CN106886485B - System capacity analysis and prediction method and device - Google Patents
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Abstract
The invention provides a method and a device for analyzing and predicting system capacity, which are used for acquiring system operation data, establishing the relationship among system state data, service state data and abnormal fault data, and establishing a capacity regression model based on the above data, calculating the predicted capacity utilization data of each hardware resource in a set time interval and the system performance data of the set time interval based on the capacity regression model, referring to the capacity regression model according to the above two data, the subsequent capacity use state of the hardware resources is predicted, so that the capacity use condition of key resources in each link can be analyzed by comprehensively analyzing historical operation condition data, whether the capacity of the hardware resources such as a server and the like reaches a bottleneck or not is judged in advance, the capacity risk which possibly occurs is pre-warned, the condition that the hardware resource is repaired when the hardware resource fails is avoided, and the safety of the system is improved.
Description
Technical Field
The invention relates to a big data operation and maintenance system, in particular to a system capacity analysis and prediction method and device for a big data operation and maintenance platform.
Background
The big data operation and maintenance system in the prior art cannot predict the capacity use condition of hardware resources such as servers in the system in advance, can only repair the hardware resources when the hardware resources are in fault, and cannot prevent the fault in advance, so that certain potential safety hazard is caused.
Disclosure of Invention
The invention aims to solve the problem that the existing big data operation and maintenance system cannot predict the capacity use condition of each hardware resource in the system in advance and cannot prevent faults in advance, so that the system has potential safety hazards.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a system capacity analysis prediction method comprises the following steps:
acquiring system operation data; each system operation data comprises system state data, service state data and abnormal fault data, and the system state data, the service state data and the abnormal fault data can be classified according to a time interval, hardware resources and service application;
establishing a capacity regression model according to system operation data;
acquiring historical system operation data of a set time interval according to the system operation data;
calculating historical capacity use data of each hardware resource in a set time interval by using a capacity regression model according to historical system operation data of the set time interval;
predicting the capacity use condition of each hardware resource in a set time interval according to the historical capacity use data of each hardware resource in the set time interval to obtain predicted capacity use data of each hardware resource in the set time interval;
detecting and evaluating the performance condition of each hardware resource in a set time interval by using a capacity regression model according to historical system operation data of the set time interval to obtain system performance data of the set time interval;
predicting the system capacity use state after the set time interval by using a capacity regression model according to the predicted capacity use data of each hardware resource in the set time interval and the system performance data of the set time interval to obtain a predicted system capacity use state; the predicted system capacity usage state includes a risk level and a risk occurrence probability.
On the basis of the above embodiment, further, the method further includes:
and sending early warning information to the user according to the risk level.
On the basis of any of the above embodiments, further, the step of detecting and evaluating the performance condition of each hardware resource in the set time interval by using a capacity regression model according to the historical system operation data of the set time interval to obtain the system performance data of the set time interval specifically includes:
acquiring performance indexes and index values of all hardware resources in a set time interval by using a capacity regression model according to historical system operation data of the set time interval;
calculating the weight value of the performance index of each hardware resource in the set time interval according to the performance index and the index value of each hardware resource in the set time interval;
and acquiring system performance data of the set time interval according to the weight value of the performance index of each hardware resource in the set time interval.
On the basis of the foregoing embodiment, further, in the system performance data, the performance indicators of the hardware resources are sorted according to the weight values.
On the basis of the foregoing embodiment, further, the performance index of the hardware resource includes one or more of a CPU index, a memory index, a disk index, and a network module index.
A system capacity analysis prediction apparatus comprising:
a model building module to:
acquiring system operation data; each system operation data comprises system state data, service state data and abnormal fault data, and the system state data, the service state data and the abnormal fault data can be classified according to a time interval, hardware resources and service application;
establishing a capacity regression model according to system operation data;
a capacity analysis module to:
acquiring historical system operation data of a set time interval according to the system operation data;
calculating historical capacity use data of each hardware resource in a set time interval by using a capacity regression model according to historical system operation data of the set time interval;
predicting the capacity use condition of each hardware resource in a set time interval according to the historical capacity use data of each hardware resource in the set time interval to obtain predicted capacity use data of each hardware resource in the set time interval;
a performance analysis module to:
detecting and evaluating the performance condition of each hardware resource in a set time interval by using a capacity regression model according to historical system operation data of the set time interval to obtain system performance data of the set time interval;
a state prediction module to:
predicting the system capacity use state after the set time interval by using a capacity regression model according to the predicted capacity use data of each hardware resource in the set time interval and the system performance data of the set time interval to obtain a predicted system capacity use state; the predicted system capacity usage state includes a risk level and a risk occurrence probability.
