CN110333992B - Service performance analysis method and device, computer equipment and storage medium - Google Patents

Service performance analysis method and device, computer equipment and storage medium Download PDF

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CN110333992B
CN110333992B CN201910470646.9A CN201910470646A CN110333992B CN 110333992 B CN110333992 B CN 110333992B CN 201910470646 A CN201910470646 A CN 201910470646A CN 110333992 B CN110333992 B CN 110333992B
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service performance
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CN110333992A (en
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包晓华
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Ping An Technology Shenzhen Co Ltd
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    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment

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Abstract

The application discloses a service performance analysis method, a device, computer equipment and a storage medium, wherein the service performance analysis method comprises the following steps: acquiring monitoring data recorded in a system, wherein the monitoring data comprises service performance parameters; analyzing the service type corresponding to the service performance parameter, and determining a fuzzy membership function corresponding to the service type according to the service type; substituting the service performance parameters into the fuzzy membership function to match corresponding fuzzy semantics; obtaining fuzzy semantics corresponding to the service performance parameters output by the fuzzy membership function; and converting the fuzzy semantics into a service performance analysis language according to a preset conversion mode. The absolute eigenvalue corresponding to the service performance parameter is mapped into a relative value through the fuzzy membership function, so that each service performance can be described more visually, the method is more suitable for non-developers and operation and maintenance personnel to know the current situation of the service, and the physical significance deviation of the absolute eigenvalue corresponding to the service performance parameter under different application scenes is reduced to a certain extent.

Description

Service performance analysis method and device, computer equipment and storage medium
Technical Field
The present application relates to the field of computers, and in particular, to a method and an apparatus for analyzing service performance, a computer device, and a storage medium.
Background
Most of existing monitoring platforms in the industry are oriented to basic development and operation and maintenance personnel, results are displayed in the form of original data, but the same data has different meanings for different services, and non-professionals cannot visually and clearly describe service performance from the displayed results, so that management layer personnel of the non-developers and the non-operation and maintenance personnel cannot timely and accurately know the service current situation of each service, and global evaluation and planning are not convenient to perform.
Disclosure of Invention
The application mainly aims to provide a service performance analysis method and aims to solve the technical problems that existing service performance display results are in original data forms and service performance is not visual.
The application provides a service performance analysis method, which comprises the following steps:
acquiring monitoring data recorded in a system, wherein the monitoring data comprises service performance parameters;
analyzing the service type corresponding to the service performance parameter, and determining a fuzzy membership function corresponding to the service type according to the service type;
substituting the service performance parameters into the fuzzy membership functions to match corresponding fuzzy semantics, wherein the fuzzy semantics are contained in a group of evaluation dimension information corresponding to the service types, and each group of evaluation dimension information corresponds to each service type one to one;
obtaining fuzzy semantics corresponding to the service performance parameters output by the fuzzy membership function;
and converting the fuzzy semantics into a service performance analysis language according to a preset conversion mode.
Preferably, the step of acquiring the monitoring data recorded in the system includes:
acquiring a configuration table of specified service performance according to a preset mode, wherein fuzzy matching corresponding segments and fuzzy semantics corresponding to the segments are configured in the configuration table;
and generating the fuzzy membership function according to the segmentation and the fuzzy semantics corresponding to the segmentation respectively.
Preferably, the fuzzy membership function is a linear function, and the step of generating the fuzzy membership function according to the fuzzy semantics corresponding to the segments and each of the segments includes:
acquiring a first critical value and a first fuzzy semantic corresponding to a first segment, wherein the first segment is any segment in each segment, each segment is sequentially connected and continuously distributed, and the first critical value comprises a starting critical value and an ending critical value;
correspondingly mapping the first segmentation into a vertical coordinate interval, and correspondingly mapping the first fuzzy semantic meaning into a horizontal coordinate interval;
forming a first coordinate point by using the starting point critical value and the starting point value of the abscissa interval, and forming a second coordinate point by using the end point critical value and the starting point value of the abscissa interval;
and generating the fuzzy membership function expressed by the linear function according to a connecting line of the first coordinate point and the second coordinate point.
Preferably, the step of obtaining the configuration table of the specified service performance according to the preset mode includes:
judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user;
acquiring first historical data of the designated service performance corresponding to the first deployment level, wherein the first historical data comprises stored data of a designated time period before the current time;
arranging the first historical data into a first array according to the sequence of the data values from small to large;
according to the type number of preset fuzzy semantics, sequentially and correspondingly dividing the first array according to a preset normal distribution proportion;
and mapping each segment corresponding to the first array into a second configuration table respectively corresponding to the type of the preset fuzzy semantics.
Preferably, the step of obtaining the configuration table of the specified service performance according to the preset mode includes:
judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user;
acquiring a second deployment level which is higher than the first deployment level in level, and second historical data which respectively corresponds to each first deployment level included in the second deployment level, wherein the second historical data comprises stored data which is located in a specified time period before the current time, and the second deployment level comprises a plurality of first deployment levels;
arranging the data of the designated service performance corresponding to the same historical moment in the second historical data into a second array according to the sequence of the data values from small to large;
according to the type number of the preset fuzzy semantics, sequentially and correspondingly dividing the second array according to a preset normal distribution proportion;
and mapping each segment corresponding to the second array into a third configuration table corresponding to the type of the preset fuzzy semantics respectively.
Preferably, the step of sequentially and correspondingly dividing the second array according to the number of the types of the preset fuzzy semantics and the preset normal distribution ratio includes:
acquiring the type number of the preset fuzzy semantics;
acquiring the average corresponding to the second array;
and segmenting all data ranges in the second array by taking the average number as a middle peak value of normal distribution according to a preset ratio of the data range where the middle peak value is located to all data ranges in the second array, wherein the number of the segments is the same as the number of the types.
Preferably, the step of obtaining the configuration table of the specified service performance according to the preset manner includes:
judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user;
respectively acquiring first historical data of the designated service performance corresponding to the first deployment hierarchy and second historical data corresponding to each first deployment hierarchy in a second deployment hierarchy, wherein the second deployment hierarchy is higher in level than the first deployment hierarchy, and the first historical data and the second historical data both comprise stored data of a designated time period before the current time;
arranging the data of the designated service performance corresponding to the same historical time in each second historical data into a third array according to the sequence of the data values from small to large, and arranging the data of the designated service performance in the first historical data into a fourth array according to the sequence of the data values from small to large, wherein the data of the designated service performance in the first historical data are the data corresponding to the designated service performance at different historical times, the different historical times and the same historical time are contained in a designated time period before the current time, and the number of the different historical times is determined according to the number of the first deployment levels contained in the second deployment levels, so that the third array and the fourth array comprise the same data number;
correspondingly fitting the data in the third array and the fourth array in a one-to-one manner according to a preset weight proportion from small to large to form a fifth array;
according to the type number of the preset fuzzy semantics, sequentially and correspondingly dividing the fifth array according to a preset normal distribution proportion;
and mapping each segment corresponding to the fifth array into a fourth configuration table corresponding to the type of the preset fuzzy semantics respectively.
The present application further provides a service performance analysis apparatus, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring monitoring data recorded in the system, and the monitoring data comprises service performance parameters to be subjected to fuzzy semantic matching;
the analysis module is used for analyzing the service type corresponding to the service performance parameter and determining a fuzzy membership function corresponding to the service type according to the service type;
a substituting module, configured to substitute the service performance parameter into the fuzzy membership function to match a corresponding fuzzy semantic, where the fuzzy semantic is included in a set of evaluation dimension information corresponding to the service type, and each set of evaluation dimension information corresponds to each service type one to one;
the obtaining module is used for obtaining fuzzy semantics corresponding to the service performance parameters output by the fuzzy membership function;
and the conversion module is used for converting the fuzzy semantics into a service performance analysis language according to a preset conversion mode.
