A method and apparatus for determining service migration information, an electronic device, a storage medium, and a product

Abstract

The application discloses a kind of service migration information determination method, device, electronic equipment, storage medium and product, relate to data processing technical field, the method includes: when receiving the service migration information determination instruction of distributed system, find in database based on the instruction, obtain the initial characteristic data and reference characteristic data of distributed system, initial characteristic data is the service information of distributed system in current data acquisition period, reference characteristic data is the service information of distributed system in preset data acquisition period;Initial characteristic data and reference characteristic data are analyzed, and the service migration information determination scheme of distributed system is determined according to analysis result;Initial characteristic data and reference characteristic data are processed using service migration information determination scheme, and the target service migration information of distributed system is obtained.The application can automatically detect the service migration information of distributed system, improves the detection efficiency and accuracy of service migration information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a service migration information determination method and device, electronic equipment, storage medium and product. BACKGROUND

[0002] The service migration of the homogenized deployment cluster in the distributed system refers to the migration of part of the business or part of the instance in the cluster, and different migration contents will cause different changes of the cluster. Detailed service migration information is an important data support for intelligent operation and maintenance. Therefore, the identification efficiency and accuracy of the service migration information of the service migration are one of the key problems in the field of data processing.

[0003] Currently, the determination process of the service migration information is semi-automatic. For example, the deviation degree of the newly added data and the reference data in the distributed system is determined at regular intervals, and the deviation degree of the newly added data is evaluated. When the deviation degree is greater than the reference threshold, the management personnel is notified to further investigate whether the service migration occurs in the distributed system. If the service migration occurs, the management personnel further verifies the service migration information of the service migration, such as the migration type of the service migration and the migration point of the service migration. However, the manual intervention to verify the service migration and confirm the service migration information not only prolongs the determination time of the service migration information and reduces the identification efficiency of the service migration information, but also reduces the identification accuracy of the service migration information. SUMMARY

[0004] The present application provides a service migration information determination method, device, electronic equipment, storage medium and product, which aims to automatically detect the service migration information of the distributed system and improve the detection efficiency and accuracy of the service migration information.

[0005] According to an aspect of the present application, a service migration information determination method is provided, which comprises:

[0006] Upon receiving the service migration information determination instruction of the distributed system, the initial feature data and the reference feature data of the distributed system are obtained by searching in the database based on the service migration information determination instruction, wherein the initial feature data is the business information of the distributed system in the current data acquisition period, the reference feature data is the business information of the distributed system in the preset data acquisition period, and the preset data acquisition period is earlier than the current data acquisition period;

[0007] The initial feature data and the reference feature data are analyzed for correlation, and the service migration information determination scheme of the distributed system is determined according to the analysis result;

[0008] The initial feature data and the reference feature data are processed by using the service migration information determination scheme to obtain the target service migration information of the distributed system.

[0009] According to another aspect of the present application, there is provided a device for determining service migration information, the device being configured to implement the method for determining service migration information according to any of the embodiments of the present application, the device comprising:

[0010] an information obtaining module configured to, upon receiving a service migration information determination instruction for the distributed system, perform a search in the database based on the service migration information determination instruction, and obtain initial feature data and reference feature data of the distributed system, wherein the initial feature data is service information of the distributed system in a current data acquisition period, the reference feature data is service information of the distributed system in a preset data acquisition period, and the preset data acquisition period is earlier than the current data acquisition period;

[0011] a scheme determining module configured to perform a correlation analysis on the initial feature data and the reference feature data, and determine a service migration information determination scheme of the distributed system according to an analysis result;

[0012] an information determining module configured to process the initial feature data and the reference feature data using the service migration information determination scheme, and obtain target service migration information of the distributed system.

[0013] According to another aspect of the present application, there is provided an electronic device, the electronic device comprising:

[0014] at least one processor; and a memory connected to the at least one processor in communication;

[0015] wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for determining service migration information according to any of the embodiments of the present application.

[0016] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions, the computer instructions being configured to enable a processor to implement the method for determining service migration information according to any of the embodiments of the present application when executed by the processor.

[0017] According to another aspect of the present application, there is provided a computer program product comprising a computer program, the computer program being configured to implement the method for determining service migration information according to any of the embodiments of the present application when executed by a processor.

[0018] The method for determining service migration information provided by the application comprises the following steps: when receiving the service migration information determination instruction of the distributed system, searching in the database based on the service migration information determination instruction to obtain the initial characteristic data and the reference characteristic data of the distributed system, wherein the initial characteristic data is the service information of the distributed system in the current data acquisition period, the reference characteristic data is the service information of the distributed system in the preset data acquisition period, and the preset data acquisition period is earlier than the current data acquisition period; performing correlation analysis on the initial characteristic data and the reference characteristic data, and determining the service migration information determination scheme of the distributed system according to the analysis result; and processing the initial characteristic data and the reference characteristic data by using the service migration information determination scheme to obtain the target service migration information of the distributed system. When receiving the service migration information determination instruction, the initial characteristic data and the reference characteristic data in the database are called according to the query requirement contained in the service migration information determination instruction, the correlation analysis is performed on the two types of data, the service migration information determination scheme is determined according to the correlation analysis result, the initial characteristic data and the reference characteristic data are processed by using the determined scheme, the participation of the management personnel can be reduced, the dependence of the service migration information determination work on the artificial can be reduced, and the detection efficiency and quality of the service migration information can be effectively improved. The problems that the service migration information determination time is prolonged, the service migration information recognition efficiency is reduced, and the service migration information recognition accuracy is low are solved.

[0019] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0021] Figure 1 is a flowchart of a service migration information determination method provided by the application;

[0022] Figure 2 is a flowchart of another service migration information determination method provided by the application;

[0023] Figure 3 is a flowchart of a target service migration information determination method provided by the application;

[0024] Figure 4 is a flowchart of another target service migration information determination method provided by the present application;

[0025] Figure 5 is a structural diagram of a service migration information determination device provided by the present application;

[0026] Figure 6 is a structural diagram of an electronic device provided by the present application. DETAILED DESCRIPTION

[0027] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the scope of protection of the present application.

[0028] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices. The acquisition, transmission, storage, use, processing and the like of data in the technical scheme of the present application comply with the relevant provisions of national laws and regulations. It should be noted that in the embodiments of the present application, some industry existing schemes such as software, components, models and the like may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical scheme of the present application, but does not mean that the applicant has or will necessarily use the scheme.

[0029] Homogeneous deployment is a deployment strategy that integrates all components of a distributed system together and deploys them to each instance of the system, which can make the applications running on each instance of the system exactly the same, and simplify the complexity of the system in deployment and running. The service migration information detected by the present application is essentially the service migration information of the homogeneously deployed cluster in the distributed system.

[0030] Figure 1is a flowchart of a method for determining service migration information provided by the present application. The embodiment can be applied to determine whether service migration occurs in a distributed system, the migration type and migration time of the service migration, and the like. The method can be executed by a device for determining service migration information provided by the present application. The device can be realized in the form of hardware and / or software. In a specific embodiment, the device can be integrated in an electronic device. The following embodiment will be described by taking the device integrated in the electronic device as an example. Referring to Figure 1 , the method specifically includes the following steps.

[0031] S101, when receiving a service migration information determination instruction of a distributed system, searching in a database based on the service migration information determination instruction to obtain initial feature data and reference feature data of the distributed system.

[0032] The service migration information determination instruction can be understood as an indication of detecting service migration information of a homogeneous deployment cluster of the distributed system, and is used to instruct the processor to start the determination of the service migration information. The service migration information determination instruction can be triggered at a fixed time or determined according to the query requirement of a user. The database can be understood as a component for storing running data or business information of the distributed system. The initial feature data is the running data or business information of the distributed system in a current data acquisition period, and the reference feature data is the running data or business information of the distributed system in a preset data acquisition period. The preset data acquisition period is earlier than the current data acquisition period.

