CN106886485A - Power system capacity analyzing and predicting method and device - Google Patents
Power system capacity analyzing and predicting method and device Download PDFInfo
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- CN106886485A CN106886485A CN201710116658.2A CN201710116658A CN106886485A CN 106886485 A CN106886485 A CN 106886485A CN 201710116658 A CN201710116658 A CN 201710116658A CN 106886485 A CN106886485 A CN 106886485A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3447—Performance evaluation by modeling
Abstract
The invention provides power system capacity analyzing and predicting method and device,Obtain system operation data,Set up system state data,Relation between service condition data and abnormal failure data,And capacity regression model is set up based on this,Again on the basis of the capacity regression model,The prediction capacity for calculating the interval interior each hardware resource of setting time uses data and the interval system performance information of setting time,And according to above-mentioned two item data,With reference to capacity regression model,Capacity use state to follow-up hardware resource provides prediction,This makes it possible to pass through comprehensive analysis history run data,Analyze the capacity service condition of each link keystone resources,Whether the capacity of the hardware resource such as anticipation server reaches bottleneck in advance,Capacity risk to being likely to occur carries out early warning,Avoid being repaired again until hardware resource breaks down,Improve the security of system.
Description
Technical field
The present invention relates to big data operational system, more particularly, to the power system capacity analysis prediction of big data operation platform
Method and device.
Background technology
The big data operational system of prior art cannot look-ahead make to the capacity of the hardware resources such as server in system
With situation, can only be repaired until these hardware resources break down, it is impossible to enough to take precautions against failure in advance, thus be caused
Certain potential safety hazard.
The content of the invention
It is an object of the invention to provide, it is intended to solving existing big data operational system cannot be each hard in look-ahead system
The capacity service condition of part resource, it is impossible to enough to take precautions against failure in advance, thus causes system to there is a problem of potential safety hazard.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of power system capacity analyzing and predicting method, including：
Obtain system operation data；Every system operation data includes system state data, service condition data and exception
Fault data, the system state data, service condition data and abnormal failure data can be provided according to time interval, hardware
Source, service application are classified；
According to system operation data, capacity regression model is set up；
According to system operation data, the interval legacy system service data of setting time is obtained；
According to the legacy system service data that setting time is interval, using capacity regression model, setting time is calculated interval
The historical capacity of interior each hardware resource uses data；
Historical capacity according to each hardware resource in setting time interval uses data, interior each hardware interval to setting time
The capacity service condition of resource is predicted, and the prediction capacity for obtaining the interval interior each hardware resource of setting time uses data；
It is interval to setting time interior using capacity regression model according to the legacy system service data that setting time is interval
The behavior pattern of each hardware resource detected and assessed, and obtains the interval system performance information of setting time；
Prediction capacity according to each hardware resource in setting time interval uses the interval systematic function of data, setting time
Data, using capacity regression model, are predicted to the power system capacity use state after setting time interval, obtain prediction system
System capacity use state；The forecasting system capacity use state includes risk class and risk probability of happening.
On the basis of above-described embodiment, further, also include：
According to risk class, early warning information is sent to user.
On the basis of above-mentioned any embodiment, further, the legacy system operation interval according to setting time
Data, using capacity regression model, the behavior pattern of interior each hardware resource interval to setting time is detected and assessed, and is obtained
The step of system performance information in setting time interval, specially：
According to the legacy system service data that setting time is interval, using capacity regression model, setting time is obtained interval
The performance indications and its desired value of interior each hardware resource；
According to the performance indications and its desired value of each hardware resource in setting time interval, setting time is calculated interval interior each
The weighted value of the performance indications of hardware resource；
According to the weighted value of the performance indications of each hardware resource in setting time interval, the interval system of setting time is obtained
Performance data.
On the basis of above-described embodiment, further, in the system performance information, the performance indications of each hardware resource
It is ranked up according to weighted value size.
On the basis of above-described embodiment, further, the performance indications of the hardware resource include CPU indexs, internal memory
One or more in index, disk index, mixed-media network modules mixed-media index.
