Program performance determination method, device, and storage medium

Abstract

The application provides a program performance determination method, device and storage medium, and the specific implementation scheme comprises the following steps: obtaining a target program, and determining a target framework type of the target program and a target business category to which the target program belongs; determining a target average MIPS value of the target program in runtime; obtaining a plurality of MIPS consumption cumulative probability intervals corresponding to the target business category under the target framework type and an average MIPS value interval corresponding to each MIPS consumption cumulative probability interval, which is determined according to a pre-constructed probability cumulative distribution function of the corresponding program of the target business category under the target framework type; determining an average MIPS value interval in which the target average MIPS value is located; determining a corresponding MIPS consumption cumulative probability interval according to the average MIPS value interval in which the target average MIPS value is located; and determining the performance of the target program according to the corresponding MIPS consumption cumulative probability interval.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of data processing, and particularly relate to a program performance determination method, device and storage medium. BACKGROUND

[0002] The resource consumption of a program running in a large host is measured by how much CPU processing capacity (unit: MIPS) of the host is consumed. In actual applications, the host resources are limited, so under the condition of ensuring the normal operation of the program, how to effectively measure the resource consumption level of the program, and then determine whether the program performance is abnormal, and optimize and repair the program when the program performance is abnormal, is the key point to ensure the safe production of the IT system.

[0003] At present, when measuring the resource consumption level of a program, only the MIPS size corresponding to the program is generally used to determine the resource consumption level of the program. Since simply comparing the MIPS value of the program lacks practical significance, it cannot effectively measure and define the resource consumption level of the program, and cannot provide effective program optimization guidance for development, testing and production, and thus the accuracy is low when the resource consumption level is used to determine the performance of the corresponding program. SUMMARY

[0004] Embodiments of the present application provide a program performance determination method, device and storage medium, to solve the technical problem that in the prior art, the resource consumption level of a program cannot be effectively measured and defined, and cannot provide effective program optimization guidance for development, testing and production, and thus the accuracy is low when the resource consumption level is used to determine the performance of the corresponding program.

[0005] In a first aspect, embodiments of the present application provide a program performance determination method, comprising:

[0006] obtaining a target program, and determining the target framework type of the target program and the target business category to which the target program belongs;

[0007] determining a target average MIPS value of the target program at runtime;

[0008] obtaining a plurality of MIPS consumption cumulative probability intervals corresponding to the target business category under the target framework type and an average MIPS value interval corresponding to each MIPS consumption cumulative probability interval, which are determined according to a program probability cumulative distribution function corresponding to the target business category under the target framework type that is constructed in advance, wherein the independent variable in the program probability cumulative distribution function is the program average MIPS corresponding to the target business category under the target framework type, and the dependent variable is the MIPS consumption cumulative probability corresponding to the target business category under the target framework type;

[0009] determining an average MIPS value interval in which the target average MIPS value is located;

[0010] determining a corresponding MIPS consumption cumulative probability interval according to the average MIPS value interval in which the target average MIPS value is located;

[0011] determining the performance of the target program according to the corresponding MIPS consumption cumulative probability interval.

[0012] In a second aspect, an embodiment of the present application provides an electronic device, comprising: at least one processor, a memory and a transceiver;

[0013] the processor, the memory and the transceiver circuit are interconnected;

[0014] the memory stores computer execution instructions; and the transceiver is configured to transceive data;

[0015] the at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the method of the first aspect.

[0016] In a third aspect, an embodiment of the present application provides a computer readable storage medium, the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to implement the method of the first aspect.

[0017] In a fourth aspect, an embodiment of the present application provides a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0018] The program performance determination method, device, and storage medium provided in this invention involve: acquiring a target program and determining its target framework type and target service category; determining the target average MIPS value of the target program during runtime; acquiring multiple cumulative MIPS consumption probability intervals corresponding to the target service category under the target framework type and the average MIPS value interval corresponding to each cumulative MIPS consumption probability interval; the multiple cumulative MIPS consumption probability intervals and the corresponding average MIPS value intervals are determined based on a pre-constructed cumulative distribution function of the program probability corresponding to the target service category under the target framework type, where the independent variable in the cumulative distribution function is the program average MIPS corresponding to the target service category under the target framework type, and the dependent variable is the cumulative MIPS consumption probability corresponding to the target service category under the target framework type; determining the average MIPS value interval in which the target average MIPS value lies; determining the corresponding cumulative MIPS consumption probability interval based on the average MIPS value interval in which the target average MIPS value lies; and determining the performance of the target program based on the corresponding cumulative MIPS consumption probability interval. Since the average MIPS of the program follows a normal distribution, a corresponding cumulative probability distribution function can be constructed for the program under each framework type and business category. Then, based on the program probability distribution function, multiple cumulative MIPS consumption probability intervals corresponding to the average MIPS value intervals can be configured. Each cumulative MIPS consumption probability interval and its corresponding average MIPS value interval can effectively measure the resource consumption level of the program under the corresponding framework type and business category. Therefore, after obtaining the target program, determining the target framework type and target business category, and then determining the cumulative MIPS consumption probability interval for the target program based on the multiple cumulative MIPS consumption probability intervals and their corresponding average MIPS value intervals for the target business category under the target framework type, the performance of the target program can be accurately determined, providing effective program optimization guidance for development, testing, and production. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0020] Figure 1 This is a network architecture diagram that can implement the program performance determination method of the embodiments of the present invention;