[0033] According to the detection logic of the service migration information, the feature data can be acquired by hours, days, or weeks. Taking the acquisition of the feature data by weeks as an example, the current data acquisition period can be understood as a week in which the current time is located or a week generated by extending 168 hours from the current time, and the preset data acquisition period can be understood as a week set in advance. The preset data acquisition period is earlier than the current data acquisition period, for example, a week before the current week, a preset number of weeks before the current week, and the like. The embodiment is not limited in this regard.

[0034] In an embodiment, assuming that the feature data is business transaction volume data in the homogeneous cluster, the preset data acquisition period can be understood as a data acquisition period before the current data acquisition period. When receiving the service migration information determination instruction, the processor will call the business transaction volume data of the distributed system in the current data acquisition period and the previous data acquisition period stored in the database, and take the called business transaction volume data in the current data acquisition period as the initial feature data and take the called business transaction volume data in the previous data acquisition period as the reference feature data.

[0035] The advantage of such arrangement is that the initial feature data and the reference feature data can be quickly retrieved, and the determination efficiency of the service migration information is accelerated.

[0036] In S102, correlation analysis is performed on the initial feature data and the reference feature data, and a service migration information determination scheme of the distributed system is determined according to an analysis result.

[0037] The correlation analysis can be understood as analyzing the correlation or similarity of the initial feature data and the reference feature data, the analysis result can be understood as an analysis conclusion of the correlation or similarity of the initial feature data and the reference feature data, and the service migration information determination scheme can be understood as a determination manner of the service migration information.

[0038] The analysis result includes high correlation of the initial feature data and the reference feature data and low correlation of the initial feature data and the reference feature data, and the analysis result is used to indicate the determination manner of the service migration information, and different analysis results correspond to different service migration information determination schemes.

[0039] Specifically, the analysis result of high correlation corresponds to a first service migration information determination scheme, and the first service migration information determination scheme is used to instruct the server to calculate the service migration information according to a first data processing rule; the analysis result of low correlation corresponds to a second service migration information determination scheme, and the second service migration information determination scheme is used to instruct the server to calculate the service migration information according to a second data processing rule.

[0040] In one embodiment, the correlation analysis is performed on the initial feature data and the reference feature data, if the analysis result indicates that the initial feature data and the reference feature data have high correlation, the service migration information determination scheme of the distributed system is determined as the first service migration information determination scheme; if the analysis result indicates that the initial feature data and the reference feature data have low correlation, the service migration information determination scheme of the distributed system is determined as the second service migration information determination scheme.

[0041] The advantage of such arrangement is that the service migration information determination scheme of the distributed system can be quickly located, so that the initial feature data and the reference feature data can be processed in a targeted manner, and the target service migration information can be obtained more quickly and with higher quality.

[0042] In S103, the initial feature data and the reference feature data are processed by using the service migration information determination scheme, and target service migration information of the distributed system is obtained.

[0043] The target service migration information can be understood as detailed migration information of a service migration action of a homogeneous deployment cluster of the distributed system, including but not limited to service migration relocation time.

[0044] In one embodiment, assuming that the service migration information determination scheme is to fit the initial feature data and the reference feature data according to the weight preset in the scheme, the fitting result is determined as the service migration migration time. The processor will fit the initial feature data and the reference feature data according to the weight in the service migration information determination scheme determined in S102, and the fitted time is taken as the service migration migration time of the distributed system, that is, the target service migration information of the distributed system.

[0045] The advantage of such arrangement is that the target service migration information can be determined automatically, reducing the participation of the management personnel in the work of the target service migration information, saving human resources while improving the determination efficiency and accuracy of the target service migration information.

[0046] The technical scheme of the embodiment can call the initial feature data and the reference feature data in the database according to the query requirement contained in the service migration information determination instruction when receiving the service migration information determination instruction, analyze the correlation of the two types of data, determine the determination scheme of the service migration information according to the correlation analysis result, and process the initial feature data and the reference feature data by using the determined scheme, which can reduce the participation of the management personnel, reduce the dependence of the determination work of the service migration information on the human, and effectively improve the detection efficiency and quality of the service migration information. The problems of prolonging the determination time of the service migration information, reducing the identification efficiency of the service migration information, and reducing the identification accuracy of the service migration information are solved.

[0047] Figure 2 is a flowchart of another method for determining service migration information provided by the application. The embodiment can be applied to determine whether the distributed system has service migration, the migration type and migration time of the service migration, and the like, and is used to determine the service migration information of the machine migration. The method can be executed by the service migration information determination device provided by the application. The device can be realized in the form of hardware and / or software. In one specific embodiment, the device can be integrated in an electronic device. The following embodiment will be described by taking the device integrated in the electronic device as an example. For reference Figure 2 , the method specifically includes the following steps:

[0048] S201, when receiving the service migration information determination instruction of the distributed system, analyzing the service migration information determination instruction to obtain the data query duration and the first data query time of the distributed system.

[0049] The data query duration can be understood as a feature data retrieval time period contained in the service migration information determination instruction, for example, three days, five days, one week, etc., and the first data query time can be understood as an instruction generation or sending time contained in the service migration information determination instruction, used to represent the generation time of the detection requirement of the distributed system. The advantage of such setting is that the acquisition range of the initial feature data and the reference feature data can be quickly located.

[0050] In S202, the first query time period and the second data query time are determined according to the data query duration and the first data query time, and the initial feature data is obtained by searching in the database based on the first query time period.

[0051] The initial feature data is the business transaction volume data of the distributed system in the current data acquisition period, the first query time period can be understood as a data retrieval parameter generated according to the first data query time and the data query duration, used to retrieve the initial feature data from the database, the first data query time is defined by the present application as the end time of the first query time period, the time corresponding to the data query duration before the first data query time is the start time of the first query time period, the transaction volume data in the first query time period is the initial feature data of the distributed system, and whether the initial feature data exists service migration represents whether the homogeneous deployment cluster of the distributed system has service migration. The second data query time can be understood as a time node generated according to the first data query time and the data query duration, used to retrieve the reference feature data.

[0052] In one specific example, it is assumed that the data query duration is one week, the data acquisition period of the reference feature data is one period before the data acquisition period of the initial feature data, the first data query time is A time, and A time has uniqueness within one week, which can be specifically to a certain minute of a certain day within one week in minute unit, or to a certain hour of a certain day within one week in hour unit. Then, the first query time period is the time period from A time of the last week to A time of the current week, the second data query time is A time of the last week, and the initial feature data is the business transaction volume data of the distributed system in the time period from A time of the last week to A time of the current week.

[0053] In S203, the second query time period is determined according to the data query duration and the second data query time, and the reference feature data is obtained by searching in the database based on the second query time period.

[0054] The reference feature data is the business transaction volume data of the distributed system in a preset data acquisition period, and the preset data acquisition period is earlier than the current data acquisition period. The second query time period can be understood as a data retrieval parameter generated according to the second data query time and the data query duration, and is used to retrieve the reference feature data from the database. The application defines the transaction volume data in the second query time period as the reference feature data of the distributed system, and the reference feature data is used to evaluate whether the initial feature data has migration.

[0055] In a specific example, assuming that the data query duration is one week and the second data query time is A time of the last week, the second query time period is the time period between A time of the week before last and A time of the last week, and the reference feature data is the business transaction volume data of the distributed system in the time period between A time of the week before last and A time of the last week.

[0056] The advantage of such setting is that complete initial feature data and reference feature data are obtained, so as to improve the accuracy of the correlation analysis conclusion.

