data backup node, and from cloud service provider C's data backup node back to cloud service provider A's data storage node. When data traffic returns to the data storage node of Cloud Service Provider A, a loop of data traffic is recorded in the corresponding row and column position of Cloud Service Provider A's data storage node in the cross-cloud traffic loopback identification matrix, indicating that the data traffic has undergone one round of forwarding. The cloud service provider calculates the charge once when the data enters and leaves the node. Every time the data traffic returns to Cloud Service Provider A's data storage node, a duplicate charge is generated. Therefore, by searching the record position corresponding to each node in the cross-cloud traffic loopback identification matrix, the number of duplicate charges that occur when the same data traffic returns to the initial node through the loop path is counted one by one. For example, Cloud Service Provider A's data storage node charges once for each loop of the path. If the data traffic loops multiple times, the number of duplicate charges will also accumulate multiple times. By searching and counting each node in the cross-cloud traffic loopback identification matrix, the number of duplicate charges for each cloud service node in the multi-cloud computing architecture can be obtained.

[0027] The number of repeated billing records is accumulated and counted according to the data flow loop order to obtain the set of repeated billing counts for each node; The node duplicate billing count set is the total number of times each cloud service node is billed due to repeated data traffic passing through it during the data traffic loop transmission process. For example, data traffic starts from node A, passes through nodes B and C in sequence, and then returns to node A, forming a loop path. If the data traffic executes the above path several times, nodes A, B, and C each record the accumulated duplicate billing count, which is the node duplicate billing count set.

[0028] Based on the cross-cloud traffic loopback identification matrix, the difference between the outbound billing amount and the inbound billing amount generated by the corresponding cloud service node during the forward and reverse transmission of data traffic is calculated to obtain the billing asymmetry set. The billing asymmetry set is the difference in billing amounts generated when any two adjacent node pairs (e.g., node A - node B) transmit data traffic in the forward and reverse directions. The calculation method is as follows: Billing asymmetry = Billing amount per unit of data transmitted in the forward direction - Billing amount per unit of data transmitted in the reverse direction.

[0029] The billing asymmetry set is the set of billing amount differences calculated for each node pair.

[0030] The cumulative premium risk assessment index is generated by weighting and combining the set of node repeated billing times with the set of billing asymmetry. The cumulative premium risk assessment index is calculated by multiplying the number of repeated billings for each node pair in the set of repeated billings with the billing difference for the corresponding node pair in the set of billing asymmetry, and then adding them together. It reflects the cumulative additional billing cost risk brought about by the data traffic loopback transmission path.

[0031] S4: Based on the cross-cloud traffic loopback identification matrix, analyze the differences in forwarding costs and reverse billing thresholds between cloud service nodes, and generate path reconstruction priority scoring data, including: Based on the cross-cloud traffic loopback identification matrix, the continuous cloud service node pairs in each data traffic forwarding path are determined; A continuous cloud service node pair refers to a combination of two adjacent cloud service nodes along a forwarding path defined by the business service process. For example, a user-deployed business service system defines a cross-cloud data traffic forwarding path, starting from the data storage node of cloud service provider A, transmitting to the data processing node of cloud service provider B, then to the data backup node of cloud service provider C, and finally returning to the data storage node of cloud service provider A. In this data traffic forwarding path, adjacent nodes form continuous cloud service node pairs. That is, the data storage node of cloud service provider A to the data processing node of cloud service provider B is a continuous cloud service node pair, the data processing node of cloud service provider B to the data backup node of cloud service provider C is the next continuous cloud service node pair, and the data backup node of cloud service provider C returning to the data storage node of cloud service provider A forms the third continuous cloud service node pair. By analyzing each data traffic forwarding path in the cross-cloud traffic loopback identification matrix, all adjacent nodes on each path can be identified and recorded as multiple continuous cloud service node pairs.

