A method and device for determining system capacity

Abstract

The embodiment of the present invention provides a method and device for determining system capacity, the method comprising collecting current operation information of each business subsystem in an application system, determining the real-time information of each capacity influencing factor from the current operation information of the business subsystem for at least one business subsystem, and determining the system capacity of the business subsystem according to the real-time information of each capacity influencing factor and the capacity calculation model of the business subsystem. Since the system capacity is automatically calculated by combining the real-time information of each capacity influencing factor, the weight of each capacity influencing factor and the capacity calculation model, excessive manual intervention can be avoided, which helps to reduce the time and manpower consumed by manually determining the system capacity, and helps to ensure the real-time and accuracy of the system capacity determination, thereby solving the problem of low evaluation efficiency and low accuracy caused by manual evaluation of system capacity in the prior art.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of financial technology (Fintech), and in particular, to a method and device for determining system capacity. Background Art

[0002] With the development of computer technology, more and more technologies are applied in the financial field. Traditional finance is gradually transforming to financial technology. However, due to the security and real-time requirements of the financial industry, higher requirements are also placed on technology. In the financial field, due to the concurrency of financial business execution, the banking system may execute a large number of financial businesses in a period of time, which requires the banking system to provide sufficient capacity to handle these financial businesses. Therefore, timely evaluation of the capacity of the banking system is extremely important to ensure the normal operation of the banking system.

[0003] The existing capacity assessment method of the banking system is mainly based on manual estimation by operation and maintenance personnel or cost operation planners. Specifically, when the capacity of the banking system needs to be assessed, the operation and maintenance personnel or cost operation planners obtain the operating status of the banking system during operation (such as the request processing time of each transaction in the system, the number of machines in the system, the machine load, etc.), and calculate the operating data in the operating status to obtain the evaluation result of the capacity of the banking system. However, since this processing method mainly relies on manual calculation by operation and maintenance personnel or cost operation planners, it takes a long time and effort, and since the evaluated banking systems are mostly online systems, the application after the assessment is lagging. In addition, due to inaccurate evaluation results in the early stage or omissions in manual calculations, the assessed capacity is less than the actual capacity. In the process of going online, the system may cause abnormal transactions due to insufficient system load, resulting in abnormal customer funds, thereby causing serious losses to customers. It is also possible that the assessed capacity is much larger than the actual required capacity, which will lead to waste of server resources and increase in bank operating costs.

[0004] In summary, there is an urgent need for a method for determining system capacity to solve the problem in the prior art of manually evaluating system capacity, which results in low evaluation efficiency and low accuracy. Summary of the invention

[0005] The embodiments of the present invention provide a method and device for determining system capacity, so as to solve the problem in the prior art that manual evaluation of system capacity leads to low evaluation efficiency and low accuracy.

[0006] In a first aspect, an embodiment of the present invention provides a method for determining system capacity, including:

[0007] Collect the current operation information of each business subsystem in the application system;

[0008] For at least one business subsystem, determining real-time information of each capacity influencing factor from current operation information of the business subsystem;

[0009] The system capacity of the business subsystem is determined according to the real-time information of each capacity influencing factor and the capacity calculation model of the business subsystem; wherein the capacity calculation model is provided with the weight of each capacity influencing factor determined by the historical operation information of the business subsystem.

[0010] In the above technical solution, the current operation information of each business subsystem in the application system is obtained in real time, and for at least one business subsystem, the real-time information of each capacity influencing factor is determined from the current operation information of the business subsystem. Then, the system capacity of the business subsystem is determined based on the real-time information of each capacity influencing factor and the capacity calculation model of the business subsystem. Since the system capacity is automatically calculated by combining the real-time information of each capacity influencing factor, the weight of each capacity influencing factor and the capacity calculation model, excessive manual intervention can be avoided, which helps to reduce the time and manpower consumed by manually determining the system capacity, and helps to ensure the real-time and accuracy of the system capacity determination, thereby solving the problem of low evaluation efficiency and low accuracy caused by manual evaluation of system capacity in the prior art.

[0011] Optionally, the weight of each capacity influencing factor is determined according to the following method:

[0012] Based on the hierarchical analysis method, the historical operation information of the application system is analyzed and processed to determine the first influencing factors as the solution layer, the second influencing factors as the criterion layer and the system capacity as the target layer; each capacity influencing factor includes the first influencing factors and the second influencing factors;

[0013] Based on the hierarchical analysis method, a first weight of each of the first influencing factors and a second weight of each of the second influencing factors are determined.

[0014] In the above technical scheme, by analyzing and processing the historical operation information of the application system through the hierarchical analysis method, the first weight of each first influencing factor and the second weight of each second influencing factor can be quickly and accurately determined, so that the subsequent capacity calculation model can determine the system capacity of the business subsystem according to the first weight of each first influencing factor and the second weight of each second influencing factor.

[0015] Optionally, the first influencing factors include single-machine processing capacity and the number of machines; the second influencing factors include disaster recovery capacity, machine load, storage load and business volume load.

[0016] Optionally, determining the system capacity of the service subsystem according to the real-time information of each capacity influencing factor and a capacity calculation model of the service subsystem includes:

[0017] Determine the maximum system capacity of the service subsystem according to the following formula (1);

[0018] The formula (1) is:

[0019] Maximum system capacity = maximum business processing capacity / (load status × load weight) ………… (1)

[0020] Determine the disaster recovery system capacity of the business subsystem according to the following formula (2);

[0021] The formula (2) is:

[0022] Disaster recovery system capacity = disaster recovery capability × maximum system capacity × weight of disaster recovery capability…………(2)

[0023] The load condition includes machine load, storage load and business load, and the disaster recovery capacity is the ratio of the number of available machines to the total number of machines.

