A multi-cloud service based resource charging method

Abstract

The application discloses a resource charging method based on multi-cloud services and particularly relates to the technical field of resource management and cost optimization; the method comprises the following steps: acquiring service traffic charging related data configured by a user in a multi-cloud computing architecture, and constructing standardized multi-cloud service interaction path data; extracting data forwarding directions, node switching frequencies and loop transmission modes among different cloud service providers, and constructing a cross-cloud traffic loop identification matrix; analyzing repeated charging and asymmetric charging characteristics of cross-cloud traffic loops, and generating cumulative premium risk assessment indexes; analyzing forwarding cost differences and reverse charging thresholds among cloud service nodes, and generating path reconstruction priority score data; constructing a cross-cloud path optimization objective function for minimizing cumulative charging costs, outputting optimized recommended path data, and reconstructing cross-cloud transmission paths of business services and adjusting cross-cloud node configurations; and the method improves cross-cloud resource utilization efficiency and effectively reduces charging costs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource management and cost optimization, and more particularly, to a resource billing method based on multi-cloud services. BACKGROUND

[0002] With the wide application of multi-cloud computing mode in enterprise business architecture, more and more enterprises choose to use resources of multiple different cloud service providers at the same time to improve the reliability and performance of business systems.

[0003] The existing multi-cloud service resource billing method fails to effectively identify and control the non-linear cost accumulation problem of cross-cloud data traffic in multiple cross-node transmission processes caused by the differences in billing rules between different cloud service providers. SUMMARY

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

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] A resource billing method based on multi-cloud services, comprising the following steps:

[0007] S1: Obtain the multi-cloud business traffic billing related data configured by the user in the multi-cloud computing architecture, standardize the path node identification and synchronize the time sequence data, and generate multi-cloud service interaction path data;

[0008] S2: Based on the multi-cloud service interaction path data, extract the data forwarding direction, node switching frequency and loop transmission mode between different cloud service providers, and construct a cross-cloud traffic loop identification matrix;

[0009] S3: Based on the cross-cloud traffic loop identification matrix, analyze the repeated billing and asymmetric billing characteristics of cross-cloud traffic loops, and generate cumulative premium risk assessment indicators;

[0010] S4: Based on the cross-cloud traffic loop identification matrix, analyze the forwarding cost difference and reverse billing threshold between cloud service nodes, and generate path reconstruction priority score data;

[0011] S5: Based on the cumulative premium risk assessment indicators and the path reconstruction priority score data, construct a cross-cloud path optimization objective function that minimizes the cumulative billing cost, and output optimized recommended path data;

[0012] S6: Based on the optimized recommended path data, reconstruct the cross-cloud transmission path of the business service and adjust the cross-cloud node configuration, and realize data traffic distribution optimization and resource billing control under the multi-cloud service architecture.

[0013] In a preferred embodiment, S1, specifically:

[0014] Obtain multi-cloud service traffic billing related data configured by the user in the multi-cloud computing architecture, including user service flow data, cross-cloud data transmission path data, and cloud service provider outbound and inbound billing rule data;

[0015] Uniformly encode each cloud service node involved in the multi-cloud service traffic billing related data;

[0016] Uniformly time series mark the multi-cloud service traffic billing related data after node identifier encoding to form multi-cloud service interaction path data.

[0017] In a preferred embodiment, S2, specifically:

[0018] Based on the multi-cloud service interaction path data, extract the data traffic forwarding path between each cloud service node;

[0019] According to the data traffic forwarding path, count the data traffic transmission direction between adjacent two cloud service nodes;

[0020] According to the data traffic forwarding path, count the number of cloud service node switching occurring within a unit time;

[0021] According to the data traffic forwarding path, identify the loopback path of data traffic after passing through the loopback path between multiple cloud service nodes and returning to the initial cloud service node;

[0022] Based on the data traffic transmission direction, the number of cloud service node switching occurring within a unit time, and the loopback path, establish a cross-cloud traffic loopback identification matrix.

[0023] In a preferred embodiment, S3, specifically:

[0024] Based on the cross-cloud traffic loopback identification matrix, retrieve the data traffic loopback path and record the number of repeated billing occurring at the same cloud service node for the same data traffic;

[0025] Cumulatively count the recorded number of repeated billing according to the data traffic loopback order to obtain a node repeated billing number set;

[0026] Based on the cross-cloud traffic loopback identification matrix, calculate the difference between the outbound billing amount and the inbound billing amount of the corresponding cloud service node in the forward transmission and reverse transmission process of the data traffic to obtain a billing asymmetry degree set;

[0027] Weight and combine the node repeated billing number set and the billing asymmetry degree set to generate a cumulative premium risk assessment index.

