CN115292146B - System capacity estimation method, system, equipment and storage medium - Google Patents
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Abstract
One embodiment of the invention discloses a method, a system, equipment and a storage medium for estimating system capacity, wherein the method comprises the following steps: obtaining historical operation index data of an application instance of a target cluster, and calculating historical TPS according to the historical operation index data, wherein the historical operation index data comprises a historical operation index peak value; obtaining a corresponding relation between the historical operation index peak value of the application example and the historical TPS according to the historical operation index data and the historical TPS; acquiring real-time operation index data of an application example of a target cluster, calculating real-time TPS according to the real-time operation index data, and calculating an estimated real-time operation index peak value according to the corresponding relation and the real-time TPS; and judging whether the target cluster needs capacity expansion or not according to the estimated real-time operation index peak value. The capacity evaluation method and the capacity evaluation system simplify the capacity evaluation flow, reduce the complexity of the capacity evaluation process and improve the capacity evaluation efficiency.
Description
Technical Field
The invention relates to the technical field of computers. And more particularly, to a system capacity estimation method, system, device, and storage medium.
Background
Currently, in the internet industry, in order to determine the configuration and the number of hardware resources of a software service, capacity planning needs to be performed on the resources generally, and an existing capacity planning scheme mainly includes three forms, namely an empirical theory, a model creation and a stress test. For example, in chinese patent document, with application number CN 201811403940, entitled "pressure testing method, system and server pressure testing system", a pressure testing method is introduced for a press machine, which includes: acquiring load parameters of a tested server; comparing the load parameter with a preset threshold value of the tested server; when the load parameter is smaller than the difference value between the preset threshold value and the fluctuation value, adjusting the output pressure measurement TPS value; and sending the pressure measurement TPS value to the tested server. Although the method overcomes the defect of manual adjustment of the TPS value in the current stage, the participation degree of testers in the pressure testing process of the server is reduced, the pressure testing cannot be combined with the actual service, and the accuracy of the pressure testing is reduced.
For example, in chinese patent document, with application number CN 201510425487.2, entitled "a pressure testing method and system", a pressure testing method is introduced, which includes: setting a business model, and respectively performing model testing on each business scene in the business model; carrying out accuracy verification on the service model according to the model test, and taking the service model as a pressure test service model when the accuracy verification is passed; selecting a service scene according to the pressure measurement service model, and sending a service request according to the service scene; and generating a database request according to the service request, and carrying out pressure test on the database. Although the pressure test is executed based on the service model, the execution of the service driving pressure test is realized, the test is closer to the actual environment, and the accuracy of the pressure test is improved, but the capacity planning of resources can not be carried out according to the test result.
Disclosure of Invention
In view of the above, a first embodiment of the present invention provides a method for estimating system capacity, including:
obtaining historical operation index data of an application instance of a target cluster, and calculating a historical TPS according to the historical operation index data, wherein the historical operation index data comprises a historical operation index peak value;
obtaining a corresponding relation between the historical operation index peak value of the application example and the historical TPS according to the historical operation index data and the historical TPS;
acquiring real-time operation index data of an application example of a target cluster, calculating real-time TPS according to the real-time operation index data, and calculating an estimated real-time operation index peak value according to the corresponding relation and the real-time TPS;
and judging whether the target cluster needs capacity expansion or not according to the estimated real-time operation index peak value.
In a specific embodiment, obtaining a correspondence between the historical operation index peak value of the application instance and the historical TPS according to the historical operation index data and the historical TPS includes:
and modeling according to the relation between the historical operation index peak value and the historical TPS, and obtaining a linear regression model for representing the relation between the historical operation index peak value and the historical TPS of the application example.
In a specific embodiment, the obtaining of the historical operation index data of the application instance of the target cluster includes:
and acquiring a plurality of application examples operated by the target cluster and operation index data of a plurality of preset time points in a historical time period from a time sequence database, wherein the historical time period is a preset time period close to the current time point.