[0021] Figure 2 This is a flowchart illustrating a method for determining program performance according to an embodiment of the present invention;

[0022] Figure 3is a flowchart of the program performance determination method provided by another embodiment of the present application;

[0023] Figure 4 is a flowchart of step 302 in the program performance determination method provided by another embodiment of the present application;

[0024] Figure 5 is a flowchart of step 303 in the program performance determination method provided by another embodiment of the present application;

[0025] Figure 6 is a flowchart of step 304 in the program performance determination method provided by another embodiment of the present application;

[0026] Figure 7 is a flowchart of step 305 in the program performance determination method provided by another embodiment of the present application;

[0027] Figure 8 is a structural diagram of an electronic device for implementing the program performance determination method of the embodiment of the present application;

[0028] Figure 9 is a block diagram of an electronic device for implementing the program performance determination method of the embodiment of the present application.

[0029] Through the above-mentioned drawings, the explicit embodiments of the present disclosure have been shown, and will be described in more detail hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present disclosure by any means, but to illustrate the concept of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0030] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, the same numbers are used to indicate the same or similar components. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0031] First, the terms involved in the embodiments of the present application are explained:

[0032] Host / mainframe: using a dedicated processor instruction set, operating system and application software. The mainframe has strong processing capacity and good stability, but is expensive and technically closed, and is generally used by large banks, insurance companies and other enterprises.

[0033] Host program: a program running on a host.

[0034] MIPS: the number of millions of instruction sets consumed per second, used to describe the size of program consumption of host resources.

[0035] Transaction: represents an interactive operation of a business, which is bound to the program and belongs to the application level concept.

[0036] Program business category: in the business field, a large number of programs belong to different business categories, and different business categories have different business elements. For example, in the banking business field, according to the business attribute, it is divided into query class, account class, non-account class, internal class and other business categories. Different business category programs have different effects on system operation, different security requirements, and different transaction volumes.

[0037] In order to clearly understand the technical solutions of the present application, the solutions of the prior art are described in detail.

[0038] At present, when measuring the resource consumption level of a program, only the MIPS size of the program is used to determine the resource consumption level of the program. However, in actual scenarios, programs in the host have different business attributes, unbalanced transaction sizes, and other differentiating factors. Therefore, the MIPS size of the program only provides a digital size concept of resource consumption. Simply comparing the MIPS size of the program lacks practical significance, cannot determine the overall consumption state of the program in the same business program, cannot effectively measure and define the resource consumption level of the program, and cannot provide effective program optimization guidance for development, testing, and production. Therefore, the accuracy of determining the performance of the corresponding program by measuring the resource consumption level is low.

[0039] In order to effectively measure the resource consumption level of a program and provide effective program optimization guidance for development, testing, and production, there are two methods for measuring the resource consumption level of a program.

[0040] The first method is expert evaluation. This method collects some experts from host operation and maintenance management and development and testing departments, and with the help of actual work experience and observation, evaluates and formulates resource standards for different business category programs. Since experts are generally familiar with host resources and programs of different business types, the formulated standards have a certain guiding significance. Therefore, after expert evaluation, different business category program resource consumption standards are issued, and the performance of the corresponding program is determined by measuring the resource consumption level.

[0041] The second method is the key transaction consumption weighted average method. The key transactions are obtained, and the business category and resource consumption of the key transactions are determined. Then, the key transactions under each business category are weighted and summed in MIPS according to the transaction volume, and the resource consumption standards of different business category programs are obtained. Then, the performance of the corresponding program is determined by measuring the resource consumption level.

[0042] In the first method, although it can be helpful by the work experience of relevant experts, due to the huge number of host programs and the variety of business categories, the subjectivity of this method is too strong, and the estimation is too rough to accurately measure.

[0043] In the second method, only the key transactions are selected, resulting in too few samples, and because the simple weighted sum calculation is too rough, the generated standard cannot fully reflect the performance of various programs and lacks precision.

[0044] Therefore, the current method of measuring program resource consumption level still cannot effectively measure the resource consumption level of the program, and cannot provide effective program optimization guidance for development, testing, and production, thereby reducing the accuracy of determining the corresponding program performance by measuring the resource consumption level.

[0045] Therefore, in the face of the technical problems of the prior art, the inventors found through creative research that different development frameworks can be used during the development of a program. Different development frameworks use different languages, resulting in natural resource consumption differences in developed programs. This performance consumption difference is determined by the language underlying implementation and execution framework. Moreover, all program executions have business scenarios and different business categories, and programs belonging to different business categories have significant differences in actual transaction volume, importance, and resource consumption size. Taking a bank scenario as an example, various programs can be classified into different business scenarios, some programs involve account calculation, and some programs only involve querying. Due to different business requirements, the resource consumption of each program differs more obviously. Therefore, when measuring the resource consumption level of a program, the framework type and business category of the program need to be distinguished. Moreover, when measuring the resource consumption level of each program in each business category under each framework type, since the resource consumption level of the program satisfies a normal distribution function, a program probability cumulative distribution function corresponding to each business category under each framework type can be constructed. In this program probability cumulative distribution function, the independent variable is the average MIPS of the program under the corresponding framework and business category, and the dependent variable is the cumulative probability of MIPS consumption under the corresponding framework and business category. Moreover, a plurality of MIPS consumption cumulative probability intervals and corresponding average MIPS value intervals can be determined according to the program probability cumulative distribution function under each framework and business category. Each MIPS consumption cumulative probability interval and the corresponding average MIPS value interval are used to perform performance grading on a target program under the same architecture and business category.