[0024] In the above technical solution, the maximum system capacity and disaster recovery system capacity of the business subsystem can be determined efficiently and accurately through the capacity calculation model, and the system capacity of the business subsystem can be determined in real time and accurately based on the maximum system capacity and disaster recovery system capacity of the business subsystem without relying on excessive manual intervention, which helps to reduce the time and manpower consumed by manual determination of system capacity, and helps to ensure the real-time and accuracy of system capacity determination.

[0025] Optionally, the business subsystem is an online business subsystem;

[0026] Determine the maximum service processing capacity of the online service subsystem according to the following formula (3);

[0027] The formula (3) is:

[0028]

[0029] The maximum TPS request volume (Transactions Per Second) indicates the maximum number of request transactions per unit time.

[0030] Optionally, the business subsystem is a batch business subsystem;

[0031] Determine the maximum service processing capacity of the batch service subsystem according to the following formula (4);

[0032] The formula (4) is:

[0033]

[0034] Optionally, the method further comprises:

[0035] The dynamic system capacity of the service subsystem is determined according to the current operation information of the service subsystem.

[0036] In the above technical solution, the current operating information of the business subsystem is obtained in real time, and based on the current operating information of the business subsystem, the dynamic system capacity of the business subsystem can be accurately determined in real time, so as to provide support for the subsequent real-time and accurate determination of the system capacity of the business subsystem according to the capacity calculation model.

[0037] In a second aspect, an embodiment of the present invention further provides a device for determining system capacity, including:

[0038] The collection unit is used to collect the current operation information of each business subsystem in the application system;

[0039] A processing unit is used to determine, for at least one business subsystem, real-time information of each capacity influencing factor from current operation information of the business subsystem; determine the system capacity of the business subsystem based on the real-time information of each capacity influencing factor and a capacity calculation model of the business subsystem; wherein the capacity calculation model is provided with weights of each capacity influencing factor determined by historical operation information of the business subsystem.

[0040] Optionally, the processing unit is specifically configured to:

[0041] The weights of the capacity influencing factors are determined as follows:

[0042] Based on the hierarchical analysis method, the historical operation information of the application system is analyzed and processed to determine the first influencing factors as the solution layer, the second influencing factors as the criterion layer and the system capacity as the target layer; each capacity influencing factor includes the first influencing factors and the second influencing factors;

[0043] Based on the hierarchical analysis method, a first weight of each of the first influencing factors and a second weight of each of the second influencing factors are determined.

[0044] Optionally, the processing unit is specifically configured to:

[0045] The first influencing factors include the processing capacity of a single machine and the number of machines; the second influencing factors include disaster recovery capacity, machine load, storage load and business volume load.

[0046] Optionally, the processing unit is specifically configured to:

[0047] Determine the maximum system capacity of the service subsystem according to the following formula (1);

[0048] The formula (1) is:

[0049] Maximum system capacity = maximum business processing capacity / (load status × load weight) ………… (1)

[0050] Determine the disaster recovery system capacity of the business subsystem according to the following formula (2);

[0051] The formula (2) is:

[0052] Disaster recovery system capacity = disaster recovery capability × maximum system capacity × weight of disaster recovery capability…………(2)

[0053] The load condition includes machine load, storage load and business load, and the disaster recovery capacity is the ratio of the number of available machines to the total number of machines.

[0054] Optionally, the business subsystem is an online business subsystem;

[0055] The processing unit is specifically used for:

[0056] Determine the maximum service processing capacity of the online service subsystem according to the following formula (3);

[0057] The formula (3) is:

[0058]

[0059] The maximum TPS request volume (Transactions Per Second) indicates the maximum number of request transactions per unit time.

[0060] Optionally, the business subsystem is a batch business subsystem;

[0061] The processing unit is specifically used for:

[0062] Determine the maximum service processing capacity of the batch service subsystem according to the following formula (4);

[0063] The formula (4) is:

[0064]

[0065] Among them, the storage performance coefficient and network delay coefficient are both constants.

[0066] Optionally, the processing unit is further configured to:

[0067] The dynamic system capacity of the service subsystem is determined according to the current operation information of the service subsystem.

[0068] In a third aspect, an embodiment of the present invention provides a computing device, including:

[0069] Memory for storing computer programs;

[0070] The processor is used to call the computer program stored in the memory and execute the method for determining the system capacity according to the obtained program.

[0071] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer-executable program, and the computer-executable program is used to enable a computer to execute a method for determining system capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0073] Figure 1 A schematic diagram of a system architecture provided for an embodiment of the present invention;

[0074] Figure 2 A schematic diagram of a flow chart of a method for determining system capacity provided by an embodiment of the present invention;

[0075] Figure 3 A schematic diagram of a hierarchical structure model provided by an embodiment of the present invention;

[0076] Figure 4 A schematic diagram of the relationship between system capacity and number of requests in an online subsystem capacity calculation model provided by an embodiment of the present invention;

[0077] Figure 5 A schematic diagram of the relationship between system capacity and the number of faulty machines in an online subsystem capacity calculation model provided by an embodiment of the present invention;

[0078] Figure 6 A schematic diagram of capacity calculation results of an online subsystem provided in an embodiment of the present invention;

[0079] Figure 7 A schematic diagram of the relationship between the capacity of a batch task subsystem and the running time of batch tasks provided by an embodiment of the present invention;

[0080] Figure 8 A schematic diagram of the relationship between the number of accounts involved in a batch task and the processing time of the batch task provided by an embodiment of the present invention;

[0081] Fig. 9 A schematic diagram of capacity calculation results of a batch task subsystem provided in an embodiment of the present invention;

[0082] Fig.10 A schematic diagram of the structure of a device for determining system capacity provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] First, some terms involved in the embodiments of the present invention are explained below to facilitate understanding by those skilled in the art.