On the basis of the above embodiment, further, the method further includes:
an early warning module for:
and sending early warning information to the user according to the risk level.
On the basis of any of the above embodiments, further, the performance analysis module is configured to:
acquiring performance indexes and index values of all hardware resources in a set time interval by using a capacity regression model according to historical system operation data of the set time interval;
calculating the weight value of the performance index of each hardware resource in the set time interval according to the performance index and the index value of each hardware resource in the set time interval;
and acquiring system performance data of the set time interval according to the weight value of the performance index of each hardware resource in the set time interval.
On the basis of the foregoing embodiment, further, in the system performance data, the performance indicators of the hardware resources are sorted according to the weight values.
On the basis of the foregoing embodiment, further, the performance index of the hardware resource includes one or more of a CPU index, a memory index, a disk index, and a network module index.
The invention has the beneficial effects that:
the invention provides a system capacity analysis and prediction method and a device, which are used for acquiring system operation data, establishing a relation among system state data, service state data and abnormal fault data, establishing a capacity regression model on the basis, calculating predicted capacity use data of each hardware resource in a set time interval and system performance data of the set time interval on the basis of the capacity regression model, referring to the capacity regression model according to the two data, predicting the capacity use state of subsequent hardware resources, analyzing the capacity use condition of key resources (including a host, a network, application and the like) of each link by comprehensively analyzing historical operation condition data, prejudging whether the capacity of the hardware resources of a server and the like reaches a bottleneck in advance, carrying out early warning on the possible capacity risk, avoiding repairing the hardware resources when the hardware resources are in fault, the safety of the system is improved.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a flow chart illustrating a system capacity analysis prediction method provided by an embodiment of the invention;
fig. 2 is a schematic structural diagram illustrating a system capacity analysis and prediction apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Detailed description of the preferred embodiment
As shown in fig. 1, an embodiment of the present invention provides a system capacity analysis and prediction method, which includes the following steps.
Step S101, acquiring system operation data; each system operation data comprises system state data, service state data and abnormal fault data, and the system state data, the service state data and the abnormal fault data can be classified according to a time interval, hardware resources and service application.
And step S102, establishing a capacity regression model according to the system operation data.
And step S103, acquiring historical system operation data of a set time interval according to the system operation data.
And step S104, calculating the historical capacity use data of each hardware resource in the set time interval by using a capacity regression model according to the historical system operation data of the set time interval. Namely, the capacity use condition of each hardware resource in the service chain in the period of time is calculated by setting the system operation data in the time interval.
Step S105, according to the historical capacity utilization data of each hardware resource in the set time interval, predicting the capacity utilization condition of each hardware resource in the set time interval to obtain the predicted capacity utilization data of each hardware resource in the set time interval. From the historical capacity usage data calculated in step S104, the future capacity usage within the set time interval can be predicted. For example, based on past month capacity usage data, future month capacity usage data may be predicted.
And step S106, detecting and evaluating the performance condition of each hardware resource in the set time interval by using a capacity regression model according to the historical system operation data of the set time interval, and acquiring the system performance data of the set time interval.
Step S107, predicting the system capacity use state after the set time interval by using a capacity regression model according to the predicted capacity use data of each hardware resource in the set time interval and the system performance data of the set time interval to obtain a predicted system capacity use state; the predicted system capacity usage state includes a risk level and a risk occurrence probability. The risk level can be divided into five levels of high, medium, general and low.
The system state data, the service state data and the abnormal fault data can be classified according to time intervals, hardware resources and service applications, and the system state data comprise system state data of different time intervals, different hardware resources and different service applications, the service state data comprise service state data of different time intervals, different hardware resources and different service applications, and the abnormal fault data comprise abnormal fault data of different time intervals, different hardware resources and different service applications.
The embodiment of the invention can obtain system state data (comprising hardware resource state data), service state data (mainly comprising concurrent data and transaction data) and abnormal fault data (mainly comprising abnormal occurrence data and severity index data) of the system at different time periods and different dimensions by analyzing the performance indexes and the working logs of each hardware resource in the system; the data are correlated to form a scatter diagram, so that the relation between the service state and the abnormal fault under the condition of the same hardware resource can be analyzed, and the relation between the hardware resource and the abnormal fault under the same service scene can also be analyzed; the relation can calculate the service capacity borne by the current hardware resources and the evaluation value of the performance index of each hardware resource under the expected service volume in the set time interval and on the premise of maintaining low abnormal faults; and predicting the system capacity state after the set time interval according to the two data. Therefore, the capacity use condition of key resources (including a host, a network, application and the like) in each link can be analyzed by comprehensively analyzing historical operation condition data, whether the capacity of the hardware resources such as a server and the like reaches a bottleneck or not is judged in advance, the possible capacity risk is early warned, the hardware resources are prevented from being repaired when the hardware resources are failed, and the safety of the system is improved.