[0057] It is worth noting that, on the one hand, the application will divide the obtained initial feature data and reference feature data according to weekdays and weekends, and respectively perform service migration detection, so as to achieve the goal of divide and conquer. On the other hand, the application will preliminarily screen the obtained initial feature data and reference feature data, and delete the transaction volume data of abnormal services, so as to guarantee the detection accuracy of the service migration information.

[0058] S204, performing correlation analysis on the initial feature data and the reference feature data, and determining an initial migration type of the distributed system according to an analysis result.

[0059] The correlation analysis can be understood as analyzing the correlation or similarity of the initial feature data and the reference feature data, the analysis result can be understood as an analysis conclusion of the correlation or similarity of the initial feature data and the reference feature data, and the initial migration type can be understood as a preliminary conclusion of the migration type of the service migration action of the distributed system. Specifically, the migration type of the service migration includes machine migration and business migration, the machine migration type is used to indicate that there is instance migration in the distributed system, and the business migration type is used to indicate that there is business migration in the distributed system.

[0060] The analysis result is used to indicate an initial migration type of the service migration, and the initial migration type includes high correlation of the initial feature data and the reference feature data and low correlation of the initial feature data and the reference feature data, different analysis results correspond to different initial migration types, for example, the analysis result of low correlation of the initial feature data and the reference feature data corresponds to a business migration type, and the analysis result of high correlation of the initial feature data and the reference feature data corresponds to a machine migration type. The advantage of such setting is that the type of service migration can be quickly located, the detailed service migration information can be determined in a targeted manner, and the determination efficiency of the service migration information is improved.

[0061] In an embodiment, S204 can specifically include: determining a first standard deviation of the initial feature data, a first covariance of the initial feature data, a second standard deviation of the reference feature data, and a second covariance of the reference feature data; processing the first standard deviation, the first covariance, the second standard deviation, and the second covariance to obtain an analysis result containing a correlation value; and comparing a preset correlation threshold with the correlation value to obtain the initial migration type according to a comparison result.

[0062] The correlation value can be understood as a data representing the correlation degree of the initial feature data and the reference feature data, and the preset correlation threshold can be understood as a basis for judging the correlation of the initial feature data and the reference feature data according to the correlation value in the analysis result, for example, 0.75, 0.8, 0.85, etc. The specific value is related to the detection logic of the service migration information, and is not limited in the present embodiment. When the analysis result is greater than the preset correlation threshold, the initial migration type is determined as a machine migration type, and it is preliminarily determined that there may be instance migration in the distributed system. When the analysis result is not greater than the preset correlation threshold, the initial migration type is determined as a business migration type, and it is preliminarily determined that there may be business migration in the distributed system. The advantage of such setting is that the judgment standard of the service migration type can be quantified, and the determination time of the service migration type is shortened.

[0063] In an embodiment, the correlation value Wherein, x represents the product of the standard deviations of the initial feature data, that is, the product of the plurality of standard deviations in the first standard deviation, y represents the product of the standard deviations of the reference feature data, that is, the product of the plurality of standard deviations in the second standard deviation, Cov(x, x) represents the covariance of the initial feature data, that is, the first covariance, Cov(y, y) represents the covariance of the reference feature data, that is, the second covariance, and n represents the total number of data.

[0064] Taking the preset correlation threshold of 0.8 as an example, when the correlation value is greater than 0.8, it is considered that the initial feature data and the reference feature data are highly correlated, the initial migration type is determined as a machine migration type, otherwise, the initial migration type is determined as a business migration type.

[0065] S205, determine that the migration point detection scheme corresponding to the initial migration type is a service migration information determination scheme.

[0066] The migration point can be understood as the time when the service migration action occurs, and the service migration information determination scheme can be understood as a method for further verifying the service migration type and determining the service migration action occurrence time. Specifically, the migration point detection scheme includes a horizontal migration detection scheme corresponding to the machine migration type and a trend mutation detection scheme corresponding to the service migration type. When the initial migration type is the service migration type, the service migration information determination scheme is determined to be the trend mutation detection scheme; when the initial migration type is the machine migration type, the service migration information determination scheme is determined to be the horizontal migration detection scheme.

[0067] The advantage of such a setting is that the service migration information determination scheme can be quickly located so as to detect the service migration information in a targeted manner.

[0068] S206, processing the initial feature data and the reference feature data by using the service migration information determination scheme to obtain target service migration information of the distributed system.

[0069] The target service migration information can be understood as detailed migration information of the service migration action of the homogenization deployment cluster of the distributed system, including but not limited to the migration type and the migration point of the service migration.

[0070] In this step, the server will process the initial feature data and the reference feature data according to the service migration information determination scheme determined in the S205 step, and determine the target service migration information of the distributed system according to the processing result.

[0071] The technical scheme of the embodiment can analyze the service migration information determination instruction when it is received, obtain the query requirement contained in the instruction, for example, the data query time length and the first data query time of the distributed system directly contained and the second data query time and the determination of the second query time period indirectly contained, call the initial feature data and the reference feature data in the database according to the query requirement contained in the service migration information determination instruction, perform correlation analysis on the two types of data, and determine the initial migration type of the distributed system and the determination scheme of the service migration information according to the correlation analysis result. Processing the initial feature data and the reference feature data by using the determined scheme can reduce the participation of the management personnel, reduce the dependence of the determination of the service migration information on the artificial, effectively improve the detection efficiency and the quality of the service migration information. The service migration information can be detected in all directions, the detection efficiency is improved while the labor input is reduced, and the purpose of reducing cost and increasing benefit is achieved. The problems that the service migration action is verified and the service migration information is confirmed by the management personnel, the determination time of the service migration information is prolonged, the identification efficiency of the service migration information is reduced, and the identification accuracy of the service migration information is low are solved.

[0072] The application can use different service migration information detection methods to process different migration types, and achieve the goal of divide and conquer. Figure 3 It is a flowchart of a method for determining target service migration information provided by the application, which is used to determine the service migration information of the machine migration type. On the basis of the above embodiment, when the service migration information determination scheme is the horizontal relocation detection scheme, S206 includes the following steps:

[0073] S301, calculate the offset proportion of the initial feature data and the reference feature data to obtain at least one offset data.

[0074] The initial feature data includes transaction volume data at multiple time points, and the reference feature data also includes reference transaction volume data at multiple time points. The transaction volume data in the initial feature data and the reference transaction volume data in the reference feature data are one-to-one corresponding, with the position of the data in the data acquisition period or the generation time as the limiting condition. The offset proportion can be understood as the offset degree of each transaction volume data in the initial feature data and the corresponding reference transaction volume data, and the offset data can be understood as a quantitative representation value of the offset degree.

[0075] In one embodiment, S301 can specifically include: dividing the initial feature data and the reference feature data based on the machine migration clustering duration respectively to obtain at least one sub-initial feature data and at least one sub-reference feature data, wherein the sub-initial feature data and the sub-reference feature data correspond one by one; processing the at least one sub-initial feature data and the at least one sub-reference feature data respectively by using a preset data screening rule to obtain at least one to-be-solved sub-initial feature data and at least one to-be-solved sub-reference feature data, wherein the to-be-solved sub-initial feature data and the to-be-solved sub-reference feature data correspond one by one; and performing offset ratio calculation on the at least one to-be-solved sub-initial feature data and the at least one to-be-solved sub-reference feature data to obtain at least one offset data; wherein the offset data corresponds to the to-be-solved sub-initial feature data and the to-be-solved sub-reference feature data one by one.