[0085] (1) IT system: IT stands for Internet Technology. IT system refers to Internet application system. The application system mentioned in the embodiments of the present invention may refer to the Internet application system of bank deposit business, and of course may also refer to other transaction application systems in the financial field, which is not limited in the embodiments of the present invention.

[0086] (2) Capacity: The amount of server resources required by an application system (such as a banking IT system) to support terminal service requests.

[0087] (3) Capacity platform: used to calculate the capacity of application systems (such as banking IT systems) and provide reasonable capacity assessment results for reference by operation and maintenance personnel.

[0088] (4) TPS: Transactions Per Second, which means the number of transactions per second. A transaction refers to the process in which a client sends a request to a server and the server responds. The timing starts when the client sends a request and ends when the client receives a response from the server. This is used to calculate the time used and the number of completed transactions. A transaction is a transaction.

[0089] As mentioned above, some terms involved in the embodiments of the present invention are introduced. Now, the technical features involved in the embodiments of the present invention are introduced.

[0090] Since the prior art relies on manual evaluation of the capacity of the bank system, it takes a long time and effort, and since the bank systems to be evaluated are mostly online systems, the application after the evaluation is lagging. In addition, due to the inaccurate manual evaluation results, the evaluated capacity is less than the actual capacity or the evaluated capacity is greater than the actual capacity, resulting in transaction anomalies or waste of server resources due to insufficient load in the system. Therefore, the embodiment of the present invention constructs a server capacity platform to replace manual evaluation to quickly and accurately evaluate the system capacity, so as to solve the problem of low evaluation efficiency and low accuracy caused by manual evaluation of system capacity in the prior art.

[0091] In order to facilitate understanding of the embodiments of the present invention, first Figure 1 The system architecture shown in the figure is used as an example to illustrate the system architecture applicable to capacity calculation in the embodiment of the present invention. Figure 1 As shown, Figure 1 As shown, the system architecture may include a capacity platform and external systems. Since application systems (such as bank deposit IT systems, etc.) generally include online systems and batch task systems, the objects collected by the capacity platform are online systems and batch task systems.

[0092] The capacity platform may include a collection module 101 and a capacity calculation module 102. The capacity calculation module may include an online system calculation module 1021 and a batch task system calculation module 1022.

[0093] The collection module 101 is used to collect the operation information of the application system (such as a bank deposit IT system, etc.) (for example, regular collection, the collection frequency is once every 1 second). At the same time, the collected operation information of the application system is analyzed, classified and integrated. For example, the number of requests of the online system collected per second is regularized (weighted average), sorted into 1 minute of data, and input to the capacity calculation module 102 for capacity calculation. Among them, the collection module can be set in the server to facilitate the collection of the operation information of the application system, or the operation information of the application system can be manually collected and classified and sorted before being input to the capacity calculation module 102 for capacity calculation.

[0094] The capacity calculation module 102 is used to input the data sorted by the collection module into a pre-configured capacity calculation model for capacity calculation (for example, the operating data of the online system is input into the online system calculation module 1021 for capacity calculation, and the operating data of the batch task system is input into the batch task system calculation module 1022 for capacity calculation), and obtain the capacity calculation result, which is the real-time capacity of the application system (such as a bank IT system, etc.), and feed back the real-time capacity to the user (such as operation and maintenance personnel or cost operation planning personnel, etc.). The user can determine whether the application system needs to be expanded or reduced based on the capacity calculation result.

[0095] External systems may include application systems (such as bank deposit IT systems, etc.) and users (such as operation and maintenance personnel or cost operation planning personnel, etc.). The application system is used to provide the capacity platform with the operation information of the application system (such as the actual request volume of the online system, the execution time of each online transaction, the number of batch tasks of the batch task system, the execution time of batch tasks, the CPU load of the host in the system, the memory load, the hard disk I / O load, the storage service usage, the number of machines, etc.); the user is used to receive the capacity calculation results obtained by the capacity platform for the operation information of the application system, and determine whether the application system needs to be expanded or reduced according to the capacity calculation results.

[0096] It should be noted that the above Figure 1 The structure shown is only an example and is not limited to this embodiment of the present invention.

[0097] Based on the above description, Figure 2 The process of a method for determining system capacity provided by an embodiment of the present invention is exemplarily shown, and the process can be executed by a device for determining system capacity.

[0098] like Figure 2 As shown in the figure, the process specifically includes:

[0099] Step 201, collecting the current operation information of each business subsystem in the application system.

[0100] Step 202: for at least one business subsystem, determine real-time information of each capacity influencing factor from current operation information of the business subsystem.

[0101] Step 203: Determine the system capacity of the service subsystem according to the real-time information of each capacity influencing factor and the capacity calculation model of the service subsystem.