In a specific embodiment, the determining whether the target cluster needs to be expanded includes:
calculating a real-time water level according to the real-time TPS and the estimated real-time operation index peak value;
and periodically detecting whether the real-time water level exceeds a preset first threshold value, and if so, expanding the capacity of the target cluster.
In a specific embodiment, the estimation method further includes: and displaying the real-time water level, the estimated real-time operation index peak value, the real-time TPS and the historical operation index peak value through a visual operation interface.
A second embodiment of the present invention provides a system capacity estimation system, including:
the obtaining historical data module is used for obtaining historical operation index data of an application instance of a target cluster and calculating historical TPS according to the historical operation index data, wherein the historical operation index data comprise a historical operation index peak value;
the obtaining corresponding relation module is used for obtaining the corresponding relation between the historical operation index peak value of the application example and the historical TPS according to the historical operation index data and the historical TPS;
the real-time data acquisition module is used for acquiring real-time operation index data of an application instance of a target cluster and calculating real-time TPS according to the real-time operation index data, wherein the real-time operation index data comprises a real-time operation index peak value;
the calculation module is used for calculating the estimated real-time operation index peak value according to the corresponding relation and the real-time TPS;
and the judging module judges whether the target cluster needs to be expanded according to the estimated real-time operation index peak value.
In a specific embodiment, the obtaining correspondence module is further configured to perform modeling according to a relationship between the historical operation index peak value and the historical TPS, and obtain a linear regression model for characterizing a relationship between the historical operation index peak value and the historical TPS of the application instance.
In a specific embodiment, the historical data obtaining module is further configured to obtain, from a time sequence database, a plurality of application instances operated by the target cluster and operation index data of a plurality of preset time points in a historical time period, where the historical time period is a predetermined time period close to a current time point.
A third embodiment of the invention provides a computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the method according to the first embodiment.
A fourth embodiment of the invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as described in the first embodiment when executing the program.
The invention has the following beneficial effects:
the invention provides a system capacity estimation method, a system, equipment and a storage medium, which can calculate the estimated real-time operation index peak value according to the corresponding relation and real-time TPS by acquiring the historical operation index data and the historical TPS and acquiring the corresponding relation between the historical operation index peak value and the historical TPS of an application example so as to judge whether a target cluster needs to be expanded, thereby avoiding the problems that the existing capacity estimation process is high in complexity due to dependence on manual pressure measurement, and the capacity estimation result is inaccurate due to the fact that the pressure measurement process cannot cover all services needing pressure measurement, pressure measurement data is easy to lag, distort, inaccurate and the like, simplifying the capacity estimation process, reducing the complexity of the capacity estimation process and improving the capacity estimation efficiency.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart illustrating a method for system capacity estimation according to an embodiment of the invention;
FIG. 2 illustrates a visual operator interface diagram according to one embodiment of the present invention;
FIG. 3 shows a schematic diagram of a system capacity estimation system according to an embodiment of the invention;
fig. 4 shows a schematic structural diagram of a computer device according to another embodiment of the present invention.
Detailed Description
In order to make the technical solutions and advantages of the present invention more apparent, the following detailed description of the embodiments of the present invention will be made with reference to the accompanying drawings.
In the existing typical scene of carrying out flow estimation aiming at service and outputting a system capacity estimation result by combining service pressure measurement data, a tester carries out service pressure measurement on the system to obtain pressure measurement data, then an operator predicts the flow of a flow peak or a sales promotion activity, an operation and maintenance worker carries out capacity estimation according to the pressure measurement data and the predicted flow, and purchases a host machine and increases the number of application examples based on the capacity estimation result.