[0032] For each pair of consecutive cloud service nodes, obtain the outbound billing amount generated by forward transmission and the inbound billing amount generated by reverse transmission per unit of data volume, and calculate the forward transmission cost and the reverse transmission cost. Forward transmission refers to data traffic moving along the normal transmission direction defined by the business service process, from one node to the next adjacent node. Reverse transmission refers to data traffic moving along the opposite path, from one node to the next adjacent node. For example, in a continuous node pair between the data storage node of cloud service provider A and the data processing node of cloud service provider B, the billing amount incurred for transferring a unit of data from cloud service provider A's data storage node to cloud service provider B's data processing node is the outbound billing amount for forward transmission. When data is transferred back from cloud service provider B's data processing node to cloud service provider A... When data storage nodes of service providers are used, the resulting billing amount is the inbound billing amount for reverse transmission. Based on the cloud service provider's billing rules, the outbound billing amount for forward transmission and the inbound billing amount for reverse transmission are determined and recorded separately. These billing amounts are then converted into corresponding transmission costs. Specifically, the forward transmission cost is equivalent to the outbound billing amount for a unit of data transmitted from one node to the next, and the reverse transmission cost is equivalent to the inbound billing amount for a unit of data transmitted back from the next node to the previous node. Following this method, each pair of continuous cloud service nodes is analyzed to obtain the forward and reverse transmission costs for all pairs of continuous cloud service nodes.

[0033] Calculate the difference in forwarding costs based on the forward transmission cost and the reverse transmission cost; Forwarding cost variance refers to the difference between the transmission cost per unit of data in the forward transmission direction and the transmission cost in the reverse transmission direction, reflecting the degree of cost difference for the same node pair in different transmission directions. For example, if the forward transmission cost from the data storage node of cloud service provider A to the data processing node of cloud service provider B is high, while the reverse transmission cost is low, then the forwarding cost variance for this pair of continuous cloud service node pairs will be relatively large. The forwarding cost variance is calculated by subtracting the corresponding reverse transmission cost from the forward transmission cost of each continuous cloud service node pair. If the forward transmission cost is higher than the reverse transmission cost, the variance is positive, indicating that forward transmission is more expensive; if the reverse transmission cost is higher than the forward transmission cost, the variance is negative, indicating that reverse transmission is more expensive. By calculating the variance for each continuous cloud service node pair using the above method, a complete set of forwarding cost variance data is obtained, called the forwarding cost variance set.

[0034] Based on the tiered rules for outbound billing amount and the paginated rules for inbound billing amount of cloud service nodes, the reverse billing threshold is determined. The reverse billing threshold refers to the allowed billing amount limit when data traffic is transmitted in the reverse direction (from the next node to the previous node) in a single continuous cloud service node pair. The process involves: statistically analyzing the data transmission volume of the continuous cloud service node pair in the reverse direction; determining the outbound billing amount corresponding to the data transmitted from the starting node (Node B) to the adjacent node (Node A) according to the cloud service node outbound billing amount tier rules; then determining the inbound billing amount corresponding to the data received by the adjacent node (Node A) from Node B according to the inbound billing amount paging rules; and finally, adding the outbound billing amount of Node B to the inbound billing amount of Node A to obtain the reverse billing threshold for the continuous cloud service node pair. The cloud service node outbound billing amount tier rules are segmented charging rules set by cloud service providers for outbound data transmission. When the cumulative unit data volume reaches a standard, the data traffic exceeding the standard will be charged according to a higher or lower tiered price. The inbound billing amount paging rules are segmented or tiered charging standards set by cloud service providers for inbound data transmission.

[0035] The forwarding cost difference value and the reverse billing threshold are weighted according to the preset weight coefficients to generate path reconstruction priority score data; The path reconstruction priority score data reflects the priority order of path optimization and reconstruction for each continuous cloud service node pair. Node pairs with higher path reconstruction priority scores have higher priority when optimizing paths. The forwarding cost difference value corresponding to each continuous cloud service node pair in the forwarding cost difference value set and the reverse billing threshold corresponding to the same continuous cloud service node pair in the reverse billing threshold set are multiplied by their respective pre-set weight coefficients and then summed to obtain the path reconstruction priority score data for the continuous cloud service node pair. The above calculation is performed on all continuous cloud service node pairs to form the path reconstruction priority score data set.

