Porting method and capacity estimation method of application system, device, storage medium

Abstract

The present disclosure provides a method for migrating an application system, as well as a method, device, and storage medium for estimating capacity specifications. The migration method includes: obtaining initial capacity evaluation factors and original system capacity parameters based on the evaluation objectives of the original host platform; obtaining multi-level capacity parameters, and calculating the target capacity specifications of the new system based on the original system capacity parameters and the multi-level capacity parameters; determining the capacity specifications corresponding to the respective physical subsystems of the target system according to the target capacity specifications of the new system, so as to migrate the application system to the respective physical subsystems. The migration method provided by the present disclosure calculates the new system capacity specifications of the target system and the capacity specifications corresponding to the respective physical subsystems based on the evaluation objectives of the original host platform and the obtained multi-level capacity parameters, and then the application system can be migrated from the original host platform to the respective physical subsystems of the target system in combination with production requirements, which can not only maintain the service ability of the application system, but also effectively save the investment in hardware resources.

Description

Technical Field

[0001] The present invention relates to the field of computer application technologies, and more specifically, to a method for transplanting an application system, as well as a method, device, and storage medium for estimating capacity specifications. Background Art

[0002] With the rapid development of banking business, the transaction volume based on mainframes has been growing rapidly at a rate of 20-25% per year, and it is expected to reach more than 20 billion in 2019. The demand for mainframe resources continues to expand, the dependence on mainframes is becoming increasingly serious, and the cost is getting higher and higher. At the same time, with the rapid development of the Internet industry, distributed technologies have become increasingly mature. It has become an optimal technical option to implement the core banking services from mainframes to open platforms using distributed technologies. A main goal of the transformation of the bank's distributed architecture is to transplant the bank's key application systems from the traditional mainframe-based centralized architecture to an open, distributed platform.

[0003] However, due to the structural differences between the mainframe platform and the open distributed platform, when transplanting an application system from the mainframe platform to an open, distributed platform, the problem of how to estimate the capacity of the application system is first faced. Specifically, the mainframe platform adopts an integrated design, and the application systems share the computing resources of the mainframe, and there are mature methods for resource estimation. Under the distributed architecture, each application system needs to be equipped with an independent application server and database server. In the process of transplanting the core application system of a commercial bank from the mainframe platform to a target system based on an open, distributed architecture, the functions of the application system remain unchanged. However, due to the structural differences and the detailed differences in the operating systems, databases, middleware, and hardware resources used, the resource evaluation methods or tools based on the mainframe platform in the past cannot be directly used for the capacity evaluation of the target system for downward migration. Therefore, there is an urgent need to develop a method for estimating the capacity specifications of application systems to achieve the transplantation of application systems. Summary of the Invention

[0004] To solve the above problems or some of the problems existing in the prior art, embodiments of the present invention provide a method for transplanting an application system, as well as a method, device, and storage medium for estimating capacity specifications. Based on the evaluation objectives of the original mainframe platform, initial capacity evaluation elements and original system capacity parameters are determined, and based on the original system capacity parameters and the obtained multi-level capacity parameters, the new system target capacity specifications of the target system are calculated to obtain the operating resources required by the target system and the capacity specifications corresponding to each physical subsystem in the target system. Thus, based on the capacity specifications corresponding to each physical subsystem and production requirements, the application system can be transplanted from the original mainframe platform to each physical subsystem of the target system, effectively saving the investment in hardware resources and reducing resource waste while keeping the service capacity of the system unchanged and having appropriate resource redundancy.

[0005] According to the first aspect of the present invention, an embodiment of the present invention provides a method for migrating an application system. By this migration method, the application system is migrated from the original host platform to a target system based on an open and distributed architecture. The method includes: determining an evaluation target of the original host platform, and obtaining initial capacity evaluation elements based on the evaluation target; determining original system capacity parameters of the original host platform based on the initial capacity evaluation elements; obtaining multi-level capacity parameters, and calculating a new system target capacity specification based on the original system capacity parameters and the multi-level capacity parameters; determining a scale baseline of the operating resources required after migrating the application system from the original host platform to the target system according to the new system target capacity specification; decomposing the scale baseline based on the attributes of each physical subsystem of the target system to obtain the capacity specification corresponding to each physical subsystem; and migrating the application system to each physical subsystem of the target system based on the capacity specification corresponding to each physical subsystem and production requirements.

[0006] Based on the evaluation target of the original host platform, the above embodiment of the present invention determines the initial capacity evaluation elements and the original system capacity parameters, and calculates the new system target capacity specification of the target system based on the original system capacity parameters and the obtained multi-level capacity parameters, so as to determine the operating resources required by the target system and the capacity specification corresponding to each physical subsystem in the target system. Thus, the application system can be migrated from the original host platform to each physical subsystem of the target system based on the capacity specification corresponding to each physical subsystem and production requirements, which can not only maintain the service ability of the application system, but also effectively save the investment in hardware resources and reduce resource waste.

[0007] In some embodiments of the present invention, determining the evaluation target of the original host platform includes: for the online applications of the original host platform, taking the number of transaction processes processed by the online application server per unit time as one of the basic indicators of the evaluation target; for the batch applications of the original host platform, taking the number of accounts processed by the batch application server per unit time as one of the basic indicators of the evaluation target.