However, the above method has the following drawbacks:
the existing pressure measurement process depends heavily on manpower, and all services needing pressure measurement cannot be covered under the condition of massive service categories; the performance indexes of the service built each time may be different, and under the condition that the service version is iterated frequently, the pressure measurement data is easy to lag and distort, so that the capacity evaluation result is inaccurate. Finally, in the production environment and the test environment, the configuration of the CPU and the memory of the application example is different, which may cause the pressure measurement data to be inaccurate, and further cause the capacity evaluation result to be inaccurate. In this case, after capacity expansion of the host, if the predicted traffic is smaller than the actual traffic, resource waste of the host may be caused, and if the predicted traffic is larger than the actual traffic, service stability may be affected.
In order to solve the above problem, an embodiment of the present invention provides a system capacity estimation method, including:
obtaining historical operation index data of an application instance of a target cluster, and calculating historical TPS according to the historical operation index data, wherein the historical operation index data comprises a historical operation index peak value;
obtaining a corresponding relation between the historical operation index peak value of the application example and the historical TPS according to the historical operation index data and the historical TPS;
acquiring real-time operation index data of an application instance of a target cluster, and calculating a real-time TPS according to the real-time operation index data, wherein the real-time operation index data comprises a real-time operation index peak value;
calculating the estimated real-time operation index peak value according to the corresponding relation and the real-time TPS;
and judging whether the target cluster needs capacity expansion or not according to the estimated real-time operation index peak value.
In this embodiment, by obtaining the historical operation index data and the historical TPS and obtaining the corresponding relationship between the historical operation index peak value and the historical TPS of the application example, the estimated real-time operation index peak value may be calculated according to the corresponding relationship and the real-time TPS to determine whether the target cluster needs to be expanded, thereby avoiding the problems of high complexity of the capacity evaluation process due to the fact that the existing pressure measurement process depends on manual pressure measurement, inaccurate capacity evaluation results due to the fact that the pressure measurement process cannot cover all services that need pressure measurement, pressure measurement data is easy to lag behind, distort, and inaccurate, etc., simplifying the capacity evaluation process, reducing complexity of the capacity evaluation process, and improving capacity evaluation efficiency.
In an embodiment, the method for estimating system capacity further includes:
firstly, obtaining historical operation index data of an application instance of a target cluster, and calculating historical TPS according to the historical operation index data, wherein the historical operation index data comprises a historical operation index peak value;
in this embodiment, historical operation index data of an application instance of a target cluster is obtained and sent to a kafka message queue, and kafka streaming obtains the historical operation index data from kafka, so as to calculate and obtain a historical TPS, where a TPS (Transactions Per Second, the number of Transactions transmitted Per Second, that is, the number of Transactions processed Per Second by a server) value can also be obtained by monitoring a corresponding log, so as to obtain a corresponding relationship between the historical operation index peak value of the application instance and the historical TPS in the subsequent process.
The target cluster is a service system deployed in a cluster, and the application instance of the target cluster is a group of containers running in a shared storage space and running options of the service system. The historical operation index data of the application example refers to: and the target cluster comprises operation index data of the application instance in a certain historical event segment, and the operation index data is used for representing application instance resource utilization indexes, such as container CPU utilization, memory utilization, disks, network resources and the like. The historical operation index peak value refers to the maximum value of operation index data of the application instance contained in the target cluster in a certain historical event segment, for example, the maximum value of the utilization rate of the CPU of the container.
In this embodiment, the historical operation index data may specifically be: the CPU utilization of a plurality of application instances (which may be all application instances) run by the target cluster, a plurality of preset time points (time points corresponding to a predetermined time interval, for example, monitoring data is acquired every 5 seconds) within a historical time period (for example, the day before the current time), where the historical time period is a predetermined time period close to the current time point, for example, one or two days before the current time point; the historical operation index peak value refers to a maximum value of the historical operation index data, for example, a maximum value of the CPU usage rate in one or two days before the current time point.
In a specific embodiment, the obtaining historical operation index data of the application instance of the target cluster further includes: and acquiring a plurality of application instances operated by the target cluster and operation index data of a plurality of preset time points in a historical time period from a time sequence database, wherein the historical time period is a preset time period close to the current time point.