[0076] The machine migration clustering duration can be understood as a time unit for dividing the initial feature data during horizontal relocation detection. In the present application, the machine migration clustering duration is one hour, that is, one week is taken as a data acquisition period. The initial feature data and the reference feature data are divided based on the machine migration clustering duration to obtain at least one sub-initial feature data and at least one sub-reference feature data, which can be understood as dividing the initial feature data into 168 sub-initial feature data and dividing the reference feature data into 168 sub-reference feature data according to one hour as the division basis. The data in a week is 120, and the data on the weekend is 48. The data in a week and the data on the weekend are processed separately.

[0077] The preset data screening rule can be understood as a data screening rule, for example, selecting data between the tenth and the ninth tenths, selecting data between the twentieth and the eightieth tenths, and the like. The purpose of this is to screen out the maximum data and the minimum data and reduce the influence of discrete data on the detection conclusion. The to-be-solved sub-initial feature data can be understood as the sub-initial feature data after screening, and the to-be-solved sub-reference feature data can be understood as the sub-reference feature data after screening.

[0078] The offset ratio calculation on the at least one to-be-solved sub-initial feature data and the at least one to-be-solved sub-reference feature data to obtain at least one offset data can be understood as calculating the offset degree of each to-be-solved sub-initial feature data and its corresponding to-be-solved sub-reference feature data respectively to obtain the offset numerical value of the two, so as to quantify the offset degree of each to-be-solved sub-initial feature data.

[0079] Specifically, for any one to be solved operator initial feature data, the offset ratio calculation is performed on the to-be-solved operator initial feature data and the to-be-solved operator reference feature data corresponding to the to-be-solved operator initial feature data, to obtain offset data corresponding to the to-be-solved operator initial feature data, including: determining the offset data based on the aggregated mean of the to-be-solved operator initial feature data, the aggregated mean of the to-be-solved operator reference feature data, and the data minimum value.

[0080] The aggregated mean of the to-be-solved operator initial feature data can be understood as the average of the plurality of transaction volume data in the to-be-solved operator initial feature data, the aggregated mean of the to-be-solved operator reference feature data can be understood as the average of the plurality of transaction volume data in the to-be-solved operator reference feature data, and the data minimum value can be understood as a weight parameter for calculating the offset data.

[0081] Specifically, the offset data Wherein, c represents the aggregated mean of the to-be-solved operator initial feature data, m represents the aggregated mean of the to-be-solved operator reference feature data, and l represents the data minimum value.

[0082] At least one offset data of the application is a sequence of offset data, including 120 offset data or 48 offset data.

[0083] S302, clustering at least one offset data to obtain at least one data cluster.

[0084] The data cluster can be understood as a cluster of offset data, for example, a data cluster formed by dividing offset data that are relatively close according to distance values.

[0085] In one embodiment, S302 can specifically include: sorting at least one offset data according to the order from small to large data values; determining the distance values of each offset data and adjacent offset data, and clustering the offset data whose distance values are less than a preset neighborhood radius to obtain at least one data cluster.

[0086] The preset neighborhood radius can be understood as a basis for evaluating the distance between two offset data and whether they can be clustered into a data cluster. Only two offset data whose distance values are less than the preset neighborhood radius can be clustered.

[0087] Specifically, the preset neighborhood radius s=α*(1 / w), wherein w represents the size of the homogenization deployment cluster, and a represents the attenuation coefficient, which is 0.8 by default. The minimum neighborhood number is 1, indicating that a single data can also be grouped.

[0088] S303, determining target service migration information based on a preset data threshold, data values and data time identifiers of at least one data cluster.

[0089] The data value of the data cluster can be understood as the value of each transaction data in the data cluster, and the data time identifier of the data cluster can be understood as the generation time of each transaction data in the data cluster, which is similar to an index and is used to locate the migration point of service migration.

[0090] The preset data threshold can be understood as a basis for determining whether the data cluster needs to be further detected. The data cluster that needs to be further detected is a data cluster that may exist service migration. The advantage of this setting is that the data cluster is preliminarily screened, and only the data cluster that may exist service migration is detected, thereby reducing the detection workload of service migration information.

[0091] Specifically, the preset data threshold is -a*(1 / w), where w represents the size of the homogenized deployment cluster, and a represents a decay coefficient, which is 0.8 by default.

[0092] In an embodiment, S303 can specifically include: determining a data cluster with a data value less than the preset data threshold as a candidate data cluster; determining whether there is a data cluster with a deviation degree higher than a preset deviation in the candidate data cluster; if there is no data cluster with a deviation degree higher than the preset deviation, determining that the first target migration type is a no-migration type, and determining the first target migration type as the target service migration information; and if there is a data cluster with a deviation degree higher than the preset deviation, determining that the second target migration type is a machine migration type, determining a target migration point based on the data time identifier of the data cluster with the deviation degree higher than the preset deviation, and determining the second target migration type and the target migration point as the target service migration information.

[0093] The candidate data cluster can be understood as a data cluster that needs to be further detected for service migration information. The preset deviation can be understood as a basis for evaluating whether each transaction data in the candidate data cluster is migration data, and the deviation degree can be understood as the degree of deviation of each transaction data in the candidate data cluster. The no-migration type is used to indicate that there is no service migration action in the distributed system, and the machine migration type indicates that there is a machine migration action in the distributed system, i.e., the distributed system has a partial instance migration, and the target migration point is the time when the machine migration action occurs.

[0094] Specifically, assuming that the preset deviation is 0.9, this embodiment will determine the deviation degree of each transaction data in the candidate data cluster in the order of the data time identifier from early to late, and when the deviation degree of a certain transaction data is higher than 0.9, the calculation will be stopped, and the data time identifier corresponding to the data will be determined as the target migration point. It should be noted that the transaction data in the candidate data cluster is the processed initial feature data to be solved.

[0095] Deviation degree Wherein, p represents the size of the candidate data cluster, that is, the number of transaction volume data in the candidate data cluster, q represents the length of the initial feature data to be calculated, that is, the number of initial feature data to be calculated, and the embodiment is 120 or 48, and j represents the data time identifier.

[0096] Specifically, the process of determining the target migration point based on the data time identifier includes: 1) calculating the migration day, migration day day = j / / 24 + 1; 2) calculating the migration time, migration time hour = j % 24, wherein j represents the data time identifier.

[0097] The advantage of such a setting is that the characteristics of the overall transaction volume feature change by machine migration are utilized to quickly and accurately determine the service migration information of the distributed system.

[0098] Figure 4 is a flowchart of another method for determining target service migration information provided by the application, which is used to determine the service migration information of the business migration type, and when the service migration information determination scheme is the trend mutation detection scheme, S206 includes the following steps based on the above embodiment:

[0099] S401, processing the initial feature data and the reference feature data to obtain at least one trend mutation factor.

[0100] Wherein, the initial feature data includes transaction volume data at multiple time points, and the reference feature data also includes reference transaction volume data at multiple time points. The transaction volume data in the initial feature data and the reference transaction volume data in the reference feature are one-to-one corresponding, with the position of the data in the data acquisition period or the generation time as the limiting condition. The trend mutation factor can be understood as a quantitative value of the mutation degree of each transaction volume data in the initial feature data compared with its corresponding reference transaction volume data.

[0101] In one embodiment, S401 can specifically include: dividing the initial feature data and the reference feature data based on the business migration clustering duration to obtain at least one sub-initial feature data and at least one sub-reference feature data, wherein the sub-initial feature data and the sub-reference feature data are one-to-one corresponding; processing the at least one sub-initial feature data and the at least one sub-reference feature data using a preset data screening rule to obtain at least one initial feature data to be calculated and at least one reference feature data to be calculated, wherein the initial feature data to be calculated and the reference feature data to be calculated are one-to-one corresponding; performing sum-difference calculation on the at least one initial feature data to be calculated and the at least one reference feature data to be calculated to obtain at least one trend mutation factor; wherein the trend mutation factor is one-to-one corresponding to the initial feature data to be calculated and the reference feature data to be calculated.