[0102] In the above step 201, the embodiment of the present invention can use the acquisition module to collect the current operation information of each business subsystem in the application system in real time. For example, the acquisition module can be a single server or a server cluster, and the current operation information of each business subsystem is collected using a single server or a service cluster. Among them, the server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud computing, cloud storage, network services, and artificial intelligence platforms. Of course, the acquisition module can also manually collect the current operation information of each business subsystem and classify and organize it. In actual application scenarios, the embodiment of the present invention does not limit this. When collecting the current operation information of each business subsystem, for example, collecting the current operation information of each business subsystem once every 1 second or once every 2 seconds, etc., it can be set according to the needs of the actual business scenario, and the embodiment of the present invention does not limit this. The current operation information may include real-time information of various capacity influencing factors of each business subsystem. For example, the collected current operation information is described by taking the business subsystem as an online subsystem and a batch task subsystem as an example. The current operation information may include the actual request volume (TPS) of the online system, the execution time of each online transaction, the number of batch tasks of the batch task system, the execution time of batch tasks, the host CPU load, memory load, hard disk I / O load in the system, storage service usage (such as database capacity, database access times, etc.), the number of machines, etc. After collecting the current operation information of each business subsystem, the collected current operation information of each business subsystem is stored in the database.

[0103] In the above steps 202 and 203, for at least one business subsystem, based on the capacity influencing factors of the capacity calculation model determined through the historical operation information of the business subsystem and the weights of the capacity influencing factors, the real-time information of each capacity influencing factor can be determined from the current operation information of the business subsystem, and the real-time information of each capacity influencing factor can be input into the capacity calculation model of the business subsystem to determine the system capacity of the business subsystem. Since the system capacity is automatically calculated by combining the real-time information of each capacity influencing factor and the capacity calculation model, excessive manual intervention can be avoided, which helps to reduce the time and manpower consumed by manually determining the system capacity, and helps to ensure the real-time and accuracy of the system capacity determination, thereby solving the problem of low evaluation efficiency and low accuracy caused by manual evaluation of system capacity in the prior art. Among them, the capacity influencing factors can include first influencing factors and second influencing factors. The weight of each capacity influencing factor can be determined based on the analytic hierarchy process and the historical operation information of the application system. The specific process is as follows: based on the analytic hierarchy process, the historical operation information of the application system is analyzed and processed to determine the first influencing factors as the solution layer, the second influencing factors as the criterion layer, and the system capacity as the target layer; based on the analytic hierarchy process, the first weight of each first influencing factor and the second weight of each second influencing factor are determined. By analyzing and processing the historical operation information of the application system through the analytic hierarchy process, the first weight of each first influencing factor and the second weight of each second influencing factor can be quickly and accurately determined, so that the subsequent capacity calculation model can determine the system capacity of the business subsystem according to the first weight of each first influencing factor and the second weight of each second influencing factor. Among them, each first influencing factor can include the processing capacity of a single machine, the number of machines, and the number of threads; each second influencing factor can include disaster recovery capacity, machine load, storage load, and business volume load.

[0104] For example, the embodiment of the present invention takes the bank deposit IT system as an example to describe the process of determining the system capacity of the business subsystem. The bank deposit IT system may include an online business subsystem and a batch business subsystem. It should be noted that the embodiment of the present invention does not limit the scope of the application system. In other business application scenarios, the application systems corresponding to other business application scenarios may also use the technical solution provided by the present invention to calculate the system capacity.

[0105] The maximum system capacity of the service subsystem is determined according to the following formula (1), which is specifically:

[0106] Maximum system capacity = maximum business processing capacity / (load status × load weight) ... (1) According to formula (2), the disaster recovery system capacity of the business subsystem is determined. The specific formula (2) is:

[0107] Disaster recovery system capacity = disaster recovery capability × maximum system capacity × weight of disaster recovery capability…………(2)

[0108] The load condition includes machine load, storage load and business load, and the disaster recovery capacity is the ratio of the number of available machines to the total number of machines.

[0109] It should be noted that if the service subsystem is an online service subsystem, the maximum service processing capacity of the online service subsystem is determined according to the following formula (3). The formula (3) is specifically:

[0110]

[0111] The maximum TPS request volume (Transactions Per Second) indicates the maximum number of request transactions per unit time.

[0112] If the business subsystem is a batch business subsystem, the maximum business processing capacity of the batch business subsystem is determined according to the following formula (4). The formula (4) is specifically:

[0113]

[0114] Among them, the storage performance coefficient and network delay coefficient are both constants.

[0115] In addition, in the specific implementation process of the embodiment of the present invention, the dynamic system capacity of the business subsystem can be quickly and accurately determined based on the current operation information of the business subsystem. For example, the dynamic system capacity of the business subsystem can be determined by monitoring the real-time request volume or batch task processing volume of the business subsystem.

[0116] By way of example, the specific implementation process of the capacity calculation module involved in the embodiment of the present invention is described below by taking the bank deposit IT system as an example.

[0117] Since the bank deposit IT system can include two parts, an online subsystem and a batch task subsystem, the capacity calculation model involved in the technical solution of the present invention can also be composed of two parts, namely, an online subsystem capacity calculation model and a batch task subsystem capacity calculation model. It should be noted that the various calculation factors that the capacity calculation model involved in the technical solution of the present invention relies on mainly come from various collected data, such as the number of machines, the processing capacity of a single-instance application, the processing threads of a single instance, etc. These calculation factors have different influence weights respectively, and will have different influences on the output results of the capacity calculation model. Therefore, before executing the capacity calculation model, it is necessary to combine the actual business scenario to obtain the influence weights of different calculation factors, and then combine the capacity calculation model to calculate to obtain the capacity of the bank deposit IT system.