[0008] In some embodiments of the present invention, obtaining multi-level capacity parameters and calculating the new system target capacity specification based on the original system capacity parameters and the multi-level capacity parameters includes: obtaining the common-level capacity parameters, and calculating the new system baseline capacity based on the original system capacity parameters and the common-level capacity parameters according to the following formula: R = (A * p) / W, where R is the new system baseline capacity, A is the processing capacity of the application system, p is the difference coefficient between the processing capabilities of the original host platform and the target system application, and W is the single-core processing capacity of the application server transaction; obtaining the operation and maintenance-level capacity parameters, and calculating the new system production capacity based on the new system baseline capacity and the operation and maintenance-level capacity parameters according to the following formula: R / s, where s is the operation and maintenance threshold of the application server; obtaining the business-level capacity parameters, and calculating the new system business capacity based on the new system production capacity and the business-level capacity parameters according to the following formula: (R * (1 + c) Yr ) / s, where c is the annual business growth rate and Yr is the business planning period; obtaining the instance-level capacity parameters, and calculating the application server capacity in the new system target capacity specification based on the new system business capacity and the instance-level capacity parameters according to the following formula: Integer(((R * (1 + c) Yr ) / s / a) + 1) * a, where a is the number of CPU cores of a single application server.

[0009] According to the original system capacity parameters, the obtained multi-level capacity parameters and the calculation relationships between the parameters, the above embodiments of the present invention obtain the new system target capacity specification of the target system, realizing scientific and reasonable resource calculation and evaluation, and avoiding the inaccuracy of manual evaluation and the resulting resource waste.

[0010] In some embodiments of the present invention, the migration method further includes: calculating the database server capacity in the new system target capacity specification according to the following formula: T * (integer(Co / b) + 1) * b, where T is the number of database instances, Co is the number of CPU cores required for the system to process the data volume of a single instance, and b is the number of CPU cores of a single database server.

[0011] In some embodiments of the present invention, the evaluation objectives include: the online processing capacity of the application system and the batch processing capacity of the application system; the initial capacity evaluation factors include: the evaluation objectives, the difference coefficient of the online processing capacity of the original host platform and the target system application, and the difference coefficient of the batch processing capacity of the original host platform and the target system application; the common-level capacity parameters include: the single-core processing capacity of online application server transactions, the single-core processing capacity of batch application server transactions, the single-core processing capacity of online database servers, and the single-core processing capacity of batch database servers; the operation and maintenance-level capacity parameters include: the operation and maintenance thresholds of online application servers, the operation and maintenance thresholds of batch application servers, the operation and maintenance thresholds of online database servers, and the operation and maintenance thresholds of batch database servers; the business-level capacity parameters include: the annual business growth rate and the business planning cycle; the instance-level capacity parameters include: the number of CPU cores of a single application server, the number of CPU cores of a single database server, the data volume of a single instance of the application system, and the number of CPU cores required for the data volume of a single instance of the application system.

[0012] According to the second aspect of the present invention, an embodiment of the present invention provides a method for estimating the capacity specification of an application system. This estimation method can determine the capacity specification required to transplant the application system from the original host platform to a target system based on an open, distributed architecture. The method includes: determining the evaluation objectives of the original host platform and obtaining the initial capacity evaluation factors based on the evaluation objectives; determining the original system capacity parameters of the original host platform based on the initial capacity evaluation factors; obtaining multi-level capacity parameters and calculating the target capacity specification of the new system based on the original system capacity parameters and the multi-level capacity parameters; decomposing the scale baseline based on the attributes of each physical subsystem of the target system to obtain the capacity specification corresponding to each physical subsystem.

[0013] The above embodiments of the present invention determine the initial capacity evaluation factors and the original system capacity parameters based on the evaluation objectives of the original host platform, and calculate the target capacity specification of the new system based on the original system capacity parameters and the obtained multi-level capacity parameters, thereby determining the operating resources required by the target system and the capacity specifications corresponding to each physical subsystem in the target system, achieving scientific and reasonable resource calculation and evaluation, avoiding the inaccuracy of manual evaluation and the resulting resource waste, and thus providing a basis for further application system transplantation.

[0014] In some embodiments of the present invention, determining the evaluation objectives of the original host platform includes: for the online applications of the original host platform, taking the number of transaction processes processed by the online application server per unit time as one of the basic indicators of the evaluation objectives; for the batch applications of the original host platform, taking the number of accounts processed by the batch application server per unit time as one of the basic indicators of the evaluation objectives.

[0015] In some embodiments of the present invention, obtaining multi-level capacity parameters and calculating the new system target capacity specification based on the original system capacity parameters and the multi-level capacity parameters includes: obtaining the common-level capacity parameters, and calculating the new system baseline capacity based on the original system capacity parameters and the common-level capacity parameters according to the following formula: R = (A * p) / W, where R is the new system baseline capacity, A is the processing capacity of the application system, p is the difference coefficient of the processing capacity between the original host platform and the target system application, and W is the single-core processing capacity of the application server transaction; obtaining the operation and maintenance level capacity parameters, and calculating the new system production capacity based on the new system baseline capacity and the operation and maintenance level capacity parameters according to the following formula: R / s, where s is the operation and maintenance threshold of the application server; obtaining the business level capacity parameters, and calculating the new system business capacity based on the new system production capacity and the business level capacity parameters according to the following formula: (R * (1 + c) Yr ) / s, where c is the annual business growth rate and Yr is the business planning cycle; obtaining the instance level capacity parameters, and calculating the application server capacity in the new system target capacity specification based on the new system business capacity and the instance level capacity parameters according to the following formula: Integer(((R * (1 + c) Yr ) / s / a) + 1) * a, where a is the number of CPU cores of a single application server.