[0102] The business migration clustering duration can be understood as a time unit for dividing the initial feature data in the mutation trend detection. In the present application, the business migration clustering duration is two hours, that is, the initial feature data and the reference feature data are divided based on the business migration clustering duration, to obtain at least one sub-initial feature data and at least one sub-reference feature data. It can be understood that the initial feature data is divided into 84 sub-initial feature data, and the reference feature data is divided into 84 sub-reference feature data. The data in the week is 60, and the data in the weekend is 24. The data in the week and the data in the weekend are processed separately.

[0103] Similarly, the preset data screening rule is a data screening rule, for example, selecting data between the tenth and the ninth tenth, selecting data between the twentieth and the eighth tenth, and the like. The purpose of this is to filter out the maximum data and the minimum data, and to reduce the influence of discrete data on the detection conclusion. The to-be-solved sub-initial feature data can be understood as the sub-initial feature data after screening, and the to-be-solved sub-reference feature data can be understood as the sub-reference feature data after screening.

[0104] The sum-difference calculation is performed on the at least one to-be-solved sub-initial feature data and the at least one to-be-solved sub-reference feature data to obtain at least one trend mutation factor. It can be understood that the trend mutation factors of each to-be-solved sub-initial feature data and its corresponding to-be-solved sub-reference feature data are calculated respectively, so as to quantify the mutation of each to-be-solved sub-initial feature data.

[0105] For any one to-be-solved sub-initial feature data, the sum-difference calculation is performed on the to-be-solved sub-initial feature data and the to-be-solved sub-reference feature data corresponding to the to-be-solved sub-initial feature data to obtain the trend mutation factor corresponding to the to-be-solved sub-initial feature data, including: based on the aggregate mean of the to-be-solved sub-initial feature data and the aggregate mean of the to-be-solved sub-reference feature data, determining the trend mutation factor.

[0106] The aggregate mean of the to-be-solved sub-initial feature data can be understood as the average of the plurality of transaction volume data in the to-be-solved sub-initial feature data, and the aggregate mean of the to-be-solved sub-reference feature data can be understood as the average of the plurality of transaction volume data in the to-be-solved sub-reference feature data. The trend mutation factor c represents the aggregate mean of the to-be-solved sub-initial feature data, and m represents the aggregate mean of the to-be-solved sub-reference feature data. The at least one trend mutation factor of the present application is a trend mutation factor sequence, including 60 offset data or 24 offset data.

[0107] S402, processing the at least one trend mutation factor to obtain a first business statistic and a second business statistic.

[0108] The first service statistic can be understood as a positive order arrangement sequence of the at least one trend mutation factor, and the second service statistic can be understood as a reverse order arrangement sequence of the at least one trend mutation factor. Specifically, the first service statistic and the second service statistic are used to determine whether each trend mutation factor has a mutation, and if the trend mutation factor has a mutation, the mutation time of the mutation needs to be further detected so as to determine a migration point of the service migration.

[0109] In one embodiment, S402 can specifically include: arranging the at least one trend mutation factor in an order from early to late based on the generation time to obtain a first arrangement result; processing the at least one trend mutation factor according to the first arrangement result to obtain a first rank sequence factor, and determining a first service statistic based on a variance and an expectation of the first rank sequence factor, wherein the first rank sequence factor includes at least one sub-first rank sequence factor, the first service statistic includes at least one sub-first service statistic, and the sub-first service statistic corresponds to the sub-first rank sequence factor in a one-to-one manner; arranging the at least one trend mutation factor in an order from late to early based on the generation time to obtain a second arrangement result; processing the at least one trend mutation factor according to the second arrangement result to obtain a second rank sequence factor, and determining a second service statistic based on a variance and an expectation of the second rank sequence factor, wherein the second rank sequence factor includes at least one sub-second rank sequence factor, the second service statistic includes at least one sub-second service statistic, and the sub-second service statistic corresponds to the sub-second rank sequence factor in a one-to-one manner.

[0110] The first rank sequence factor can be understood as a sum of sequences constructed based on the trend mutation factors arranged in a positive direction, and the second rank sequence factor can be understood as a sum of sequences constructed based on the trend mutation factors arranged in a reverse direction.

[0111] Specifically, the first service statistic wherein k represents the number of the trend mutation factors, E(s k ) represents the expectation of the first rank sequence factor, Var(s k ) represents the variance of the first rank sequence factor, and s k represents the first sequence factor.

[0112] The variance of the first rank sequence factor The expectation of the first rank sequence factor n represents the total number of the trend mutation factors, the first rank sequence factor r i represents the i-th rank sequence parameter, 1≤j≤i≤n. The second service statistic UB kThe determination method of the second service statistical quantity is the same as that of the first service statistical quantity, which is not described herein. Generally, the value of the second service statistical quantity is negative to the value of the first service statistical quantity.

[0113] S403, determining target service migration information based on intersection information of the first service statistical quantity and the second service statistical quantity.

[0114] The intersection information can be understood as intersection point information of curves represented by the first service statistical quantity and the second service statistical quantity, for example, confidence level of the intersection point of the curve of the first service statistical quantity and the curve of the second service statistical quantity, data time identifier of the intersection point, etc., which is used to locate the migration point of the service migration.

[0115] In one embodiment, S403 can specifically include: determining whether the intersection information of the first service statistical quantity and the second service statistical quantity satisfies a service migration condition, wherein the service migration condition is that the intersection point of the first service statistical quantity and the second service statistical quantity is located in a confidence level interval; if the intersection information of the first service statistical quantity and the second service statistical quantity does not satisfy the service migration condition, determining that the first target migration type is a no-migration type, and determining the first target migration type as the target service migration information; if the intersection information of the first service statistical quantity and the second service statistical quantity satisfies the service migration condition, determining that the second target migration type is a service migration type, determining a target migration point based on the first service statistical quantity and the second service statistical quantity, and determining the second target migration type and the target migration point as the target service migration information.

[0116] The confidence level interval is a basis for measuring whether the intersection information is a trend mutation point. Only the intersection information located in the confidence level interval represents that the distributed system has service migration. The confidence level interval is generally [-1.96, 1.96]. Specifically, when the first service statistical quantity curve and the second service statistical quantity curve do not have an intersection point or the intersection information of the two is not in the confidence level interval [-1.96, 1.96], it is determined that the sequence has no trend mutation, the distributed system has no service migration, and the target migration type is a no-migration type. When the intersection information of the first service statistical quantity curve and the second service statistical quantity curve is in the confidence level interval [-1.96, 1.96], it is determined that the sequence has a trend mutation, the distributed system has service migration, and the position corresponding to the intersection point is the position of the beginning of the mutation.

[0117] Optionally, determining the target migration point based on the first service statistics and the second service statistics comprises: performing difference processing on the first service statistics and the second service statistics to obtain at least one difference data, wherein the difference data corresponds to the sub-first service statistics and the sub-second service statistics one by one; if there is difference data whose difference result meets the preset service migration condition, determining the target migration point based on the data time identifier corresponding to the difference data meeting the preset service migration condition; if there is no difference data whose difference result meets the preset service migration condition, arranging the at least one difference data in reverse order, constructing a target service function based on the arranged result, the first service statistics and the second service statistics, solving the target service function, and determining the target migration point according to the function solution result located in the confidence level interval.