[0036] S5: Based on the cumulative premium risk assessment index and path reconstruction priority scoring data, construct a cross-cloud path optimization objective function that minimizes the cumulative billing cost, and output optimized suggested path data, including: Based on the cumulative premium risk assessment index and path reconstruction priority scoring data, a cross-cloud path optimization objective function is set, with the cumulative billing cost as the parameter to be minimized. The cross-cloud path optimization objective function is a mathematical expression used to describe how to minimize the cumulative billing cost by adjusting the transmission path configuration during the transmission of data traffic between multiple cloud service nodes. When setting the cross-cloud path optimization objective function, the cumulative premium risk assessment indicator and path reconstruction priority score data are used as input variables. The cumulative premium risk assessment indicator reflects the degree of cost accumulation caused by duplicate billing and billing asymmetry in the data traffic loop path, while the path reconstruction priority score data reflects the cost optimization potential between different node pairs. Together, they determine the cost assessment of different path optimization options for the objective function. The objective function specifies that the billing cost of all cross-cloud data traffic is calculated cumulatively, assuming the data traffic passes through each cloud service node. All inbound and outbound billing amounts are added together sequentially to form the total path cost, which is the output of the objective function. Setting the objective function as minimizing parameters means that when optimizing cross-cloud paths for data traffic, the final criterion for selecting an optimization scheme is to minimize the cumulative billing cost of the objective function. In other words, among all possible path schemes, the one with the lowest total billing cost is the optimal scheme. For example, if there are two different cross-cloud transmission paths, and the total cost of the first path (from the data storage node of cloud service provider A to the data processing node of cloud service provider B and then to the data backup node of cloud service provider C) is higher than that of the second path, then the second path will be selected as the better scheme by the objective function.

[0037] Set business service process continuity constraints, cloud service node capacity constraints, data traffic inbound and outbound bandwidth constraints, and dynamic billing threshold constraints for the cross-cloud path optimization objective function. Business service process continuity constraints require that data traffic follow the user-defined business service process during transmission, without arbitrarily interrupting or skipping any necessary service nodes. For example, a user's disaster recovery backup service stipulates that data traffic must be transmitted in the order of storage node, processing node, and backup node, and processing nodes cannot be arbitrarily omitted. Therefore, when setting business service process continuity constraints, this is expressed as data traffic must pass through all cloud service nodes specified in the business process. Cloud service node capacity constraints refer to the actual upper limit of the storage, processing, or backup capacity of each cloud service node in the data traffic transmission path. Data traffic transmitted to a node must not exceed the actual processing or storage capacity of that node. For example, if the data backup node of cloud service provider C has a low capacity and can only handle a limited amount of data traffic, if the data traffic exceeds the node's maximum allowable capacity, it will violate the cloud service node capacity constraint. Therefore, cloud service nodes... Capacity constraints mean that data traffic arriving at any node must not exceed the node's specified actual processing or storage capacity. Data traffic inbound and outbound bandwidth constraints refer to the maximum outbound and inbound data traffic transmission rate that each cloud service node can withstand per unit time, requiring that data traffic transmission must not exceed the node's bandwidth limit. For example, a cloud service provider's data processing node may stipulate that the data traffic transmission rate per unit time must not exceed the maximum allowable value; otherwise, node congestion or service failure will occur. Therefore, data traffic inbound and outbound bandwidth constraints mean that at any node, the data traffic transmission rate must not exceed the node's maximum allowed bandwidth rate. Dynamic billing threshold constraints refer to the dynamic changes in the billing rules applicable to cloud service nodes during actual data transmission. When the data transmission volume reaches a certain value, the billing rules will change; for example, the node's outbound billing amount will change in stages as the data volume increases. Therefore, dynamic billing threshold constraints mean that the cumulative amount of data traffic transmission must not exceed the threshold that generates additional billing, or, if the threshold is exceeded, the resulting additional billing changes must be accepted. These constraints together ensure that the optimized data traffic path scheme is practically operable.

[0038] Solve the objective function for cross-cloud path optimization to obtain a set of candidate cross-cloud transmission paths; The solution is jointly obtained based on the constraints and the minimized parameters defined by the optimization objective function. Existing optimization algorithms, such as linear programming or integer programming, are used to solve the problem. All data traffic path schemes that meet the requirements of business service process continuity, node capacity limit, bandwidth limit, and dynamic billing threshold are substituted into the objective function one by one. The cumulative billing cost of each path scheme is calculated, and the results are compared and recorded to obtain all the path schemes that meet the conditions, forming a candidate cross-cloud transmission path set. For example, multiple candidate schemes are input into the optimization function in sequence, and the cumulative billing cost of each scheme is calculated in sequence to form a set of paths to be selected. The candidate cross-cloud transmission path set covers all path schemes that meet the constraints and optimization objective requirements.