[0046] Therefore, based on the above creative findings, the inventors propose the technical scheme of the embodiments of the present application. The network architecture of the program performance determination method provided by the embodiments of the present application is introduced below.

[0047] Figure 1is a network architecture diagram of a program performance determination method that can implement embodiments of the present application, as shown in Figure 1 The network structure includes a host 1, a data storage device 2, and an electronic device 3. The data storage device 2 is communicatively connected to the host 1 and the electronic device 3. A plurality of production programs run on the host 1, and the production programs are monitored every day during a monitoring period to form monitoring data, which is sent to the data storage device 2. The data storage device 2 stores the monitoring data. Each monitoring data includes an average MIPS value of the monitoring period every day, a transaction volume of the monitoring period every day, and a total transaction volume of the whole day. After the data storage device 2 receives the monitoring data, the monitoring data is reported to the electronic device 3, which obtains monitoring data of a plurality of production programs within a preset range when the programs run on the host. The program probability cumulative distribution function under each framework and business category is constructed based on the monitoring data of the plurality of production programs when the programs run on the host using the scheme provided in the embodiments of the present application. The plurality of MIPS consumption cumulative probability intervals corresponding to the production programs of each framework and business category are configured based on the program probability cumulative distribution function under each framework and business category, and the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval is determined. The electronic device 3 obtains a target program and determines the performance of the target program using the program performance determination method provided in the embodiments of the present application. If the performance of the target program meets the requirements, the target program is sent to the host 1 for production on the host 1.

[0048] The technical solutions of the present application and how the technical solutions solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0049] Embodiment one

[0050] Figure 2 is a flowchart of a program performance determination method provided in an embodiment of the present application, as shown in Figure 2 The execution subject of the program performance determination method provided in the embodiment is a program performance determination device. The program performance determination device is located in an electronic device. The program performance determination method provided in the embodiment includes the following steps:

[0051] Step 201, obtaining a target program and determining the target framework type and the target business category to which the target program belongs.

[0052] The target program is the program whose performance needs to be determined. This target program is either fully developed or fully tested. After the performance is determined according to the program performance determination method provided in this embodiment of the invention, if the performance requirements are met, the program is released and deployed to the host. If the performance is determined according to the program performance determination method provided in this embodiment of the invention but does not meet the performance requirements, the target program is modified and optimized to meet the performance requirements before being released and deployed to the host.

[0053] The target framework type is the framework type corresponding to the target program.

[0054] In this embodiment, since programs developed using different development frameworks have different naming rules, the program name of the target program can be obtained, and the framework type of the target program can be determined according to the naming rules. For example, the determined framework type of the target program is Appbuilder framework or C framework.

[0055] In this embodiment, a mapping relationship between program names and business categories is pre-stored, so the mapping relationship can be obtained, the program name corresponding to the target program can be found based on the mapping relationship, and then the business category that has a mapping relationship with the program name of the target program can be obtained and determined as the business category of the target program.

[0056] Among them, the business category of the target program is the target business category.

[0057] Step 202: Determine the target average MIPS value of the target program during runtime.

[0058] In this embodiment, the target program is run on an electronic device, and the target program is monitored during its operation to obtain monitoring data, which includes the target program's transaction volume and average MIPS value.

[0059] The average MIPS value of the target program is the target average MIPS value.

[0060] Step 203: Obtain multiple cumulative MIPS consumption probability intervals and the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval under the pre-configured target framework type and target business category. The multiple cumulative MIPS consumption probability intervals and the corresponding average MIPS value intervals are determined according to the pre-built cumulative program probability distribution function corresponding to the target business category under the target framework type. The independent variable in the cumulative program probability distribution function is the average MIPS of the program corresponding to the target business category under the target framework type, and the dependent variable is the cumulative MIPS consumption probability of the target business category under the target framework type.

[0061] In this embodiment, the program probability cumulative distribution function corresponding to each business category under each framework type is constructed in advance. The program probability cumulative distribution function corresponding to each business category under each framework type represents the relationship between the average MIPS of the program and the cumulative probability of MIPS consumption in the program of each business category under each framework type.

[0062] In this embodiment, the multiple MIPS consumption cumulative probability intervals corresponding to each business category under each framework type and the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval are configured according to the program probability cumulative distribution function corresponding to each business category under each framework type in advance.

[0063] For example, the MIPS consumption cumulative probability interval is divided into three, and the corresponding average MIPS value interval is also divided into three. The average MIPS value can be represented as X, and the MIPS consumption cumulative probability can be represented as Y. The first average MIPS value interval is X∈(0, 2], and the corresponding MIPS consumption cumulative probability interval is Y∈(0, 70%]. The meaning is that in a certain business category under a certain framework type, when the average MIPS value of the program is less than or equal to 2, the average MIPS value consumed is less than or equal to 70% of the average MIPS value consumed by the program in the business category under the framework type. The second average MIPS value interval is X∈(2, 4], and the corresponding MIPS consumption cumulative probability interval is Y∈(70%, 90%]. The meaning is that in a certain business category under a certain framework type, when the average MIPS value of the program is greater than 2 and less than or equal to 4, the average MIPS value consumed is greater than 70% and less than or equal to 90% of the average MIPS value consumed by the program in the business category under the framework type. The third average MIPS value interval is X∈(4, +∞), and the corresponding MIPS consumption cumulative probability interval is Y∈(90%, 100%). The meaning is that in a certain business category under a certain framework type, when the average MIPS value of the program is greater than 4, the average MIPS value consumed is greater than 90% and less than 100% of the average MIPS value consumed by the program in the business category under the framework type.