In this embodiment, the service is deployed in a target cluster in a containerized manner, and the target cluster can monitor historical operation index data labels (CPU, memory, etc.) corresponding to the service and store the historical operation index data labels into a time sequence database, so as to obtain a corresponding relationship between a historical operation index peak value and a historical TPS of an application example in the subsequent step, and thus, according to the corresponding relationship and the real TPS, a pre-estimated real-time operation index peak value can be calculated to determine whether the target cluster needs to be expanded, thereby avoiding the existing problems that a pressure measurement process is high in complexity, and all services needing pressure measurement cannot be covered by a pressure measurement process, and a pressure measurement data is easy to lag, distort, inaccurate, and the like, which lead to inaccurate capacity estimation results.
Secondly, according to the historical operation index data and the historical TPS, obtaining a corresponding relation between the historical operation index peak value of the application example and the historical TPS;
in a specific embodiment, obtaining a correspondence between the historical operation index peak value of the application instance and the historical TPS according to the historical operation index data and the historical TPS further includes:
and modeling according to the relation between the historical operation index peak value and the historical TPS, and obtaining a linear regression model for representing the relation between the historical operation index peak value and the historical TPS of the application example.
In this embodiment, modeling is performed according to a relationship between the historical operation index peak value and the historical TPS, and a linear regression model for characterizing a relationship between the historical operation index peak value and the historical TPS of the application instance is obtained. The correspondence may be represented by a straight line, and this type of regression analysis is called a unary linear regression analysis. Linear regression is a regression analysis for modeling the relationship between one or more independent variables and dependent variables using a least square function called a linear regression equation, for example, a linear equation representing the correspondence between the CPU usage and QPS may be fitted by a least square method, for example, a straight line for y = bx + a is calculated, and the equation is a linear regression model representing the relationship between the operation index data and the application index data of the application example.
Thirdly, acquiring real-time operation index data of the application example of the target cluster, calculating real-time TPS according to the real-time operation index data, and calculating an estimated real-time operation index peak value according to the corresponding relation and the real-time TPS;
in this embodiment, real-time operation index data of an application instance of a target cluster is obtained and sent to a kafka message queue, and kafka streaming obtains the real-time operation index data from kafka, so as to obtain real-time TPS through calculation, for example, the real-time TPS is 134 through calculation;
and calculating an estimated real-time operation index peak value according to the corresponding relation and the real-time TPS, and concretely, substituting the real-time TPS of 134 into the linear regression equation to obtain an operation index peak value which is the estimated real-time operation index peak value, namely 3660.
And finally, judging whether the target cluster needs capacity expansion or not according to the estimated real-time operation index peak value.
In a specific embodiment, the determining whether the target cluster needs to be extended further includes:
calculating a real-time water level according to the real-time TPS and the estimated real-time operation index peak value;
and periodically detecting whether the real-time water level exceeds a preset first threshold value, and if so, expanding the capacity of the target cluster.
In this embodiment, the real-time TPS is divided by the estimated real-time operation index peak value to calculate a real-time water level, for example, when the real-time TPS is 134 and the estimated real-time operation index peak value is 3660, the real-time water level is 3.602%. And periodically detecting whether the real-time water level exceeds a preset first threshold, if so, performing capacity expansion on the target cluster, for example, setting the first threshold to be 90%, and if not, performing capacity expansion on the target cluster.
As shown in fig. 2, the method further comprises: and displaying the real-time water level, the estimated real-time operation index peak value, the real-time TPS and the historical operation index peak value through a visual operation interface.
In this embodiment, the real-time water level, the estimated real-time operation index peak value, the real-time TPS and the historical operation index peak value are presented through a visual operation interface, where the historical operation index peak value may be a maximum value of historical operation index data within a predetermined time period close to a current time point, for example, a maximum value of historical operation index data within one or two days before the current time point, and a worker may adjust the target cluster according to content presented through the visual operation interface, so that a capacity evaluation process is simplified, complexity of a capacity evaluation process is reduced, and capacity evaluation efficiency is improved.