The set time interval is not limited in the embodiment of the invention, and preferably, the set time interval can be 7-30 days.
The performance index of the hardware resource is not limited in the embodiments of the present invention, and preferably, the performance index of the hardware resource may include one or more of a CPU index, a memory index, a disk index, and a network module index.
The embodiment of the present invention does not limit the data type in the capacity usage data, and preferably, the data type in the capacity usage data may include a maximum concurrent number, an IOPS, and a maximum thread number.
The maximum number of concurrent requests in the embodiment of the present invention refers to the maximum number of requests being processed at the same time.
The IOPS (Input/Output Operations Per Second) in the embodiment of the present invention refers to the number of times of read/write Operations Per Second performed by a computer storage device, and is often used in occasions such as databases to measure the performance of random access. The computer storage device may include a hard disk (HDD), a Solid State Disk (SSD), or a Storage Area Network (SAN).
The thread is the minimum unit of the program execution flow, is an entity in the process, and is the basic unit independently scheduled and dispatched by the system.
In the embodiment of the present invention, sample data of system operation data may be as follows:
IT resource CPU utilization: 50%, IT resource memory usage: 60%, IT resource disk IO: 70%, IT resource network traffic: 100M/s, the transaction concurrency is 10000, and the abnormal occurrence data is as follows: none;
IT resource CPU utilization: 60%, IT resource memory usage: 60%, IT resource disk IO: 70%, IT resource network traffic: 10M/s, the concurrent transaction amount at the time is 12000, and the abnormal occurrence data: none;
the IT resource CPU utilization rate, the IT resource memory utilization rate, the IT resource disk IO and the IT resource network flow are system state data, the transaction concurrency amount at that time is service state data, and the abnormal occurrence data is abnormal fault data. With large data resources similar to the sample data described above, a capacity regression model can be built.
Preferably, the embodiment of the present invention may further include: and step S108, sending early warning information to the user according to the risk level. The method has the advantages that the method can actively send out early warning information to the user, improves the safety of the system, enables the system to be more humanized, and improves the user experience.
In the embodiment of the present invention, the step S106 is not limited, and preferably, in the embodiment of the present invention, the step S106 may specifically be: acquiring performance indexes and index values of all hardware resources in a set time interval by using a capacity regression model according to historical system operation data of the set time interval; calculating the weight value of the performance index of each hardware resource in the set time interval according to the performance index and the index value of each hardware resource in the set time interval; and acquiring system performance data of the set time interval according to the weight value of the performance index of each hardware resource in the set time interval. The embodiment of the present invention does not limit the presentation and sorting manner of the system performance data, and preferably, in the system performance data, the performance indexes of the hardware resources may be sorted according to the weight value. In the embodiment of the present invention, the performance index of each hardware resource may include an IT resource CPU utilization rate, an IT resource memory utilization rate, an IT resource disk IO utilization rate, an IT resource network flow rate, and the like.
After step S106, the embodiment of the present invention may further include: and step S109, generating a performance analysis report according to the system performance data of the set time interval, and pushing the performance analysis report to a user, wherein the performance analysis report comprises the system performance data of the set time interval.
The embodiment of the invention does not limit the acquisition mode of the historical system operation data, preferably, the historical system operation data can be acquired from the log data stored in the hbase, the historical log data is analyzed through spark, the load condition and the performance index value of each hardware resource of the system in a time interval are comprehensively set, the weight value of the performance index of each hardware resource is calculated, the key factor influencing the performance in the set time interval is obtained, and the result is displayed in a sequencing mode through the weight value. HBase is a distributed, column-oriented open source database, and the technology is derived from the Google paper "Bigtable: a distributed storage system of structured data.
Spark is a general parallel framework derived from UC Berkeley AMP lab (AMP labs in Berkeley university, California), and can be better applied to algorithms requiring iteration, such as data mining and machine learning.
In the first embodiment, a method for analyzing and predicting system capacity is provided, and correspondingly, a device for analyzing and predicting system capacity is also provided. Since the apparatus embodiments are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for relevant points. The device embodiments described below are merely illustrative.