A kind of power system capacity analyzes prediction meanss, including：
Model building module, is used for：
Obtain system operation data；Every system operation data includes system state data, service condition data and exception
Fault data, the system state data, service condition data and abnormal failure data can be provided according to time interval, hardware
Source, service application are classified；
According to system operation data, capacity regression model is set up；
Capacity analysis module, is used for：
According to system operation data, the interval legacy system service data of setting time is obtained；
According to the legacy system service data that setting time is interval, using capacity regression model, setting time is calculated interval
The historical capacity of interior each hardware resource uses data；
Historical capacity according to each hardware resource in setting time interval uses data, interior each hardware interval to setting time
The capacity service condition of resource is predicted, and the prediction capacity for obtaining the interval interior each hardware resource of setting time uses data；
Performance evaluation module, is used for：
It is interval to setting time interior using capacity regression model according to the legacy system service data that setting time is interval
The behavior pattern of each hardware resource detected and assessed, and obtains the interval system performance information of setting time；
State prediction module, is used for：
Prediction capacity according to each hardware resource in setting time interval uses the interval systematic function of data, setting time
Data, using capacity regression model, are predicted to the power system capacity use state after setting time interval, obtain prediction system
System capacity use state；The forecasting system capacity use state includes risk class and risk probability of happening.
On the basis of above-described embodiment, further, also include：
Warning module, is used for：
According to risk class, early warning information is sent to user.
On the basis of above-mentioned any embodiment, further, the performance evaluation module is used for：
According to the legacy system service data that setting time is interval, using capacity regression model, setting time is obtained interval
The performance indications and its desired value of interior each hardware resource；
According to the performance indications and its desired value of each hardware resource in setting time interval, setting time is calculated interval interior each
The weighted value of the performance indications of hardware resource；
According to the weighted value of the performance indications of each hardware resource in setting time interval, the interval system of setting time is obtained
Performance data.
On the basis of above-described embodiment, further, in the system performance information, the performance indications of each hardware resource
It is ranked up according to weighted value size.
On the basis of above-described embodiment, further, the performance indications of the hardware resource include CPU indexs, internal memory
One or more in index, disk index, mixed-media network modules mixed-media index.
The beneficial effects of the invention are as follows：
The invention provides power system capacity analyzing and predicting method and device, system operation data is obtained, set up system mode
Relation between data, service condition data and abnormal failure data, and capacity regression model is set up based on this, then at this
On the basis of capacity regression model, the prediction capacity for calculating the interval interior each hardware resource of setting time uses data and setting time
Interval system performance information, and according to above-mentioned two item data, with reference to capacity regression model, to the capacity of follow-up hardware resource
Use state provides prediction, this makes it possible to pass through comprehensive analysis history run data, analyzes each link keystone resources (bag
Include main frame, network, using etc.) capacity service condition, in advance the capacity of the hardware resource such as anticipation server whether reach bottleneck,
Capacity risk to being likely to occur carries out early warning, it is to avoid is repaired again until hardware resource breaks down, improves system
Security.
Brief description of the drawings
The present invention is further described with reference to the accompanying drawings and examples.
Fig. 1 shows the flow chart of power system capacity analyzing and predicting method provided in an embodiment of the present invention；
Fig. 2 shows that power system capacity provided in an embodiment of the present invention analyzes the structural representation of prediction meanss.
Specific embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the present invention, not
Limit the present invention.
Specific embodiment one
As shown in figure 1, a kind of power system capacity analyzing and predicting method is the embodiment of the invention provides, including following step
Suddenly.
Step S101, obtains system operation data；Every system operation data includes system state data, service condition number
According to abnormal failure data, the system state data, service condition data and abnormal failure data can according to time interval,
Hardware resource, service application are classified.
Step S102, according to system operation data, sets up capacity regression model.
Step S103, according to system operation data, obtains the interval legacy system service data of setting time.
Step S104, according to the legacy system service data that setting time is interval, using capacity regression model, calculates setting
The historical capacity of each hardware resource uses data in time interval.I.e. by the system operation data in setting time interval, meter
Calculate the capacity service condition of each hardware resource in the business chain in this period.
