CN106549772B - Resource prediction method, system and capacity management device - Google Patents
Resource prediction method, system and capacity management device Download PDFInfo
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- CN106549772B CN106549772B CN201510590666.1A CN201510590666A CN106549772B CN 106549772 B CN106549772 B CN 106549772B CN 201510590666 A CN201510590666 A CN 201510590666A CN 106549772 B CN106549772 B CN 106549772B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0896—Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/82—Miscellaneous aspects
- H04L47/822—Collecting or measuring resource availability data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/83—Admission control; Resource allocation based on usage prediction
Abstract
The present invention provides a kind of resource prediction method, system and capacity management device and obtains the prediction result of the initial service index this method comprises: predicting according to the historical data of traffic forecast algorithm and initial service index the initial service index；The association factor of the initial service index is obtained, and obtains the historical data of the association factor；According to the historical data of the association factor and traffic forecast algorithm, the association factor is predicted, obtains the prediction result of the association factor；According to the prediction result of the prediction result of the initial service index and the association factor, the prediction result of final service index is obtained, and according to the prediction result of the final service index, the resource consumption of predictive server.Resource prediction method, system and capacity management device provided by the invention, can be improved the accuracy of resources.
Description
Technical field
The present invention relates to field of cloud calculation more particularly to a kind of resource prediction methods, system and capacity management device.
Background technique
Capacity management is used to assess the capacity of existing net office point, that is, acquires and analyze under current portfolio scale CPU, interior
The Trend Prediction system depositing, store, the capacity of network and other resources, and being increased based on current business scale and portfolio
Following sustainable degree.To which support carries out capacity scheduling or dilatation, safeguards system even running in advance.Then it needs in one kind
Longer term resource prediction and evaluation method.
Currently, resources and estimation to server or service node, are adjusted by business in predictive server
Resource consumed by service itself predicted, however service impacting resource consumption be it is complicated, may be by
To the influence of other service calls, therefore the usual error of result of current server prediction can be bigger, not accurate enough.
Summary of the invention
In view of the above-mentioned problems, the purpose of the present invention is to provide a kind of resource for considering the incidence relation between service is pre-
Survey method, system and capacity management device.
In a first aspect, the present invention provides a kind of methods of resources, comprising:
According to the historical data of traffic forecast algorithm and initial service index, the initial service index is predicted,
Obtain the prediction result of the initial service index；
The association factor of the initial service index is obtained, and obtains the historical data of the association factor；
According to the historical data of the association factor and traffic forecast algorithm, the association factor is predicted, is obtained
The prediction result of the association factor；
According to the prediction result of the prediction result of the initial service index and the association factor, obtains final service and refer to
Target prediction result, and according to the prediction result of the final service index, the resource consumption of predictive server.
With reference to first aspect, in a first possible implementation of that first aspect, the acquisition initial service refers to
Target association factor, specifically includes: obtain the association factor of the initial service index from database, the association factor by
What cloud controller was determined in deployment services.
With reference to first aspect, in a second possible implementation of that first aspect, the initial service index is obtained
Association factor specifically includes: the association factor of the initial service index is obtained from database, wherein the association factor
By calling chain apparatus by calling path to obtain between Analysis Service, the calling path is recorded in record log.
With reference to first aspect, in first aspect in the third possible implementation, when running multiple business on server
When, it is described to be referred to according to the prediction result of the initial service index with the prediction result of the association factor, acquisition final service
Target prediction result, and according to the prediction result of the final service index, the resource consumption of predictive server specifically includes:
According to the prediction result of the prediction result of the initial service index and the association factor, each industry is obtained respectively
The prediction result of the final service index of business；
According to the prediction result of the final service index of each business and the corresponding unit resource consumption of each business
Value, the resource consumption of predictive server.
Second aspect, the present invention also provides a kind of capacity management devices, comprising:
First prediction module, for the historical data according to traffic forecast algorithm and initial service index, to described initial
Operational indicator is predicted, the prediction result of the initial service index is obtained；
Module is obtained, for obtaining the association factor of the initial service index, and obtains the history of the association factor
Data；
Second prediction module, for the historical data and traffic forecast algorithm according to the association factor, to the association
The factor is predicted, and obtains the prediction result of the association factor；
Third prediction module, for according to the prediction result of the initial service index and the prediction knot of the association factor
Fruit obtains the prediction result of final service index, and according to the prediction result of the final service index, the money of predictive server
Source consumption.
In conjunction with second aspect, in second aspect in the first possible implementation, module is obtained, is specifically used for from data
The association factor of the initial service index is obtained in library, the association factor is determined by cloud controller in deployment services.
In conjunction with second aspect, in second of second aspect possible implementation, module is obtained, is specifically used for from data
The association factor of the initial service index is obtained in library, wherein the association factor passes through Analysis Service by calling chain apparatus
Between call path obtain, the calling path is recorded in record log.
In conjunction with second aspect, in second aspect in the third possible implementation, the third prediction module is specific to use
In when running multiple business on server, according to the prediction of the prediction result of the initial service index and the association factor
As a result, obtaining the prediction result of the final service index of each business respectively；According to the final service index of each business
Prediction result and the corresponding unit resource consumption value of each business, the resource consumption of predictive server.
The third aspect, the present invention also provides a kind of resources systems, comprising: capacity management device and database,
In,
The capacity management device, for the historical data according to traffic forecast algorithm and initial service index, to described
Initial service index is predicted, the prediction result of the initial service index is obtained；Obtain the pass of the initial service index
Join the factor, and obtains the historical data of the association factor；According to the historical data of the association factor and traffic forecast algorithm,
The association factor is predicted, the prediction result of the association factor is obtained；According to the prediction of the initial service index
As a result with the prediction result of the association factor, the prediction result of final service index is obtained, and is referred to according to the final service
Target prediction result, the resource consumption of predictive server；The database, for storing the history number of the initial service index
According to the historical data of association factor corresponding to, the initial service index and the association factor.