[0118] The construction process of the capacity calculation model is as follows:

[0119] Step 1: Use the Analytic Hierarchy Process (AHP) to analyze the influence weights of the factors affecting the bank deposit IT system and obtain the influence factors and their influence weights of the capacity calculation model.

[0120] First, we introduce the hierarchical model in the hierarchical analysis method. Figure 3 , Figure 3 A schematic diagram of a hierarchical model provided for an embodiment of the present invention. The hierarchical model includes a target layer (system capacity), a criterion layer (A1 disaster recovery capability, A2 machine load, A3 database load, A4 system load) and a solution layer (B1 number of machines, B2 single machine processing capability, B3 number of configured threads).

[0121] The following is an introduction to the process of determining influencing factors and their influence weights using the hierarchical analysis method.

[0122] a. Determine the influencing factors of the capacity calculation model.

[0123] When determining the influence weights between factors at each level, if only qualitative results are obtained (for example, A1 disaster recovery capacity accounts for 0.3, A2 machine load accounts for 0.2, etc.), it is not easy to be recognized by users or may easily lead to inaccurate results calculated by the capacity calculation model. Therefore, the embodiment of the present invention adopts the consistent matrix method in the hierarchical analysis method, that is: do not compare all factors together, but compare them two by two; at this time, a relative scale is used to minimize the difficulty of comparing factors of different natures to improve accuracy. Among them, the consistent matrix method is a comparison of the relative importance of all factors in this layer to a factor in the previous layer (such as the criterion layer or the target layer). As shown in Table 1, the element a of the consistent matrix method ij It represents the comparison result of the i-th factor relative to the j-th factor. This value is given using Santy's 1-9 scale method.

[0124] Table 1

[0125]

[0126] b. Through the pairwise comparison of the consistent matrix method, it can be obtained that in the system capacity calculation model, the key influencing factors are the number of machines, the processing power of a single machine, and the number of threads configured for the application instance deployed on the single machine. After determining the influencing factors that affect the capacity calculation formula, it is also necessary to analyze the weight ratio of each of these influencing factors. Among them, the embodiment of the present invention defines the number of machines as B1, the processing power of a single machine as B2, and the number of threads configured for the application instance as B3.

[0127] c. Determine the weight of the influencing factors of the capacity calculation model.

[0128] Normalize the factors of each layer of layer B (number of machines B1, single machine processing capacity B2, number of configured threads B3) to obtain a normalized vector W, that is, W = {W 1 ,W 2 ,…,W n},and W i It represents the relative importance of the i-th factor in the same layer to a factor in the previous layer. It calculates the composite weight of each element (i.e., influencing factor) in each layer to the total target, and finally obtains the total hierarchical ranking of layer B.

[0129] In view of this, the ranking of the three factors in layer B for the overall goal (system capacity) is a1, a2, and a3, and the ranking of each factor in layer B for the factors in layer A is A. j The hierarchical order of 1j ,b 2j ,…b nj Therefore, the total hierarchical order of layer B is:

[0130]

[0131] That is, the influence weight of the i-th factor in layer B on the overall goal is Since the factors in layer B are obtained by comparing the factors in layer A with each other, the influence weights of the factors in layer A can be inferred from the influence weights of the factors in layer B, that is, the influence weight of the i-th factor in layer A on the overall target (system capacity) can be obtained.

[0132] d. Since in the actual operation of the system, there will be other qualitative rules controlling the factors in layer A (such as business system high availability rules, load requirement rules, etc.), it is also necessary to obtain the influence weight of each factor in layer A. The embodiment of the present invention adopts the control variable method. For example, when calculating the influence weight of A1, the values ​​of the control unit processing capacity B2 and the number of configured threads B3 remain unchanged. Since the factors in layer B are obtained by comparing the factors in layer A with each other, the influence weights of the factors in layer A can be inferred from the influence weights of the factors in layer B. That is, the influence weight of A1 on B1 can be calculated based on the i-th factor in layer B (since the values ​​of B2 and B3 remain unchanged, the influence weight can be considered to be the influence weight of A1 on the overall goal). Among them, the influence weight of the i-th factor in layer B on the overall goal is In addition, since the factors in layer B are obtained by comparing the factors in layer A, there are multiple influence weights of A1 on B1. Therefore, it is necessary to perform weighted average on the multiple influence weights of A1 to obtain the final influence weight of A1, that is, the influence weight ratio of influencing factor A1 is K. 1The calculation method of other factors in layer A is similar. At the same time, the calculation formula of the weight of each factor in layer A can be obtained, which is:

[0133] f 1 =A×K i +(P+Q)……………………(6)

[0134] Among them, f 1 is the influence weight of the first factor in layer B on the overall goal, A represents each factor in layer A, K i is the weight of a factor in layer A, that is, the influence weight ratio coefficient of a factor in layer A, P is the fixed value corresponding to the single-machine processing capacity B2, which is a constant, and Q is the fixed value corresponding to the number of configured threads B3, which is a constant.