Step S105, the historical capacity according to each hardware resource in setting time interval uses data, to setting time area
The capacity service condition of interior each hardware resource is predicted, and the prediction capacity for obtaining the interval interior each hardware resource of setting time makes
Use data.Historical capacity according to being calculated in step S104 uses data, can predict following in setting time interval
Capacity service condition.Data were for example used according to the capacity of past month, can predict that following one month capacity is used
Data.
Step S106, according to the legacy system service data that setting time is interval, using capacity regression model, during to setting
Between it is interval in the behavior pattern of each hardware resource detected and assessed, obtain the interval system performance information of setting time.
Step S107, the prediction capacity according to each hardware resource in setting time interval is interval using data, setting time
System performance information, using capacity regression model, the power system capacity use state after setting time interval is predicted,
Obtain forecasting system capacity use state；The forecasting system capacity use state includes risk class and risk probability of happening.
The risk class can be divided into it is very high, high, in, general, low five kinds of grades.
The system state data, service condition data and abnormal failure data can according to time interval, hardware resource,
Service application is classified, and referring to system state data should including different time interval, different hardware resource, different business
System state data, service condition data include different time interval, different hardware resource, the business of different business application
Status data, abnormal failure data include different time interval, different hardware resource, the abnormal failure number of different business application
According to.
The embodiment of the present invention is analyzed by the performance indications of each hardware resource, daily record of work in system, can obtain
Go out the system state data (including hardware resource status data) of system under different periods different dimensions, service condition data (main
To include concurrent data, transaction data), abnormal failure data (main include abnormal data, severity data occur)；
By these data correlations into scatter diagram, it is possible to analyze in the case of same hardware resource, service condition and abnormal failure
Between relation, it is also possible to analyze, under identical services scene, the relation between hardware resource and abnormal failure；By these passes
System is it can be calculated that in setting time is interval, on the premise of low abnormal failure is maintained, Current hardware resource can bear
Volume of business, and under expected traffic amount, the valuation of the performance indications of each hardware resource；It is right further according to above-mentioned two item data
Power system capacity state behind setting time interval is predicted.This makes it possible to pass through comprehensive analysis history run data,
Analyze each link keystone resources (including main frame, network, using etc.) capacity service condition, in advance the hardware such as anticipation server money
Whether the capacity in source reaches bottleneck, and the capacity risk to being likely to occur carries out early warning, it is to avoid until hardware resource breaks down
Repaired again, improve the security of system.
The embodiment of the present invention is not limited setting time interval, it is preferred that it can be 7~30 days.
The embodiment of the present invention is not limited the performance indications of hardware resource, it is preferred that the performance of the hardware resource refers to
Mark can include one or more in CPU indexs, indicator memory, disk index, mixed-media network modules mixed-media index.
The embodiment of the present invention is not limited capacity using the data type in data, it is preferred that capacity is used in data
Data type can include maximum and hair number, IOPS, maximum thread.
Maximum and hair number in the embodiment of the present invention, refers to the largest request number that synchronization is being processed.
IOPS (Input/Output Operations Per Second) in the embodiment of the present invention, refers to computer and deposits
The storage equipment number of times for being written and read operation per second, is used for database occasion, weighs the performance of random access.Computer storage sets
It is standby to include hard disk (HDD), solid state hard disc (SSD) or storage area network (SAN).
Thread is the minimum unit that program performs stream, is an entity in process, is independently dispatched and is assigned by system
Base unit, the maximum thread in the embodiment of the present invention referred to while the maximum of the Thread Count for carrying out.
In the embodiment of the present invention, the sample data of system operation data can be with as follows：
IT resource CPU usages：50%, IT resource memory usage：60%, IT resource disk I/O：70%, IT resource network
Network flow：100M/s, concurrency of concluding the business at that time is 10000, abnormal that data occur：Nothing；
IT resource CPU usages：60%, IT resource memory usage：60%, IT resource disk I/O：70%, IT resource network
Network flow：10M/s, concurrency of concluding the business at that time is 12000, abnormal that data occur：Nothing；
Wherein, IT resources CPU usage, IT resources memory usage, IT resources disk I/O, IT resource networks flow are to be
System status data, concurrency of concluding the business at that time is service condition data, and abnormal generation data are abnormal failure data.By with it is above-mentioned
The similar big data resource of sample data, can set up capacity regression model.