[0016] According to the original system capacity parameters, the obtained multi-level capacity parameters and the calculation relationships between the parameters, the above embodiments of the present invention obtain the new system target capacity specification of the target system, realizing scientific and reasonable resource calculation and evaluation, and avoiding the inaccuracy of manual evaluation and the resulting resource waste.

[0017] In some embodiments of the present invention, the migration method further includes: calculating the database server capacity in the new system target capacity specification according to the following formula: T * (integer(Co / b) + 1) * b, where T is the number of database instances, Co is the number of CPU cores required for the data volume processing capacity of a single system instance, and b is the number of CPU cores of a single database server.

[0018] In some embodiments of the present invention, the evaluation objectives include: the online processing ability of the application system and the batch processing ability of the application system; the initial capacity evaluation factors include: the evaluation objectives, the difference coefficient of the online processing ability of the original host platform and the target system application, and the difference coefficient of the batch processing ability of the original host platform and the target system application; the common-level capacity parameters include: the single-core processing ability of the online application server transaction, the single-core processing ability of the batch application server transaction, the single-core processing ability of the online database server, and the single-core processing ability of the batch database server; the operation and maintenance-level capacity parameters include: the operation and maintenance threshold of the online application server, the operation and maintenance threshold of the batch application server, the operation and maintenance threshold of the online database server, and the operation and maintenance threshold of the batch database server; the business-level capacity parameters include: the annual business growth rate and the business planning cycle; the instance-level capacity parameters include: the number of CPU cores of a single application server, the number of CPU cores of a single database server, the data volume of a single instance of the application system, and the number of CPU cores required for the data volume of a single instance of the application system.

[0019] According to the third aspect of the present invention, an embodiment of the present invention provides a computer storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the computer performs the following operations: the operations include the steps included in the estimation method according to any one of the above embodiments.

[0020] According to the fourth aspect of the present invention, an embodiment of the present invention provides a computer device including a memory and a processor. The memory is used to store one or more computer instructions. When the one or more computer instructions are executed by the processor, the estimation method according to any one of the above embodiments can be implemented.

[0021] As can be seen from the above, the application system transplantation method, capacity specification estimation method, device and storage medium provided by the embodiments of the present invention determine the initial capacity evaluation factors and the original system capacity parameters based on the evaluation objectives of the original host platform, and then calculate the new system target capacity specification of the target system based on the original system capacity parameters and the obtained multi-level capacity parameters, so as to obtain the operating resources required by the target system and the capacity specifications corresponding to each physical subsystem in the target system. Furthermore, based on the capacity specifications corresponding to each physical subsystem and the production requirements, the application system is transplanted from the original host platform to each physical subsystem of the target system, which can realize effective application system transplantation based on scientific and reasonable target system resource calculation and evaluation, and reduce the waste of hardware resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a schematic flowchart of the application system transplantation method according to an embodiment of the present invention;

[0023] Figure 2 is a schematic flowchart of a method for estimating the capacity specification based on an application system according to an embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of an interface for obtaining initial capacity evaluation elements of an application system in an original host platform according to an embodiment of the present invention;

[0025] Figure 4 is a schematic diagram showing the specific content of multi - level capacity parameters according to an embodiment of the present invention;

[0026] Figure 5 is a schematic flowchart of a method for estimating the capacity specification based on an application system according to another embodiment of the present invention;

[0027] Figure 6 is a schematic diagram of an interface for obtaining the baseline capacity of a new system by adding common - level capacity parameters based on the initial capacity evaluation elements according to an embodiment of the present invention;

[0028] Figure 7 is a schematic diagram of an interface for obtaining the production capacity of a new system by adding production - level capacity parameters based on the baseline capacity of the new system according to an embodiment of the present invention;

[0029] Figure 8 is a schematic diagram of an interface for obtaining the business capacity of a new system by adding business - level capacity parameters based on the production capacity of the new system according to an embodiment of the present invention;

[0030] Figure 9 is a schematic diagram of an interface for obtaining the target capacity specification of a new system based on the business capacity of the new system and instance - level capacity parameters according to an embodiment of the present invention;

[0031] Figure 10 is a schematic diagram of an interface for the obtained target capacity specification according to an embodiment of the present invention;

[0032] Figure 11 is a specific parameter diagram for an application capacity evaluator to estimate the total number of cores of application servers required for application system E using the empirical method according to an embodiment of the present invention. Detailed implementation manners

[0033] The following elaborates on various aspects of the present invention in conjunction with the accompanying drawings and specific embodiments. Among them, well-known modules, units, and their connections, links, communications, or operations are not shown or described in detail. Moreover, the described features, architectures, or functions can be combined in any manner in one or more embodiments. Those skilled in the art should understand that the following various embodiments are only for illustrative purposes and not for limiting the protection scope of the present invention. It can also be easily understood that the modules, units, or processing methods in the embodiments described herein and shown in the drawings can be combined and designed in various different configurations.

[0034] The following briefly explains the terms used in this article.

[0035] Host platform: mainly refers to a mainframe, a large computer system that uses a dedicated processor instruction set, operating system, and application software. Due to its integrated design, it has high reliability, high availability, and strong I / O processing capabilities, and currently occupies a dominant position in the core systems of the financial industry.

[0036] Distributed Architecture: a brand-new architecture described from the perspectives of architecture and technology. Its application microservices are based on microservice design, data distribution is characterized by physical segmentation and distribution of data, and the distribution of infrastructure such as computing nodes, network nodes, and storage nodes constitutes the basic framework of the new-generation bank distributed information architecture.

[0037] TPS (Transactions Per Second): the number of transaction processes processed per second, which is one of the basic indicators for measuring the online transaction processing capabilities of commercial bank IT systems.