[0135] It should be noted that when calculating the influence weight of A1, the values ​​of the number of machines B1 and the number of configured threads B3 can also be controlled to remain unchanged, and the influence weight of A1 on B2 can be calculated based on the i-th factor in the B layer (since the values ​​of B1 and B3 remain unchanged, the influence weight can be considered to be the influence weight of A1 on the overall goal). In this way, the above calculation ideas can be used to calculate the final influence weight of A1, that is, the influence weight ratio of the influence factor A1, and the calculation methods of the remaining factors in the A layer are similar. Or when calculating the influence weight of A1, the values ​​of the number of machines B1 and the single-machine processing capacity B2 can also be controlled to remain unchanged, and the influence weight of A1 on B3 can be calculated based on the i-th factor in the B layer (since the values ​​of B1 and B2 remain unchanged, the influence weight can be considered to be the influence weight of A1 on the overall goal). In this way, the above calculation ideas can be used to calculate the final influence weight of A1, that is, the influence weight ratio of the influence factor A1, and the calculation methods of the remaining factors in the A layer are similar. Of course, when determining the influence weight of A2, A3 or A4, the technical solution of the embodiment of the present invention provided above may also be used for calculation, which will not be described in detail here.

[0136] Step 2: Build a capacity calculation model.

[0137] After determining the influencing factors of system capacity and the influence weights of each influencing factor, the capacity calculation model can be constructed.

[0138] (1) Construct an online system capacity calculation model.

[0139] a. The sources of the values ​​of the influencing factors at each layer.

[0140] Since the single-machine processing performance of a bank deposit IT system is mainly determined by the system program (i.e., the transaction requests M that N servers can process within 1 second should be close to a constant), it can be considered that the capacity of the online subsystem is equal to the number of requests (TPS) that the system can process. After analysis, the capacity of the online subsystem is mainly determined by the number of requests (TPS) of the online subsystem, the request processing delay, the host CPU load, and the system disaster recovery strategy (disaster recovery capability). For example, Figure 4 A schematic diagram of the relationship between system capacity and number of requests in an online subsystem capacity calculation model provided by an embodiment of the present invention. Figure 4 As shown, when the number of requests is larger, the online subsystem needs to process more requests, and the corresponding CPU computing resources are consumed more, and the host CPU load will also increase.

[0141] b. Substitute the impact factors of each layer and the impact weights of each layer into the formula for calculation.

[0142] For example, Figure 5 A schematic diagram of the relationship between system capacity and the number of faulty machines in an online subsystem capacity calculation model provided by an embodiment of the present invention. Figure 5 As shown in the figure, when an online subsystem fails in the cluster, the corresponding cluster processing performance will also decline (when there are N machines in the cluster and n machines fail, the processing performance loss in the cluster is n / N). Therefore, when a machine fails abnormally, it will also affect the evaluation and calculation of the system capacity by the online subsystem capacity calculation model. Therefore, the system disaster recovery strategy (disaster recovery capability) is also a factor that needs to be considered in the online subsystem capacity calculation model.

[0143] According to the exploration relationship obtained by the above analytic hierarchy process, the influence weights of the influencing factors at each level can be inserted, and the capacity formula model of the online subsystem can be derived to be approximately:

[0144]

[0145]

[0146] Among them, the maximum TPS request volume (Transactions Per Second) represents the maximum number of request transactions per unit time. The load condition may include machine load, storage load and business load. The load weight is the impact weight corresponding to each load, and α is the impact weight of the single-machine processing capacity.

[0147] Since disaster recovery scenarios also need to be considered, that is, scenarios where machines in the cluster fail and become unavailable, the real-time capacity model of the online subsystem is:

[0148]

[0149] Among them, K 1 It is the impact weight of disaster recovery capability in the disaster recovery scenario.

[0150] In view of this, for the online subsystem capacity calculation module, the current operation information of the online subsystem is input into the online subsystem capacity calculation module for capacity calculation, so that the real-time dynamic capacity and real-time maximum capacity of the online subsystem can be obtained, and the real-time dynamic capacity, real-time maximum capacity and the ratio of the real-time dynamic capacity to the real-time maximum capacity of the online subsystem are fed back to the user, so that the user can determine whether to expand or reduce the capacity according to the results fed back by the capacity platform. Figure 6 , which is a schematic diagram of the capacity calculation result of an online subsystem provided in an embodiment of the present invention. According to the schematic diagram, the user can intuitively understand the real-time dynamic changes of the system capacity, which is convenient for the user to make decisions.

[0151] (2) Construct a batch task system capacity calculation model.

[0152] a. The sources of the values ​​of the influencing factors at each layer.

[0153] Since the batch task subsystem of the bank deposit system mainly handles batch entry, account verification, interest accrual, etc., the processing time of batch tasks is closely related to the capacity of the batch task subsystem, the number of accounts processed in the task, the computing power of the batch thread, the storage service performance / capacity, and the network transmission time. The capacity of the batch task subsystem is related to various capacity influencing factors (including machine load, database load, disaster recovery capability, number of machines, single-machine processing capability, number of configured threads, etc.) and the influence weight of each capacity influencing factor. Among them, reference Figure 7 , which is a schematic diagram of the relationship between the capacity of a batch task subsystem and the running time of a batch task provided by an embodiment of the present invention. According to the schematic diagram, when the capacity of the batch task subsystem is larger, the running time of the batch task subsystem is longer. Figure 8 , which is a schematic diagram of the relationship between the number of accounts involved in a batch task and the processing time of the batch task provided by an embodiment of the present invention. According to the schematic diagram, when the number of accounts involved in the batch task gradually increases, the processing time of the batch task gradually increases, and then gradually tends to be stable.