[0028] In a preferred embodiment, S4, specifically:

[0029] Determine the continuous cloud service node pairs in each data traffic forwarding path based on the cross-cloud traffic loop recognition matrix;

[0030] For each continuous cloud service node pair, obtain the outbound billing amount generated by forward transmission and the inbound billing amount generated by reverse transmission per unit data volume, and calculate the forward transmission cost and the reverse transmission cost;

[0031] Calculate the forwarding cost difference value according to the forward transmission cost and the reverse transmission cost;

[0032] Determine the reverse billing threshold based on the cloud service node outbound billing amount step rule and the inbound billing amount paging rule;

[0033] According to the preset weight coefficient, the forwarding cost difference value and the reverse billing threshold are weighted and operated to generate path reconstruction priority score data.

[0034] In a preferred embodiment, S5, specifically:

[0035] Based on the cumulative premium risk evaluation index and the path reconstruction priority score data, set the cross-cloud path optimization target function, and set the cumulative billing cost as the minimum parameter;

[0036] Set the business service process continuity constraint, cloud service node capacity constraint, data traffic import and export bandwidth constraint, and dynamic billing threshold constraint for the cross-cloud path optimization target function;

[0037] Solve the cross-cloud path optimization target function to obtain a candidate cross-cloud transmission path set;

[0038] Sort the candidate cross-cloud transmission path set in ascending order according to the cumulative billing cost, select the cross-cloud transmission path with the lowest cumulative billing cost, and generate optimization suggestion path data.

[0039] In a preferred embodiment, S6, specifically:

[0040] Based on the optimization suggestion path data, obtain the cross-cloud transmission path and the corresponding cloud service node identifier contained therein;

[0041] According to the order of the cloud service node identifier in the cross-cloud transmission path, adjust the business service process configuration of the user in the multi-cloud computing architecture, and update the data traffic forwarding order of the business service process;

[0042] According to the cloud service node identifier in the cross-cloud transmission path, adjust the cross-cloud node configuration in the multi-cloud computing architecture, and update the cross-cloud data traffic transmission path;

[0043] The updated cross-cloud data flow transmission path is subjected to path node identification standardization and timing data synchronization processing, and a reconstructed service service cross-cloud transmission path and node configuration data are obtained.

[0044] The technical effects and advantages of the resource billing method based on the multi-cloud service are as follows:

[0045] Through standardization and synchronization processing of the service flow billing data in the multi-cloud environment, the cross-cloud data loop transmission mode can be accurately identified, the cumulative overcharge risk caused by repeated billing and asymmetric billing in the cross-cloud transmission process can be effectively found and quantified, and through evaluation of the forwarding cost difference between the cloud nodes, the priority of path optimization is determined, so as to build and solve the cross-cloud path optimization objective function and output the optimization suggestion path with the lowest cost. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 A resource billing method based on a multi-cloud service is shown in the figure. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0048] EMBODIMENT

[0049] Figure 1 A resource billing method based on a multi-cloud service is given, which includes the following steps:

[0050] S1: Obtain the multi-cloud service flow billing related data configured by the user in the multi-cloud computing architecture, perform path node identification standardization and timing data synchronization, and generate multi-cloud service interaction path data;

[0051] S2: Based on the multi-cloud service interaction path data, extract the data forwarding direction, node switching frequency and loop transmission mode between different cloud service providers, and build a cross-cloud flow loop identification matrix;

[0052] S3: Based on the cross-cloud flow loop identification matrix, analyze the repeated billing and asymmetric billing characteristics of the cross-cloud flow loop, and generate a cumulative overcharge risk evaluation index;

[0053] S4: Based on the cross-cloud traffic loop identification matrix, analyze the difference in forwarding cost between cloud service nodes and the reverse billing threshold, and generate path reconstruction priority score data;

[0054] S5: Based on the cumulative premium risk evaluation index and the path reconstruction priority score data, construct a cross-cloud path optimization objective function that minimizes the cumulative billing cost, and output optimization recommendation path data;

[0055] S6: Based on the optimization recommendation path data, reconstruct the cross-cloud transmission path of the business service and adjust the cross-cloud node configuration, to realize data flow distribution optimization and resource billing control under the multi-cloud service architecture.