Corresponding to the method for estimating system capacity provided in the foregoing embodiment, as shown in fig. 3, an embodiment of the present application further provides a system capacity estimating system, including:
the obtaining historical data module is used for obtaining historical operation index data of an application instance of a target cluster and calculating a historical TPS according to the historical operation index data, wherein the historical operation index data comprises a historical operation index peak value;
the obtaining corresponding relation module is used for obtaining the corresponding relation between the historical operation index peak value of the application example and the historical TPS according to the historical operation index data and the historical TPS;
the real-time data acquisition module is used for acquiring real-time operation index data of an application instance of a target cluster and calculating real-time TPS according to the real-time operation index data, wherein the real-time operation index data comprises a real-time operation index peak value;
the calculation module is used for calculating the estimated real-time operation index peak value according to the corresponding relation and the real-time TPS;
and the judging module judges whether the target cluster needs to be expanded according to the estimated real-time operation index peak value.
In this embodiment, the historical operating index data and the historical TPS are obtained by the obtaining historical data module, and the corresponding relationship between the historical operating index peak value and the historical TPS of the application instance is obtained by the obtaining corresponding relationship module, so that the calculation module can calculate the estimated real-time operating index peak value according to the corresponding relationship and the real-time TPS to determine whether the target cluster needs to be expanded, thereby avoiding the problems that the existing capacity evaluation process is high in complexity due to manual pressure measurement, and the capacity evaluation result is inaccurate because the pressure measurement process cannot cover all services needing pressure measurement, the pressure measurement data is easy to lag, distort, and inaccurate, and the like, simplifying the capacity evaluation process, reducing the complexity of the capacity evaluation process, and improving the capacity evaluation efficiency.
In a specific embodiment, the obtaining correspondence module is further configured to perform modeling according to a relationship between the historical operation index peak value and the historical TPS, and obtain a linear regression model for characterizing a relationship between the historical operation index peak value and the historical TPS of the application instance.
In a specific embodiment, the historical data obtaining module is further configured to obtain, from a time sequence database, a plurality of application instances operated by the target cluster and operation index data of a plurality of preset time points in a historical time period, where the historical time period is a predetermined time period close to a current time point.
Since the system capacity estimation system provided in the embodiments of the present application corresponds to the system capacity estimation methods provided in the foregoing embodiments, the foregoing embodiments are also applicable to the system capacity estimation system provided in the embodiments, and detailed description is omitted in this embodiment.
Another embodiment of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements: obtaining historical operation index data of an application instance of a target cluster, and calculating historical TPS according to the historical operation index data, wherein the historical operation index data comprises a historical operation index peak value; obtaining a corresponding relation between the historical operation index peak value of the application example and the historical TPS according to the historical operation index data and the historical TPS; acquiring real-time operation index data of an application example of a target cluster, calculating real-time TPS according to the real-time operation index data, and calculating an estimated real-time operation index peak value according to the corresponding relation and the real-time TPS; and judging whether the target cluster needs capacity expansion or not according to the estimated real-time operation index peak value.
In practice, the computer-readable storage medium may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
As shown in fig. 4, another embodiment of the present invention provides a schematic structural diagram of a computer device. The computer device 12 shown in FIG. 4 is only an example and should not impose any limitations on the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 4, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processor unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement an access method of a mobile terminal product provided by an embodiment of the present invention.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations or modifications may be made on the basis of the above description, and all embodiments may not be exhaustive, and all obvious variations or modifications may be included within the scope of the present invention.