[0064] It can be understood that the number of MIPS consumption cumulative probability intervals and the corresponding average MIPS value intervals can also be two or four, and the boundary values of each MIPS consumption cumulative probability interval and the corresponding average MIPS value interval are not limited.

[0065] Step 204, determining the average MIPS value interval in which the target average MIPS value is located.

[0066] Step 205, determining the corresponding MIPS consumption cumulative probability interval according to the average MIPS value interval in which the target average MIPS value is located.

[0067] In this embodiment, the target average MIPS value is compared with each average MIPS value interval to determine the average MIPS value interval in which the target average MIPS value is located. The MIPS consumption cumulative probability interval corresponding to the average MIPS value interval in which the target average MIPS value is located is obtained.

[0068] In step 206, the performance of the target program is determined according to the corresponding MIPS consumption cumulative probability interval.

[0069] In this embodiment, the above example is continued to be described. For example, if the average MIPS value interval in which the target average MIPS value is located is X∈(0, 2], the MIPS consumption cumulative probability interval corresponding to the average MIPS value interval in which the target average MIPS value is located is Y∈(0, 70%], and it is determined that the performance of the target program is high-grade, which indicates that the performance of the target program is excellent. If the average MIPS value interval in which the target average MIPS value is located is X∈(2, 4], the MIPS consumption cumulative probability interval corresponding to the average MIPS value interval in which the target average MIPS value is located is Y∈(70%, 90%], and it is determined that the performance of the target program is middle-grade, which indicates that the performance of the target program is good. If the average MIPS value interval in which the target average MIPS value is located is X∈(4, +∞), the MIPS consumption cumulative probability interval corresponding to the average MIPS value interval in which the target average MIPS value is located is Y∈(90%, 100%), which indicates that the performance of the target program is poor.

[0070] It can be understood that if the performance grade of the target program is high-grade, the target program can be directly put into production and loaded into the host. If the performance grade of the target program is low-grade, the target program is prohibited from being put into production and needs to be optimized. If the performance grade of the target program is middle-grade, the transaction volume proportion of the target program needs to be further determined. If the transaction volume proportion is high, the target program is not put into production and is optimized. Conversely, if the transaction volume proportion is low, the target program is put into production and loaded into the host.

[0071] The program performance determination method provided in this embodiment involves: acquiring the target program and determining its target framework type and target business category; determining the target average MIPS value of the target program during runtime; acquiring multiple cumulative probability intervals of MIPS consumption corresponding to the target business category under the target framework type and the average MIPS value interval corresponding to each cumulative probability interval; the multiple cumulative probability intervals of MIPS consumption and the corresponding average MIPS value intervals are determined based on a pre-constructed cumulative probability distribution function of the program corresponding to the target business category under the target framework type, where the independent variable in the cumulative probability distribution function is the average MIPS of the program corresponding to the target business category under the target framework type, and the dependent variable is the cumulative probability of MIPS consumption corresponding to the target business category under the target framework type; determining the average MIPS value interval in which the target average MIPS value lies; determining the corresponding cumulative probability interval of MIPS consumption based on the average MIPS value interval in which the target average MIPS value lies; and determining the performance of the target program based on the corresponding cumulative probability interval of MIPS consumption. Since the average MIPS of the program conforms to a normal distribution, a corresponding cumulative probability distribution function of the program can be constructed for the program under the business category of each framework type, and then the average MIPS value intervals corresponding to multiple cumulative probability intervals of MIPS consumption can be configured based on the program probability distribution function. Each MIPS cumulative probability interval and its corresponding average MIPS value interval can effectively measure the resource consumption level of a program under a business category of a corresponding framework type. Therefore, after obtaining the target program, determining the target framework type and target business category, and then determining the MIPS cumulative probability interval for the target program based on multiple MIPS cumulative probability intervals and their corresponding average MIPS value intervals for the target business category under the target framework type, and finally determining the performance of the target program based on its corresponding MIPS cumulative probability interval, can accurately determine the performance of the target program and provide effective program optimization guidance for development, testing, and production.

[0072] Example 2

[0073] Figure 3 This is a flowchart illustrating a method for determining program performance according to another embodiment of the present invention, as shown below. Figure 3 As shown, the program performance determination method provided in this embodiment, based on the program performance determination method provided in Embodiment 1, further includes other steps. Therefore, the program performance determination method provided in this embodiment also includes the following steps:

[0074] Step 301: Obtain monitoring data of multiple production programs running on the host within a preset time range. Each monitoring data includes the average MIPS value for each monitoring period, the transaction volume for each monitoring period, and the total transaction volume for the whole day.

[0075] The production program is a program running on the host.