[0118] The difference processing on the first service statistics and the second service statistics to obtain at least one difference data can be understood as the difference between each transaction data in the first service statistics and the corresponding transaction data in the second service statistics, to obtain the difference data corresponding to the transaction data. The preset service migration condition is the basis for evaluating whether each transaction data is a trend mutation point. In the present application, the preset service migration condition is that the sequence values (sub-first service statistics and sub-second service statistics) corresponding to the transaction data whose difference result is 0 in the first service statistics and the second service statistics are both in the confidence level interval [-1.96, 1.96]. If there is difference data whose difference result meets the preset service migration condition, the data time identifier corresponding to the difference data meeting the preset service migration condition is determined as the mutation time, and the target migration point is obtained by analyzing the data time identifier. The target migration point analysis process is the same as the above embodiment, which will not be described here.

[0119] The target service function can be understood as a solution algorithm of a mutation point. Specifically, when there is no difference data whose difference result meets the preset service migration condition in the difference result, the target service function needs to be constructed to solve the service migration information. The solving process specifically comprises: 1) solving the positive and negative transformation positions n and n+1 of the first service statistics and the second service statistics; 2) arranging the at least one difference data in reverse order to obtain at least one intermediate difference data; 3) using (n, UFn) and (n+1, UFn+1) to construct a function f(x), and using (n, UBn) and (n+1, UBn+1) to construct a function g(x); 4) setting f(x)-g(x)=0, and determining whether rk in the solution result (k, rk) is located in the confidence level interval [-1.96, 1.96], if it is located in the confidence level interval, determining the time point of the upward rounding and restoration of k as the target migration point, i.e. the occurrence time of the service migration action; if rk is not located in the confidence level interval [-1.96, 1.96], updating the positive and negative transformation positions, and returning to execute step 3).

[0120] The advantage of such an arrangement is that the trend mutation point is detected by taking advantage of the characteristics of the overall transaction volume feature change caused by service migration, and the service migration information of the distributed system is quickly and accurately determined.

[0121] Figure 5 is a structural schematic diagram of a service migration information determination device provided by the present application. As shown in Figure 5 the device comprises an information acquisition module 501, a scheme determination module 502, and an information determination module 503.

[0122] The information acquisition module 501 is configured to, when a service migration information determination instruction of a distributed system is received, perform a search in a database based on the service migration information determination instruction to obtain initial feature data and reference feature data of the distributed system, wherein the initial feature data is business information of the distributed system in a current data acquisition period, the reference feature data is business information of the distributed system in a preset data acquisition period, and the preset data acquisition period is earlier than the current data acquisition period.

[0123] The scheme determination module 502 is configured to perform correlation analysis on the initial feature data and the reference feature data, and determine a service migration information determination scheme of the distributed system according to an analysis result.

[0124] The information determination module 503 is configured to process the initial feature data and the reference feature data by using the service migration information determination scheme to obtain target service migration information of the distributed system.

[0125] Optionally, the information acquisition module 501 is specifically configured to parse the service migration information determination instruction to obtain a data query duration and a first data query time; determine a first query time period and a second data query time according to the data query duration and the first data query time, and perform a search in the database based on the first query time period to obtain the initial feature data; determine a second query time period according to the data query duration and the second data query time, and perform a search in the database based on the second query time period to obtain the reference feature data.

[0126] Optionally, the scheme determination module 502 is specifically configured to perform correlation analysis on the initial feature data and the reference feature data, and determine an initial migration type of the distributed system according to an analysis result; and determine a relocation point detection scheme corresponding to the initial migration type as the service migration information determination scheme.

[0127] Optionally, the scheme determining module 502 is specifically configured to determine a first standard deviation of the initial feature data, a first covariance of the initial feature data, a second standard deviation of the reference feature data, and a second covariance of the reference feature data; process the first standard deviation, the first covariance, the second standard deviation, and the second covariance to obtain an analysis result containing a correlation value; compare the preset correlation threshold and the correlation value, and obtain the initial migration type according to a comparison result.

[0128] Optionally, when the analysis result is greater than the preset correlation threshold, the scheme determining module 502 is specifically configured to determine that the initial migration type is a machine migration type, where the machine migration type is used to indicate that there is instance migration in the distributed system; and when the analysis result is not greater than the preset correlation threshold, the scheme determining module 502 is specifically configured to determine that the initial migration type is a service migration type, where the service migration type is used to indicate that there is service migration in the distributed system.

[0129] Optionally, the migration point detection scheme includes a horizontal migration detection scheme corresponding to the machine migration type and a trend mutation detection scheme corresponding to the service migration type.

[0130] Optionally, when the initial migration type is the service migration type, the scheme determining module 502 is specifically configured to determine that the service migration information determination scheme is the trend mutation detection scheme; and when the initial migration type is the machine migration type, the scheme determining module 502 is specifically configured to determine that the service migration information determination scheme is the horizontal migration detection scheme.

[0131] Optionally, when the service migration information determination scheme is the horizontal migration detection scheme, the information determining module 503 is specifically configured to calculate offset ratios of the initial feature data and the reference feature data to obtain at least one offset data; cluster the at least one offset data to obtain at least one data cluster; and determine target service migration information based on a preset data threshold, data values, and data time identifiers of the at least one data cluster.

[0132] Optionally, the information determining module 503 is specifically configured to divide the initial feature data and the reference feature data based on a machine migration clustering duration to obtain at least one sub-initial feature data and at least one sub-reference feature data, where the sub-initial feature data and the sub-reference feature data are in one-to-one correspondence; process the at least one sub-initial feature data and the at least one sub-reference feature data by using a preset data screening rule to obtain at least one to-be-solved sub-initial feature data and at least one to-be-solved sub-reference feature data, where the to-be-solved sub-initial feature data and the to-be-solved sub-reference feature data are in one-to-one correspondence; and calculate offset ratios of the at least one to-be-solved sub-initial feature data and the at least one to-be-solved sub-reference feature data to obtain at least one offset data, where the offset data is in one-to-one correspondence with the to-be-solved sub-initial feature data and the to-be-solved sub-reference feature data.

[0133] Optionally, for any one to-be-solved operator initial characteristic data, the information determination module 503 is specifically configured to perform offset ratio calculation on the to-be-solved operator initial characteristic data and to-be-solved operator reference characteristic data corresponding to the to-be-solved operator initial characteristic data, to obtain offset data corresponding to the to-be-solved operator initial characteristic data. For example, the offset data is determined based on the aggregate mean of the to-be-solved operator initial characteristic data, the aggregate mean of the to-be-solved operator reference characteristic data, and the data minimum value.

[0134] Optionally, the information determination module 503 is specifically configured to sort at least one offset data in the order from small to large data value; determine distance values of each offset data and adjacent offset data, and cluster offset data with distance values less than a preset neighborhood radius to obtain at least one data cluster.

[0135] Optionally, the information determination module 503 is specifically configured to determine a data cluster with a data value less than a preset data threshold as a candidate data cluster; determine whether there is a data cluster with an offset degree higher than a preset offset in the candidate data cluster; if there is no data cluster with an offset degree higher than the preset offset, determine that the first target migration type is a no-migration type, and determine the first target migration type as the target service migration information; if there is a data cluster with an offset degree higher than the preset offset, determine that the second target migration type is a machine migration type, determine a target migration point based on a data time identifier of the data cluster with the offset degree higher than the preset offset, and determine the second target migration type and the target migration point as the target service migration information.

[0136] Optionally, when the service migration information determination scheme is a trend mutation detection scheme, the information determination module 503 is specifically configured to process the initial characteristic data and the reference characteristic data to obtain at least one trend mutation factor; process the at least one trend mutation factor to obtain a first business statistic and a second business statistic; and determine the target service migration information based on intersection information of the first business statistic and the second business statistic.

