CN105979532B - Performance capacity analysis early warning method and device of service processing system - Google Patents
Performance capacity analysis early warning method and device of service processing system Download PDFInfo
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Abstract
The invention discloses a performance capacity analysis early warning method and a device of a business processing system, wherein the method comprises the following steps: collecting multiple service index data of each service and performance index data of at least one service host for processing the service by taking the service as a unit; calculating the operation index and the capability index of the service host according to the service index data and the performance index data; calculating the variation trend of each service index data of the service according to each service index of the service; calculating the predicted value of the capability index of the at least one service host in the N preset periods in the future by combining a prediction model according to the capability index, the variation trend of each service index data of the service and the weight ratio of each service index; obtaining the performance state of the service host in the future N preset periods according to the capability index predicted value; and calculating the configuration quantity needing to be increased or decreased in the future N preset periods according to the performance state.
Description
Technical Field
The invention relates to the technical field of performance capacity analysis, in particular to a performance capacity analysis early warning method and device of a service processing system.
Background
The mobile telecommunication service processing system is a basic guarantee and support for normal operation of mobile services, and how to improve the support efficiency of the enterprise service processing system, realize high return on investment of the service processing system and realize reasonable allocation of resources while ensuring the accuracy and reliability of the service processing system is gradually becoming the focus of attention of relevant departments of mobile operators at present.
The main confusion and challenge faced by equipment capacity planning in mobile telecommunications service processing systems is:
whether the current service system can meet future requirements or not with the increase of the service data volume of each service, whether capacity expansion is needed in a future period of time or not, whether the service system needs to increase or decrease the configuration or not with the change trend of the service data volume, and whether the current capacity bottleneck points need to increase or decrease how much the configuration? or how much the potential capacity performance bottleneck problems still exist in the? service system or how to find the potential capacity performance bottleneck problems.
The main problems of the mobile users in the aspect of service system equipment capacity management currently exist:
(1) predictive analysis of future development of services (type of service, service functionality, user volume, performance requirements, etc.) is lacking.
(2) There is a lack of clear understanding of the capacity management role, as well as its process documentation, normalization, and standardization.
(3) There is a lack of analysis of the source of the capacity problems that cause the business system.
(4) Lack of collection and accumulation of large, accurate performance data.
In a word, due to the lack of an effective relation analysis tool for business and equipment performance, when data analysis is performed on massive business performance data based on a traditional database (such as oracle), the performance is slow, the efficiency is low, and future development trends of the business performance data and the equipment performance data cannot be predicted and analyzed, so that the problems are caused.
Disclosure of Invention
The invention aims to provide a performance capacity analysis early warning method and a performance capacity analysis early warning device for a business processing system, which can perform early warning analysis on the performance of the business processing system.
The technical scheme for solving the technical problems is as follows:
in one aspect, the present invention provides a performance capacity analysis and early warning method for a service processing system, where the service processing system includes a plurality of service hosts, and the method includes:
s1, collecting multiple service index data of each service in preset time and performance index data of at least one service host for processing the service by taking the service as a unit;
s2a, parallel computing is carried out on the collected data of the plurality of services by adopting a big data analysis platform, and the operation index of the at least one service host is obtained through computing according to the performance index data of the at least one service host and the operation index computing model; calculating the capability index of the at least one service host according to the operation index and the capability index calculation model;
s2b, calculating the change trend of the data volume of each service index data of the service in each predetermined period according to the plurality of service index data of each service collected in the predetermined time, wherein the change trend comprises an increase trend or a decrease trend;
s3, calculating a predicted value of the capability index of the at least one service host in N predetermined periods in the future by combining a prediction model according to the capability index of the at least one service host, the variation trend of the data volume of each service index data of the service and the weight ratio of each service index data of the service;
s4, comparing the capability index predicted value of the at least one service host with each performance state index threshold value, and obtaining the performance state of the at least one service host in N predetermined periods in the future according to the comparison result;
and S5, calculating the capacity allocation quantity which needs to be increased or decreased by the at least one service host in the future N preset periods according to the predicted performance state of the at least one service host.