[0076] In this embodiment, multiple production programs run on the host, and the production programs are monitored to obtain monitoring data in real time. The monitoring data obtained in real time is uploaded and stored in the data storage device. After the data storage device receives the monitoring data, the monitoring data is reported to the electronic device, and the electronic device locally stores the monitoring data. When the locally stored monitoring data reaches a preset time range, the monitoring data of multiple production programs in the preset time range is obtained.

[0077] The preset time range can be a quarter, half a year, or a year, and the embodiment is not limited thereto.

[0078] The monitoring data of each production program includes a transaction name, and an average MIPS value of each monitoring period per day, a transaction volume of each monitoring period per day, and a total transaction volume per day under each transaction name.

[0079] Step 302, determine the production framework type of each production program and the production business category to which it belongs.

[0080] As an optional implementation manner, as shown in Figure 4 Step 302 includes the following steps:

[0081] Step 3021, obtain the mapping relationship of the transaction name, the production program name, and the production business category.

[0082] In this embodiment, the data storage device stores a constant table, and the constant table stores the mapping relationship of the transaction name, the production program name, and the production business category.

[0083] The production business category is the business category to which the production program belongs. If the target program and the production program are of the same business category, the target business category and the production business category are the same.

[0084] The transaction name, the production program name, and the production business category are one-to-one mapping relationship. That is, one transaction name corresponds to one production program name, and one transaction name corresponds to one production business category.

[0085] Step 3022, determine the program name of the production program having a mapping relationship with each transaction name.

[0086] In this embodiment, the monitoring data of each production program further includes a transaction name, so the program name of the production program having a mapping relationship with each transaction name is obtained in the mapping relationship according to the transaction name in the monitoring data of each production program.

[0087] At step 3023, the naming rules of the program names are determined, and the production framework types of the production programs are determined according to the naming rules of the program names.

[0088] In this embodiment, the program names of the production programs are parsed to determine the naming rules of the program names. Since the naming rules of the program names corresponding to different development frameworks are different, the production framework types of the production programs are determined according to the naming rules of the program names.

[0089] The production framework types of the production programs can be Appbuilder framework or C framework.

[0090] At step 3024, the production business categories to which the production programs belong are determined according to the mapping relationship.

[0091] In this embodiment, the business categories having the mapping relationship with the transaction names in the mapping relationship are obtained according to the transaction names in the monitoring data of the production programs, and the production business categories having the mapping relationship with the transaction names are determined as the production business categories to which the production programs belong.

[0092] It can be understood that the types of the corresponding business categories are different according to the application scenarios of the production programs loaded in the host. For example, the production programs in the bank scenario are loaded in the host of the bank, and the corresponding production business categories can include query category, cash account category, non-cash account category, internal management category, and the like.

[0093] At step 303, the average MIPS value of each production program in each framework and business category within a preset time range and the transaction volume of each production program in each framework and business category within the preset time range are determined according to the average MIPS value of each monitoring period of each day, the transaction volume of each monitoring period of each day, and the total transaction volume of the whole day.

[0094] As an optional implementation manner, as shown in FIG. 3B, step 303 includes the following steps. Figure 5

[0095] At step 3031, the average MIPS value of each production program in each framework and business category within a preset time range is determined according to the average MIPS value of each monitoring period of each day and the transaction volume of each monitoring period of each day.

[0096] Optionally, step 3031 includes the following steps.

[0097] At step 3031a, the total transaction volume of each production program in each framework and business category within a monitoring period of a preset time range is determined.

[0098] For convenience of description, hereinafter, each production program in each framework and business category is each production program in each production business category of each production framework type. ​

[0099] In this embodiment, the transaction volume of each monitoring period per day is accumulated to obtain the total transaction volume of the monitoring period within the preset time range for each production procedure under each framework and business category. The total transaction volume of the monitoring period within the preset time range for the ith production procedure can be represented as i D(i).

[0100] Wherein, i represents the ith production procedure. D(i) represents the transaction volume of the monitoring period per day of the ith production procedure.

[0101] Step 3031b, determining the first proportion of the transaction volume of the monitoring period per day of each production procedure under each framework and business category to the total transaction volume of the monitoring period within the preset time range.

[0102] Wherein, the first proportion of the transaction volume of the monitoring period per day of each production procedure under each framework and business category to the total transaction volume of the monitoring period within the preset time range can be represented as W1(i) = D(i) / ∑ i D(i).

[0103] Step 3031c, taking each first proportion as a first weight, and performing weighted summation on the average MIPS value of the monitoring period per day of each production procedure under each framework and business category to obtain the average MIPS value of each production procedure within the preset time range under each framework and business category.

[0104] Wherein, the average MIPS value of each production procedure within the preset time range under each framework and business category is represented as shown in formula (1):

[0105] EMIPS(i) = ∑ i MIPS(i) x W1(i) formula (1)

[0106] Wherein, EMIPS(i) is the average MIPS value of the ith production procedure within the preset time range. MIPS(i) is the average MIPS value of the ith production procedure in the monitoring period per day. W1(i) is the first weight corresponding to the average MIPS value of the monitoring period per day.

[0107] In this embodiment, since the production procedure is monitored all day long, it is very resource-consuming to obtain the all-day average MIPS value of the production procedure, so the average MIPS value of the monitoring period is used to represent the all-day average MIPS value, and the average MIPS value of the monitoring period within the preset time range is used to represent the average MIPS value within the preset time range.