[0038] APM (Account Per Minute): the number of accounts processed per minute, which is one of the basic indicators for measuring the batch transaction processing capabilities of commercial bank IT systems.

[0039] AP server: application server.

[0040] DB server: database server.

[0041] Capacity measurement: starting from the perspective of architecture design, around the system's functional / non-functional design goals, and on the premise of reasonable design, based on objective test data and other determining factors, calculate the operating resources required by the target system.

[0042] Figure 1FIG. 0 is a schematic flow chart of a method for migrating an application system according to an embodiment of the present invention. Through the migration method, the application system can be migrated from the original host platform to a target system based on an open and distributed architecture.

[0043] As Figure 1 shown, in an embodiment of the present invention, the migration method may include: step S11, step S12, step S13, step S14, step S15, and step S16. The above steps will be specifically described below.

[0044] In step S11, determine the evaluation objectives of the original host platform, and obtain initial capacity evaluation factors based on the evaluation objectives. In one embodiment, the evaluation objectives of the original host platform may include, but are not limited to, one or both of the following: the online processing capacity of the application system in the original host platform and the batch processing capacity of the application system. Among them, the online processing capacity of the application system is the number of transaction processes processed by the online application server per unit time, such as the number of transaction processes processed per second (TPS); the batch processing capacity of the application system is the number of accounts processed by the batch application server per unit time, such as the number of accounts processed per minute (APM). In other words, determining the evaluation objectives of the original host platform may include, but are not limited to, the following two: for the online applications of the original host platform, taking the number of transaction processes processed by the online application server per unit time as one of the basic indicators of the evaluation objective; for the batch applications of the original host platform, taking the number of accounts processed by the batch application server per unit time as one of the basic indicators of the evaluation objective. In addition, for the database server of the original host platform, specific evaluation indicators can be determined based on the deployment modes (hybrid deployment, independent deployment, hybrid deployment + independent deployment, etc.) of the online applications and batch applications.

[0045] In another embodiment, the initial capacity evaluation factors may include, but are not limited to, one or more of the following: the evaluation objective, the difference coefficient of the online processing capacity of the application between the original host platform and the target system, and the difference coefficient of the batch processing capacity of the application between the original host platform and the target system.

[0046] In step S12, determine the original system capacity parameters of the original host platform based on the initial capacity evaluation factors. Among them, the original system capacity parameters of the original host platform may include, but are not limited to, one or more of the following: evaluation objective A (online processing capacity X of the application system, batch processing capacity Y of the application system), difference coefficient p of the application processing capacity between the original host platform and the target system (difference coefficient px of the online processing capacity of the application between the original host platform and the target system, difference coefficient py of the batch processing capacity of the application between the original host platform and the target system). Among them, px and py vary according to different algorithms, and their mean values of the evaluated data can be obtained by the expert method.

[0047] In step S13, multi-level capacity parameters are obtained, and a new system target capacity specification is calculated based on the original system capacity parameters and the multi-level capacity parameters. Among them, the multi-level capacity parameters include the following four types: common-level (Common) capacity parameters, operation-level (Operation) capacity parameters, business-level (Business) capacity parameters, and instance-level (Instance) capacity parameters. Specifically, the common-level capacity parameters include, but are not limited to, one or more of the following: the single-core processing capacity W of the application server transaction (the single-core processing capacity Wx of the online application server transaction, the single-core processing capacity Wy of the batch application server transaction), the single-core processing capacity Dx of the online database server, and the single-core processing capacity Dy of the batch database server. And Dx and Dy are obtained according to the test results. The operation-level capacity parameters include at least one of the following: the operation threshold s of the application server (the upper limit s1 of the production safety usage rate of the online AP server (CPU utilization percentage), the upper limit s2 of the production safety usage rate of the batch AP server (CPU utilization percentage), the upper limit s3 of the production safety usage rate of the online database server (CPU utilization percentage), the upper limit s4 of the production safety usage rate of the batch database server (CPU utilization percentage)). And this operation threshold is proposed by the operation and maintenance department based on the production safety upper limit. The business-level capacity parameters include, but are not limited to: the annual business growth rate c, and the business planning cycle Yr (this cycle can be selected in years as the time unit). The instance-level capacity parameters include at least one of the following: the number of CPU cores a of a single application server, the number of CPU cores b of a single database server, and the data volume Z of a single instance of the application system.

[0048] In one implementation, the single-core processing capacity Wx of the online application server transaction and the single-core processing capacity Wy of the batch application server transaction can be from the non-functional test baseline of the application system.

[0049] Specifically, the calculation formula for the processing capacity of a single application server for online applications (the single-core processing capacity Wx of the online application server transaction) is as follows:

[0050] Wx i = X i / (C i* S i )

[0051]

[0052] Among them, Xi is the online processing capacity measured by the application system i in the non-functional test environment, Ci is the total number of CPU cores of the application server corresponding to the non-functional online processing capacity, and Si is the average CPU utilization rate of these application servers;

[0053] The calculation formula for the processing capacity of a single application server for batch applications (the single-core processing capacity Wy of batch application server transactions) is as follows:

[0054] Wy i = Y i / (C i* S i )

[0055]

[0056] Among them, Yi is the number of batch processing accounts measured by application system i in the non-functional test environment, Ci is the total number of CPU cores of the application server corresponding to the batch processing capacity, and Si is the average CPU utilization rate of these application servers.