[0056] S1: Obtain the multi-cloud business traffic billing related data configured by the user in the multi-cloud computing architecture, standardize the path node identifier and synchronize the time series data, and generate multi-cloud service interaction path data, including:

[0057] Obtain the multi-cloud business traffic billing related data configured by the user in the multi-cloud computing architecture;

[0058] The multi-cloud business traffic billing related data includes user business service process data, cross-cloud data transmission path data, and cloud service provider's outbound and inbound billing rule data; the user business service process data refers to the data flow sequence, logical relationship of service nodes, and calling rules defined by the user in the business service system constructed by multiple cloud service providers; for example, the user builds a disaster recovery system that spans multiple cloud service providers, and specifies 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 when a failure occurs, the data is returned from the backup node to the original node; the above defined data flow sequence, logical relationship of service nodes, and calling rules constitute the user business service process data; the cross-cloud data transmission path data refers to the actual path through which the user transmits data between different cloud service providers, and the involved data includes the names, locations, and communication sequences of the source node, target node, and intermediate nodes; for example, when the 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 located in the North China region, and then to the backup center of cloud service provider C located in the South China region; at this time, the cross-cloud data transmission path data records the relevant information of the path nodes passed through in the entire data transmission process; the outbound and inbound billing rule data of the cloud service provider refers to the rules formulated by different cloud service providers for charging for the outbound transmission or inbound reception of user data, including charging standards and step billing methods for different traffic sizes and different transmission directions.

[0059] Each cloud service node involved in the multi-cloud business traffic billing related data is uniformly encoded with a node identifier;

[0060] The unified node identifier coding refers to coding each service node provided by different cloud service providers according to a unified data identifier standard, so that each node can be uniquely identified. The method of unified node identifier coding is to assign each node a unique node identifier code according to a specific coding rule. First, the service nodes are classified according to the name of the cloud service provider, and then they are subdivided according to the geographical location, service type, and function type of the node, and are respectively coded with a unified identifier. For example, the node identifier code can be described as a storage node identifier of a specific cloud service provider's North China data center, which is coded in the order of cloud service provider, geographical location, and data center function type, so that each node has a unique, determined, and non-repeating node identifier code. The unified node identifier coding process includes all service nodes involved in the user business service process data and cross-cloud data transmission path data.

[0061] The multi-cloud business traffic billing-related data coded with the node identifier is processed with a unified time sequence label to form multi-cloud service interaction path data.

[0062] A unified time sequence label is assigned to all obtained data types, that is, for each record item of each data type, a unified time label is recorded in the order of actual physical time when the data is generated or flows through the service node. For example, after the data flow defined in the user business service process data reaches a node and records a timestamp, the cross-cloud data transmission path data and the cloud service provider's outbound and inbound billing rules data record the same timestamp at the same time, so that all data types have a unified and consistent time label identification. The unified time sequence label processing process not only covers the start time and end time of the data flow through the cloud service node, but also covers the time when the data stays, forwards, and is called in the node, ensuring that all data types have complete and consistent time sequence labels. After all data types are processed and unified, multi-cloud service interaction path data is formed. The multi-cloud service interaction path data formed after the unified node identifier coding and the unified time sequence label processing includes the data identification and time label of all cross-cloud business service processes, node-to-node data flow paths, and node-related billing rules.

[0063] S2: Based on the multi-cloud service interaction path data, the data forwarding direction, node switching frequency, and loop transmission mode between different cloud service providers are extracted to construct a cross-cloud traffic loop identification matrix, including:

[0064] Based on the multi-cloud service interaction path data, the data flow forwarding path between each cloud service node is extracted.

[0065] The data flow forwarding path refers to a path process that data travels from a starting cloud service node, through multiple cloud service nodes, and finally reaches a target cloud service node when performing a business process; for example, the data of a business system deployed by a user starts from a data processing node of a cloud service provider A, passes through a storage node of a cloud service provider B and a computing node of a cloud service provider C, and finally returns to a data storage node of the cloud service provider A, and the data flow forwarding path is the complete path information of the data processing node of the cloud service provider A, the storage node of the cloud service provider B, the computing node of the cloud service provider C, and the data storage node of the cloud service provider A; each cross-cloud data flow forwarding path can be extracted by searching the multi-cloud service interaction path data one by one; the extraction process of the data flow forwarding path is performed according to the order of the unified node identifier code and the order of the unified time sequence marker, which can ensure the completeness and accuracy of the data flow forwarding path record.

[0066] According to the data flow forwarding path, the data flow transmission direction between two adjacent cloud service nodes is counted.

[0067] The data flow transmission direction refers to the transmission direction of data when flowing between two adjacent cloud service nodes, that is, the path order from one node to another node; for example, data is transmitted from a data processing node of a cloud service provider A to a storage node of a cloud service provider B, and when counting the data flow transmission direction, the number of flows from the data processing node of the cloud service provider A to the storage node of the cloud service provider B needs to be recorded respectively; if the data is transmitted from the storage node of the cloud service provider B to a computing node of a cloud service provider C, the number of flows from the storage node of the cloud service provider B to the computing node of the cloud service provider C also needs to be recorded respectively; by counting the transmission direction and the corresponding transmission times between each two adjacent nodes in the statistical data flow forwarding path one by one, a complete data flow transmission direction record can be obtained, forming a data flow transmission direction statistical set between adjacent cloud service nodes.

[0068] According to the data flow forwarding path, the number of cloud service node switching times in a unit time is counted.