The present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method as described above.
According to the method and the device, the absolute eigenvalues corresponding to the service performance parameters are mapped into relative values through the fuzzy membership functions, so that each service performance can be described more visually, the method and the device are more suitable for non-developers and operation and maintenance personnel to know the service current situation, and the physical significance deviation of the absolute eigenvalues corresponding to the service performance parameters under different application scenes is reduced to a certain extent. According to the method, an investigation range is defined according to the service, the average values of all relevant characteristic values, such as 95line and 99line, are counted so as to determine a reasonable investigation data range, segmentation is carried out according to the determined investigation data range and the normal data distribution characteristics to form each segment, and fuzzy semantics are matched according to the service state meaning corresponding to each segment. And the automatic mapping from the original recorded data to the fuzzy semantics is realized by using JAVA codes or CASE statements of SQL (structured query language) of a relational database, so that the automation effect is improved. The configuration table can be automatically set and stored through manual experience, and can also be automatically formed through a statistical method, so that different requirements of users can be met. The method and the device comprehensively consider the longitudinal data corresponding to the first deployment level and the transverse data of the last deployment level of the first deployment level, and increase the reliability of obtaining the membership critical value.
Drawings
Fig. 1 is a schematic flow chart of a service performance analysis method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a service performance analysis apparatus according to an embodiment of the present application;
fig. 3 is a schematic diagram of an internal structure of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
Referring to fig. 1, a service performance analysis method according to an embodiment of the present application includes:
s1: and acquiring monitoring data recorded in a system, wherein the monitoring data comprises service performance parameters.
S2: and analyzing the service type corresponding to the service performance parameter, and determining a fuzzy membership function corresponding to the service type according to the service type.
The monitoring data of this embodiment includes feature data in service management, including service performance parameters and service types corresponding to the service performance parameters, such as service types of "time consumption, total data amount, average service request size, abnormal rate", and the like. In this embodiment, different service types correspond to different fuzzy rules, and the different fuzzy rules form a fuzzy rule base and are stored so as to be called in time. For example, the time consumption is mapped to the response efficiency of the service, and the corresponding fuzzy semantics in the fuzzy rule include: slow, normal, high speed, top speed four states; the abnormal rate is mapped as the stability of the service, and the corresponding fuzzy semantics in the fuzzy rule comprise: high risk, normal, more stable, excellent four dimensions, etc., where fuzzy rules are not listed.
S3: and substituting the service performance parameters into the fuzzy membership functions to match corresponding fuzzy semantics, wherein the fuzzy semantics are contained in a group of evaluation dimension information corresponding to the service types, and each group of evaluation dimension information corresponds to each service type one to one.
In the embodiment, the absolute eigenvalue corresponding to the service performance parameter is mapped into the relative value through the fuzzy membership function, so that each service performance can be described more visually, the method is more suitable for non-developers and operation and maintenance personnel to know the current service situation, and the physical significance deviation of the absolute eigenvalue corresponding to the service performance parameter in different application scenes is reduced to a certain extent. For example, for the system a with external calling, the average time is 2000ms, then the response efficiency of the service a1 with 200ms is high; for internal service B, it takes 20ms on average, and then for 200ms service B1, its response efficiency is slow. Namely, the "time-consuming" service type of the response efficiency of the mapping service, the corresponding set of evaluation dimension information includes: slow, normal, high speed, top speed four dimensions.
S4: and obtaining fuzzy semantics corresponding to the service performance parameters output by the fuzzy membership function.
In this embodiment, the absolute eigenvalue corresponding to the service performance parameter is substituted into the fuzzy membership function, so as to obtain the corresponding fuzzy semantics according to the segmentation of the fuzzy membership function corresponding to the absolute eigenvalue and according to the segmentation. For example, in the BIS (business information system) service, the setting segmentation is performed according to the theoretical extremely small value of network delay of about 20ms, the time consumed by the dedicated line to route the dual-port network is about 36 ms, and at least three round-trip experiences of one non-empty tcp request. The fuzzy rule corresponding to the network delay performance comprises 8 fuzzy semantics, wherein 8 subsections are sequentially corresponding according to the size of a critical value, adjacent subsections are demarcated and associated by the corresponding critical value, the 8 fuzzy semantics correspond to the critical value which respectively corresponds to 8 subsection partitions from small to large, and the 8 subsections comprise 'excellent, very good, better, normal, slower, very slow and abnormal', the corresponding 8 subsections are (0,20), [20,60), [60,120), [120,300), [300,10000), [10000,30000), [30000,60000), [60000,120000) in millisecond, if the absolute feature value corresponding to the current network delay is 45ms, the corresponding subsection is [20,60 ], and the corresponding fuzzy semantics is 'very good', so that the current service state can be known more intuitively.
S5: and converting the fuzzy semantics into a service performance analysis language according to a preset conversion mode.
In this embodiment, in the process of converting the fuzzy semantics into the service performance analysis language and matching the fuzzy semantics with a parameter table corresponding to the analysis language, the parameter table includes two parameters, a preset fixed composition parameter and a matching parameter selected by a user, for example, the preset fixed composition parameter includes "company, system, service, environment, time period" and the like, the matching parameter may obtain a parameter of the corresponding fuzzy semantics through a fuzzy rule, for example, "average consumed time" and the like, and the current service performance is displayed more intuitively through the parameter table. In another embodiment of the present application, the parameters in the parameter table are correspondingly matched into the preset sentence pattern according to the preset sentence pattern, and the report described in human language is automatically converted. Finally, the service performance analysis based on the fuzzy semantic description can be output by depending on a front-end page or an Ireport reporting tool. The Ireport reporting tool can realize the functions of a Table Table, a cross Table, a sub-report, a multi-data source report, word export, excel, pdf and the like.
Further, before the step S1 of acquiring the monitoring data recorded in the system, the method includes:
s11: and acquiring a configuration table of the designated service performance according to a preset mode, wherein the fuzzy matching corresponding sections and fuzzy semantics respectively corresponding to the sections are configured in the configuration table.
The configuration table of this embodiment includes a critical point value of a segment, fuzzy semantics corresponding to the segment, a representative meaning of the critical point value, and the like. Firstly, defining an investigation range according to services, if an investigation object is a certain service inquiry interface and belongs to one service of the service function A, counting the mean values of the related characteristic values of all the service interfaces in the service A, such as the mean values of 95line, 99line and the like, by taking the service A as the background so as to determine a reasonable investigation data range, segmenting according to the determined investigation data range and the normal data distribution characteristics to form segments, and matching fuzzy semantics according to the service state meanings corresponding to the segments. And the automatic mapping from the original recorded data to the fuzzy semantics is realized by using JAVA codes or CASE statements of SQL (structured query language) of a relational database, so that the automation effect is improved. The CASE statement in the SQL statement and the switch statement in the high-level language are grammars of standard SQL, and are suitable for executing different operations under the condition that one condition judgment has multiple values.
S12: and generating the fuzzy membership function according to the fuzzy semantics corresponding to the segments and each segment.
Further, the step S12 of generating the fuzzy membership function according to the fuzzy semantics corresponding to the segments and the segments respectively includes:
s121: acquiring a first critical value and a first fuzzy semantic corresponding to a first segment, wherein the first segment is any segment in each segment, each segment is sequentially connected and continuously distributed, and the first critical value comprises a starting critical value and an ending critical value.
S122: and correspondingly mapping the first segmentation into a vertical coordinate interval, and correspondingly mapping the first fuzzy semantic meaning into a horizontal coordinate interval.