Claims (8)
1. A method for estimating system capacity, comprising:
obtaining historical operation index data of an application instance of a target cluster, and calculating historical TPS according to the historical operation index data, wherein the historical operation index data comprises a historical operation index peak value;
obtaining a corresponding relation between the historical operation index peak value of the application example and the historical TPS according to the historical operation index data and the historical TPS;
acquiring real-time operation index data of an application example of a target cluster, calculating real-time TPS according to the real-time operation index data, and calculating an estimated real-time operation index peak value according to the corresponding relation and the real-time TPS;
judging whether the target cluster needs to be expanded according to the estimated real-time operation index peak value;
obtaining the corresponding relation between the historical operation index peak value of the application example and the historical TPS according to the historical operation index data and the historical TPS comprises the following steps:
and modeling according to the relation between the historical operation index peak value and the historical TPS, and obtaining a linear regression model for representing the relation between the historical operation index peak value and the historical TPS of the application example.
2. The method of claim 1, wherein obtaining historical operating metric data for the application instance of the target cluster comprises:
and acquiring a plurality of application examples operated by the target cluster and operation index data of a plurality of preset time points in a historical time period from a time sequence database, wherein the historical time period is a preset time period close to the current time point.
3. The method of claim 1, wherein determining whether the target cluster needs to be expanded comprises:
calculating a real-time water level according to the real-time TPS and the estimated real-time operation index peak value;
and periodically detecting whether the real-time water level exceeds a preset first threshold value, and if so, expanding the capacity of the target cluster.
4. The method of claim 3, wherein the predictive method further comprises: and displaying the real-time water level, the estimated real-time operation index peak value, the real-time TPS and the historical operation index peak value through a visual operation interface.
5. A system capacity estimation system, comprising:
the obtaining historical data module is used for obtaining historical operation index data of an application instance of a target cluster and calculating historical TPS according to the historical operation index data, wherein the historical operation index data comprise a historical operation index peak value;
the acquisition corresponding relation module is used for acquiring a corresponding relation between the historical operation index peak value of the application example and the historical TPS according to the historical operation index data and the historical TPS;
the real-time data acquisition module is used for acquiring real-time operation index data of an application instance of a target cluster and calculating real-time TPS according to the real-time operation index data, wherein the real-time operation index data comprises a real-time operation index peak value;
the calculation module is used for calculating the estimated real-time operation index peak value according to the corresponding relation and the real-time TPS;
the judging module judges whether the target cluster needs to be expanded according to the estimated real-time operation index peak value;
the obtaining corresponding relation module is also used for modeling according to the relation between the historical operation index peak value and the historical TPS, and obtaining a linear regression model for representing the relation between the historical operation index peak value and the historical TPS of the application example.
6. The system according to claim 5, wherein the historical data obtaining module is further configured to obtain, from a time sequence database, operation index data of a plurality of application instances operated by the target cluster at a plurality of preset time points within a historical time period, where the historical time period is a predetermined time period close to a current time point.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-4.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-4 when executing the program.
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| CN104978232A (en) * | 2014-04-09 | 2015-10-14 | 阿里巴巴集团控股有限公司 | Computation resource capacity expansion method for real-time stream-oriented computation, computation resource release method for real-time stream-oriented computation, computation resource capacity expansion device for real-time stream-oriented computation and computation resource release device for real-time stream-oriented computation |
| CN107231264A (en) * | 2017-07-25 | 2017-10-03 | 北京百度网讯科技有限公司 | For the method and apparatus for the capacity for managing Cloud Server |
| CN108845881A (en) * | 2018-05-30 | 2018-11-20 | 有米科技股份有限公司 | The method and device of server capacity dynamic adjustment |
| CN108897673B (en) * | 2018-07-05 | 2022-04-12 | 京东科技控股股份有限公司 | System capacity evaluation method and device |
| CN114154668A (en) * | 2020-09-08 | 2022-03-08 | ***通信集团河北有限公司 | IT system capacity expansion prediction method and device |
| CN113407426A (en) * | 2021-06-17 | 2021-09-17 | 北京字跳网络技术有限公司 | Server cluster capacity evaluation method and device, electronic equipment and storage medium |
| CN114356577A (en) * | 2022-01-08 | 2022-04-15 | 时时同云科技(成都)有限责任公司 | System capacity estimation method and device |
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