[0039] The candidate cross-cloud transmission path set is sorted in ascending order according to the cumulative billing cost, and the cross-cloud transmission path with the lowest cumulative billing cost is selected to generate optimized suggested path data. The cumulative billing cost value corresponding to each path scheme is extracted from the candidate cross-cloud transmission path set and compared. The path scheme with the lowest billing cost is placed first, and so on, forming a sorted sequence from low to high. After sorting, the path scheme with the lowest cumulative billing cost is determined as the optimized suggested path data. The optimized suggested path data records the name of each cloud service node traversed by the path with the lowest cumulative billing cost, the transmission order between nodes, the billing amount per unit of data volume, and the total cumulative billing cost. The optimized suggested path data is used to guide users to adjust data transmission paths in actual cross-cloud computing architectures. For example, if the sorting reveals that the path from the data processing node of cloud service provider B to the data backup node of cloud service provider C, and then back to the data storage node of cloud service provider A has the lowest cumulative billing cost, then this path is recommended to the user as the optimized suggested path data for actual path reconstruction and adjustment, thereby achieving the goal of cost minimization.

[0040] S6: Based on the optimized suggested path data, reconstruct the cross-cloud transmission path of business services and adjust the cross-cloud node configuration, including: Based on the optimized suggested path data, obtain the cross-cloud transmission paths and corresponding cloud service node identifiers. The optimized suggested path data records the data traffic transmission path, including the node identifier of each cloud service node through which the data flows and the data transmission order between nodes. For example, if the user's initial defined data traffic transmission path is from the East China data center storage node of cloud service provider A to the North China data processing node of cloud service provider B, and then to the South China backup node of cloud service provider C, the optimized suggested path data, after optimization by the objective function, recommends direct transmission from the East China data center storage node of cloud service provider A to the South China backup node of cloud service provider C, and then to the North China data processing node of cloud service provider B, minimizing the cumulative billing cost of the path. The cloud service node identifier is used to mark the unified identification code of each cloud service node. The node identifier consists of the cloud service provider name, geographical location, service type, and node function type in sequence. For example, the identifier of the East China data center storage node of cloud service provider A is expressed as cloud service provider A, East China region, data storage center. The process of obtaining the cross-cloud transmission path and the corresponding cloud service node identifier is to extract all nodes and node identifier information through which the data traffic passes from the optimized suggested path data, and finally form the correspondence between the cross-cloud transmission path and the cloud service node identifier, which serves as the input data for path adjustment.

[0041] Adjust the user's business service process configuration in the multi-cloud computing architecture according to the order of cloud service node identifiers in the cross-cloud transmission path, and update the data traffic forwarding order of the business service process; Business service process configuration refers to the node order and logical call rules for data flow predefined by the user in a multi-cloud computing architecture. The user's initial data traffic forwarding order may not have taken into account the differences in billing rules among different cloud service providers in the cross-cloud computing architecture, which may lead to increased billing costs. The order of cloud service node identifiers in the cross-cloud transmission path is the order of data flow between nodes determined by the optimized suggested path data, reflecting the node forwarding order that data traffic should take under the optimal cost conditions. For example, the user's original data traffic forwarding order is defined as from the data storage node of cloud service provider A to the data processing node of cloud service provider B, and then to the data backup node of cloud service provider C, through the path After optimization, the adjusted node order is from the data storage node of cloud service provider A to the data backup node of cloud service provider C, and then to the data processing node of cloud service provider B. Therefore, the user's initial business service process configuration is readjusted according to the node identifier order determined by the optimized path data. That is, the data traffic forwarding order of the business service process is redefined and configured so that the adjusted business service process conforms to the optimal path scheme determined by the optimized path data. The process of updating the business service process configuration requires checking the node order specified in the optimized path data node by node and updating the business service process node calling logic of the user's multi-cloud computing architecture, ultimately forming the adjusted business service process configuration.