[0069] The number of cloud service node switching occurring in a unit of time refers to the total number of times that data is switched from one cloud service node to another cloud service node in a service process in a fixed time period, for example, in units of hours or minutes; for example, in a one-hour statistical period, the data of a user service system experiences a data node of a cloud service provider A to a data node of a cloud service provider B, and then from the data node of the cloud service provider B to a data node of a cloud service provider C, a total of two cloud service node switching occurs; when counting the number of cloud service node switching occurring in a unit of time, the number of node switching of each data flow forwarding path recorded in the multi-cloud service interaction path data needs to be counted, and finally a set of statistical results of the number of cloud service node switching occurring in a unit of time is obtained; the set of statistical results of the number of node switching can reflect the frequency of data flow switching nodes in the cross-cloud service architecture.

[0070] According to the data flow forwarding path, a loopback path is identified in which the data flow returns to the initial cloud service node after passing through a circulating path between multiple cloud service nodes;

[0071] The loopback path refers to a loop-shaped transmission path formed by data returning to the initial node after passing through a series of different cloud service nodes in the data transmission process; for example, the data of a user service system initially departs from a data storage node of a cloud service provider A, passes through a data processing node of a cloud service provider B and a data backup node of a cloud service provider C, and finally returns to the data storage node of the cloud service provider A, thereby constituting a data loopback path; the loopback path identification process tracks each node of each data flow forwarding path one by one to determine whether the data flow returns to the initial node after passing through different nodes; if the data flow returns to the initial node in the forwarding process, the complete data node sequence is identified and recorded as a loopback path to form a loopback path set.

[0072] Based on the data flow transmission direction, the number of cloud service node switching occurring in a unit of time, and the loopback path, a cross-cloud flow loopback identification matrix is established;

[0073] The rows and columns of the cross-cloud flow loopback identification matrix respectively represent different cloud service nodes, and the data elements in the matrix represent the data flow transmission relationship between the cloud service nodes, the node switching frequency, and whether there is a loopback path; for example:

[0074]

[0075] The cross-cloud flow loopback identification matrix can comprehensively reflect the specific forwarding path, node switching frequency, and loopback situation of the data flow in the multi-cloud service environment.

[0076] S3: Based on the cross-cloud traffic loop identification matrix, analyze the repeated charging and charging asymmetry characteristics of the cross-cloud traffic loop, and generate cumulative premium risk assessment indicators, including:

[0077] Based on the cross-cloud traffic loop identification matrix, retrieve the data traffic loop path, and record the number of repeated charges of the same data traffic at the same cloud service node;

[0078] The data traffic loop path refers to the path sequence in which the data traffic starts from a certain cloud service node, passes through multiple other cloud service nodes in turn, and then returns to the initial cloud service node. The above data traffic path constitutes a data traffic loop path. The cross-cloud traffic loop identification matrix records the transmission from the data storage node of the A cloud service provider to the data processing node of the B cloud service provider, from the data processing node of the B cloud service provider to the data backup node of the C cloud service provider, and from the data backup node of the C cloud service provider back to the data storage node of the A cloud service provider. When the data traffic returns to the data storage node of the A cloud service provider, the cross-cloud traffic loop identification matrix records the loop of the data traffic in the corresponding position of the data storage node row and column of the A cloud service provider, indicating that the data traffic has been circulated once. The cloud service provider calculates the cost once when the data enters and leaves the node, and each time the data traffic returns to the data storage node of the A cloud service provider, it generates a repeated charge. Therefore, retrieve the recorded position of each node of the cross-cloud traffic loop identification matrix, and successively count the number of repeated charges of the same data traffic when it returns to the initial node through the loop path. For example, the data storage node of the A cloud service provider is charged once for each loop. If the data traffic loops multiple times, the number of repeated charges also accumulates multiple times. All nodes of the cross-cloud traffic loop identification matrix are retrieved and counted, and the number of repeated charges of each cloud service node in the multi-cloud computing architecture is finally obtained.

[0079] Cumulatively count the recorded number of repeated charges according to the data traffic loop order to obtain a node repeated charge number set;

[0080] The node repeated charge number set is the total charge number of each cloud service node accumulated due to repeated data traffic during the data traffic loop transmission process. For example, the data traffic starts from node A, passes through nodes B and C in turn, 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 will record the cumulative number of repeated charges, respectively, that is, the node repeated charge number set.

[0081] Based on the cross-cloud traffic loop identification matrix, calculate the difference between the outbound charge amount and the inbound charge amount of the corresponding cloud service node during the forward transmission and reverse transmission of the data traffic, and obtain a charging asymmetry degree set;

[0082] The charging asymmetry set is the difference between the charging amount generated when transmitting data traffic in the forward direction and the reverse direction for any two adjacent node pairs (e.g., node A-node B), and the calculation method is:

[0083] Charging asymmetry = forward transmission unit data volume charging amount-reverse transmission unit data volume charging amount.