[0137] Optionally, the information determination module 503 is specifically configured to divide the initial characteristic data and the reference characteristic data based on a business migration clustering time length to obtain at least one sub-initial characteristic data and at least one sub-reference characteristic data, wherein the sub-initial characteristic data and the sub-reference characteristic data correspond one by one; process the at least one sub-initial characteristic data and the at least one sub-reference characteristic data respectively by using a preset data screening rule to obtain at least one to-be-solved operator initial characteristic data and at least one to-be-solved operator reference characteristic data, wherein the to-be-solved operator initial characteristic data and the to-be-solved operator reference characteristic data correspond one by one; and perform sum-difference calculation on the at least one to-be-solved operator initial characteristic data and the at least one to-be-solved operator reference characteristic data to obtain at least one trend mutation factor, wherein the trend mutation factor corresponds one by one to the to-be-solved operator initial characteristic data and the to-be-solved operator reference characteristic data.

[0138] Optionally, the information determining module 503 is specifically configured to sort the at least one trend mutation factor based on the generation time from early to late to obtain a first sorting result; process the at least one trend mutation factor according to the first sorting result to obtain a first order sequence factor, and determine a first service statistical quantity based on the variance and expectation of the first order sequence factor, wherein the first order sequence factor includes at least one sub-first order sequence factor, the first service statistical quantity includes at least one sub-first service statistical quantity, and the sub-first service statistical quantity corresponds to the sub-first order sequence factor one by one; sort the at least one trend mutation factor based on the generation time from late to early to obtain a second sorting result; process the at least one trend mutation factor according to the second sorting result to obtain a second order sequence factor, and determine a second service statistical quantity based on the variance and expectation of the second order sequence factor, wherein the second order sequence factor includes at least one sub-second order sequence factor, the second service statistical quantity includes at least one sub-second service statistical quantity, and the sub-second service statistical quantity corresponds to the sub-second order sequence factor one by one.

[0139] Optionally, the information determining module 503 is specifically configured to determine whether the intersection information of the first service statistical quantity and the second service statistical quantity satisfies a service migration condition, wherein the service migration condition is that the intersection of the first service statistical quantity and the second service statistical quantity is located in a confidence level interval; if the intersection information of the first service statistical quantity and the second service statistical quantity does not satisfy the service migration condition, determine that the first target migration type is a no-migration type, and determine the first target migration type as the target service migration information; if the intersection information of the first service statistical quantity and the second service statistical quantity satisfies the service migration condition, determine that the second target migration type is a service migration type, determine a target migration point based on the first service statistical quantity and the second service statistical quantity, and determine the second target migration type and the target migration point as the target service migration information.

[0140] Optionally, the information determining module 503 is specifically configured to perform differential processing on the first service statistical quantity and the second service statistical quantity to obtain at least one differential data, wherein the differential data corresponds to the sub-first service statistical quantity and the sub-second service statistical quantity one by one; if there is differential data whose differential result satisfies a preset service migration condition, determine the target migration point based on the data time identifier corresponding to the differential data that satisfies the preset service migration condition; if there is no differential data whose differential result satisfies the preset service migration condition, reverse at least one differential data, construct a target service function based on the reverse result, the first service statistical quantity and the second service statistical quantity, solve the target service function, and determine the target migration point according to the function solution result located in the confidence level interval.

[0141] The service migration information determination apparatus provided in the embodiment can execute the service migration information determination method provided in any embodiment of the application, and has the function modules and beneficial effects corresponding to the execution method.

[0142] Figure 6 is a structural schematic diagram of an electronic device provided by the application. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the application described and / or claimed in this document.

[0143] As shown in Figure 6 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (also referred to as a random access memory, RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0144] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0145] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the method of determining service migration information.

[0146] In some embodiments, the method of determining service migration information can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the method of determining service migration information described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the method of determining service migration information by any other suitable means, such as by means of firmware.

[0147] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0148] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor of the machine, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, and partially on a machine or a remote machine or a server.

[0149] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0150] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0151] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0152] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are a host product in the cloud computing service system to solve the defects of large management difficulty and weak business scalability in traditional physical hosts and VPS services.

[0153] In an embodiment, the present application further includes a computer program product comprising a computer program which, when executed by a processor, implements the method of determining service migration information according to any one of the embodiments of the present application.

[0154] The computer program product can be written in any one of a number of programming languages or combinations thereof, including an object oriented programming language such as Java, Smalltalk, C++, or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0155] It should be understood that the steps shown in the above-described flowcharts can be reordered, added to, or deleted from, as appropriate. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, without limitation, as long as the desired results of the present application are achieved.

[0156] The specific embodiments described above are not intended to limit the scope of the present application. Those skilled in the art will understand that various modifications, combinations, sub-combinations, and alternatives can be made to the specific embodiments without departing from the spirit and principles of the present application. Any further modifications, equivalents, and / or alternatives come within the scope of the present application as recited by the claims.

Claims

1. A method of determining service migration information, characterized by, The method comprises the steps of: Upon receiving a service migration information determination instruction of a distributed system, performing a search in a database based on the service migration information determination instruction to obtain initial feature data and reference feature data of the distributed system, wherein the initial feature data is service information of the distributed system in a current data acquisition period, and the reference feature data is service information of the distributed system in a preset data acquisition period, and the preset data acquisition period is earlier than the current data acquisition period; performing correlation analysis on the initial feature data and the reference feature data, and determining a service migration information determination scheme of the distributed system according to an analysis result; The method comprises the steps of: performing correlation analysis on the initial feature data and the reference feature data, and determining an initial migration type of the distributed system according to an analysis result; determining a relocation point detection scheme corresponding to the initial migration type as the service migration information determination scheme; processing the initial feature data and the reference feature data by using the service migration information determination scheme to obtain target service migration information of the distributed system, wherein the target service migration information is migration information of a service migration action of the distributed system.

2. The method of claim 1, wherein, The method comprises the steps of: parsing the service migration information determination instruction to obtain a data query duration and a first data query time point of the distributed system; determining a first query time period and a second data query time point according to the data query duration and the first data query time point, and performing a search in the database based on the first query time period to obtain the initial feature data; determining a second query time period according to the data query duration and the second data query time point, and performing a search in the database based on the second query time period to obtain the reference feature data.

3. The method of claim 1, wherein, The method comprises the steps of: determining a first standard deviation of the initial feature data, a first covariance of the initial feature data, a second standard deviation of the reference feature data, and a second covariance of the reference feature data; processing the first standard deviation, the first covariance, the second standard deviation, and the second covariance to obtain the analysis result containing a correlation value; comparing a preset correlation threshold value and the correlation value to obtain the initial migration type according to a comparison result.

4. The method of claim 3, wherein, The method comprises the steps of: when the analysis result is greater than the preset correlation threshold value, determining that the initial migration type is a machine migration type, wherein the machine migration type is used to indicate that there is an instance migration in the distributed system. determining that the initial migration type is a service migration type when the analysis result is not greater than the preset correlation threshold, wherein the service migration type is used to indicate that there is service migration in the distributed system.

5. The method of claim 4, wherein, The migration point detection scheme includes a horizontal migration detection scheme corresponding to the machine migration type and a trend mutation detection scheme corresponding to the service migration type. The determination of the migration point detection scheme corresponding to the initial migration type is the service migration information determination scheme, including: When the initial migration type is the service migration type, the service migration information determination scheme is determined to be the trend mutation detection scheme. When the initial migration type is the machine migration type, the service migration information determination scheme is determined to be the horizontal migration detection scheme.