S123: and combining the starting point critical value and the starting point value of the abscissa interval into a first coordinate point, and combining the ending point critical value and the starting point value of the abscissa interval into a second coordinate point.
S124: and generating the fuzzy membership function expressed by a linear function according to a connecting line of the first coordinate point and the second coordinate point.
The embodiment adopts the linear function as the fuzzy membership function, and has the advantages of monotony, easy control and small algorithm complexity. Because the service attributes are in the same trend, the monotone linear function can better meet the requirements in terms of function and efficiency. The concept of the above trend is a monotonicity trend of data, such as that the average time is 33ms less than 66ms, which means 33ms is faster, 66ms is less than 99ms, which means 66ms is faster than 99ms, and then 33ms must be faster than 99 ms. The linear function of this embodiment is a linear function, and each segment is mapped into a function value y, the fuzzy semantics corresponding to each segment is mapped into a fuzzy independent variable x, when the fuzzy independent variable x corresponds to a same span interval, the data ranges corresponding to each segment are different, and the data amount covered by each segment is different, that is, the equal abscissa interval corresponds to the segments of different data ranges, but the overall distribution state is normal, that is, the data amount occupation ratio corresponding to the "normal" fuzzy semantics is the maximum, so as to conform to the natural law of the conventional service, and the linear change slope expressed as the "normal" fuzzy semantics is the minimum.
Further, the step S11 of acquiring the configuration table of the specified service performance according to the preset manner includes:
s111: identifying the specified service performance to be fuzzily analyzed, which is input by a user.
S112: and judging whether a first configuration table corresponding to the specified service performance exists in advance through manual matching.
S113: and if the first configuration table corresponding to the specified service performance which is manually matched in advance exists, calling the first configuration table.
The first configuration table of the embodiment is autonomously set through manual experience and then stored so as to be called in time. According to the method and the device, the configuration table is set through manual participation, fuzzy semantics can meet the requirements of service analysis, and the configuration table is set through manual participation and is automatically called specifically according to the type of the service performance during service performance analysis through the link of the storage address.
Further, the step S11 of acquiring the configuration table of the specified service performance according to the preset manner includes:
s114: and judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by the user.
S115: acquiring first historical data of the designated service performance corresponding to the first deployment level, wherein the first historical data comprises stored data of a designated time period before the current time.
S116: and arranging the first historical data into a first array according to the sequence of the data values from small to large.
S117: and according to the type quantity of the preset fuzzy semantics, sequentially and correspondingly dividing the first array according to a preset normal distribution proportion.
S118: and mapping each segment corresponding to the first array and the type of the preset fuzzy semantic into a second configuration table.
In the embodiment, data is automatically analyzed by a statistical method, so that data segmentation is automatically realized, and further, corresponding mapping between the data segmentation and fuzzy semantics is realized. According to the embodiment, data statistics is realized according to the same layer attribute by counting the longitudinal historical data of the same deployment level, and the variation trend of the data is obtained. For example, the time of the target survey is year, month, and day, and the unit is day; the minimum granularity of the attribute tags is 'BIS service system', the current first deployment level is 'system', and the second deployment level higher than the first deployment level is 'company'. And counting the time consumption distribution of the BIS service in the previous 30 days, wherein the original recorded data are shown in the following table 1, the time in the table is specifically replaced by the first day, the second day and the like, and the actual time is year, month and day, and represents the average value corresponding to the data of one day.
TABLE 1
Time Average elapsed time/ms
Day one 55
The next day 50
.. Process for preparing the same .. its advantages are simple structure
Last day 98
The average time consumption of the original record is sequentially divided into segments corresponding to five critical values after being sorted from small to large according to the average time consumption value, a segment region is divided according to a normal distribution proportion mode, for example, the segments are divided according to the normal distribution proportion of 3,6,12,6 and 3 to obtain each segment shown in the following table 2, then a linear membership function is obtained according to the data of the table 2, then the fuzzy semantics of 'average time consumption 33' of the current data is judged according to the obtained linear membership function, the 'average time consumption 33' is known to belong to a second segment according to the linear membership function, and the corresponding fuzzy semantics is 'better'.
TABLE 2
Figure BDA0002080734650000091
Further, the step S11 of obtaining the configuration table of the specified service performance according to the preset manner includes:
s1100: and judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by the user.
S1101: the method comprises the steps of obtaining a second deployment hierarchy which is higher than the first deployment hierarchy, and second historical data corresponding to each first deployment hierarchy included in the second deployment hierarchy, wherein the second historical data comprise stored data which are located in a specified time period before the current time, and the second deployment hierarchy comprises a plurality of first deployment hierarchies.
S1102: and arranging the data of the specified service performance corresponding to the same historical moment in the second historical data into a second array according to the sequence of the data numerical values from small to large.
S1103: and according to the type number of the preset fuzzy semantics, sequentially and correspondingly dividing the second array according to a preset normal distribution proportion.
S1104: and mapping each segment corresponding to the second array into a third configuration table corresponding to the type of the preset fuzzy semantics respectively.
According to the attribute of the second deployment level with the level higher than that of the first deployment level, the present embodiment calculates all historical data of the first deployment level included under the second deployment level, for example, the first deployment level is "system", the last deployment level of the first deployment level is "company", the "company" is the second deployment level, the "company" includes a plurality of "systems", and the present embodiment has an advantage that the "systems" can be compared horizontally, and the cause of the problem is found more easily. For example, services of each system served by each "system" in the same time dimension are very different, for example, there is a service which consumes a lot of time such as file transmission, etc., and a membership critical value of the whole system is adjusted upward, so that part of services with high performance requirements cannot be objectively evaluated. The process of data statistics and mapping into the third configuration table is similar to the process of forming the second configuration table, except that the data in the third configuration table is horizontal data of each system in the same time dimension, the data in the second configuration table is vertical data of the same system in different time dimensions, and the same time dimension and the different time dimensions both take 'day' as a unit.
Further, the step S1103 of correspondingly dividing the second array in sequence according to the number of the types of the preset fuzzy semantics and the preset normal distribution ratio includes:
and S1103a, acquiring the type number of the preset fuzzy semantics.
S1103b, obtaining the average corresponding to the second array.
And S1103c, taking the average number as a middle peak of normal distribution, and segmenting all data ranges in the second array according to the preset ratio of the data ranges in which the middle peak is located to all data ranges in the second array, wherein the number of the segments is the same as the number of the types.
The number of segments corresponding to the normal distribution in this embodiment is the same as the number of types of the preset fuzzy semantics, and the data range of the middle peak is the largest than the preset occupation ratio of all the data ranges in the second array. For example, the service performance with 5 evaluation dimensions is divided according to the normal distribution ratio of 3,6,12,6, 3.
Further, the step of obtaining the configuration table of the specified service performance according to the preset mode includes:
s1105: and judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by the user.
S1106: and respectively acquiring first historical data of the specified service performance corresponding to the first deployment hierarchy and second historical data corresponding to each first deployment hierarchy included in a second deployment hierarchy, wherein the second deployment hierarchy is higher in level than the first deployment hierarchy, and the first historical data and the second historical data both include stored data of a specified time period before the current time.
S1107: arranging the data of the designated service performance corresponding to the same historical time in the second historical data into a third array according to the sequence of the data values from small to large, and arranging the data of the designated service performance in the first historical data into a fourth array according to the sequence of the data values from small to large, wherein the data of the designated service performance in the first historical data are the data corresponding to the designated service performance at different historical times, the different historical times and the same historical time are contained in a designated time period before the current time, and the number of the different historical times is determined according to the number of the first deployment levels contained in the second deployment levels, so that the third array and the fourth array comprise the same data number.