[0084] The charging asymmetry set is the set of charging amount differences calculated by all node pairs.

[0085] The node repeated charging number set and the charging asymmetry set are weighted and synthesized to generate a cumulative premium risk evaluation index;

[0086] The cumulative premium risk evaluation index is obtained by multiplying the repeated charging number of each node pair in the node repeated charging number set and the charging difference of the corresponding node pair in the charging asymmetry set, and then adding them up, which reflects the cumulative additional charging cost risk brought by the data traffic loop transmission path.

[0087] S4: Based on the cross-cloud traffic loop identification matrix, analyze the difference in forwarding cost between cloud service nodes and the reverse charging threshold, and generate path reconstruction priority score data, including:

[0088] Determine the continuous cloud service node pairs in each data traffic forwarding path based on the cross-cloud traffic loop identification matrix;

[0089] Continuous cloud service node pairs refer to two cloud service nodes that are sequentially adjacent in the data traffic forwarding path defined by the business service process; for example, a user-deployed business service system defines a cross-cloud data traffic forwarding path that starts from the data storage node of cloud service provider A, transmits to the data processing node of cloud service provider B, then transmits to the data backup node of cloud service provider C, and finally returns to the data storage node of cloud service provider A; in the data traffic forwarding path, the sequentially adjacent nodes form continuous cloud service node pairs, i.e., the data storage node of cloud service provider A to the data processing node of cloud service provider B is a pair of continuous cloud service node pairs, the data processing node of cloud service provider B to the data backup node of cloud service provider C is the next pair of continuous cloud service node pairs, and the data backup node of cloud service provider C returning to the data storage node of cloud service provider A forms the third pair of continuous cloud service node pairs; by analyzing each data traffic forwarding path in the cross-cloud traffic loop identification matrix, all adjacent nodes on each path can be identified and recorded as multiple continuous cloud service node pairs.

[0090] For each continuous cloud service node pair, the outbound billing amount generated by the forward transmission of unit data volume and the inbound billing amount generated by the reverse transmission are obtained, and the forward transmission cost and the reverse transmission cost are calculated;

[0091] The forward transmission refers to the data flow along the normal transmission direction defined by the business service flow, from the previous node to the next adjacent node for data transmission. The reverse transmission refers to the data flow along the path direction opposite to the forward direction, from the next node to the previous adjacent node for data backhaul. For example, in the continuous node pair of the data storage node of the cloud service provider A and the data processing node of the cloud service provider B, the billing amount generated by the transmission of unit data volume from the data storage node of the cloud service provider A to the data processing node of the cloud service provider B is the outbound billing amount of the forward transmission. When the data is backhauled from the data processing node of the cloud service provider B to the data storage node of the cloud service provider A, the generated billing amount is the inbound billing amount of the reverse transmission. The outbound billing amount generated by the forward transmission and the inbound billing amount generated by the reverse transmission are determined and recorded according to the billing rules of the cloud service provider, and the billing amount is converted into the corresponding transmission cost, that is, the forward transmission cost is equal to the outbound billing amount of unit data volume transmitted from the previous node to the next node, and the reverse transmission cost is equal to the inbound billing amount of unit data volume backhauled from the next node to the previous node. According to the above method, each pair of continuous cloud service nodes is analyzed to obtain the forward and reverse transmission costs of all pairs of continuous cloud service nodes.

[0092] The forwarding cost difference value is calculated according to the forward transmission cost and the reverse transmission cost;

[0093] The forwarding cost difference value refers to the difference between the transmission cost generated by unit data volume in the forward transmission direction and the transmission cost generated by unit data volume in the reverse transmission direction, which reflects the cost difference degree of the same node pair in different transmission directions. For example, the forward transmission cost from the data storage node of the cloud service provider A to the data processing node of the cloud service provider B is high, and the reverse transmission cost is low, so the forwarding cost difference value of the continuous cloud service node pair of the data storage node of the cloud service provider A and the data processing node of the cloud service provider B is a large value. The calculation method of the forwarding cost difference value is: subtracting the corresponding reverse transmission cost from the forward transmission cost of each continuous cloud service node pair to obtain the difference value. If the forward transmission cost is higher than the reverse transmission cost, the difference value is positive, indicating that the forward transmission is more expensive. If the reverse transmission cost is higher than the forward transmission cost, the difference value is negative, indicating that the reverse transmission is more expensive. According to the above method, the difference value is calculated for all continuous cloud service node pairs to obtain a complete set of forwarding cost difference values, which is called the forwarding cost difference value set.