On the other hand, the invention provides a performance capacity analysis early warning device of a business processing system, which comprises a data acquisition server and a big data analysis platform;
the data acquisition server includes:
the acquisition module is used for acquiring multiple service index data of each service in preset time and performance index data of at least one service host for processing the service by taking the service as a unit;
the sending module is used for sending the acquired data to the big data analysis platform;
the big data analysis platform comprises a receiving module, a data dividing module and a plurality of data processing modules;
the receiving module is used for receiving the acquired data sent by the data acquisition module;
the data dividing module is used for dividing the received acquired data into a plurality of data blocks, and each data block at least comprises data corresponding to one service;
each data processing module includes:
the operation index calculation module is used for calculating the operation index of the at least one service host according to the performance index data of the at least one service host and the operation index calculation model;
the capability index calculation module is used for calculating the capability index of the at least one service host according to the operation index and the capability index calculation model;
the change trend calculation module is used for calculating the change trend of the data volume of each service index data of the service in each preset period according to a plurality of service index data of the service collected in preset time, wherein the change trend comprises an increase trend or a decrease trend;
the predicted value calculation module is used for calculating the predicted value of the capability index of the at least one service host in the future N preset periods by combining a prediction model according to the capability index of the at least one service host, the change trend of the data quantity of each service index data of the service and the weight ratio of each service index data of the service;
the performance state obtaining module is used for comparing the predicted value of the capability index of the at least one service host with each performance state index threshold value, and obtaining the performance state of the at least one service host in the future N preset periods according to the comparison result;
and the configuration calculation module is used for calculating the capacity configuration quantity which needs to be increased or decreased in the future N preset periods according to the predicted performance state of the at least one service host.
The invention provides a performance capacity analysis early warning method and device of a service processing system, which take a service as a unit, respectively obtain the capability index of a service host and the change trend of the service data volume by adopting a corresponding calculation model according to the data volume of the service and the performance index data of the service host for processing the service, then calculate the predicted value of the capability index of the service host after N preset periods in the future by utilizing a prediction model according to the capability index of the service host and the growth trend of the service data volume, and judge the performance state of the service host according to the predicted value. The performance state of the service host is maintained in a performance health state in a future period of time, and when the performance state of the service host is a performance bottleneck state, a warning prompt is given to a user so as to take corresponding measures in time.
Drawings
Fig. 1 is a flowchart of a performance capacity analysis early warning method of a service processing system according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of a performance capacity analysis early warning apparatus of a service processing system according to a second embodiment of the present invention;
FIG. 3 is a diagram illustrating internal connections of data processing modules according to a second embodiment.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
The embodiment I provides a performance capacity analysis early warning method of a business processing system. The method provided by the present embodiment is described below with reference to fig. 1.
Referring to fig. 1, S1, in units of services, collects a plurality of service index data of each service in a predetermined time and performance index data of at least one service host that processes the service.
Specifically, a service processing system includes many service hosts, and since the data volume of each service may be very large, a service may require multiple service hosts to process together. A service comprises a plurality of service index data, for example, in the mobile telecommunication service, a plurality of service index data including the number of call orders, the number of account orders and the number of user requests. In this embodiment, a service is taken as a unit, and multiple service index data of each service and performance index data of at least one service host (usually multiple service hosts) that processes the service are collected in real time, where the performance index data refers to a CPU utilization rate or a memory utilization rate of the service host. When there are multiple service hosts corresponding to one service, the performance index data of each service host is collected first, and then the performance index data of each service host is accumulated to be used as the overall performance index data.
S2a, parallel computing is carried out on the collected data of the plurality of services by adopting a big data analysis platform, and the operation index of the at least one service host is obtained through computing according to the performance index data of the at least one service host and the operation index computing model; and calculating the capability index of the at least one service host according to the operation index and the capability index calculation model.
Specifically, after the data corresponding to each service is collected in step S1, because the collected data is very large in volume, the collected data is imported into the big data analysis platform, and the big data analysis platform divides the collected data into blocks and performs parallel computation on each part, where each part includes at least data corresponding to one service. And calculating the operation index of the at least one service host according to the performance index data of the at least one service host corresponding to each service and a pre-established operation index calculation model aiming at each part of data, wherein the higher the operation index of the service host is, the more busy the service host is. And then, calculating to obtain the capability index of the at least one service host according to the operation index of the at least one service host and a pre-established capability index calculation model.
S2b, calculating the variation trend of the data volume of each service index data of the service in each preset period according to the service index data of the service collected in real time, wherein the variation trend comprises an increasing trend or a decreasing trend.
Specifically, according to each service index data amount of each service collected in real time, that is, according to each collected service index data amount of each service, a change trend of each service index of the service in each predetermined period is calculated by using an inertial prediction algorithm, for example, a change trend of each service index data amount of the service in each day may be calculated, or a change trend of each service index data amount of the service in each month may be calculated, where the change trend may be a growth trend or a reduction trend.