[0057] An embodiment of the present invention gives a calculation example of the single-core processing capacity Wx of the online application server and the single-core processing capacity Wy of the batch application server of an application system according to the above calculation method:

[0058] When the online server configuration of the non-functional test environment of the application system is: 3 8C16G virtual machines (virtual machines with 8 cores and 16G of memory) for application servers, and 1 28C256G physical machine for the database server, the non-functional test result is 393 transactions per second, and the average CPU utilization rate is 53%, then Wxi = 393 / 24 / 53% = 30 TPS / C. After deducting part of the loss, 25 TPS / C is taken.

[0059] The non-functional test environment batch processing test results of this application system are as follows: 6 million accounts, using 8 8C32G application servers (load 55%) and 1 28C256G database server (load 61%), it takes 4 hours and 25 minutes to complete the accounting batch processing, and the CPU utilization rate of the application server is 55%. Then the single-core processing capacity of the batch application server of this application system is: Wyi = 6 million / 265 Min / 64C / 55% ≈ 650 APM.

[0060] In another embodiment, obtaining multi-level capacity parameters and calculating the target capacity specification of the new system based on the original system capacity parameters and multi-level capacity parameters may specifically include the following steps:

[0061] I. For the online application server

[0062] (1) Obtain the common-level capacity parameter Wx (the single-core processing capacity of the online application server), and calculate the new system benchmark capacity according to the following formula based on the original system capacity parameters (the online processing capacity X of the application system, the difference coefficient px between the original host platform and the online processing capacity of the target system application) and the common-level capacity parameter Wx: R = (X * px) / Wx, where R is the new system benchmark capacity;

[0063] (2) Obtain the operation and maintenance level capacity parameter s1 (the operation and maintenance threshold of the online application server), and calculate the new system production capacity based on the new system benchmark capacity R and the operation and maintenance level capacity parameter s1 according to the formula "R / s1".

[0064] (3) Obtain the business level capacity parameters c (business annual growth rate), Yr (business planning cycle), and calculate the new system business capacity based on the new system production capacity and the business level capacity parameters (c, Yr) according to the formula "(R*(1 + c) Yr ) / s1".

[0065] (4) Obtain the instance level capacity parameter a (the number of CPU cores of a single application server), and calculate the new system target capacity specification based on the new system business capacity and the instance level capacity parameter a according to the following formula "Integer(((R*(1 + c) Yr ) / s1 / a)+1)*a". Among them, the number of CPU cores a of a single application server can be exemplarily selected as 4, that is, the number of CPU cores of a single application server is 4C.

[0066] II. For batch application servers

[0067] (1) Obtain the public level capacity parameter Wy (the single-core processing capacity of batch application server transactions), and calculate the new system benchmark capacity based on the original system capacity parameters (the batch processing capacity Y of the application system, the difference coefficient py between the original host platform and the target system application batch processing capacity) and the public level capacity parameter Wy according to the following formula: R = (Y * py) / Wy, where R is the new system benchmark capacity;

[0068] (2) Obtain the operation and maintenance level capacity parameter s2 (the operation and maintenance threshold of the batch application server), and calculate the new system production capacity based on the new system benchmark capacity R and the operation and maintenance level capacity parameter s2 according to the formula "R / s2".

[0069] (3) Obtain the business level capacity parameters c (business annual growth rate), Yr (business planning cycle), and calculate the new system business capacity based on the new system production capacity and the business level capacity parameters (c, Yr) according to the formula "(R*(1 + c) Yr ) / s2".

[0070] (4) Obtain the instance level capacity parameter a (the number of CPU cores of a single application server), and calculate the new system business capacity based on the new system production capacity and the instance level capacity parameter a according to the following formula "Integer(((R*(1 + c) Yr) / s2 / a) + 1) * a” to calculate the new system target capacity specification. Among them, the number of CPU cores a of a single application server can be exemplarily selected as 8, that is, the number of CPU cores of a single application server is 8C.

[0071] III. For the database server

[0072] (1) Obtain the public-level capacity parameters D (the single-core processing capacity Dx of the online database server, the single-core processing capacity Dy of the batch database server), the operation and maintenance-level capacity parameters (the upper limit s3 of the production safety utilization rate of the online database server, the upper limit s4 of the production safety utilization rate of the batch database server), and the instance-level capacity parameters (the number of CPU cores b of a single database server, the data volume Z of a single instance of the application system)

[0073] (2) Calculate the number of CPU cores required for the processing capacity of the system single-instance data volume Z according to the following formula:

[0074] Co = Max(Xz * px / (Dx * s3), Yz * py / (Dy * s4))

[0075] Among them, Xz is the online design capacity corresponding to this database instance, and Yz is the batch design capacity corresponding to this database instance.

[0076] In one implementation, in addition to the method of taking the maximum value in the above formula for Co, the sum of the two can also be taken as the number of CPU cores required for the processing capacity of the system single-instance data volume Z.

[0077] (3) Calculate the number of CPU cores required for the processing capacity of the system single-instance data volume Z according to the following formula:

[0078] T * (integer(Co / b) + 1) * b

[0079] Among them, T is the number of database instances, and b is the number of CPU cores of a single database server. Among them, b can be exemplarily selected as 40, that is, the number of CPU cores of a single database server is 40C.

[0080] In step S14, determine the scale baseline of the operating resources required after migrating the application system from the original host platform to the target system according to the new system target capacity specification. For example, for a certain application system, calculate the total number of cores of the target capacity of the online application server, the total number of cores of the target capacity of the batch application server, and the total capacity of the database server in the target capacity specification according to steps S11 - S13. Then, the scale baseline of the operating resources required after migrating the application system from the original host platform to the target system includes the total number of cores of the application server, which is the sum of the total number of cores of the target capacity of the online application server and the total number of cores of the target capacity of the batch application server, and the total capacity of the database server.