[0154] b. Substitute the impact factors of each layer and the impact weights of each layer into the formula for calculation.

[0155] According to the exploration relationship obtained by the above analytic hierarchy process, the capacity formula model of the batch task subsystem can be derived by inserting the influence weights of the influencing factors at each level:

[0156] Maximum capacity = single machine batch task processing capacity × number of machines ÷ (load status × load weight) ... (10)

[0157]

[0158] The load conditions may include machine load, storage load and business load, the load weight is the impact weight corresponding to each load condition, and the storage performance coefficient and network delay coefficient are both constants.

[0159] According to the above system capacity derivation, the batch processing capacity of the system can be obtained (if there is no change or optimization during the operation of the system, the batch processing capacity of the system can be considered to be a constant. If there is a change or optimization, the capacity calculation model can also be calculated and corrected according to the collected operation information, and the constant value can be revised). In this way, the capacity calculation model of the batch task subsystem can be obtained as follows:

[0160] Maximum capacity = number of machines × batch processing capacity………………(12)

[0161] Among them, the batch processing capacity can be considered as a constant under normal conditions (that is, no changes or optimizations are performed during system operation).

[0162] Since disaster recovery scenarios also need to be considered, that is, scenarios where machines in the cluster fail and become unavailable, the real-time capacity model of the batch task subsystem is:

[0163]

[0164] Among them, K 1 It is the impact weight of disaster recovery capability in the disaster recovery scenario.

[0165] In view of this, for the batch task subsystem capacity calculation module, after collecting the current operating information of the bank deposit IT system, the processing capacity of the batch subsystem in the bank deposit IT system can be calculated. Similarly, the capacity calculation of the batch task subsystem can be performed on the current operating information of the batch task subsystem according to the batch task subsystem capacity calculation module, and the real-time dynamic capacity and real-time maximum capacity of the batch task subsystem can be obtained. The real-time dynamic capacity, real-time maximum capacity and the ratio of the real-time dynamic capacity to the real-time maximum capacity of the batch task subsystem are fed back to the user, so that the user can determine whether to expand or reduce the capacity according to the feedback results of the capacity platform. Among them, the real-time dynamic capacity of the batch task subsystem can determine the dynamic system capacity of the batch task subsystem by monitoring the current operating information of the batch task subsystem (such as batch task processing volume, batch task execution time, batch task processing capacity, etc.). Reference Fig. 9 , which is a schematic diagram of capacity calculation results of a batch task subsystem provided in an embodiment of the present invention. According to the schematic diagram, the user can intuitively understand the real-time dynamic changes of the system capacity, which is convenient for the user to make decisions.

[0166] The above embodiments show that by acquiring the current operation information of each business subsystem in the application system in real time, and for at least one business subsystem, determining the real-time information of each capacity influencing factor from the current operation information of the business subsystem. Then, based on the real-time information of each capacity influencing factor and the capacity calculation model of the business subsystem, the system capacity of the business subsystem is determined. Since the system capacity is automatically calculated by combining the real-time information of each capacity influencing factor, the weight of each capacity influencing factor and the capacity calculation model, excessive manual intervention can be avoided, which helps to reduce the time and manpower consumed by manually determining the system capacity, and helps to ensure the real-time and accuracy of the system capacity determination, thereby solving the problem of low evaluation efficiency and low accuracy caused by manual evaluation of system capacity in the prior art.

[0167] Based on the same technical concept, Fig.10 An apparatus for determining system capacity provided by an embodiment of the present invention is exemplarily shown, and the apparatus can execute the process of the method for determining system capacity.

[0168] like Fig.10 As shown, the device comprises:

[0169] The collection unit 1001 is used to collect the current operation information of each business subsystem in the application system;

[0170] Processing unit 1002 is used to determine, for at least one business subsystem, real-time information of each capacity influencing factor from the current operation information of the business subsystem; determine the system capacity of the business subsystem based on the real-time information of each capacity influencing factor and a capacity calculation model of the business subsystem; wherein the capacity calculation model is provided with weights of each capacity influencing factor determined by historical operation information of the business subsystem.

[0171] Optionally, the processing unit 1002 is specifically configured to:

[0172] The weights of the capacity influencing factors are determined as follows:

[0173] Based on the hierarchical analysis method, the historical operation information of the application system is analyzed and processed to determine the first influencing factors as the solution layer, the second influencing factors as the criterion layer and the system capacity as the target layer; each capacity influencing factor includes the first influencing factors and the second influencing factors;

[0174] Based on the hierarchical analysis method, a first weight of each of the first influencing factors and a second weight of each of the second influencing factors are determined.

[0175] Optionally, the processing unit 1002 is specifically configured to:

[0176] The first influencing factors include the processing capacity of a single machine and the number of machines; the second influencing factors include disaster recovery capacity, machine load, storage load and business volume load.

[0177] Optionally, the processing unit 1002 is specifically configured to:

[0178] Determine the maximum system capacity of the service subsystem according to the following formula (1);

[0179] The formula (1) is:

[0180] Maximum system capacity = maximum business processing capacity / (load status × load weight) ………… (1)

[0181] Determine the disaster recovery system capacity of the business subsystem according to the following formula (2);

[0182] The formula (2) is:

[0183] Disaster recovery system capacity = disaster recovery capability × maximum system capacity × weight of disaster recovery capability…………(2)

[0184] The load condition includes machine load, storage load and business load, and the disaster recovery capacity is the ratio of the number of available machines to the total number of machines.