S1108: and correspondingly fitting the data in the third array and the fourth array from small to large according to a preset weight proportion to form a fifth array.
S1109: and correspondingly dividing the fifth array in sequence according to the type number of the preset fuzzy semantics and the preset normal distribution proportion.
S1110: and mapping each segment corresponding to the fifth array to a fourth configuration table corresponding to the type of the preset fuzzy semantics respectively.
In this embodiment, data in the fourth configuration table is formed according to different weight ratios by simultaneously combining the second configuration table and the third configuration table, and the respective corresponding membership critical values are obtained according to the formation processes of the second configuration table and the third configuration table, and then fusion is performed according to preset weight parameters, for example, the weight of the first deployment level is 0.8, the weight of the second deployment level is 0.2, so as to obtain the membership critical value corresponding to the fourth configuration table. For example, the upper threshold values of the first historical data corresponding to the first deployment level are, in order from small to large: 20. 60, 150, 200, 220; the upper critical values of the second historical data corresponding to the second deployment level are as follows from small to large: 40. 80, 230, 300, 330; the upper thresholds corresponding to the fourth configuration table obtained according to the weights are as follows: 24. 64, 166, 220, 242, wherein 24 is 20 x 0.8+40 x 0.2, the reliability of obtaining the membership threshold is increased by comprehensively considering the longitudinal data corresponding to the first deployment level and the lateral data of the previous deployment level of the first deployment level. In other embodiments of the present application, when a previous deployment level of the second deployment level needs to be added, the same can be achieved by adding a weight setting, for example, the weight of the first deployment level is 0.7, the weight of the second deployment level is 0.2, and the weight of the previous deployment level of the second deployment level is 0.1, which is not described in detail herein. The critical value algorithm of the embodiment can be adjusted according to needs, developers can make various critical value algorithms into tool bags, users can select and use the critical value algorithms according to needs, the configuration table can be dynamically generated through the critical value algorithm tool bags, the linear membership functions are adjusted through the changed configuration table, then language description suitable for non-development users to understand can be obtained according to the linear membership functions, and more non-developers and operation and maintenance personnel can know various performances of the system more intuitively.
In the embodiment, the absolute eigenvalue corresponding to the service performance parameter is mapped into the relative value through the fuzzy membership function, so that each service performance can be described more visually, the method is more suitable for non-developers and operation and maintenance personnel to know the current service situation, and the physical significance deviation of the absolute eigenvalue corresponding to the service performance parameter in different application scenes is reduced to a certain extent. In this embodiment, an investigation range is defined according to a service, a mean value of all relevant feature values, such as a mean value attribute of 95line, 99line, and the like, is counted to determine a reasonable investigation data range, segmentation is performed according to the determined investigation data range and normal data distribution characteristics to form segments, and fuzzy semantics are matched according to service state meanings corresponding to the segments. And the automatic mapping from the original recorded data to the fuzzy semantics is realized by using JAVA codes or CASE statements of SQL (structured query language) of a relational database, so that the automation effect is improved. The configuration table of the embodiment can be autonomously set and stored through manual experience, and can also be autonomously formed through a statistical method, so that different requirements of users can be met. In this embodiment, the longitudinal data corresponding to the first deployment level and the lateral data of the previous deployment level of the first deployment level are considered comprehensively, so as to increase the reliability of obtaining the membership critical value.
Referring to fig. 2, a service performance analysis apparatus according to an embodiment of the present application includes:
the first obtaining module 1 is configured to obtain monitoring data recorded in a system, where the monitoring data includes a service performance parameter.
And the analysis module 2 is used for analyzing the service type corresponding to the service performance parameter and determining a fuzzy membership function corresponding to the service type according to the service type.
The monitoring data of this embodiment includes feature data in service management, including service performance parameters and service types corresponding to the service performance parameters, such as service types of "time consumption, total data amount, average service request size, abnormal rate", and the like. In this embodiment, different service types correspond to different fuzzy rules, and the different fuzzy rules form a fuzzy rule base and are stored so as to be called in time. For example, the time consumption is mapped to the response efficiency of the service, and the corresponding fuzzy semantics in the fuzzy rule include: slow, normal, high speed, top speed four states; the abnormal rate is mapped as the stability of the service, and the corresponding fuzzy semantics in the fuzzy rule comprise: high risk, normal, more stable, excellent four dimensions, etc., and fuzzy rules are not enumerated here.
And a substituting module 3, configured to substitute the service performance parameter into the fuzzy membership function to match a corresponding fuzzy semantic, where the fuzzy semantic is included in a set of evaluation dimension information corresponding to the service type, and each set of evaluation dimension information corresponds to each service type one to one.
In the embodiment, the absolute eigenvalue corresponding to the service performance parameter is mapped into the relative value through the fuzzy membership function, so that each service performance can be described more visually, the method is more suitable for non-developers and operation and maintenance personnel to know the current service situation, and the physical significance deviation of the absolute eigenvalue corresponding to the service performance parameter under different application scenes is reduced to a certain extent. For example, for the system a with external calling, the average time is 2000ms, then the response efficiency of the service a1 with 200ms is high; for internal service B, it takes 20ms on average, and then for 200ms service B1, its response efficiency is slow. Namely, the service type of "time consumption" for mapping the response efficiency of the service, and the corresponding set of evaluation dimension information includes: slow, normal, high speed, top speed four dimensions.
And the obtaining module 4 is used for obtaining the fuzzy semantics corresponding to the service performance parameters output by the fuzzy membership function.
In this embodiment, the absolute eigenvalue corresponding to the service performance parameter is substituted into the fuzzy membership function, so as to obtain the corresponding fuzzy semantics according to the segmentation of the fuzzy membership function corresponding to the absolute eigenvalue and according to the segmentation. For example, in the BIS (business information system) service, the setting segmentation is performed according to the theoretical extremely small value of network delay of about 20ms, the time taken by the dedicated routing dual-port network is about 36 ms, and at least three round-trip experiences of one non-empty tcp request. The fuzzy rule corresponding to the network delay performance comprises 8 fuzzy semantics, wherein 8 subsections are sequentially corresponding according to the size of a critical value, adjacent subsections are demarcated and associated by the corresponding critical value, the 8 fuzzy semantics correspond to the critical value which respectively corresponds to 8 subsection partitions from small to large, and the 8 subsections comprise 'excellent, very good, better, normal, slower, very slow and abnormal', the corresponding 8 subsections are (0,20), [20,60), [60,120), [120,300), [300,10000), [10000,30000), [30000,60000), [60000,120000) in millisecond, if the absolute feature value corresponding to the current network delay is 45ms, the corresponding subsection is [20,60 ], and the corresponding fuzzy semantics is 'very good', so that the current service state can be known more intuitively.
And the conversion module 5 is used for converting the fuzzy semantics into the service performance analysis language according to a preset conversion mode.
In the embodiment, in order to match the fuzzy semantics with the parameter table corresponding to the analysis language, the parameter table includes two parameters, and a preset fixed composition parameter and a matching parameter selected by a user, for example, the preset fixed composition parameter includes "company, system, service, environment, time period" and the like, and the matching parameter can obtain a parameter of the corresponding fuzzy semantics through a fuzzy rule, for example, "average consumed time" and the like, and the current service performance is displayed more intuitively through the parameter table. In another embodiment of the present application, each parameter in the parameter table is correspondingly matched into a preset sentence pattern according to the preset sentence pattern, and a report of human language description is automatically converted. Finally, the service performance analysis based on the fuzzy semantic description can be output by depending on a front-end page or an Ireport reporting tool. The Ireport reporting tool can realize the functions of a Table Table, a cross Table, a sub-report, a multi-data source report, word export, excel, pdf and the like.