[0042] Adjust the cross-cloud node configuration in the multi-cloud computing architecture according to the cloud service node identifier in the cross-cloud transmission path, and update the cross-cloud data traffic transmission path. Cross-cloud node configuration refers to the deployment of each cloud service node and the setting of network connections between nodes in a multi-cloud computing architecture. The original cross-cloud node configuration may lead to uneconomical data traffic transmission paths between nodes, increasing billing costs. Optimization suggestion path data provides the node identifier order in the cross-cloud transmission path, determining the optimal data traffic transmission path in the cross-cloud computing architecture, which can then be used to adjust existing cross-cloud node configurations. For example, in the user's original cross-cloud node configuration, a dedicated network is configured between the storage node in the East China data center of cloud service provider A and the data processing node in North China of cloud service provider B. However, path optimization reveals that data traffic transmission should preferentially pass through the backup node in South China of cloud service provider C before reaching the data processing node in North China of cloud service provider B. Therefore, it is necessary to adjust the node configuration. The network connections between nodes are replanned and redeployed. For example, a new network connection is established from the storage node of Cloud Service Provider A's East China data center to the backup node of Cloud Service Provider C's South China data center, while updating the network connection relationships between nodes. The process of updating the cross-cloud node configuration includes determining the connection relationship between nodes node by node based on the node identifier in the cross-cloud transmission path, deleting old unnecessary connections, and adding new necessary connections. Through these adjustments, the cross-cloud data traffic transmission path can conform to the path order and connection relationship between nodes specified in the optimized recommended path data, thereby reducing the total cost incurred by data traffic during cross-cloud transmission. After the cross-cloud data traffic transmission path is updated, the connection relationship between nodes and the data flow path are both adjusted to conform to the definition of the optimized recommended path data.

[0043] The updated cross-cloud data traffic transmission path is standardized with path node identification and time-series data synchronization to obtain the reconstructed cross-cloud transmission path and node configuration data for business services. Path node identification standardization involves re-encoding all cloud service nodes involved in the updated cross-cloud data traffic transmission path according to a defined, unified node identifier encoding rule. This ensures the consistency and uniqueness of each node identifier in the reconstructed path information. For example, if the adjusted cross-cloud data traffic transmission path involves storage nodes in the East China data center of cloud service provider A, backup nodes in South China of cloud service provider C, and data processing nodes in North China of cloud service provider B, the nodes will be re-identified according to the unified encoding rule, thus forming consistent, standardized node identifier data. Time-series data synchronization processing involves recording the time stamp of each node identifier and the data transmission process involved in the reconstructed data traffic transmission path according to a unified time-series labeling rule. That is, each... The data inflow and outflow at each node, the data dwell time at each node, and the actual time of transmission to the next node are uniformly time-series marked to ensure consistency of time data for all nodes and the data transmission process in the reconstructed cross-cloud transmission path. After the path node identification standardization and time-series data synchronization are completed, the reconstructed business service cross-cloud transmission path and node configuration data include node identification data and time-series data, recording the transmission time information of each node in the data traffic transmission path and between nodes. The reconstructed business service cross-cloud transmission path and node configuration data can be used for node configuration and data transmission path deployment in actual multi-cloud computing architectures to ensure that the actual implemented data traffic transmission process is completely consistent with the optimal solution determined by the optimized path data, thereby achieving the goal of minimizing cumulative billing costs.

[0044] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0045] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0046] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0047] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0048] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0049] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0050] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 this application. 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.

[0051] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0052] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A resource billing method based on multi-cloud services, characterized in that, Includes the following steps: S1: Obtain multi-cloud service traffic billing related data configured by the user in the multi-cloud computing architecture, standardize path node identification and synchronize time-series data, and generate multi-cloud service interaction path data; S2: Based on multi-cloud service interaction path data, extract the data forwarding direction, node switching frequency and loopback transmission mode between different cloud service providers, and construct a cross-cloud traffic loopback identification matrix; S3: Based on the cross-cloud traffic loop identification matrix, analyze the characteristics of duplicate billing and billing asymmetry in cross-cloud traffic loops, and generate cumulative premium risk assessment indicators. S4: Based on the cross-cloud traffic loopback identification matrix, analyze the differences in forwarding costs and reverse billing thresholds between cloud service nodes, and generate path reconstruction priority scoring data; S5: Based on the cumulative premium risk assessment index and path reconstruction priority scoring data, construct a cross-cloud path optimization objective function that minimizes the cumulative billing cost, and output optimized suggested path data; S6: Based on the optimized suggested path data, reconstruct the cross-cloud transmission path of business services and adjust the cross-cloud node configuration to achieve data traffic distribution optimization and resource billing control under the multi-cloud service architecture.