Preferably, the embodiment of the present invention can also include：Step S108, according to risk class, sends early warning and believes to user
Breath.Advantage of this is that, early warning information can be actively issued the user with, the security of system is improve, also use system
More hommization, improves Consumer's Experience.
The embodiment of the present invention is not limited step S106, it is preferred that in the embodiment of the present invention, and step S106 can be specific
For：According to the legacy system service data that setting time is interval, using capacity regression model, setting time is obtained interval interior each hard
The performance indications and its desired value of part resource；According to the performance indications and its desired value of each hardware resource in setting time interval,
Calculate the weighted value of the performance indications of the interval interior each hardware resource of setting time；According to each hardware resource in setting time interval
The weighted value of performance indications, obtains the interval system performance information of setting time.The embodiment of the present invention is to system performance information
Present and sortord is not limited, it is preferred that in the system performance information, the performance indications of each hardware resource can basis
Weighted value size is ranked up.In the embodiment of the present invention, the performance indications of each hardware resource can be used including IT resources CPU
Rate, IT resource memory usages, IT resource disk I/O utilization rates, IT resource network flows etc..
The embodiment of the present invention can also include after step S106：Step S109, according to the systematicness that setting time is interval
Energy data, generate performance analysis report and are simultaneously pushed to user, and the performance analysis report includes the interval system of setting time
Performance data.
The embodiment of the present invention is not limited the acquisition modes of legacy system service data, it is preferred that can be existed from storage
Daily record data in hbase obtains legacy system service data, then history log data is analyzed by spark, comprehensive
The loading condition and its size of performance index value of each hardware resource of system, calculate each hardware resource in setting time is interval
The size of the weighted value of performance indications, obtains the key factor of the interval interior influence performance of setting time, and result is passed through into weight
Value is ranked up displaying.HBase is a PostgreSQL database distributed, towards row, and the Technology origin is in Fay Chang institutes
Google papers " the Bigtable for writing：One distributed memory system of structural data ".
Spark be UC Berkeley AMP lab (the AMP laboratories of University of California Berkeley) increased income it is general
Parallel framework, can preferably be applied to data mining and machine learning etc. needs the algorithm of iteration.
It is corresponding in above-mentioned specific embodiment one, there is provided a kind of power system capacity analyzing and predicting method, this
Application also provides a kind of power system capacity analysis prediction meanss.Because device embodiment is substantially similar to embodiment of the method, so retouching
State fairly simple, the relevent part can refer to the partial explaination of embodiments of method.Device embodiment described below is only
Schematically.
Specific embodiment two
As shown in Fig. 2 a kind of power system capacity analysis prediction meanss are the embodiment of the invention provides, including following mould
Block.
Model building module 201, is used for：Obtain system operation data；Every system operation data includes system mode number
According to, service condition data and abnormal failure data, the system state data, service condition data and abnormal failure data
Classified according to time interval, hardware resource, service application；According to system operation data, capacity regression model is set up.This hair
Bright embodiment sets up the capacity regression model of whole system from different time, multiple dimensions, such as by the hardware resource of main frame
Configuration and the service application for carrying.
Capacity analysis module 202, is used for：According to system operation data, the interval legacy system operation of setting time is obtained
Data；According to the legacy system service data that setting time is interval, using capacity regression model, setting time is calculated interval interior each
The historical capacity of hardware resource uses data；Historical capacity according to each hardware resource in setting time interval uses data, right
The capacity service condition of each hardware resource is predicted in setting time is interval, obtains the interval interior each hardware resource of setting time
Prediction capacity uses data.
Performance evaluation module 203, is used for：According to the legacy system service data that setting time is interval, returned using capacity
Model, the behavior pattern of interior each hardware resource interval to setting time is detected and is assessed that obtain setting time interval is
System performance data.According to history data, the behavior pattern to set time interval is detected, assessed and is generated report
Accuse, systematic function situation in displaying time interval, the key factor to influenceing performance is ranked up and points out with risk, to user
Provide related advisory.