Further, the service performance analysis apparatus includes:
and the second acquisition module is used for acquiring a configuration table of the specified service performance according to a preset mode, wherein fuzzy matching corresponding sections and fuzzy semantics corresponding to the sections are configured in the configuration table.
The configuration table of this embodiment includes the critical point value of the segment, the fuzzy semantics corresponding to the segment, the representative meaning of the critical point value, and the like. Firstly, defining an investigation range according to a service, if an investigation object is a certain service inquiry interface and belongs to a service of the A service function, counting the average values of the related characteristic values of all service interfaces in the A service, such as the average values of 95line, 99line and the like, by taking the A service as a background so as to determine a reasonable investigation data range, segmenting according to the determined investigation data range and the normal data distribution characteristics to form segments, and matching fuzzy semantics according to the service state meaning corresponding to each segment. And the automatic mapping from the original recorded data to the fuzzy semantics is realized by using JAVA codes or CASE statements of SQL (structured query language) of a relational database, so that the automation effect is improved. The CASE statement in the SQL statement and the switch statement in the high-level language are grammars of standard SQL, and are suitable for executing different operations under the condition that one condition judgment has multiple values.
And the generating module is used for generating the fuzzy membership function according to the fuzzy semantics respectively corresponding to the segments and the segments.
Further, the generating module includes:
the device comprises a first obtaining unit, a second obtaining unit and a third obtaining unit, wherein the first obtaining unit is used for obtaining a first critical value and a first fuzzy semantic corresponding to a first segment, the first segment is any segment in all the segments, all the segments are connected in sequence and distributed continuously, and the first critical value comprises a starting critical value and an ending critical value.
And the mapping unit is used for correspondingly mapping the first segmentation into a longitudinal coordinate interval and correspondingly mapping the first fuzzy semantic meaning into a horizontal coordinate interval.
And the composition unit is used for composing the starting point critical value and the starting point value of the abscissa interval into a first coordinate point and composing the end point critical value and the starting point value of the abscissa interval into a second coordinate point.
And the generating unit is used for generating the fuzzy membership function expressed by a linear function according to a connecting line of the first coordinate point and the second coordinate point.
The embodiment adopts the linear function as the fuzzy membership function, and has the advantages of monotony, easy control and small algorithm complexity. Because the service attributes are homodromous, the monotonic linear function can better meet the requirements in terms of function and efficiency. The concept of the above trend is a monotonicity trend of data, such as that the average time is 33ms less than 66ms, which means 33ms is faster, 66ms is less than 99ms, which means 66ms is faster than 99ms, and then 33ms must be faster than 99 ms. The linear function of this embodiment is a linear function, each segment is mapped into a function value y, the fuzzy semantics corresponding to each segment are mapped into a fuzzy independent variable x, when the fuzzy independent variable x corresponds to a same span interval, the data ranges corresponding to each segment are different, and the data amount covered by each segment is different, that is, the equal abscissa intervals correspond to the segments of different data ranges, but the overall distribution state is normal, that is, the data amount occupation ratio corresponding to the "normal" fuzzy semantics is maximum, so as to conform to the natural law of the conventional service, and the linear change slope expressed as the "normal" fuzzy semantics is minimum.
Further, the second obtaining module includes:
and the identification unit is used for identifying the specified service performance to be subjected to fuzzy analysis and input by the user.
And the first judgment unit is used for judging whether a first configuration table which is manually matched in advance and corresponds to the specified service performance exists.
And the calling unit is used for calling the first configuration table if the first configuration table corresponding to the specified service performance is matched manually in advance.
The first configuration table of the embodiment is autonomously set through manual experience and then stored so as to be called in time. According to the embodiment, the configuration table is set through manual participation, so that fuzzy semantics can meet the requirements of service analysis, and the configuration table is set through manual participation, so that the service performance analysis can be automatically carried out according to the category of the service performance in a targeted mode.
Further, the second obtaining module includes:
and the second judging unit is used for judging the first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by the user.
A second obtaining unit, configured to obtain first history data of the specified service performance corresponding to the first deployment level, where the first history data includes stored data of a specified time period before a current time.
The first arrangement unit is used for arranging the first historical data into a first array according to the sequence of the data numerical values from small to large.
And the first dividing unit is used for sequentially and correspondingly dividing the first array according to the type number of the preset fuzzy semantics and the preset normal distribution proportion.
And the first mapping unit is used for mapping each segment corresponding to the first array and the type of the preset fuzzy semantic meaning into a second configuration table respectively.
The embodiment automatically analyzes the data by a statistical method, automatically realizes the segmentation of the data, and further realizes the corresponding mapping of the data segmentation and fuzzy semantics. According to the embodiment, data statistics is realized according to the same layer attribute by counting the longitudinal historical data of the same deployment level, and the variation trend of the data is obtained. For example, the time of the target survey is year, month, and day, and the unit is day; the minimum granularity of the attribute labels is 'BIS service system', the current first deployment level is 'system', and the second deployment level higher than the first deployment level is 'company'. And counting the time consumption distribution of the BIS service in the previous 30 days, wherein the original recorded data are shown in the following table 1, the time in the table is specifically replaced by the first day, the second day and the like in sequence, and the actual time is the average value corresponding to the data of one day.
TABLE 1
Time Average elapsed time/ms
Day one 55
The next day 50
.. Process for preparing the same .. its advantages are simple structure
Last day 98
The average time consumption of the original record is sequentially divided into segments corresponding to five critical values after being sorted from small to large according to the average time consumption value, a segment region is divided according to a normal distribution proportion mode, for example, the segments are divided according to the normal distribution proportion of 3,6,12,6 and 3 to obtain each segment shown in the following table 2, then a linear membership function is obtained according to the data of the table 2, then the fuzzy semantics of 'average time consumption 33' of the current data is judged according to the obtained linear membership function, the 'average time consumption 33' is known to belong to a second segment according to the linear membership function, and the corresponding fuzzy semantics is 'better'.
TABLE 2
Figure BDA0002080734650000161
Figure BDA0002080734650000171
Further, the second obtaining module includes:
and the third judging unit is used for judging the first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by the user.
A third obtaining unit, configured to obtain a second deployment hierarchy that is higher in level than the first deployment hierarchy, and second historical data corresponding to each of the first deployment hierarchies included in the second deployment hierarchy, where the second historical data includes stored data of a specified time period before a current time, and the second deployment hierarchy includes multiple first deployment hierarchies.
And the second arrangement unit is used for arranging the data of the specified service performance corresponding to the same historical moment in the second historical data into a second array according to the sequence of the data numerical values from small to large.
And the second dividing unit is used for correspondingly dividing the second array in sequence according to the type number of the preset fuzzy semantics and the preset normal distribution proportion.
And the second mapping unit is used for mapping each segment corresponding to the second array into a third configuration table respectively corresponding to the type of the preset fuzzy semantics.
According to the attribute of the second deployment level with the level higher than that of the first deployment level, the present embodiment calculates all historical data of the first deployment level included under the second deployment level, for example, the first deployment level is "system", the last deployment level of the first deployment level is "company", the "company" is the second deployment level, the "company" includes a plurality of "systems", and the present embodiment has an advantage that the "systems" can be compared horizontally, and the cause of the problem is found more easily. For example, services of each system of each "system" in the same time dimension are very different, and for example, there is a service that consumes a lot of time such as file transmission, the membership critical value of the whole system is adjusted upward, so that part of services with high performance requirements cannot be objectively evaluated. The process of data statistics and mapping into the third configuration table is similar to the process of forming the second configuration table, except that the data in the third configuration table is horizontal data of each system in the same time dimension, the data in the second configuration table is vertical data of the same system in different time dimensions, and the same time dimension and the different time dimensions are both in a day unit.