Detailed description of the invention
As shown in fig. 2, an embodiment of the present invention provides a system capacity analyzing and predicting apparatus, which includes the following modules.
A model building module 201 configured to: acquiring system operation data; each system operation data comprises system state data, service state data and abnormal fault data, and the system state data, the service state data and the abnormal fault data can be classified according to a time interval, hardware resources and service application; and establishing a capacity regression model according to the system operation data. The embodiment of the invention establishes a capacity regression model of the whole system from different time and multiple dimensions, for example, through hardware resource configuration and loaded service application of a host.
A capacity analysis module 202 configured to: acquiring historical system operation data of a set time interval according to the system operation data; calculating historical capacity use data of each hardware resource in a set time interval by using a capacity regression model according to historical system operation data of the set time interval; and predicting the capacity use condition of each hardware resource in the set time interval according to the historical capacity use data of each hardware resource in the set time interval to obtain the predicted capacity use data of each hardware resource in the set time interval.
A performance analysis module 203 for: and detecting and evaluating the performance condition of each hardware resource in the set time interval by using a capacity regression model according to the historical system operation data of the set time interval to obtain the system performance data of the set time interval. According to historical operating data, the performance condition of the set time interval is detected, evaluated and generated to be a report, the system performance condition in the time interval is displayed, key factors and risks influencing the performance are ranked and prompted, and relevant suggestions are given to a user.
A state prediction module 204 to: predicting the system capacity use state after the set time interval by using a capacity regression model according to the predicted capacity use data of each hardware resource in the set time interval and the system performance data of the set time interval to obtain a predicted system capacity use state; the predicted system capacity usage state includes a risk level and a risk occurrence probability. The risk level can be divided into five levels of high, medium, general and low.
The embodiment of the invention can obtain system state data (comprising hardware resource state data), service state data (mainly comprising concurrent data and transaction data) and abnormal fault data (mainly comprising abnormal occurrence data and severity index data) of the system at different time periods and different dimensions by analyzing the performance indexes and the working logs of each hardware resource in the system; the data are correlated to form a scatter diagram, so that the relation between the service state and the abnormal fault under the condition of the same hardware resource can be analyzed, and the relation between the hardware resource and the abnormal fault under the same service scene can also be analyzed; the relation can calculate the service capacity borne by the current hardware resources and the evaluation value of the performance index of each hardware resource under the expected service volume in the set time interval and on the premise of maintaining low abnormal faults; and predicting the system capacity state after the set time interval according to the two data. Therefore, the capacity use condition of key resources (including a host, a network, application and the like) in each link can be analyzed by comprehensively analyzing historical operation condition data, whether the capacity of the hardware resources such as a server and the like reaches a bottleneck or not is judged in advance, the possible capacity risk is early warned, the hardware resources are prevented from being repaired when the hardware resources are failed, and the safety of the system is improved.
The set time interval is not limited in the embodiment of the invention, and preferably, the set time interval can be 7-30 days.
Preferably, the embodiment of the present invention may further include an early warning module 205, configured to: and sending early warning information to the user according to the risk level. The method has the advantages that the method can actively send out early warning information to the user, improves the safety of the system, enables the system to be more humanized, and improves the user experience.
The performance analysis module 203 is not limited in the embodiment of the present invention, and preferably, the performance analysis module 203 may be configured to: acquiring performance indexes and index values of all hardware resources in a set time interval by using a capacity regression model according to historical system operation data of the set time interval; calculating the weight value of the performance index of each hardware resource in the set time interval according to the performance index and the index value of each hardware resource in the set time interval; and acquiring system performance data of the set time interval according to the weight value of the performance index of each hardware resource in the set time interval. The embodiment of the present invention does not limit the presentation and sorting manner of the system performance data, and preferably, in the system performance data, the performance indexes of the hardware resources may be sorted according to the weight value. The performance index of the hardware resource is not limited in the embodiments of the present invention, and preferably, the performance index of the hardware resource may include one or more of a CPU index, a memory index, a disk index, and a network module index.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. Although the present invention has been described to a certain extent, it is apparent that appropriate changes in the respective conditions may be made without departing from the spirit and scope of the present invention. It is to be understood that the invention is not limited to the described embodiments, but is to be accorded the scope consistent with the claims, including equivalents of each element described.