State prediction module 204, is used for：According to setting time it is interval in each hardware resource prediction capacity use data,
The interval system performance information of setting time, using capacity regression model, uses the power system capacity after setting time interval
State is predicted, and obtains forecasting system capacity use state；The forecasting system capacity use state include risk class and
Risk probability of happening.The risk class can be divided into it is very high, high, in, general, low five kinds of grades.
The embodiment of the present invention is analyzed by the performance indications of each hardware resource, daily record of work in system, can obtain
Go out the system state data (including hardware resource status data) of system under different periods different dimensions, service condition data (main
To include concurrent data, transaction data), abnormal failure data (main include abnormal data, severity data occur)；
By these data correlations into scatter diagram, it is possible to analyze in the case of same hardware resource, service condition and abnormal failure
Between relation, it is also possible to analyze, under identical services scene, the relation between hardware resource and abnormal failure；By these passes
System is it can be calculated that in setting time is interval, on the premise of low abnormal failure is maintained, Current hardware resource can bear
Volume of business, and under expected traffic amount, the valuation of the performance indications of each hardware resource；It is right further according to above-mentioned two item data
Power system capacity state behind setting time interval is predicted.This makes it possible to pass through comprehensive analysis history run data,
Analyze each link keystone resources (including main frame, network, using etc.) capacity service condition, in advance the hardware such as anticipation server money
Whether the capacity in source reaches bottleneck, and the capacity risk to being likely to occur carries out early warning, it is to avoid until hardware resource breaks down
Repaired again, improve the security of system.
The embodiment of the present invention is not limited setting time interval, it is preferred that it can be 7~30 days.
Preferably, the embodiment of the present invention can also include warning module 205, be used for：According to risk class, sent to user
Early warning information.Advantage of this is that, can actively issue the user with early warning information, improve the security of system, also make be
System uses more hommization, improves Consumer's Experience.
The embodiment of the present invention is not limited performance evaluation module 203, it is preferred that the performance evaluation module 203 can be with
For：According to the legacy system service data that setting time is interval, using capacity regression model, setting time is obtained interval interior each
The performance indications and its desired value of hardware resource；According to the performance indications and its index of each hardware resource in setting time interval
Value, calculates the weighted value of the performance indications of the interval interior each hardware resource of setting time；According to each hardware money in setting time interval
The weighted value of the performance indications in source, obtains the interval system performance information of setting time.The embodiment of the present invention is to systematic function number
According to presentation and sortord do not limit, it is preferred that in the system performance information, the performance indications of each hardware resource can be with
It is ranked up according to weighted value size.The embodiment of the present invention is not limited the performance indications of hardware resource, it is preferred that described hard
The performance indications of part resource can include one or more in CPU indexs, indicator memory, disk index, mixed-media network modules mixed-media index.
It should be noted that in the case where not conflicting, the embodiment in the present invention and the feature in embodiment can phases
Mutually combination.Although present invention has been a certain degree of description, it will be apparent that, do not departing from the bar of the spirit and scope of the present invention
Under part, the appropriate change of each condition can be carried out.It is appreciated that the invention is not restricted to the embodiment, and be attributed to right and want
The scope asked, its equivalent for including each factor.
Claims (10)
1. a kind of power system capacity analyzing and predicting method, it is characterised in that including：
Obtain system operation data；Every system operation data includes system state data, service condition data and abnormal failure
Data, the system state data, service condition data and abnormal failure data can be according to time interval, hardware resource, industry
Business application is classified；
According to system operation data, capacity regression model is set up；
According to system operation data, the interval legacy system service data of setting time is obtained；
According to the legacy system service data that setting time is interval, using capacity regression model, setting time is calculated interval interior each
The historical capacity of hardware resource uses data；
Historical capacity according to each hardware resource in setting time interval uses data, interior each hardware resource interval to setting time
Capacity service condition be predicted, obtain setting time it is interval in the prediction capacity of each hardware resource use data；
It is interval to setting time interior each hard using capacity regression model according to the legacy system service data that setting time is interval
The behavior pattern of part resource detected and assessed, and obtains the interval system performance information of setting time；
Prediction capacity according to each hardware resource in setting time interval uses the interval systematic function number of data, setting time
According to, using capacity regression model, the power system capacity use state after setting time interval is predicted, obtain forecasting system
Capacity use state；The forecasting system capacity use state includes risk class and risk probability of happening.