Further, the second dividing unit includes:
and the first acquisition subunit is used for acquiring the type number of the preset fuzzy semantics.
And the second obtaining subunit is used for obtaining the average number corresponding to the second array.
And the dividing subunit is used for taking the average number as a middle peak value of normal distribution, and dividing all data ranges in the second array into sections according to a preset ratio of the data range in which the middle peak value is located to all data ranges in the second array, wherein the number of the sections is the same as the number of the types.
The number of segments corresponding to the normal distribution in this embodiment is the same as the number of types of the preset fuzzy semantics, and the data range of the middle peak is the largest than the preset occupation ratio of all the data ranges in the second array. For example, the service performance with 5 evaluation dimensions is divided according to the normal distribution ratio of 3,6,12,6, 3.
Further, the second obtaining module includes:
and the fourth judging unit is used for judging the first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by the user.
A fourth obtaining unit, configured to obtain first historical data of the specified service performance corresponding to the first deployment hierarchy and second historical data corresponding to each of the first deployment hierarchies included in a second deployment hierarchy, where the second deployment hierarchy is higher in level than the first deployment hierarchy, and the first historical data and the second historical data each include stored data of a specified time period before a current time.
The third arrangement unit is used for arranging the data of the specified service performance corresponding to the same historical time in the second historical data into a third array according to the sequence of the data values from small to large, and arranging the data of the specified service performance in the first historical data into a fourth array according to the sequence of the data values from small to large, wherein the data of the specified service performance in the first historical data are the data of the specified service performance corresponding to the specified service performance at different historical times, and the different historical times and the same historical time are contained in a specified time period before the current time, according to the second deployment levels contain the number of the first deployment levels, the number of the different historical times is determined, and the third array and the fourth array comprise the same data number.
And the fitting unit is used for fitting the data in the third array and the fourth array in a one-to-one correspondence manner according to a preset weight proportion from small to large respectively to form a fifth array.
And the third dividing unit is used for correspondingly dividing the fifth array in sequence according to the type number of the preset fuzzy semantics and the preset normal distribution proportion.
And the third mapping unit is used for mapping each segment corresponding to the fifth array into a fourth configuration table respectively corresponding to the type of the preset fuzzy semantics.
In this embodiment, data in the fourth configuration table is formed according to different weight ratios by simultaneously combining the second configuration table and the third configuration table, and the respective corresponding membership critical values are obtained according to the formation processes of the second configuration table and the third configuration table, and then fusion is performed according to preset weight parameters, for example, the weight of the first deployment level is 0.8, the weight of the second deployment level is 0.2, so as to obtain the membership critical value corresponding to the fourth configuration table. For example, the upper threshold values of the first historical data corresponding to the first deployment level are, in order from small to large: 20. 60, 150, 200, 220; the upper critical values of the second historical data corresponding to the second deployment level are as follows from small to large: 40. 80, 230, 300, 330; the upper thresholds corresponding to the fourth configuration table obtained according to the weights are as follows: 24. 64, 166, 220, 242, wherein 24 is 20 × 0.8+40 × 0.2, the reliability of obtaining the membership threshold is increased by comprehensively considering the longitudinal data corresponding to the first deployment level and the lateral data of the previous deployment level of the first deployment level. In other embodiments of the present application, when a previous deployment level of the second deployment level needs to be added, the same operation can be performed by adding a weight setting, for example, the weight of the first deployment level is 0.7, the weight of the second deployment level is 0.2, and the weight of the previous deployment level of the second deployment level is 0.1, which is not repeated herein. The critical value algorithm of the embodiment can be adjusted according to needs, developers make various critical value algorithms into a tool kit, users select to use the critical value algorithm tool kit according to needs, a configuration table can be dynamically generated through the critical value algorithm tool kit, the linear membership function is adjusted through the changed configuration table, then according to the linear membership function, language description suitable for non-development users to understand can be obtained, and more non-developers and operation and maintenance personnel can know various current performances of the system more intuitively.
Referring to fig. 3, an embodiment of the present application further provides a computer device, where the computer device may be a server, and an internal structure of the computer device may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer designed processor is used to provide computational and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store all data required by the service performance analysis process. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a service performance analysis method.
The processor executes the service performance analysis method, and includes: acquiring monitoring data recorded in a system, wherein the monitoring data comprises service performance parameters; analyzing the service type corresponding to the service performance parameter, and determining a fuzzy membership function corresponding to the service type according to the service type; substituting the service performance parameters into the fuzzy membership functions to match corresponding fuzzy semantics, wherein the fuzzy semantics are contained in a group of evaluation dimension information corresponding to the service types, and each group of evaluation dimension information corresponds to each service type one to one; obtaining fuzzy semantics corresponding to the service performance parameters output by the fuzzy membership function; and converting the fuzzy semantics into a service performance analysis language according to a preset conversion mode.
According to the computer equipment, the absolute eigenvalue corresponding to the service performance parameter is mapped into the relative value through the fuzzy membership function, so that each service performance can be described more visually, the computer equipment is more suitable for non-developers and operation and maintenance personnel to know the current service situation, and the physical significance deviation of the absolute eigenvalue corresponding to the service performance parameter under different application scenes is reduced to a certain extent. Defining an investigation range according to the service, counting the average values of all related characteristic values, such as 95line, 99line and other average values, so as to determine a reasonable investigation data range, segmenting according to the determined investigation data range and the normal data distribution characteristics to form segments, and matching fuzzy semantics according to the service state meaning corresponding to each segment. And the automatic mapping from the original recorded data to the fuzzy semantics is realized by using JAVA codes or CASE statements of SQL (structured query language) of a relational database, so that the automation effect is improved. The configuration table can be automatically set and stored through manual experience, and can also be automatically formed through a statistical method so as to meet different requirements of users. And increasing the reliability of obtaining the membership critical value by comprehensively considering the longitudinal data corresponding to the first deployment level and the transverse data of the last deployment level of the first deployment level.
In one embodiment, the step of acquiring the monitoring data recorded in the system by the processor includes: acquiring a configuration table of specified service performance according to a preset mode, wherein fuzzy matching corresponding segments and fuzzy semantics corresponding to the segments are configured in the configuration table; and generating the fuzzy membership function according to the segmentation and the fuzzy semantics corresponding to the segmentation respectively.
In one embodiment, the fuzzy membership function is a linear function, and the step of generating the fuzzy membership function by the processor according to the fuzzy semantics corresponding to the segments and each of the segments includes: acquiring a first critical value and a first fuzzy semantic corresponding to a first segment, wherein the first segment is any segment in the segments, the segments are sequentially connected and continuously distributed, and the first critical value comprises a starting critical value and an end critical value; correspondingly mapping the first segmentation into a vertical coordinate interval, and correspondingly mapping the first fuzzy semantic meaning into a horizontal coordinate interval; forming a first coordinate point by using the starting point critical value and the starting point value of the abscissa interval, and forming a second coordinate point by using the end point critical value and the starting point value of the abscissa interval; and generating the fuzzy membership function expressed by the linear function according to a connecting line of the first coordinate point and the second coordinate point.
In an embodiment, the step of acquiring, by the processor, the configuration table of the specified service performance according to a preset manner includes: judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user; acquiring first historical data of the designated service performance corresponding to the first deployment level, wherein the first historical data comprises stored data of a designated time period before the current time; arranging the first historical data into a first array according to the sequence of the data values from small to large; according to the type number of preset fuzzy semantics, sequentially and correspondingly dividing the first array according to a preset normal distribution proportion; and mapping each segment corresponding to the first array into a second configuration table respectively corresponding to the type of the preset fuzzy semantics.