[0081] In step S15, decompose the scale baseline based on the attributes of each physical subsystem of the target system to obtain the capacity specifications corresponding to each physical subsystem. For example, if the total number of cores of an application server is undertaken by 6 major physical subsystems, the cores of the application server can be horizontally sliced according to the scale and function of each physical subsystem, and the number of cores of the application server for each physical subsystem can be determined in turn.

[0082] In step S16, transplant the application system to each physical subsystem of the target system based on the capacity specifications corresponding to each physical subsystem and production requirements. For example, according to the configuration specifications of the application server in production (e.g., 4C16G or 8C32G), combined with the production high-availability requirements, such as at least three application servers in a cluster and dual-active deployment in the same city, the model and quantity of the application server for each physical subsystem deployed in the data center of the same city can be determined. Thus, the 6 physical subsystems including the deployment location, server model, and server quantity can together form a solution for transplanting the application system to each physical subsystem of the target system. In addition, the deployment decomposition of the database server is similar and will not be elaborated here.

[0083] Adopting the above method of the embodiment of the present invention, determine the initial capacity evaluation elements and the original system capacity parameters based on the evaluation target of the original host platform, and then calculate the new system target capacity specifications of the target system based on the original system capacity parameters and the obtained multi-level capacity parameters, so as to obtain the operating resources required by the target system and the capacity specifications corresponding to each physical subsystem in the target system. Furthermore, transplant the application system from the original host platform to each physical subsystem of the target system based on the capacity specifications corresponding to each physical subsystem and production requirements, which can realize effective application system transplantation based on scientific and reasonable target system resource calculation and evaluation, and reduce the waste of hardware resources.

[0084] Figure 2 It is a schematic flowchart of a capacity specification estimation method based on an application system according to an embodiment of the present invention. Through the estimation method, the capacity specifications required to transplant the application system from the original host platform to a target system based on an open and distributed architecture can be determined.

[0085] As Figure 2 shown, in an embodiment of the present invention, the estimation method may include: step S21, step S22, step S23, and step S24. The above steps will be specifically described below.

[0086] In step S21, determine the evaluation target of the original host platform, and obtain the initial capacity evaluation elements based on the evaluation target.

[0087] In step S22, determine the original system capacity parameter of the original host platform based on the initial capacity evaluation factor.

[0088] In step S23, obtain multi-level capacity parameters, and calculate the new system target capacity specification based on the original system capacity parameter and the multi-level capacity parameters.

[0089] In step S24, determine the scale baseline of the operating resources required after migrating the application system from the original host platform to the target system according to the new system target capacity specification.

[0090] The above steps S21 to S24 correspond to Figure 1 steps S11 to S14 in

[0091] Adopting the above method of the embodiment of the present invention, determine the initial capacity evaluation factor and the original system capacity parameter according to the evaluation target of the original host platform, and calculate the new system target capacity specification of the target system based on the original system capacity parameter and the obtained multi-level capacity parameters, so as to determine the operating resources required by the target system and the capacity specifications corresponding to each physical subsystem in the target system, realizing scientific and reasonable resource calculation and evaluation, avoiding the inaccuracy of manual evaluation and the resulting resource waste, thereby providing a basis for further application system migration. Under the premise of keeping the service capacity of the system unchanged and having appropriate resource redundancy, the scale of infrastructure resources can be greatly reduced, effectively saving hardware investment. It can not only guide the distributed transformation of applications based on the host platform, but also has reference value for the distributed transformation of open centralized applications.

[0092] The embodiment of the present invention provides a calculation example of the capacity specification required to migrate the application system E from the original host platform to a target system based on an open, distributed architecture according to the above estimation method:

[0093] When migrating the application system E with about 4800 MIPS of production resources under the host platform to a target system based on an open, distributed architecture, the capacity evaluation process of the required application server and database server is as follows:

[0094] First, determine the evaluation target (as shown in Figure 3 , the transaction per second (TPS) of the application system in the original host platform is 300, and the application per minute (APM) of the application system is 8400), and convert the main control elements of the capacity evaluation process into capacity evaluation parameters familiar to evaluators, that is, Figure 4 the common-level capacity parameters, operation and maintenance-level capacity parameters (that is, the production-level capacity parameters in Figure 3 ), business-level capacity parameters, and instance-level capacity parameters shown in

[0095] Secondly, calculate the capacity parameters at each level according to the following logical relationships:

[0096] Out i =func i (Out i-1 ,Para i ), i = 1, 2, 3, 4

[0097] Among them, Out i-1 represents the output of the previous capacity state, Out i represents the data of the applied capacity in this state, Para i represents the input of the capacity evaluation parameters at this level, and func i represents the algorithm for capacity evaluation at this level. As Figure 5 shown, the state order of the capacity parameters at each level is: common-level capacity parameters, operation and maintenance-level capacity parameters (i.e., production-level capacity parameters), business-level capacity parameters, instance-level capacity parameters; the calculation order is: based on the algorithm between the initial capacity evaluation elements and the common-level capacity parameters, the benchmark capacity of the new system can be obtained, based on the algorithm between the benchmark capacity of the new system and the operation and maintenance-level capacity parameters (i.e., production-level capacity parameters), the production capacity of the new system can be obtained, based on the algorithm between the production capacity of the new system and the business-level capacity parameters, the business capacity of the new system can be obtained, and based on the algorithm between the business capacity of the new system and the instance-level capacity parameters, the target capacity specification of the new system can be obtained. Among them, the specific algorithms can be seen in Figure 1 the description of step S13.