Claims (10)
1. A system capacity analysis prediction method is characterized by comprising the following steps:
acquiring system operation data; each system operation data comprises system state data, service state data and abnormal fault data, and the system state data, the service state data and the abnormal fault data can be classified according to a time interval, hardware resources and service application;
establishing a capacity regression model according to system operation data;
acquiring historical system operation data of a first set time interval according to the system operation data;
calculating historical capacity use data of each hardware resource in the first set time interval by using a capacity regression model according to historical system operation data of the first set time interval;
predicting the capacity use condition of each hardware resource in a second set time interval according to the historical capacity use data of each hardware resource in the first set time interval to obtain the predicted capacity use data of each hardware resource in the second set time interval;
detecting and evaluating the performance condition of each hardware resource in a second set time interval by using a capacity regression model according to the historical system operation data of the first set time interval to obtain the system performance data of the second set time interval;
predicting the system capacity use state after the second set time interval by using a capacity regression model according to the predicted capacity use data of each hardware resource in the second set time interval and the system performance data of the second set time interval to obtain a predicted system capacity use state; the predicted system capacity usage state includes a risk level and a risk occurrence probability.
2. The method of claim 1, further comprising:
and sending early warning information to the user according to the risk level.
3. The method for analyzing and predicting system capacity according to claim 1 or 2, wherein the step of detecting and evaluating performance conditions of each hardware resource in a second set time interval by using a capacity regression model according to historical system operation data of the first set time interval to obtain system performance data of the second set time interval specifically comprises:
acquiring performance indexes and index values of all hardware resources in a first set time interval by using a capacity regression model according to historical system operation data of the first set time interval;
calculating the weight value of the performance index of each hardware resource in a second set time interval according to the performance index and the index value of each hardware resource in the first set time interval;
and acquiring system performance data of the second set time interval according to the weight value of the performance index of each hardware resource in the second set time interval.
4. The method according to claim 3, wherein the performance indicators of the hardware resources in the system performance data are sorted according to the weight value.
5. The method according to claim 4, wherein the performance index of the hardware resource comprises one or more of a CPU index, a memory index, a disk index, and a network module index.
6. A system capacity analysis prediction apparatus, comprising:
a model building module to:
acquiring system operation data; each system operation data comprises system state data, service state data and abnormal fault data, and the system state data, the service state data and the abnormal fault data can be classified according to a time interval, hardware resources and service application;
establishing a capacity regression model according to system operation data;
a capacity analysis module to:
acquiring historical system operation data of a first set time interval according to the system operation data;
calculating historical capacity use data of each hardware resource in the first set time interval by using a capacity regression model according to historical system operation data of the first set time interval;
predicting the capacity use condition of each hardware resource in a second set time interval according to the historical capacity use data of each hardware resource in the first set time interval to obtain the predicted capacity use data of each hardware resource in the second set time interval;
a performance analysis module to:
detecting and evaluating the performance condition of each hardware resource in a second set time interval by using a capacity regression model according to the historical system operation data of the first set time interval to obtain the system performance data of the second set time interval;
a state prediction module to:
predicting the system capacity use state after the second set time interval by using a capacity regression model according to the predicted capacity use data of each hardware resource in the second set time interval and the system performance data of the second set time interval to obtain a predicted system capacity use state; the predicted system capacity usage state includes a risk level and a risk occurrence probability.
7. The system capacity analysis and prediction apparatus according to claim 6, further comprising:
an early warning module for:
and sending early warning information to the user according to the risk level.
8. The system capacity analysis prediction device of claim 6 or 7, wherein the performance analysis module is configured to:
acquiring performance indexes and index values of all hardware resources in a first set time interval by using a capacity regression model according to historical system operation data of the first set time interval;
calculating the weight value of the performance index of each hardware resource in a second set time interval according to the performance index and the index value of each hardware resource in the first set time interval;
and acquiring system performance data of the second set time interval according to the weight value of the performance index of each hardware resource in the second set time interval.
9. The system capacity analysis prediction apparatus according to claim 8, wherein the performance indicators of the hardware resources in the system performance data are sorted according to a weight value.
10. The system capacity analysis and prediction apparatus according to claim 9, wherein the performance index of the hardware resource includes one or more of a CPU index, a memory index, a disk index, and a network module index.
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Address after: 518000 2203/2204, Building 1, Huide Building, Beizhan Community, Minzhi Street, Longhua District, Shenzhen, Guangdong Patentee after: SHENZHEN AUDAQUE DATA TECHNOLOGY Ltd. Address before: 518000 units J and K, 12 / F, block B, building 7, Baoneng Science Park, Qinghu Industrial Zone, Qingxiang Road, Longhua New District, Shenzhen City, Guangdong Province Patentee before: SHENZHEN AUDAQUE DATA TECHNOLOGY Ltd. |