In an embodiment, the step of acquiring, by the processor, the configuration table specifying the service performance according to a preset manner includes: judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user; acquiring a second deployment level which is higher than the first deployment level in level, and second historical data which respectively corresponds to each first deployment level included in the second deployment level, wherein the second historical data comprises stored data which is located in a specified time period before the current time, and the second deployment level comprises a plurality of first deployment levels; arranging the data of the designated service performance corresponding to the same historical moment in the second historical data into a second array according to the sequence of the data values from small to large; according to the type number of the preset fuzzy semantics, sequentially and correspondingly dividing the second array according to a preset normal distribution proportion; and mapping each segment corresponding to the second array into a third configuration table respectively corresponding to the type of the preset fuzzy semantics.
In an embodiment, the step of sequentially and correspondingly dividing, by the processor according to the number of types of the preset fuzzy semantics and according to the preset normal distribution ratio, the second array includes: acquiring the type number of the preset fuzzy semantics; acquiring the average corresponding to the second array; and segmenting all data ranges in the second array by taking the average number as a middle peak value of normal distribution according to a preset ratio of the data range in which the middle peak value is located to all data ranges in the second array, wherein the number of the segments is the same as the number of the types.
In an embodiment, the step of acquiring, by the processor, the configuration table specifying the service performance according to a preset manner includes: judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user; respectively acquiring first historical data of the designated service performance corresponding to the first deployment hierarchy and second historical data corresponding to each first deployment hierarchy in a second deployment hierarchy, wherein the second deployment hierarchy is higher in level than the first deployment hierarchy, and the first historical data and the second historical data both comprise stored data of a designated time period before the current time; arranging the data of the specified service performance corresponding to the same historical time in each second historical data into a third array according to the sequence of the data numerical values from small to large, and arranging the data of the specified service performance in the first historical data into a fourth array according to the sequence of the data numerical values from small to large, wherein the data of the specified service performance in the first historical data are the data of the specified service performance corresponding to different historical times, the different historical times and the same historical time are contained in a specified time period before the current time, and the quantity of the different historical times is determined according to the quantity of the first deployment levels contained in the second deployment levels, so that the third array and the fourth array comprise the same quantity of data; correspondingly fitting the data in the third array and the fourth array in a one-to-one manner according to a preset weight proportion from small to large to form a fifth array; according to the type number of the preset fuzzy semantics, sequentially and correspondingly dividing the fifth array according to a preset normal distribution proportion; and mapping each segment corresponding to the fifth array into a fourth configuration table corresponding to the type of the preset fuzzy semantics respectively.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is only a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects may be applied.
An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing a service performance analysis method, including: acquiring monitoring data recorded in a system, wherein the monitoring data comprises service performance parameters; analyzing the service type corresponding to the service performance parameter, and determining a fuzzy membership function corresponding to the service type according to the service type; substituting the service performance parameters into the fuzzy membership functions to match corresponding fuzzy semantics, wherein the fuzzy semantics are contained in a group of evaluation dimension information corresponding to the service types, and each group of evaluation dimension information corresponds to each service type one to one; obtaining fuzzy semantics corresponding to the service performance parameters output by the fuzzy membership function; and converting the fuzzy semantics into a service performance analysis language according to a preset conversion mode.
According to the computer-readable storage medium, the absolute eigenvalue corresponding to the service performance parameter is mapped into the relative value through the fuzzy membership function, so that each service performance can be described more visually, the computer-readable storage medium is more suitable for non-developers and operation and maintenance personnel to know the current service situation, and the physical significance deviation of the absolute eigenvalue corresponding to the service performance parameter under different application scenes is reduced to a certain extent. Defining an investigation range according to the service, counting the average values of all related characteristic values, such as 95line, 99line and other average values, so as to determine a reasonable investigation data range, segmenting according to the determined investigation data range and the normal data distribution characteristics to form segments, and matching fuzzy semantics according to the service state meaning corresponding to each segment. And the automatic mapping from the original recorded data to the fuzzy semantics is realized by using JAVA codes or CASE statements of SQL (structured query language) of a relational database, so that the automation effect is improved. The configuration table can be automatically set and stored through manual experience, and can also be automatically formed through a statistical method so as to meet different requirements of users. The reliability of obtaining the membership threshold is increased by comprehensively considering the longitudinal data corresponding to the first deployment level and the lateral data of the last deployment level of the first deployment level.
In an embodiment, before the step of acquiring the monitoring data recorded in the system by the processor, the method includes: acquiring a configuration table of specified service performance according to a preset mode, wherein fuzzy matching corresponding segments and fuzzy semantics corresponding to the segments are configured in the configuration table; and generating the fuzzy membership function according to the segmentation and the fuzzy semantics corresponding to the segmentation respectively.
In one embodiment, the fuzzy membership function is a linear function, and the step of generating the fuzzy membership function by the processor according to the fuzzy semantics corresponding to the segments and the segments includes: acquiring a first critical value and a first fuzzy semantic corresponding to a first segment, wherein the first segment is any segment in each segment, each segment is sequentially connected and continuously distributed, and the first critical value comprises a starting critical value and an ending critical value; correspondingly mapping the first segmentation into a vertical coordinate interval, and correspondingly mapping the first fuzzy semantic meaning into a horizontal coordinate interval; forming a first coordinate point by using the starting point critical value and the starting point value of the abscissa interval, and forming a second coordinate point by using the end point critical value and the starting point value of the abscissa interval; and generating the fuzzy membership function expressed by the linear function according to a connecting line of the first coordinate point and the second coordinate point.
In an embodiment, the step of acquiring, by the processor, the configuration table of the specified service performance according to a preset manner includes: identifying the specified service performance to be subjected to fuzzy analysis and input by a user; judging whether a first configuration table corresponding to the specified service performance exists or not, wherein the first configuration table is manually matched in advance; if yes, calling the first configuration table.
In an embodiment, the step of acquiring, by the processor, the configuration table of the specified service performance according to a preset manner includes: judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user; acquiring first historical data of the designated service performance corresponding to the first deployment level, wherein the first historical data comprises stored data of a designated time period before the current time; arranging the first historical data into a first array according to the sequence of the data values from small to large; according to the type number of preset fuzzy semantics, sequentially and correspondingly dividing the first array according to a preset normal distribution proportion; and mapping each segment corresponding to the first array and the type of the preset fuzzy semantic into a second configuration table.
In an embodiment, the step of acquiring, by the processor, the configuration table of the specified service performance according to a preset manner includes: judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user; acquiring a second deployment hierarchy which is higher than the first deployment hierarchy in level, and second historical data which respectively correspond to each first deployment hierarchy included in the second deployment hierarchy, wherein the second historical data comprises stored data which is located in a specified time period before the current time, and the second deployment hierarchy comprises a plurality of first deployment hierarchies; arranging the data of the designated service performance corresponding to the same historical moment in the second historical data into a second array according to the sequence of the data numerical values from small to large; according to the type number of the preset fuzzy semantics, sequentially and correspondingly dividing the second array according to a preset normal distribution proportion; and mapping each segment corresponding to the second array into a third configuration table corresponding to the type of the preset fuzzy semantics respectively.
In an embodiment, the step of sequentially and correspondingly dividing, by the processor according to the number of types of the preset fuzzy semantics and according to the preset normal distribution ratio, the second array includes: acquiring the type number of the preset fuzzy semantics; acquiring the average corresponding to the second array; and segmenting all data ranges in the second array by taking the average number as a middle peak value of normal distribution according to a preset ratio of the data range where the middle peak value is located to all data ranges in the second array, wherein the number of the segments is the same as the number of the types.