2. The resource billing method based on multi-cloud services according to claim 1, characterized in that, S1, specifically: Acquire user-configured multi-cloud business traffic billing data in a multi-cloud computing architecture, including user business service process data, cross-cloud data transmission path data, and cloud service provider inbound and outbound billing rule data; A unified node identifier encoding is applied to each cloud service node involved in the multi-cloud service traffic billing data; The multi-cloud service traffic billing data after node identifier encoding is processed with unified time series labeling to form multi-cloud service interaction path data.

3. The resource billing method based on multi-cloud services according to claim 2, characterized in that, S2, specifically: Based on multi-cloud service interaction path data, extract the data traffic forwarding path between each cloud service node; Based on the data traffic forwarding path, the direction of data traffic transmission between two adjacent cloud service nodes is statistically analyzed; Based on the data traffic forwarding path, count the number of cloud service node switching events occurring per unit time. Based on the data traffic forwarding path, identify the loop path of data traffic that returns to the initial cloud service node after passing through multiple cloud service nodes. A cross-cloud traffic loopback identification matrix is ​​established based on the data traffic transmission direction, the number of cloud service node switching events per unit time, and the loopback path.

4. The resource billing method based on multi-cloud services according to claim 3, characterized in that, S3, specifically: Based on the cross-cloud traffic loopback identification matrix, the data traffic loopback path is retrieved, and the number of times the same data traffic is repeatedly billed on the same cloud service node is recorded. The number of repeated billing records is accumulated and counted according to the data flow loop order to obtain the set of repeated billing counts for each node; Based on the cross-cloud traffic loopback identification matrix, the difference between the outbound billing amount and the inbound billing amount generated by the corresponding cloud service node during the forward and reverse transmission of data traffic is calculated to obtain the billing asymmetry set. The cumulative premium risk assessment index is generated by weighting and combining the set of repeated billing times of nodes with the set of billing asymmetry.

5. A resource billing method based on multi-cloud services according to claim 4, characterized in that, S4, specifically: Based on the cross-cloud traffic loopback identification matrix, the continuous cloud service node pairs in each data traffic forwarding path are determined; For each pair of consecutive cloud service nodes, obtain the outbound billing amount generated by forward transmission and the inbound billing amount generated by reverse transmission per unit of data volume, and calculate the forward transmission cost and the reverse transmission cost. Calculate the difference in forwarding costs based on the forward transmission cost and the reverse transmission cost; Based on the tiered rules for outbound billing amount and the paginated rules for inbound billing amount of cloud service nodes, the reverse billing threshold is determined. The forwarding cost difference value and the reverse billing threshold are weighted according to the preset weight coefficients to generate path reconstruction priority score data.

6. The resource billing method based on multi-cloud services according to claim 5, characterized in that, S5, specifically: Based on the cumulative premium risk assessment index and path reconstruction priority scoring data, a cross-cloud path optimization objective function is set, with the cumulative billing cost as the parameter to be minimized. Set business service process continuity constraints, cloud service node capacity constraints, data traffic inbound and outbound bandwidth constraints, and dynamic billing threshold constraints for the cross-cloud path optimization objective function. Solve the objective function for cross-cloud path optimization to obtain a set of candidate cross-cloud transmission paths; The candidate cross-cloud transmission path set is sorted in ascending order according to the cumulative billing cost, and the cross-cloud transmission path with the lowest cumulative billing cost is selected to generate optimized path suggestion data.

7. A resource billing method based on multi-cloud services according to claim 6, characterized in that, S6, specifically: Based on the optimized suggested path data, obtain the cross-cloud transmission paths and corresponding cloud service node identifiers. Adjust the user's business service process configuration in the multi-cloud computing architecture according to the order of cloud service node identifiers in the cross-cloud transmission path, and update the data traffic forwarding order of the business service process; Adjust the cross-cloud node configuration in the multi-cloud computing architecture according to the cloud service node identifier in the cross-cloud transmission path, and update the cross-cloud data traffic transmission path. The updated cross-cloud data traffic transmission path is standardized with path node identification and time-series data synchronization to obtain the reconstructed cross-cloud transmission path and node configuration data for business services.

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