S3, calculating a predicted value of the capability index of the at least one service host in the future N preset periods according to the capability index of the at least one service host, the variation trend of the data volume of each service index of the service and the weight ratio of each service index of the service and in combination with a prediction model.
Specifically, according to the calculated capability index of the at least one service host, the variation trend of the data volume of each service index of the service, and the weight ratio of each service index of the service, a pre-established prediction model is combined to calculate a predicted value of the capability index of the at least one service host after N predetermined periods in the future, for example, the predicted value of the capability index of the at least one service host after 10 months. As before, for example, a service includes a call volume, an account volume and a user request number, the data volumes of the three service indicators have different variation trends, some are growth trends, some are reduction trends, and the variation trend percentages of the number of each service indicator are also different, so that different weight ratios are configured for each service indicator of the service, and the weight ratio of each service indicator reflects the degree of influence of the service indicator on the performance of the service host.
S4, comparing the predicted value of the capability index of the at least one service host with each performance state index threshold value, and obtaining the performance state of the at least one service host in the future N preset periods according to the comparison result.
Specifically, step S3 calculates a capability index predicted value of the at least one service host in N predetermined cycles in the future, compares the capability index predicted value with each performance state index threshold, and obtains the performance state of the at least one service host in N cycles in the future according to the comparison result, where the performance state includes a performance bottleneck state, a performance health state, and a performance idle state, that is, compares the capability index predicted value of the at least one service host with the performance bottleneck state index threshold, the performance health state index threshold, and the performance idle state index threshold, respectively, to obtain the performance state of the at least one service host in N cycles.
And S5, calculating the capacity allocation quantity which needs to be increased or decreased in the future N preset periods according to the predicted performance state of the at least one service host.
Specifically, when the performance state of the at least one service host is a performance bottleneck state, the CPU configuration should be added to keep the performance state of the at least one service host in a healthy state within N predetermined cycles; when the performance state of the at least one service host is a performance idle state, reducing the CPU configuration so as to keep the performance state of the at least one service host in a healthy state within N preset periods; when the performance state of the at least one service host is a performance health state, the CPU configuration is not required to be increased or reduced. The increase of the CPU configuration may be embodied as an increase of the CPU configuration of a certain service host, or an increase of the number of service hosts processing each service.
When the performance state of the at least one service host in the future N preset periods reaches the performance bottleneck state, calculating the time required to be increased in configuration and the capacity configuration amount required to be increased by adopting a back-stepping method so as to enable the at least one service host to reach the performance health state; and when the performance state of the at least one service host in the future N preset periods is predicted to be a performance idle state, calculating the time required to be reduced in configuration and the capacity configuration amount required to be reduced by adopting a reverse method so as to enable the at least one service host to reach the performance health state. For example, through prediction analysis, it is predicted that the service host will reach a performance bottleneck state in the future 10 months, at this time, a back-stepping method may be adopted, for example, when the CPU utilization of the service host is 60%, capacity expansion is performed, that is, configuration is increased, and the time for increasing configuration and the configuration amount that should be increased may be determined, so that the service host maintains a performance health state in the future N predetermined periods. When the at least one service host is in a performance idle state at a future time of 10 months, a back-stepping method is also adopted to determine the time for reducing the configuration and the amount of configuration required to be reduced. In addition, when the performance state of the at least one service host in the future N preset periods is predicted to be a performance bottleneck state, early warning information is generated to give an alarm, so that related personnel can process the performance bottleneck state in time.
The second embodiment relates to a performance capacity analysis early warning device of a business processing system. The apparatus provided in the present embodiment is described below with reference to fig. 2 and 3.
Referring to fig. 2, the apparatus provided in this embodiment includes a data acquisition server 1 and a big data analysis platform 2; the data collection server 1 includes:
the data acquisition module 11 is configured to acquire multiple service index data of each service in real time and performance index data of at least one service host that processes the service, in units of services;
the first sending module 12 is used for sending the acquired data to the big data analysis platform 20;
the big data analysis platform 2 comprises a receiving module 21, a data dividing module 22 and a plurality of data processing modules 23;
a receiving module 21, configured to receive the acquired data sent by the data acquisition module;
the data dividing module 22 is configured to divide the received collected data into a plurality of data blocks, where each data block at least includes data corresponding to one service.