[0108] Step 3032, determining the transaction volume of each production procedure within the preset time range under each framework and business category according to the total transaction volume of the whole day.

[0109] In this embodiment, for each framework and each production procedure under the business category, the total transaction volume of the corresponding day within the preset time range is accumulated to obtain the transaction volume within the preset time range.

[0110] In step 304, a probability cumulative distribution function of each production procedure is constructed according to the average MIPS value of each production procedure within the preset time range.

[0111] As an optional implementation, in this embodiment, as shown in Figure 6 step 304 includes the following steps:

[0112] In step 3041, the average MIPS value of each production procedure within the preset time range is determined as an expectation, and the product of the average MIPS value of each production procedure within the preset time range and a preset coefficient is determined as a standard deviation.

[0113] Wherein, the average MIPS value of each production procedure within the preset time range is represented as EMIPS(i), then the expectation is μ=EMIPS(i). The standard deviation is σ=μ*a. Wherein, a is a preset coefficient. The value of the preset coefficient can be 0.1.

[0114] In step 3042, a probability cumulative distribution function of each production procedure is constructed according to the corresponding expectation, standard deviation and preset normal probability cumulative distribution function of each production procedure.

[0115] In this embodiment, the expectation μ and the standard deviation σ of each production procedure are brought into the preset normal probability cumulative distribution function to construct the probability cumulative distribution function of each production procedure.

[0116] Wherein, the probability cumulative distribution function of each production procedure can be represented as formula (2) shown:

[0117]

[0118] Wherein, F(x;μ,σ) is the probability cumulative distribution function of each production procedure. x is an independent variable, which is specifically the average MIPS of the corresponding production procedure.

[0119] In step 305, a procedure probability cumulative distribution function under each framework and business category is constructed according to the transaction volume within the preset time range and the probability cumulative distribution function of each production procedure.

[0120] As an optional implementation, as shown in Figure 7 step 305 includes the following steps:

[0121] In step 3051, the total transaction volume of the production procedure under each framework and business category is calculated according to the transaction volume of each production procedure within the preset time range under each framework and business category.

[0122] In this embodiment, the transaction volume of all production procedures in the same business category under each production framework type within the preset time range is accumulated to obtain the total transaction volume of the production procedures in the same business category under each production framework type.

[0123] wherein the total transaction volume of the production procedures can be represented as∑ i S(i).

[0124] wherein i represents the i-th production procedure in the same business category. S(i) represents the transaction volume of the i-th production procedure in the same business category within the preset time range.

[0125] Step 3052, calculate the second proportion of the transaction volume of each production procedure within the preset time range and the total transaction volume of the production procedures under the corresponding framework and production business category.

[0126] wherein the second proportion can be represented as W2(i) = S(i) / ∑ i S(i).

[0127] Step 3053, take the second proportion as the second weight, and perform weighted summation on the probability cumulative distribution function of each production procedure under each framework and business category to construct the program probability cumulative distribution function under each framework and business category.

[0128] wherein the program probability cumulative distribution function under each framework and business category is represented as shown in equation (3):

[0129] P(x; μ, σ) =∑ i F i (x; μ, σ) × W2(i) equation (3)

[0130] wherein P(x; μ, σ) represents the program probability cumulative distribution function under each framework and business category.

[0131] Step 306, based on the program probability cumulative distribution function under each framework and business category, configure a plurality of MIPS consumption cumulative probability intervals corresponding to the production procedures under each framework and business category, and determine the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval.

[0132] In the embodiment, the curve corresponding to the cumulative distribution function of the program probability under each framework and business category is drawn, and each curve is displayed. The user inputs multiple MIPS consumption cumulative probability intervals corresponding to the production program of each framework and business category according to the displayed curves. The electronic device configures the multiple MIPS consumption cumulative probability intervals corresponding to the production program of each framework and business category according to the input, and determines the corresponding average MIPS value interval according to the corresponding curve and the multiple MIPS consumption cumulative probability intervals.

[0133] For example, the multiple MIPS consumption cumulative probability intervals corresponding to the investment program under a certain framework and business category are Y∈(0, 70%)], Y∈(70%, 90%)], and Y∈(90%, 100%) respectively. The determined corresponding average MIPS value interval is X∈(0, 2], X∈(2, 4], X∈(4, +∞).

[0134] The program performance determination method provided in the embodiment obtains monitoring data of multiple production programs running on the host in a preset time range. Each monitoring data includes the average MIPS value of the daily monitoring period, the transaction volume of the daily monitoring period, and the total transaction volume of the whole day. The framework type of each production program and the production business category to which each production program belongs are determined. The average MIPS value of each production program in the preset time range and the transaction volume of each production program in the preset time range are determined according to the average MIPS value of the daily monitoring period, the transaction volume of the daily monitoring period, and the total transaction volume of the whole day. The probability cumulative distribution function of each production program is constructed according to the average MIPS value of each production program in the preset time range. The program probability cumulative distribution function under each framework and business category is constructed according to the transaction volume in the preset time range and the probability cumulative distribution function of each production program. The multiple MIPS consumption cumulative probability intervals corresponding to the production program of each framework and business category are configured based on the program probability cumulative distribution function under each framework and business category, and the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval is determined. Since the probability cumulative distribution function of each production program is constructed according to the monitoring data corresponding to all transaction data of the production program running in the host, it can fully represent the overall consumption resource condition of each production program, so the determined probability cumulative distribution function of each production program is more accurate.