In an embodiment, the step of acquiring, by the processor, the configuration table specifying the service performance according to a preset manner includes: judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user; respectively acquiring first historical data of the designated service performance corresponding to the first deployment hierarchy and second historical data corresponding to each first deployment hierarchy in a second deployment hierarchy, wherein the second deployment hierarchy is higher than the first deployment hierarchy, and the first historical data and the second historical data both comprise stored data of a designated time period before the current time; arranging the data of the designated service performance corresponding to the same historical time in each second historical data into a third array according to the sequence of the data values from small to large, and arranging the data of the designated service performance in the first historical data into a fourth array according to the sequence of the data values from small to large, wherein the data of the designated service performance in the first historical data are the data corresponding to the designated service performance at different historical times, the different historical times and the same historical time are contained in a designated time period before the current time, and the number of the different historical times is determined according to the number of the first deployment levels contained in the second deployment levels, so that the third array and the fourth array comprise the same data number; correspondingly fitting the data in the third array and the fourth array from small to large according to a preset weight proportion one by one to form a fifth array; according to the type number of the preset fuzzy semantics, sequentially and correspondingly dividing the fifth array according to a preset normal distribution proportion; and mapping each segment corresponding to the fifth array into a fourth configuration table respectively corresponding to the type of the preset fuzzy semantics.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided herein and used in the examples may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double-rate SDRAM (SSRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, apparatus, article or method that comprises the element.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all the equivalent structures or equivalent processes that can be directly or indirectly applied to other related technical fields by using the contents of the specification and the drawings of the present application are also included in the scope of the present application.

Claims (8)

1. A method for analyzing service performance, comprising:
acquiring monitoring data recorded in a system, wherein the monitoring data comprises service performance parameters;
analyzing the service type corresponding to the service performance parameter, and determining a fuzzy membership function corresponding to the service type according to the service type;
substituting the service performance parameters into the fuzzy membership functions to match corresponding fuzzy semantics, wherein the fuzzy semantics are contained in a group of evaluation dimension information corresponding to the service types, and each group of evaluation dimension information corresponds to each service type one to one;
obtaining fuzzy semantics corresponding to the service performance parameters output by the fuzzy membership function;
converting the fuzzy semantics into a service performance analysis language according to a preset conversion mode;
before the step of acquiring the monitoring data recorded in the system, the method comprises the following steps:
acquiring a configuration table of specified service performance according to a preset mode, wherein fuzzy matching corresponding segments and fuzzy semantics corresponding to the segments are configured in the configuration table;
generating the fuzzy membership function according to the fuzzy semantics corresponding to the segments and each segment;
the fuzzy membership function is a linear function, and the step of generating the fuzzy membership function according to the fuzzy semantics corresponding to the segments and each segment respectively comprises the following steps:
acquiring a first critical value and a first fuzzy semantic corresponding to a first segment, wherein the first segment is any segment in the segments, the segments are sequentially connected and continuously distributed, and the first critical value comprises a starting critical value and an end critical value;
correspondingly mapping the first segmentation into a vertical coordinate interval, and correspondingly mapping the first fuzzy semantic meaning into a horizontal coordinate interval;
forming a first coordinate point by using the starting point critical value and the starting point value of the abscissa interval, and forming a second coordinate point by using the end point critical value and the starting point value of the abscissa interval;
and generating the fuzzy membership function expressed by the linear function according to a connecting line of the first coordinate point and the second coordinate point.
2. The method of claim 1, wherein the step of obtaining the configuration table of the specified service performance according to the preset manner comprises:
judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user;
acquiring first historical data of the designated service performance corresponding to the first deployment level, wherein the first historical data comprises stored data of a designated time period before the current time;
arranging the first historical data into a first array according to the sequence of the data values from small to large;
according to the type number of preset fuzzy semantics, sequentially and correspondingly dividing the first array according to a preset normal distribution proportion;
and mapping each segment corresponding to the first array and the type of the preset fuzzy semantic into a second configuration table.
3. The service performance analysis method of claim 1, wherein the step of obtaining the configuration table of the specified service performance according to the preset manner comprises:
judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user;
acquiring a second deployment hierarchy which is higher than the first deployment hierarchy in level, and second historical data which respectively correspond to each first deployment hierarchy included in the second deployment hierarchy, wherein the second historical data comprises stored data which is located in a specified time period before the current time, and the second deployment hierarchy comprises a plurality of first deployment hierarchies;
arranging the data of the designated service performance corresponding to the same historical moment in the second historical data into a second array according to the sequence of the data numerical values from small to large;
according to the type number of the preset fuzzy semantics, sequentially and correspondingly dividing the second array according to a preset normal distribution proportion;
and mapping each segment corresponding to the second array into a third configuration table corresponding to the type of the preset fuzzy semantics respectively.
4. The service performance analysis method of claim 3, wherein the step of sequentially and correspondingly dividing the second array according to a preset normal distribution ratio and according to the number of types of the preset fuzzy semantics comprises:
acquiring the type number of the preset fuzzy semantics;
acquiring the average corresponding to the second array;
and segmenting all data ranges in the second array by taking the average number as a middle peak value of normal distribution according to a preset ratio of the data range in which the middle peak value is located to all data ranges in the second array, wherein the number of the segments is the same as the number of the types.
5. The method of claim 1, wherein the step of obtaining the configuration table of the specified service performance according to the preset manner comprises:
judging a first deployment level corresponding to the specified service performance to be subjected to fuzzy analysis and input by a user;
respectively acquiring first historical data of the designated service performance corresponding to the first deployment hierarchy and second historical data corresponding to each first deployment hierarchy in a second deployment hierarchy, wherein the second deployment hierarchy is higher than the first deployment hierarchy, and the first historical data and the second historical data both comprise stored data of a designated time period before the current time;
arranging the data of the designated service performance corresponding to the same historical time in each second historical data into a third array according to the sequence of the data values from small to large, and arranging the data of the designated service performance in the first historical data into a fourth array according to the sequence of the data values from small to large, wherein the data of the designated service performance in the first historical data are the data corresponding to the designated service performance at different historical times, the different historical times and the same historical time are contained in a designated time period before the current time, and the number of the different historical times is determined according to the number of the first deployment levels contained in the second deployment levels, so that the third array and the fourth array comprise the same data number;
correspondingly fitting the data in the third array and the fourth array in a one-to-one manner according to a preset weight proportion from small to large to form a fifth array;
according to the type number of the preset fuzzy semantics, sequentially and correspondingly dividing the fifth array according to a preset normal distribution proportion;
and mapping each segment corresponding to the fifth array into a fourth configuration table respectively corresponding to the type of the preset fuzzy semantics.
6. A service performance analysis apparatus, comprising:
the system comprises a first acquisition module, a second acquisition module and a monitoring module, wherein the first acquisition module is used for acquiring monitoring data recorded in a system, and the monitoring data comprises service performance parameters;
the analysis module is used for analyzing the service type corresponding to the service performance parameter and determining a fuzzy membership function corresponding to the service type according to the service type;
a substitution module, configured to substitute the service performance parameter into the fuzzy membership function to match a corresponding fuzzy semantic, where the fuzzy semantic is included in a set of evaluation dimension information corresponding to the service type, and each set of evaluation dimension information corresponds to each service type one to one;
the obtaining module is used for obtaining fuzzy semantics corresponding to the service performance parameters output by the fuzzy membership function;
and the conversion module is used for converting the fuzzy semantics into a service performance analysis language according to a preset conversion mode.
7. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 5 when executing the computer program.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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