[0098] More specifically, for the capacity evaluation of the application system E, Figures 6 - 9 respectively, it is a schematic diagram of the interface for obtaining the benchmark capacity of the new system by adding common-level capacity parameters based on the initial capacity evaluation elements, obtaining the production capacity of the new system by adding operation and maintenance-level capacity parameters (i.e., Figure 3 the production-level capacity parameters in

[0099] Figure 10 the production capacity of the new system, obtaining the business capacity of the new system by adding business-level capacity parameters based on the production capacity of the new system, and obtaining the target capacity specification of the new system based on the business capacity of the new system and the instance-level capacity parameters.

[0100] is the target capacity specification required to finally transplant the application system E from the original host platform to the target system based on an open, distributed architecture, that is, the number of cores of the online transaction AP server (the total number of cores of the target capacity of the online application server) is 46C, the number of cores of the batch transaction AP server (the total number of cores of the target capacity of the batch application server) is 479C, and the number of cores of the DB server (the total number of cores of the database server capacity) is 154C.

[0100] Finally, based on the total number of cores of the online application server target capacity, the total number of cores of the batch application server target capacity, and the total number of cores of the database server capacity obtained from the above calculations, it can be known that the scale baseline of the operating resources required to transplant the application system E from the original host platform to the target system based on an open and distributed architecture includes: the total number of cores of the application server: 46 + 479 = 525C, and the total number of cores of the database server capacity is 154C.

[0101] In addition, Figure 11 It is a specific parameter diagram for the application capacity evaluator to use the empirical method to evaluate the total number of cores of the application server required for the application system E. As Figure 11 shown, the total number of cores of the application server required to evaluate the application system E using the empirical method is 3376C, and the database server is 2208C. However, when using the capacity specification estimation method provided by the embodiments of the present invention, the scale of the application server is 525 / 3376 = 15.55% of the manually evaluated scale, and the scale of the database server is 154 / 2208 = 6.97% of the manually evaluated scale. Therefore, the capacity specification estimation method provided by the embodiments of the present invention can effectively save the investment in hardware resources and reduce resource waste on the premise of maintaining the service ability unchanged and having appropriate resource redundancy.

[0102] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software combined with a hardware platform. Based on such an understanding, all or part of the technical solution of the present invention that contributes to the background technology can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0103] Correspondingly, the embodiments of the present invention further provide a computer-readable storage medium, on which computer-readable instructions or programs are stored. When the computer-readable instructions or programs are executed by a processor, the computer is enabled to perform the following operations: The operations include the steps included in the estimation method described in any one of the above embodiments, which will not be repeated here. Among them, the storage medium may include: for example, optical disc, hard disk, floppy disk, flash memory, magnetic tape, etc.

[0104] In addition, the embodiments of the present invention further provide a computer device including a memory and a processor. The memory is used to store one or more computer instructions or programs. Among them, when the one or more computer instructions or programs are executed by the processor, the estimation method described in any one of the above embodiments can be implemented. The computer device can be, for example, a server, a desktop computer, a notebook computer, a tablet computer, etc.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention. Therefore, the protection scope of the present invention should be subject to the claims.

Claims

1. A method for transplanting an application system, characterized in that, The application system is transplanted from the original host platform to the target system based on the open and distributed architecture by the transplantation method; Wherein, the transplantation method comprises: Determining an assessment target of the original host platform, and obtaining an initial capacity assessment factor based on the assessment target; Determining original system capacity parameters of the original host platform based on the initial capacity assessment factors; Acquire multi-level capacity parameters, and calculate new system target capacity specifications based on the original system capacity parameters and the multi-level capacity parameters; Determine the scale baseline of the operating resources required after the application system is transplanted from the original host platform to the target system according to the target capacity specification of the new system; Decomposing the scale baseline based on the properties of each physical subsystem of the target system, and obtaining the capacity specifications corresponding to each physical subsystem; Transplanting the application system to each physical subsystem of the target system based on the capacity specifications and production requirements corresponding to each physical subsystem; Wherein, determining the evaluation target of the original host platform includes: For the online application of the original host platform, the number of transactions processed per unit time by the online application server is used as one of the basic indicators of the evaluation target; For the batch application of the original host platform, the number of accounts processed per unit time by the batch application server is used as one of the basic indicators of the evaluation target; The step of obtaining multi-level capacity parameters and calculating the target capacity specifications of the new system based on the original system capacity parameters and the multi-level capacity parameters includes: Obtain the public level capacity parameter, and calculate the new system baseline capacity based on the original system capacity parameter and the public level capacity parameter according to the following formula: R=(A*p) / W Where R is the benchmark capacity of the new system, A is the processing capacity of the application system, p is the difference coefficient between the application processing capacity of the original host platform and the target system, and W is the single-core processing capacity of the application server transaction; The operation and maintenance level capacity parameters are obtained, and the new system production capacity is calculated based on the new system baseline capacity and the operation and maintenance level capacity parameters according to the following formula: R / s, where s is the operation and maintenance threshold of the application server; Obtain the service-level capacity parameter, and calculate the new system service capacity based on the new system production capacity and the service-level capacity parameter according to the following formula: (R*(1 + c) Yr ) / s, where c is the annual business growth rate and Yr is the business planning period; The instance-level capacity parameters are obtained, and the application server capacity in the target capacity specification of the new system is calculated according to the following formula based on the new system service capacity and the instance-level capacity parameters: Integer(((R*(1 + c) Yr ) / s / a)+1)*a, where a is the number of CPU cores of a single said application server.