[0135] The program performance determination method provided in the embodiment comprises the following steps.

[0136] In the embodiment, the average MIPS value interval corresponding to the plurality of MIPS consumption cumulative probability intervals is the average MIPS value interval corresponding to the host production program. The target program for performance determination is a program after development completion or test completion. Therefore, the average MIPS value interval corresponding to the plurality of MIPS consumption cumulative probability intervals needs to be adjusted.

[0137] As an optional implementation, the program performance determination method provided in the embodiment further comprises steps 307 and 308.

[0138] In step 307, the MIPS calculation coefficient ratio in the production environment and the development test environment is obtained.

[0139] In the embodiment, the MIPS calculation coefficient ratio in the production environment and the development test environment can be stored locally, and then the MIPS calculation coefficient ratio in the production environment and the development test environment is obtained from the local storage.

[0140] The MIPS calculation coefficient ratio in the production environment and the development test environment can be represented as k.

[0141] In step 308, the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval is adjusted according to the MIPS calculation coefficient ratio.

[0142] In the embodiment, the two end boundary values of the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval are obtained, and the quotient of each boundary value and the MIPS calculation coefficient k is calculated as the boundary value of the adjusted average MIPS value interval.

[0143] For example, if the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval is X∈(0, 2], X∈(2, 4], X∈(4, +∞), and the MIPS calculation coefficient k is 1.1, then the adjusted average MIPS value intervals are X∈(0, 1.81], X∈(1.81, 3.67], X∈(3.67, +∞), respectively.

[0144] The program performance determination method provided in this embodiment can obtain a MIPS calculation coefficient ratio in a production environment and a development test environment after determining the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval, and adjust the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval according to the MIPS calculation coefficient ratio, so that the adjusted average MIPS value interval corresponding to each MIPS consumption cumulative probability interval is more suitable for the target program, and the accuracy of determining the performance of the target program is further improved.

[0145] Embodiment Three

[0146] Figure 8 is a structural schematic diagram of an electronic device for implementing the program performance determination method of the embodiments of the present application, as shown in Figure 8 The electronic device 40 provided in this embodiment includes at least one processor 42 and a memory 41.

[0147] The memory 41 stores computer execution instructions.

[0148] The at least one processor 42 executes the computer execution instructions stored in the memory, so that the at least one processor executes the method provided in Embodiment One or Embodiment Two.

[0149] Embodiment Four

[0150] Figure 9 is a block diagram of an electronic device for implementing the program performance determination method of the embodiments of the present application, as shown in Figure 9 The electronic device can be a computer, a messaging device, a tablet device, a server, a server cluster, etc.

[0151] The electronic device 500 can include one or more of the following components: a processing component 502, a memory 504, a power supply component 506, an input / output (I / O) interface 512, a sensor component 514, and a communication component 516.

[0152] The processing component 502 generally controls the overall operation of the electronic device 500 such as the operation associated with displaying, placing phone calls, transmitting and receiving data, camera operations, and recording operations. The processing component 502 can include one or more processors 520 to execute instructions and to complete

[0153] The memory 504 is configured to store various types of data to support operations of the electronic device 500. Examples of these data include instructions for any application or methods operating on the electronic device 500, contact data, phonebook data, messages, pictures, videos, and so on. The memory 504 can be implemented by any type of volatile or non-volatile storage devices or a combination thereof such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable programmable read only memory (EPROM), programmable read only memory (PROM), read only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0154] The power component 506 provides power to the various components of the electronic device 500. The power component 506 can include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 500.

[0155] The I / O interface 512 provides an interface between the processing component 502 and peripheral interface modules, which can be a keyboard, a click wheel, buttons, and so on. These buttons can include, but are not limited to, a home button, a volume button, a start button, and a lock button.

[0156] The sensor component 514 includes one or more sensors to provide various aspects of state evaluation for the electronic device 500. For example, the sensor component 514 can detect an open / closed state of the electronic device 500, relative positioning of components, such as a display and a keypad of the electronic device 500, a change in position of the electronic device 500 or a component of the electronic device 500, presence or absence of user contact with the electronic device 500, orientation or acceleration / deceleration of the electronic device 500, and a temperature change of the electronic device 500. The sensor component 514 can include an orientation sensor configured to detect an orientation of the electronic device 500. The sensor component 514 can include a proximity sensor configured to detect presence of an object in proximity to the electronic device 500 without any physical contact. The sensor component 514 can include a light sensor such as a CMOS or CCD image sensor for use in an imaging application. In some embodiments, the sensor component 514 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0157] The communication component 516 is configured to facilitate wired or wireless communication between the electronic device 500 and other devices. The electronic device 500 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 516 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technology.

[0158] In an exemplary embodiment, the electronic device 500 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic elements, for performing the above-described methods.

[0159] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions, such as the memory 504 including instructions, is also provided, which can be executed by the processor 520 of the electronic device 500 to complete the above-described methods. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disc, and an optical data storage device, etc.

[0160] A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the methods provided by the above-described embodiment one or embodiment two.

[0161] In an exemplary embodiment, a computer program product including a computer program is also provided, which is executed by a processor to perform the methods provided by the above-described embodiment one or embodiment two.