[0185] Optionally, the business subsystem is an online business subsystem;

[0186] The processing unit 1002 is specifically used for:

[0187] Determine the maximum service processing capacity of the online service subsystem according to the following formula (3);

[0188] The formula (3) is:

[0189]

[0190] The maximum TPS request volume (Transactions Per Second) indicates the maximum number of request transactions per unit time.

[0191] Optionally, the business subsystem is a batch business subsystem;

[0192] The processing unit 1002 is specifically used for:

[0193] Determine the maximum service processing capacity of the batch service subsystem according to the following formula (4);

[0194] The formula (4) is:

[0195]

[0196] Among them, the storage performance coefficient and network delay coefficient are both constants.

[0197] Optionally, the processing unit 1002 is further configured to:

[0198] The dynamic system capacity of the service subsystem is determined according to the current operation information of the service subsystem.

[0199] Based on the same technical concept, an embodiment of the present invention provides a computing device, including:

[0200] Memory for storing computer programs;

[0201] The processor is used to call the computer program stored in the memory and execute the method for determining the system capacity according to the obtained program.

[0202] Based on the same technical concept, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer-executable program, and the computer-executable program is used to enable a computer to execute a method for determining system capacity.

[0203] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0204] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0205] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0206] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0207] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0208] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of this application and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for determining system capacity, characterized in that: include: Collect the current operation information of each business subsystem in the application system; For at least one business subsystem, determining real-time information of each capacity influencing factor from current operation information of the business subsystem; Determine the system capacity of the business subsystem according to the real-time information of each capacity influencing factor and the capacity calculation model of the business subsystem; wherein the capacity calculation model is provided with the weight of each capacity influencing factor determined by the historical operation information of the business subsystem; The weight of each capacity influencing factor is determined by the following method, including: Based on the hierarchical analysis method, the historical operation information of the application system is analyzed and processed to determine the first influencing factors as the solution layer, the second influencing factors as the criterion layer and the system capacity as the target layer; each capacity influencing factor includes the first influencing factors and the second influencing factors; Based on the hierarchical analysis method, determining the first weight of each first influencing factor and the second weight of each second influencing factor; each first influencing factor includes the processing capacity of a single machine and the number of machines; each second influencing factor includes disaster recovery capacity, machine load, storage load and business volume load; Determining the system capacity of the service subsystem according to the real-time information of each capacity influencing factor and the capacity calculation model of the service subsystem includes: Determine the maximum system capacity of the service subsystem according to the following formula (1); The formula (1) is: Maximum system capacity = maximum business processing capacity / (load status × load weight) Determine the disaster recovery system capacity of the business subsystem according to the following formula (2); The formula (2) is: Disaster recovery system capacity = disaster recovery capability × maximum system capacity × weight of disaster recovery capability The load condition includes machine load, storage load and business load, and the disaster recovery capacity is the ratio of the number of available machines to the total number of machines.

2. The method according to claim 1, characterized in that The business subsystem is an online business subsystem; Determine the maximum service processing capacity of the online service subsystem according to the following formula (3); The formula (3) is: The maximum TPS request volume (Transactions Per Second) indicates the maximum number of request transactions per unit time.

3. The method according to claim 1, characterized in that The business subsystem is a batch business subsystem; Determine the maximum service processing capacity of the batch service subsystem according to the following formula (4); The formula (4) is: Among them, the storage performance coefficient and network delay coefficient are both constants.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: The dynamic system capacity of the service subsystem is determined according to the current operation information of the service subsystem.

5. A device for determining system capacity, characterized in that: include: The collection unit is used to collect the current operation information of each business subsystem in the application system; A processing unit, configured to determine, for at least one business subsystem, real-time information of each capacity influencing factor from current operation information of the business subsystem; determine the system capacity of the business subsystem according to the real-time information of each capacity influencing factor and a capacity calculation model of the business subsystem; wherein the capacity calculation model is provided with a weight of each capacity influencing factor determined by historical operation information of the business subsystem; The weight of each capacity influencing factor is determined by the following method, including: Based on the hierarchical analysis method, the historical operation information of the application system is analyzed and processed to determine the first influencing factors as the solution layer, the second influencing factors as the criterion layer and the system capacity as the target layer; each capacity influencing factor includes the first influencing factors and the second influencing factors; Based on the hierarchical analysis method, determining the first weight of each first influencing factor and the second weight of each second influencing factor; each first influencing factor includes the processing capacity of a single machine and the number of machines; each second influencing factor includes disaster recovery capacity, machine load, storage load and business volume load; Determining the system capacity of the service subsystem according to the real-time information of each capacity influencing factor and the capacity calculation model of the service subsystem includes: Determine the maximum system capacity of the service subsystem according to the following formula (1); The formula (1) is: Maximum system capacity = maximum business processing capacity / (load status × load weight) Determine the disaster recovery system capacity of the business subsystem according to the following formula (2); The formula (2) is: Disaster recovery system capacity = disaster recovery capability × maximum system capacity × weight of disaster recovery capability The load condition includes machine load, storage load and business load, and the disaster recovery capacity is the ratio of the number of available machines to the total number of machines.

6. A computing device, characterized in that include: Memory for storing computer programs; A processor, configured to call a computer program stored in the memory, and execute the method according to any one of claims 1 to 4 according to the obtained program.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer-executable program, and the computer-executable program is used to enable a computer to execute the method according to any one of claims 1 to 4.

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