2. The transplantation method according to claim 1, wherein The transplantation method further comprises: The database server capacity in the target capacity specification of the new system is calculated according to the following formula: T*(integer(Co / b)+1)*b, where T is the number of database instances, Co is the number of CPU cores required for the system's single-instance data processing capability, and b is the number of CPU cores for a single database server.

3. The transplantation method according to claim 2, characterized in that: The evaluation targets include: the online processing capability of the application system and the batch processing capability of the application system; The initial capacity evaluation factors include: the evaluation objective, the difference coefficient of the online processing capabilities between the original host platform and the target system applications, and the difference coefficient of the batch processing capabilities between the original host platform and the target system applications; The common-level capacity parameters include: the single-core processing capacity of online application server transactions, the single-core processing capacity of batch application server transactions, the single-core processing capacity of online database server transactions, and the single-core processing capacity of batch database server transactions; The operation and maintenance-level capacity parameters include: the operation and maintenance thresholds of online application servers, the operation and maintenance thresholds of batch application servers, the operation and maintenance thresholds of online database servers, and the operation and maintenance thresholds of batch database servers; The business-level capacity parameters include: the annual business growth rate and the business planning cycle; The instance-level capacity parameters include: the number of CPU cores of a single application server, the number of CPU cores of a single database server, the data volume of a single instance of the application system, and the number of CPU cores required for the data volume of a single instance of the application system.

4. A method for estimating the capacity specification based on an application system, characterized in that, Determine the capacity specifications required to migrate the application system from the original host platform to the target system based on an open, distributed architecture through the estimation method; Among them, the estimation method includes: Determine the evaluation objective of the original host platform and obtain the initial capacity evaluation factors based on the evaluation objective; Determine the original system capacity parameters of the original host platform based on the initial capacity evaluation factors; Obtain multi-level capacity parameters and calculate the target capacity specifications of the new system based on the original system capacity parameters and the multi-level capacity parameters; Determine the scale baseline of the operating resources required after migrating the application system from the original host platform to the target system according to the target capacity specifications of the new system; Decompose the scale baseline based on the attributes of each physical subsystem of the target system to obtain the capacity specifications corresponding to each physical subsystem; Among them, determining the evaluation objective of the original host platform includes: For the online applications of the original host platform, take the number of transaction processes processed by the online application server per unit time as one of the basic indicators of the evaluation objective; For the batch applications of the original host platform, take the number of accounts processed by the batch application server per unit time as one of the basic indicators of the evaluation objective; Among them, obtaining multi-level capacity parameters and calculating the target capacity specifications of the new system based on the original system capacity parameters and the multi-level capacity parameters includes: Obtain the common-level capacity parameters and calculate the baseline capacity of the new system based on the original system capacity parameters and the common-level capacity parameters according to the following formula: R = (A * p) / W Among them, R is the baseline capacity of the new system, A is the processing capacity of the application system, p is the difference coefficient of the application processing capabilities between the original host platform and the target system, and W is the single-core processing capacity of the application server transactions; Obtain the operation and maintenance-level capacity parameters and calculate the production capacity of the new system based on the baseline capacity of the new system and the operation and maintenance-level capacity parameters according to the following formula: R / s, where s is the operation and maintenance threshold of the application server; Obtain the business-level capacity parameters and calculate the business capacity of the new system based on the production capacity of the new system and the business-level capacity parameters according to the following formula: (R * (1 + c) Yr ) / s, where c is the business annual growth rate and Yr is the business planning cycle; Obtain instance-level capacity parameters, and calculate the application server capacity in the new system target capacity specification based on the new system service capacity and the instance-level capacity parameters according to the following formula: Integer(((R * (1 + c) Yr ) / s / a) + 1) * a, where a is the number of CPU cores of a single application server mentioned above.

5. The estimation method according to claim 4, characterized in that The estimation method further includes: Calculate the database server capacity in the new system target capacity specification according to the following formula: T * (integer(Co / b) + 1) * b, where T is the number of database instances, Co is the number of CPU cores required for the system's single-instance data volume processing capacity, and b is the number of CPU cores of a single database server.

6. The estimation method according to claim 5, wherein: The evaluation objectives include: the online processing capacity of the application system and the batch processing capacity of the application system; The initial capacity evaluation factors include: the evaluation objectives, the difference coefficient of the online processing capacity of the original host platform and the target system application, and the difference coefficient of the batch processing capacity of the original host platform and the target system application; The common-level capacity parameters include: the single-core processing capacity of the online application server transaction, the single-core processing capacity of the batch application server transaction, the single-core processing capacity of the online database server, and the single-core processing capacity of the batch database server; The operation and maintenance level capacity parameters include: the operation and maintenance threshold of the online application server, the operation and maintenance threshold of the batch application server, the operation and maintenance threshold of the online database server, and the operation and maintenance threshold of the batch database server; The business-level capacity parameters include: the annual business growth rate and the business planning cycle; The instance-level capacity parameters include: the number of CPU cores of a single application server, the number of CPU cores of a single database server, the data volume of a single instance of the application system, and the number of CPU cores required for the data volume of a single instance of the application system.

7. A computer storage medium storing computer software instructions, characterized in that, The computer software instructions are executed by a processor to implement the estimation method according to any one of claims 4-6.

8. A computer device, which includes a memory and a processor; It is characterized in that The memory is used to store one or more computer instructions, and the processor executes the one or more computer instructions to implement the estimation method according to any one of claims 4-6.

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