[0094] determine the reverse charging threshold based on the cloud service node outbound charging amount ladder rule and the inbound charging amount paging rule;

[0095] The reverse charging threshold refers to the allowed charging amount limit of data traffic in the reverse transmission (from the next node to the previous node) of a single continuous cloud service node pair. The data transmission amount in the reverse transmission direction of the continuous cloud service node pair is counted, the outbound charging amount corresponding to the data transmission amount of the reverse transmission path starting node (node B) to the adjacent node (node A) is determined according to the cloud service node outbound charging amount ladder rule, and the inbound charging amount corresponding to the data amount received by the adjacent node (node A) from node B is determined according to the inbound charging amount paging rule. The outbound charging amount of node B and the inbound charging amount of node A are added, which is the reverse charging threshold of the continuous cloud service node pair. The cloud service node outbound charging amount ladder rule is a segmented charging rule set by the cloud service provider for outbound data transmission, that is, when the unit data amount accumulates to a standard, the data traffic charge amount exceeding the standard is calculated according to a higher or lower ladder price. The inbound charging amount paging rule is a segmented or hierarchical charging standard set by the cloud service provider for inbound data transmission.

[0096] The path reconstruction priority score data is generated by weighting the forwarding cost difference value and the reverse charging threshold according to the preset weight coefficient;

[0097] The path reconstruction priority score data reflects the priority processing order of path optimization reconstruction for each continuous cloud service node pair. The node pair with higher path reconstruction priority score data has higher priority in path optimization. The forwarding cost difference value corresponding to each continuous cloud service node pair in the forwarding cost difference value set and the reverse charging threshold corresponding to the same continuous cloud service node pair in the reverse charging threshold set are multiplied by the respective preset weight coefficients, and then summed to obtain the path reconstruction priority score data of the continuous cloud service node pair. The above operation is performed on all continuous cloud service node pairs to form a path reconstruction priority score data set.

[0098] S5: Based on the cumulative premium risk evaluation index and the path reconstruction priority score data, a cross-cloud path optimization objective function for minimizing the cumulative charging cost is constructed, and the optimization suggestion path data is output, including:

[0099] Based on the cumulative premium risk evaluation index and the path reconstruction priority score data, a cross-cloud path optimization objective function is set to minimize the cumulative charging cost as the target parameter;

[0100] The cross-cloud path optimization objective function is a mathematical expression used to describe the process of minimizing the cumulative billing cost of data traffic transmission between multiple cloud service nodes by adjusting the transmission path configuration. When setting the cross-cloud path optimization objective function, the cumulative premium risk assessment index and the path reconstruction priority score data are used as input variables. The cumulative premium risk assessment index reflects the degree of cost accumulation caused by repeated billing and asymmetric billing in the data traffic loop path, while the path reconstruction priority score data reflects the cost optimization potential between different nodes. Together, they determine the cost evaluation of different path optimization options by the objective function. In the objective function, the billing cost of all cross-cloud data traffic is calculated cumulatively. The total cost of the path is calculated by adding the inbound and outbound billing amounts generated by each cloud service node in sequence, and the result is used as the output of the objective function. Setting the optimization objective function to minimize the parameter means that when optimizing the cross-cloud path of data traffic, the final criterion for selecting the path 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 best scheme. For example, if there are two different cross-cloud transmission paths, and the total cost of the first path scheme through the data storage node of the A cloud service provider to the data processing node of the B cloud service provider and then to the data backup node of the C cloud service provider is higher than that of the second path scheme, then the second path scheme will be selected as the better scheme by the objective function.

[0101] The business service process continuity constraint, cloud service node capacity constraint, data traffic in-out bandwidth constraint, and dynamic billing threshold constraint are set for the cross-cloud path optimization objective function.

[0102] The service flow continuity constraint refers to a requirement that data traffic should follow a user-defined service flow in the transmission process and cannot be arbitrarily interrupted or skipped any necessary service node; for example, a user's disaster recovery service stipulates that data traffic must be transmitted in the order of storage nodes, processing nodes, and backup nodes, and does not allow the processing nodes to be arbitrarily omitted; therefore, when setting the service flow continuity constraint, it is expressed that the data traffic must pass through all the cloud service nodes specified in the service flow; the cloud service node capacity constraint refers to the actual upper limit of the storage, processing, or backup capacity of each cloud service node in the data traffic transmission path, and the data traffic transmitted to a node cannot exceed the actual processing or storage capacity of the node; for example, the data backup node of the C cloud service provider has a low capacity and can only process limited data traffic, and if the data traffic exceeds the maximum allowed capacity of the node, it will violate the cloud service node capacity constraint condition; therefore, the cloud service node capacity constraint condition is expressed as that the data traffic arriving at any node cannot exceed the actual processing or storage capacity specified by the node; the data flow in-out bandwidth constraint refers to the maximum outbound and inbound data flow transmission rate that each cloud service node can withstand per unit time, and requires that the data flow transmission cannot exceed the node bandwidth limit; for example, the data processing node of the B cloud service provider stipulates that the transmission rate of data flow per unit time cannot exceed the maximum allowed value, otherwise it will cause node congestion or service failure; therefore, the data flow in-out bandwidth constraint is expressed as that the transmission rate of data flow at any node cannot exceed the maximum bandwidth rate allowed by the node; the dynamic billing threshold constraint refers to the fact that in the actual data transmission process, the billing amount rules applicable to the cloud service node change dynamically, and when the data transmission volume reaches a certain value, the charging rules will change, for example, the outbound billing amount of the node will change in steps with the increase of the data volume. Therefore, the dynamic billing threshold constraint is expressed as that the cumulative amount of data flow transmission cannot exceed the threshold value that generates additional billing, or when the threshold value is exceeded, the additional billing change must be accepted; the above constraint conditions together ensure that the optimized data flow path scheme has actual operability.