[0162] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the aspects of the present disclosure disclosed herein. It is intended that the present disclosure cover any and all variations of the present disclosure including combinations or permutations of individual features of the disclosure. It is intended that the true scope of the present disclosure should only be limited by the claims and their full scope. The specification and examples given should be considered exemplary only and should not be used to construe the scope of the present disclosure.

[0163] It should be understood that the present disclosure is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the present disclosure. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method of program performance determination, characterized by, The method comprises the following steps: obtaining a target program, and determining a target framework type of the target program and a target business category to which the target program belongs; determining a target average MIPS value of the target program in runtime; obtaining a plurality of MIPS consumption cumulative probability intervals corresponding to the target business category under the target framework type and an average MIPS value interval corresponding to each MIPS consumption cumulative probability interval, wherein the plurality of MIPS consumption cumulative probability intervals and the corresponding average MIPS value intervals are determined according to a program probability cumulative distribution function corresponding to the target business category under the target framework type, and the program probability cumulative distribution function has an independent variable of a program average MIPS corresponding to the target business category under the target framework type and a dependent variable of a MIPS consumption cumulative probability corresponding to the target business category under the target framework type; determining an average MIPS value interval in which the target average MIPS value is located; determining a corresponding MIPS consumption cumulative probability interval according to the average MIPS value interval in which the target average MIPS value is located; determining the performance of the target program according to the corresponding MIPS consumption cumulative probability interval. Before the step of obtaining the plurality of MIPS consumption cumulative probability intervals corresponding to the target business category under the target framework type and the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval, the method further comprises the following steps: obtaining monitoring data of a plurality of production programs in runtime within a preset time range, wherein each monitoring data comprises an average MIPS value of a monitoring time period each day, a transaction volume of the monitoring time period each day, and a total transaction volume of the whole day; determining a production framework type of each production program and a production business category to which each production program belongs; determining an average MIPS value of each production program within the preset time range and a transaction volume of each production program within the preset time range according to the average MIPS value of the monitoring time period each day, the transaction volume of the monitoring time period each day, and the total transaction volume of the whole day; determining an expectation of each production program as the average MIPS value of each production program within the preset time range, and determining a standard deviation of each production program as a product of the average MIPS value of each production program within the preset time range and a preset coefficient; constructing a probability cumulative distribution function of each production program according to the expectation, the standard deviation, and a preset normal probability cumulative distribution function corresponding to each production program; constructing a program probability cumulative distribution function under each framework and business category according to the transaction volume within the preset time range and the probability cumulative distribution function of each production program; configuring a plurality of MIPS consumption cumulative probability intervals corresponding to each production program under each framework and business category based on the program probability cumulative distribution function under each framework and business category, and determining an average MIPS value interval corresponding to each MIPS consumption cumulative probability interval.

2. The method of claim 1, wherein, The monitoring data further comprises a transaction name, and the step of determining the production framework type of each production program and the production business category to which each production program belongs comprises the following steps: obtaining a mapping relationship among the transaction name, a production program name, and a production business category; determining the program name of the production program having the mapping relationship with each transaction name. determine a naming rule of each program name, and determine a production framework type of each production program according to the naming rule of each program name; determine a production business category to which each production program belongs according to the mapping relationship.

3. The method of claim 1, wherein, determine the average MIPS value of each production program in each framework and business category within a preset time range and the transaction volume of each production program in each framework and business category within the preset time range according to the average MIPS value of each monitoring period of each day, the transaction volume of each monitoring period of each day and the total transaction volume of the whole day, including: determine the average MIPS value of each production program in each framework and business category within a preset time range according to the average MIPS value of each monitoring period of each day and the transaction volume of each monitoring period of each day; determine the transaction volume of each production program in each framework and business category within a preset time range according to the total transaction volume of the whole day.

4. The method of claim 3, wherein, determine the average MIPS value of each production program in each framework and business category within a preset time range according to the average MIPS value of each monitoring period of each day and the transaction volume of each monitoring period of each day, including: determine the total transaction volume of each monitoring period within a preset time range of each production program in each framework and business category; determine a first proportion of the transaction volume of each monitoring period of each day of each production program to the total transaction volume of each monitoring period within a preset time range of each production program in each framework and business category; weight the average MIPS value of each monitoring period of each day of each production program in each framework and business category by using each first proportion as a first weight to obtain the average MIPS value of each production program in each framework and business category within a preset time range.

5. The method of claim 1, wherein, construct a program probability cumulative distribution function in each framework and business category according to the transaction volume within the preset time range and the probability cumulative distribution function of each production program, including: calculate the total transaction volume of the production program in each framework and business category according to the transaction volume of each production program in each framework and business category within a preset time range; calculate a second proportion of the transaction volume of each production program within a preset time range to the total transaction volume of the production program in the corresponding framework and business category; weight the probability cumulative distribution function of each production program in each framework and business category by using the second proportion as a second weight to construct the program probability cumulative distribution function in each framework and business category.

6. The method according to any one of claims 1 to 5, characterized in that, after determining the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval, further comprising: obtain a MIPS calculation coefficient ratio in a production environment and a development test environment; adjust the average MIPS value interval corresponding to each MIPS consumption cumulative probability interval according to the MIPS calculation coefficient ratio.

7. An electronic device, comprising: including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the method in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method in any one of claims 1-6.

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