2. power system capacity analyzing and predicting method according to claim 1, it is characterised in that also include：
According to risk class, early warning information is sent to user.
3. power system capacity analyzing and predicting method according to claim 1 and 2, it is characterised in that described according to setting time
Interval legacy system service data, using capacity regression model, the behavior pattern of interior each hardware resource interval to setting time
Detected and assessed, the step of obtain setting time interval system performance information, specially：
According to the legacy system service data that setting time is interval, using capacity regression model, setting time is obtained interval interior each
The performance indications and its desired value of hardware resource；
According to the performance indications and its desired value of each hardware resource in setting time interval, the interval interior each hardware of setting time is calculated
The weighted value of the performance indications of resource；
According to the weighted value of the performance indications of each hardware resource in setting time interval, the interval systematic function of setting time is obtained
Data.
4. power system capacity analyzing and predicting method according to claim 3, it is characterised in that in the system performance information,
The performance indications of each hardware resource are ranked up according to weighted value size.
5. power system capacity analyzing and predicting method according to claim 4, it is characterised in that the performance of the hardware resource refers to
Mark includes one or more in CPU indexs, indicator memory, disk index, mixed-media network modules mixed-media index.
6. a kind of power system capacity analyzes prediction meanss, it is characterised in that including：
Model building module, is used for：
Obtain system operation data；Every system operation data includes system state data, service condition data and abnormal failure
Data, the system state data, service condition data and abnormal failure data can be according to time interval, hardware resource, industry
Business application is classified；
According to system operation data, capacity regression model is set up；
Capacity analysis module, is used for：
According to system operation data, the interval legacy system service data of setting time is obtained；
According to the legacy system service data that setting time is interval, using capacity regression model, setting time is calculated interval interior each
The historical capacity of hardware resource uses data；
Historical capacity according to each hardware resource in setting time interval uses data, interior each hardware resource interval to setting time
Capacity service condition be predicted, obtain setting time it is interval in the prediction capacity of each hardware resource use data；
Performance evaluation module, is used for：
It is interval to setting time interior each hard using capacity regression model according to the legacy system service data that setting time is interval
The behavior pattern of part resource detected and assessed, and obtains the interval system performance information of setting time；
State prediction module, is used for：
Prediction capacity according to each hardware resource in setting time interval uses the interval systematic function number of data, setting time
According to, using capacity regression model, the power system capacity use state after setting time interval is predicted, obtain forecasting system
Capacity use state；The forecasting system capacity use state includes risk class and risk probability of happening.
7. power system capacity according to claim 6 analyzes prediction meanss, it is characterised in that also include：
Warning module, is used for：
According to risk class, early warning information is sent to user.
8. the power system capacity according to claim 6 or 7 analyzes prediction meanss, it is characterised in that the performance evaluation module
For：
According to the legacy system service data that setting time is interval, using capacity regression model, setting time is obtained interval interior each
The performance indications and its desired value of hardware resource；
According to the performance indications and its desired value of each hardware resource in setting time interval, the interval interior each hardware of setting time is calculated
The weighted value of the performance indications of resource；
According to the weighted value of the performance indications of each hardware resource in setting time interval, the interval systematic function of setting time is obtained
Data.
9. power system capacity according to claim 8 analyzes prediction meanss, it is characterised in that in the system performance information,
The performance indications of each hardware resource are ranked up according to weighted value size.
10. power system capacity according to claim 9 analyzes prediction meanss, it is characterised in that the performance of the hardware resource
Index includes one or more in CPU indexs, indicator memory, disk index, mixed-media network modules mixed-media index.
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