[0103] Solving the cross-cloud path optimization objective function obtains a candidate cross-cloud transmission path set;

[0104] The joint solution is based on the minimization parameters defined by the constraint conditions and the optimization objective function. The solution uses existing optimization solving algorithms, such as linear programming algorithms or integer programming algorithms, to gradually substitute all data flow path schemes that meet the requirements of business service process continuity, node capacity limit, bandwidth limit, and dynamic billing threshold into the objective function, calculate the cumulative billing cost value of each path scheme one by one, compare and record the results one by one, to obtain all path schemes that meet the conditions and form a candidate cross-cloud transmission path set. For example, multiple candidate schemes are input into the optimization function one by one, and the cumulative billing cost of each scheme is calculated to form a path set to be selected. The candidate cross-cloud transmission path set covers all path schemes that meet the constraint conditions and meet the optimization target requirements.

[0105] 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 optimization recommendation path data.

[0106] The cumulative billing cost value corresponding to each path scheme in the candidate cross-cloud transmission path set is extracted, compared, and placed in the front with the lowest billing cost, and so on, to form a low-to-high sorting sequence. After sorting, the path scheme with the lowest cumulative billing cost is determined as the optimization recommendation path data. The optimization recommendation path data records the name of each cloud service node through which the path passes, the transmission order between nodes, the billing amount per unit of data volume, and the cumulative total of billing costs. The optimization recommendation path data is used to guide users to adjust the data transmission path in the actual cross-cloud computing architecture. For example, if the path from the data processing node of the B cloud service provider to the data backup node of the C cloud service provider and then back to the data storage node of the A cloud service provider has the lowest cumulative billing cost after sorting, this path is recommended as the optimization recommendation path data to the user for actual path reconstruction adjustment, thereby achieving the cost minimization goal.

[0107] S6: Based on the optimization recommendation path data, reconstruct the cross-cloud transmission path of the business service and adjust the cross-cloud node configuration, including:

[0108] Based on the optimization recommendation path data, the cross-cloud transmission path and the corresponding cloud service node identifier contained therein are obtained.

[0109] The optimization suggestion path data records a path of data flow transmission, including a node identifier of each cloud service node through which the data flow is transferred and a data transmission sequence between the nodes; for example, a data flow transmission path initially defined by a user is from a data center storage node of a cloud service provider A in East China to a data processing node of a cloud service provider B in North China and then to a backup node of a cloud service provider C in South China, and after optimization of the target function, the optimization suggestion path data recommends that the data flow be transmitted from the data center storage node of the cloud service provider A in East China directly to the backup node of the cloud service provider C in South China and then to the data processing node of the cloud service provider B in North China, so that a cumulative billing cost of the path is minimized; the cloud service node identifier is used to mark a uniform identification code of each cloud service node; the node identifier is composed in sequence of a cloud service provider name, a geographical location, a service type, and a node function type, for example, an expression of the data center storage node identifier of the cloud service provider A in East China is cloud service provider A, East China region, data storage center; a process of obtaining a cross-cloud transmission path and a corresponding cloud service node identifier is a process of extracting all nodes and node identifier information through which the data flow is transmitted from the optimization suggestion path data, and finally forming a corresponding relationship between the cross-cloud transmission path and the cloud service node identifier as input data for path adjustment.

[0110] According to the sequence of the cloud service node identifiers in the cross-cloud transmission path, a business service process configuration of the user in the multi-cloud computing architecture is adjusted, and a data flow forwarding sequence of the business service process is updated.