Referring to fig. 3, each data processing module 23 includes:
the operation index calculation module 231 is configured to calculate an operation index of the at least one service host according to the performance index data of the at least one service host and an operation index calculation model;
a capability index calculation module 232, configured to calculate a capability index of the at least one service host according to the operation index and the capability index calculation model;
a variation trend calculation module 233, configured to calculate, according to multiple service indexes of the service collected in a predetermined time, a variation trend of each service index data amount of the service in each predetermined period, where the variation trend includes an increase trend or a decrease trend;
the predicted value calculating module 234 is configured to calculate, according to the capability index of the at least one service host, the variation trend of the data amount of each service index of the service, and the weight ratio of each service index of the service, a predicted value of the capability index of the at least one service host in N predetermined periods in the future by combining a prediction model;
a performance state obtaining module 235, configured to compare the predicted value of the capability index of the at least one service host with each performance state index threshold, and obtain, according to the comparison result, a performance state of the at least one service host in N predetermined cycles in the future;
and the configuration calculating module 236 is configured to calculate, according to the predicted performance state of the at least one service host, a capacity configuration amount that needs to be increased or decreased in the future N predetermined periods.
Wherein the performance states include a performance bottleneck state, a performance health state, and a performance idle state; the configuration calculation module 236 is specifically configured to: when the performance state of the at least one service host in the future N preset periods is predicted to be a performance bottleneck state, calculating the capacity allocation amount required to be increased so as to reach a performance health state; when the performance state of the at least one service host in the future N preset periods is predicted to be a performance idle state, calculating the capacity configuration amount needing to be reduced so as to achieve the performance health state.
When the performance state of the at least one service host in the future N predetermined cycles is predicted to be a performance bottleneck state, the configuration calculating module 236 is specifically configured to: calculating the time required to be increased in configuration and the capacity configuration amount required to be increased by adopting a back-stepping method so as to enable the at least one service host to reach a performance health state; or, when the performance state of the at least one service host in the future N predetermined cycles is predicted to be a performance idle state, the configuration calculating module 236 is specifically configured to: and calculating the time required to reduce the configuration and the capacity configuration amount required to reduce by adopting a reverse deduction method so as to enable the at least one service host to reach a performance health state.
The performance capacity analysis early warning device of the service processing system provided by the embodiment further comprises an early warning analysis server 3; each of the data processing modules 23 further includes:
a second sending module 237, configured to send the performance status of the at least one service host to the early warning analysis server 3;
the early warning analysis server 3 is configured to receive the performance state of the at least one service host, and generate corresponding early warning information to warn when the performance state of the at least one service host is a performance bottleneck state.
The invention provides a performance capacity analysis early warning method and device of a service processing system, which take a service as a unit, respectively obtain the capability index of a service host and the change trend of the service data volume by adopting a corresponding calculation model according to the data volume of the service and the performance index data of the service host for processing the service, then calculate the predicted value of the capability index of the service host after N preset periods in the future by utilizing a prediction model according to the capability index of the service host and the growth trend of the service data volume, and judge the performance state of the service host according to the predicted value. The performance state of the service host is maintained in a performance health state in a future period of time, and when the performance state of the service host is a performance bottleneck state, a warning prompt is given to a user so as to take corresponding measures in time; in addition, the performance of a plurality of service hosts is subjected to distributed analysis early warning by adopting a big data analysis platform, so that distributed parallel processing of data can be realized, and the data processing efficiency is improved.
In the description herein, references to the description of the terms "embodiment one," "example," "specific example," or "some examples," etc., mean that a particular method, apparatus, or feature described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, methods, apparatuses, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A performance capacity analysis early warning method of a service processing system, the service processing system includes a plurality of service hosts, characterized by comprising:
s1, collecting multiple service index data of each service in preset time and performance index data of at least one service host for processing the service by taking the service as a unit;
s2a, parallel computing is carried out on the collected data of the plurality of services by adopting a big data analysis platform, and the operation index of the at least one service host is obtained through computing according to the performance index data of the at least one service host and the operation index computing model; calculating the capability index of the at least one service host according to the operation index and the capability index calculation model;
s2b, calculating the change trend of the data volume of each service index data of the service in each predetermined period according to the plurality of service index data of each service collected in the predetermined time, wherein the change trend comprises an increase trend or a decrease trend;
s3, calculating a predicted value of the capability index of the at least one service host in N predetermined periods in the future by combining a prediction model according to the capability index of the at least one service host, the variation trend of the data volume of each service index data of the service and the weight ratio of each service index data of the service;
s4, comparing the capability index predicted value of the at least one service host with each performance state index threshold value, and obtaining the performance state of the at least one service host in N predetermined periods in the future according to the comparison result;
s5, calculating the capacity allocation quantity which needs to be increased or decreased by the at least one service host in the future N preset periods according to the predicted performance state of the at least one service host;
the performance states include a performance bottleneck state, a performance health state, and a performance idle state;
the step S5 specifically includes:
when the performance state of the at least one service host in the future N preset periods is predicted to be a performance bottleneck state, capacity allocation amount needs to be increased so as to achieve a performance health state;
when the performance state of the at least one service host in the future N preset periods is predicted to be the performance health state, the capacity configuration quantity is not changed;
when the performance state of the at least one service host in the future N preset periods is predicted to be a performance idle state, the capacity configuration amount needs to be reduced so as to achieve the performance health state.