6. The method of claim 5, wherein, Using the horizontal migration detection scheme, the initial feature data and the reference feature data are processed to obtain target service migration information of the distributed system, including: Calculate the offset ratio of the initial feature data and the reference feature data to obtain at least one offset data; Clustering the at least one offset data to obtain at least one data cluster; Based on the preset data threshold, the data value and data time identifier of the at least one data cluster, the target service migration information is determined.

7. The method of claim 6, wherein, The calculation of the offset ratio of the initial feature data and the reference feature data to obtain at least one offset data, including: Based on the machine migration clustering duration, the initial feature data and the reference feature data are divided to obtain at least one sub-initial feature data and at least one sub-reference feature data, wherein the sub-initial feature data and the sub-reference feature data correspond one by one; Using a preset data filtering rule, the at least one sub-initial feature data and the at least one sub-reference feature data are processed to obtain at least one to-be-solved sub-initial feature data and at least one to-be-solved sub-reference feature data, wherein the to-be-solved sub-initial feature data and the to-be-solved sub-reference feature data correspond one by one; The offset ratio of the at least one to-be-solved sub-initial feature data and the at least one to-be-solved sub-reference feature data is calculated to obtain the at least one offset data; wherein the offset data corresponds to the to-be-solved sub-initial feature data and the to-be-solved sub-reference feature data one by one.

8. The method of claim 7, wherein, For any one to-be-solved sub-initial feature data, the offset ratio of the to-be-solved sub-initial feature data and the to-be-solved sub-reference feature data corresponding to the to-be-solved sub-initial feature data is calculated to obtain the offset data corresponding to the to-be-solved sub-initial feature data, including: Based on the aggregation mean of the to-be-solved sub-initial feature data, the aggregation mean of the to-be-solved sub-reference feature data and the data minimum value, the offset data is determined.

9. The method of claim 6, wherein, The clustering of the at least one offset data to obtain at least one data cluster, including: According to the order of data value from small to large, the at least one offset data is sorted; Determine distance values of each offset data and adjacent offset data, and cluster offset data with distance values less than a preset neighborhood radius to obtain the at least one data cluster.

10. The method of claim 6, wherein, The target service migration information is determined based on a preset data threshold, data values and data time identifiers of the at least one data cluster, including: Determine a data cluster with a data value less than the preset data threshold as a candidate data cluster; Determine whether there is a data cluster with an offset degree higher than a preset offset in the candidate data cluster; If there is no data cluster with an offset degree higher than the preset offset, determine a first target migration type as a no-migration type, and determine the first target migration type as the target service migration information; If there is a data cluster with an offset degree higher than the preset offset, determine a second target migration type as a machine migration type, determine a target migration point based on data time identifiers of the data cluster with an offset degree higher than the preset offset, and determine the second target migration type and the target migration point as the target service migration information.

11. The method of claim 5, wherein, The target service migration information of the distributed system is obtained by processing the initial feature data and the reference feature data using the trend mutation detection scheme, including: Processing the initial feature data and the reference feature data to obtain at least one trend mutation factor; Processing the at least one trend mutation factor to obtain a first business statistic and a second business statistic; Determining the target service migration information based on intersection information of the first business statistic and the second business statistic.

12. The method of claim 11, wherein, The processing of the initial feature data and the reference feature data to obtain at least one trend mutation factor includes: Dividing the initial feature data and the reference feature data based on business migration clustering durations respectively to obtain at least one sub-initial feature data and at least one sub-reference feature data, wherein the sub-initial feature data and the sub-reference feature data correspond one-to-one; Processing the at least one sub-initial feature data and the at least one sub-reference feature data respectively using a preset data screening rule to obtain at least one to-be-solved sub-initial feature data and at least one to-be-solved sub-reference feature data, wherein the to-be-solved sub-initial feature data and the to-be-solved sub-reference feature data correspond one-to-one; Performing sum-difference calculation on the at least one to-be-solved sub-initial feature data and the at least one to-be-solved sub-reference feature data to obtain the at least one trend mutation factor; wherein the trend mutation factor corresponds one-to-one to the to-be-solved sub-initial feature data and the to-be-solved sub-reference feature data.

13. The method of claim 11, wherein, The processing of the at least one trend mutation factor to obtain a first business statistic and a second business statistic includes: Sorting the at least one trend mutation factor based on generation time from early to late to obtain a first sorting result; According to the first sorting result, the at least one trend mutation factor is processed to obtain a first order sequence factor, and the first service statistics is determined based on variance and expectation of the first order sequence factor, wherein the first order sequence factor includes at least one sub-first order sequence factor, and the first service statistics includes at least one sub-first service statistics, and the sub-first service statistics corresponds to the sub-first order sequence factor one by one; The at least one trend mutation factor is sorted based on a sequence from late to early of the generation time to obtain a second sorting result; According to the second sorting result, the at least one trend mutation factor is processed to obtain a second order sequence factor, and the second service statistics is determined based on variance and expectation of the second order sequence factor, wherein the second order sequence factor includes at least one sub-second order sequence factor, and the second service statistics includes at least one sub-second service statistics, and the sub-second service statistics corresponds to the sub-second order sequence factor one by one.

14. The method of claim 11, wherein, The intersection information of the first service statistics and the second service statistics is determined to obtain the target service migration information, including: It is determined whether the intersection information of the first service statistics and the second service statistics meets a service migration condition, wherein the service migration condition is that the intersection of the first service statistics and the second service statistics is located in a confidence level interval; If the intersection information of the first service statistics and the second service statistics does not meet the service migration condition, a first target migration type is determined as a no-migration type, and the first target migration type is determined as the target service migration information; If the intersection information of the first service statistics and the second service statistics meets the service migration condition, a second target migration type is determined as a service migration type, a target migration point is determined based on the first service statistics and the second service statistics, and the second target migration type and the target migration point are determined as the target service migration information.

15. The method of claim 14, wherein, The target migration point is determined based on the first service statistics and the second service statistics, including: The first service statistics and the second service statistics are differentially processed to obtain at least one differential data, wherein the differential data corresponds to the sub-first service statistics and the sub-second service statistics one by one; If there is differential data whose differential result meets a preset service migration condition, the target migration point is determined based on a data time identifier corresponding to the differential data that meets the preset service migration condition; If there is no differential data whose differential result meets the preset service migration condition, the at least one differential data is reversed in sequence, a target service function is constructed based on the reversed result, the first service statistics and the second service statistics, the target service function is solved, and the target migration point is determined according to a function solving result located in the confidence level interval.

16. An apparatus for determining service migration information, the apparatus comprising: including: The information obtaining module is configured to, when receiving a service migration information determination instruction of a distributed system, perform a search in a database based on the service migration information determination instruction to obtain initial feature data and reference feature data of the distributed system, wherein the initial feature data is service information of the distributed system in a current data acquisition period, and the reference feature data is service information of the distributed system in a preset data acquisition period, and the preset data acquisition period is earlier than the current data acquisition period. The scheme determination module is configured to perform correlation analysis on the initial feature data and the reference feature data, and determine a service migration information determination scheme of the distributed system according to an analysis result. The scheme determination module is specifically configured to perform correlation analysis on the initial feature data and the reference feature data, and determine an initial migration type of the distributed system according to an analysis result; and determine a relocation point detection scheme corresponding to the initial migration type as the service migration information determination scheme. The information determination module is configured to process the initial feature data and the reference feature data by using the service migration information determination scheme to obtain target service migration information of the distributed system, wherein the target service migration information is migration information of a service migration action of the distributed system.

17. An electronic device, comprising: The computer program product comprises a memory, a processor, and a computer program stored in the memory and executable by the processor, and the processor implements the service migration information determination method according to any one of claims 1 to 15 when executing the computer program.

18. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the service migration information determination method according to any one of claims 1 to 15.

19. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the service migration information determination method according to any one of claims 1 to 15.

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