[0111] The business service process configuration is a node sequence and a logical calling rule of data flow transfer defined by the user in the multi-cloud computing architecture, and the data flow forwarding sequence initially defined by the user may not consider differences in billing rules of different cloud service providers in the multi-cloud computing architecture, resulting in an increase in billing cost; the sequence of the cloud service node identifiers in the cross-cloud transmission path is a sequence of data flow transfer between the nodes determined by the optimization suggestion path data, and reflects a node forwarding sequence that should be taken by the data flow under the most optimized cost condition; for example, an original data flow forwarding sequence defined by the user is from a data storage node of a cloud service provider A to a data processing node of a cloud service provider B and then to a data backup node of a cloud service provider C, and after path optimization, an adjusted node sequence is from the data storage node of the cloud service provider A to the data backup node of the cloud service provider C and then to the data processing node of the cloud service provider B; therefore, the business service process configuration initially defined by the user is re-adjusted according to the sequence of the node identifiers determined by the optimization suggestion path data, that is, the data flow forwarding sequence of the business service process is re-defined and configured, so that the adjusted business service process meets the optimal path scheme determined by the optimization suggestion path data; a process of updating the business service process configuration needs to compare the node sequence specified in the optimization suggestion path data node by node, update a business service process node calling logic of the multi-cloud computing architecture of the user, and finally form an adjusted business service process configuration.

[0112] Adjusting the cross-cloud node configuration in the multi-cloud computing architecture according to the cloud service node identification in the cross-cloud transmission path, updating the cross-cloud data traffic transmission path;

[0113] The cross-cloud node configuration is the setting of the deployment of each cloud service node and the inter-node network connection relationship in the multi-cloud computing architecture. The original cross-cloud node configuration may cause the inter-node data traffic transmission path to be uneconomical and increase the billing cost. The node identification order in the cross-cloud transmission path provided by the optimization suggestion path data determines the optimal data traffic transmission path in the cross-cloud computing architecture, which can be used to adjust the existing cross-cloud node configuration. For example, in the user's original cross-cloud node configuration, a dedicated line network is configured between the East China data center storage node of the A cloud service provider and the North China data processing node of the B cloud service provider. However, through path optimization, it is found that the data traffic transmission should first pass through the South China backup node of the C cloud service provider and then go to the North China data processing node of the B cloud service provider. Therefore, the inter-node network connection needs to be re-planned and deployed, such as establishing a new network connection from the East China data center storage node of the A cloud service provider to the South China backup node of the C cloud service provider, and updating the inter-node network connection relationship. The updating process of the cross-cloud node configuration includes determining the connection relationship between nodes according to the node identification in the cross-cloud transmission path, deleting the old unnecessary connection, and adding the new necessary connection. Through adjustment, the cross-cloud data traffic transmission path can comply with the path order and inter-node connection relationship specified in the optimization suggestion path data, thereby reducing the total billing cost generated in the cross-cloud transmission process of the data traffic. After updating the cross-cloud data traffic transmission path, the inter-node connection relationship and the data flow path are adjusted to comply with the definition of the optimization suggestion path data.

[0114] Standardizing the path node identification and synchronizing the time sequence data of the updated cross-cloud data traffic transmission path to obtain the reconstructed business service cross-cloud transmission path and node configuration data;

[0115] The path node identification standardization is to re-identify all cloud service nodes involved in the updated cross-cloud data traffic transmission path according to the defined unified node identifier coding rule, to ensure the consistency and uniqueness of each node identification in the reconstructed path information; for example, the adjusted cross-cloud data traffic transmission path involves the storage node of the East China data center of the A cloud service provider, the backup node of the South China of the C cloud service provider, and the data processing node of the North China of the B cloud service provider, and the nodes are re-identified according to the unified coding rule, thereby forming consistent standardized node identification data; the time sequence data synchronization processing is to record the time label of each node identification and the data transmission process of the reconstructed data traffic transmission path according to the unified time sequence marking rule; that is, the inflow and outflow of each node, the residence time of data in the node, and the actual time of transmission to the next node are uniformly marked with a time sequence, to ensure the consistency of the time data of all nodes and the data transmission process in the reconstructed cross-cloud transmission path; after the path node identification standardization and the time sequence data synchronization processing are completed, the reconstructed business service cross-cloud transmission path and the node configuration data contain node identification data and time sequence data, and record the transmission time information of each node and the nodes of the data traffic transmission path; the reconstructed business service cross-cloud transmission path and the node configuration data can be used for node configuration and data transmission path deployment of the actual multi-cloud computing architecture, to ensure that the actual implementation of the data traffic transmission process is completely consistent with the optimal scheme determined by the optimized path data, thereby achieving the goal of minimizing the cumulative billing cost.

[0116] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above embodiments can be implemented in whole or in part in the form of 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, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 by wired (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0117] Those skilled in the art can clearly understand that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software manner depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0118] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and module can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0119] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are merely schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0120] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, which can be located in one place or distributed on a plurality of network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0121] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically, or two or more modules can be integrated into one module.

[0122] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0123] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0124] Finally: the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

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. A 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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