2. The method according to claim 1, wherein when the performance status of the at least one service host in the next N predetermined periods is predicted to be a performance bottleneck status, a back-stepping method is used to calculate the time required to increase the configuration and the capacity configuration amount required to increase so as to enable the at least one service host to reach the performance health status;
and when the performance state of the at least one service host in the future N preset periods is predicted to be a performance idle state, calculating the time required to be reduced in configuration and the capacity configuration amount required to be reduced by adopting a reverse method so as to enable the at least one service host to reach the performance health state.
3. The method as claimed in claim 1, wherein when the performance status of the at least one service host in the future N predetermined cycles is predicted to be a performance bottleneck status, generating an early warning message to warn.
4. A performance capacity analysis early warning device of a business processing system is characterized by comprising a data acquisition server and a big data analysis platform;
the data acquisition server includes:
the data acquisition module is used for acquiring a plurality of service index data of each service in preset time and performance index data of at least one service host for processing the service by taking the service as a unit;
the sending module is used for sending the acquired data to the big data analysis platform;
the big data analysis platform comprises a receiving module, a data dividing module and a plurality of data processing modules;
the receiving module is used for receiving the acquired data sent by the data acquisition module;
the data dividing module is used for dividing the received acquired data into a plurality of data blocks, and each data block at least comprises data corresponding to one service;
each data processing module includes: the operation index calculation module is used for calculating the operation index of the at least one service host according to the performance index data of the at least one service host and the operation index calculation model;
the capability index calculation module is used for calculating the capability index of the at least one service host according to the operation index and the capability index calculation model;
the change trend calculation module is used for calculating the change trend of the data volume of each service index data of the service in each preset period according to a plurality of service index data of the service collected in preset time, wherein the change trend comprises an increase trend or a decrease trend;
the predicted value calculation module is used for calculating the predicted value of the capability index of the at least one service host in the future N preset periods by combining a prediction model according to the capability index of the at least one service host, the change trend of the data quantity of each service index data of the service and the weight ratio of each service index data of the service;
the performance state acquisition module is used for comparing the predicted value of the capability index of the at least one service host with each performance state index threshold value and obtaining the performance state of the at least one service host in the future N preset periods according to the comparison result;
the configuration calculation module is used for calculating the capacity configuration quantity which needs to be increased or decreased in the future N preset periods according to the predicted performance state of the at least one service host;
the performance states include a performance bottleneck state, a performance health state, and a performance idle state;
the configuration calculation module is specifically configured to:
when the performance state of the at least one service host in the future N preset periods is predicted to be a performance bottleneck state, calculating the capacity allocation amount required to be increased so as to reach a performance health state;
when the performance state of the at least one service host in the future N preset periods is predicted to be a performance idle state, calculating the capacity configuration amount needing to be reduced so as to achieve the performance health state.
5. The apparatus of claim 4, wherein when the performance status of the at least one service host in the future N predetermined cycles is predicted to be a performance bottleneck status, the configuration calculation module is specifically configured to:
calculating the time required to be increased in configuration and the capacity configuration amount required to be increased by adopting a back-stepping method so as to enable the at least one service host to reach a performance health state;
alternatively, the first and second electrodes may be,
when the performance state of the at least one service host in the future N predetermined cycles is predicted to be a performance idle state, the configuration calculation module is specifically configured to:
and calculating the time required to reduce the configuration and the capacity configuration amount required to reduce by adopting a reverse deduction method so as to enable the at least one service host to reach a performance health state.
6. The apparatus of claim 5, further comprising an early warning analysis server;
the big data analysis platform further comprises:
the sending module is used for sending the performance state of the at least one service host to the early warning analysis server;
the early warning analysis server is used for receiving the performance state of the at least one service host, and generating corresponding early warning information for warning when the performance state of the at least one service host is a performance bottleneck state.
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| CN102082703A (en) * | 2009-11-26 | 2011-06-01 | 中国移动通信集团贵州有限公司 | Method and device for monitoring equipment performance of service supporting system |
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