In conjunction with the third aspect, in the third aspect in the first possible implementation, further includes: call chain apparatus, be used for
Record log is got from the database, analyzes the calling path in the record log, obtains initial service index
Association factor.
The embodiment of the present invention not only only accounts for initial service index in the resources for carrying out server, it is also necessary to examine
Consider the association factor for influencing initial service index, is in this way that resources will be more accurate to server.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without any creative labor, can be with
It obtains other drawings based on these drawings.
Fig. 1 is the system framework schematic diagram of resources provided in an embodiment of the present invention；
Fig. 2 is the method flow diagram of resources provided in an embodiment of the present invention；
The relation schematic diagram that Fig. 3 is called between service provided in an embodiment of the present invention；
Fig. 4 is the structural schematic diagram of capacity management device provided in an embodiment of the present invention；
Fig. 5 is the structural schematic diagram of capacity management device provided in an embodiment of the present invention；
Fig. 6 is the structural schematic diagram of resources system provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The embodiment of the invention provides a kind of resource prediction method, system and capacity management device, for improving cloud computing
In server resources accuracy, be described in detail separately below.
Embodiment to facilitate the understanding of the present invention below first carries out the system architecture of the resources of the embodiment of the present invention
Description.
Fig. 1 is a network environment schematic diagram of the embodiment of the present invention, in this system architecture, including following device,
Cloud controller 102, Service Source pond 104, service bus 106, capacity management device 108 call chain apparatus 110, external system
118 and each database, each database include cloud controller database 112, capacity management database 114 and call chain data
Library 116.Wherein, by 102 application deployment of cloud controller, an application is made of one group of service, this may be selected in user when deployment
A little services and which service are relevant, and such as service of ordering needs to be associated with database service, after having disposed application, cloud control
Device the incidence relation of each service can be recorded in the cloud controller database 112 of oneself, and cloud controller server 112 will change
Service between incidence relation be synchronized to capacity management database 114.102 application deployment Shi Huicong Service Source pond of cloud controller
Leisure service is searched in 104 carrys out application deployment, such as: there are resource A, resource B and resource C in Service Source pond 104, certainly may be used
With these more than resources.Sometimes one business may require that the calling between several services, when calling between each service, can arrive clothes
Searched in business bus 106 destination service it is current be deployed to where, billing of services will be searched by such as ordering service, and service bus returns
Available billing of services node is returned, the service of order can be communicated with billing of services.
Chain apparatus 110 and capacity management device 108 is called to belong to operational system, each service will record when calling
Log is called, calls chain apparatus 108 to collect the calling log of each service node, then analyzes the call relation of each service,
And by each service call relation record analyzed into call chain database 116.Pass through service between each service node
Bus 106 is communicated.
Call relation between service be deployed in deciding in cloud controller.Because under cloud scene often
New service can be disposed, administrator specifies the incidence relation of this service and other services, cloud controller record when deployment of new services
In which node and the incidence relation of this service and other services into Cloud Server database 112, capacity management is filled for this service
Incidence relation between each service can be got in cloud controller database 112 by setting, pre- so as to carry out to cloud system resource
It surveys and estimates.Certainly, capacity management device 108 can also be obtained from capacity management database 114 or call chain database 116
Service incidence relation.Capacity management device 108 with call together with chain apparatus 110 can integrate, hardware as a whole.
Under normal conditions, Cloud Server database 112 stores cloud controller 102 and disposes certain service in which node
Incidence relation between information, and service, then which node Cloud Server database 112 can service certain is disposed at
Information, and service between incidence relation be synchronized in capacity management server 114.Call chain database 116 can incite somebody to action
The incidence relation between service for calling chain apparatus 110 to analyze is synchronized to capacity management database 114.That is, capacity
The newest incidence relation between service will be stored in management database 114.In general, when needing to obtain certain clothes
When call relation between business, capacity management device 108 goes to search to capacity management database 114 first, if in volume weight tube
Database 114 is managed to search less than then capacity management device 108 will be into cloud controller database 112 or call chain database 116
It is searched.Certain above-mentioned Cloud Server database 112, capacity management database 114, call chain database 116 can merge
At a database, that is, cloud controller 102, capacity management device 108 calls chain apparatus 110 to share a public number
According to library, at this point, all data are stored in capacity management database 114.
External system 118, the service that external system 118 can be issued directly in access system, as this system has issued one
WEB service, external system can access this service by accessing network address.
Referring to FIG. 2, being a kind of flow chart of the embodiment of the method for resources of the embodiment of the present invention, specifically include:
Step 201: capacity management device determines the operational indicator for influencing the business usage amount on server.
If having run multiple business on server, capacity management device determines that the influence business of each business makes respectively
The respective operational indicator of dosage.
Step 202-204, illustrates the detailed process of one of a business specific operational indicator prediction, business
The prediction process of other operational indicators is similar with 202-204.
Step 202: according to the historical data of traffic forecast algorithm and initial service index, initial service index being carried out pre-
It surveys, and obtains the prediction result of initial service index.
Service resources prediction algorithm has very much, and the embodiment of the present invention is illustrated by taking a following specific algorithm as an example,
It is similar using the process that other service resources prediction algorithms predict operational indicator.Its process includes:
1, capacity management device detects the period of operational indicator automatically, obtains the period of operational indicator.
A certain range of newest operational indicator is obtained from database, Fast Fourier Transform (FFT) is carried out, by operational indicator
It is transformed to frequency, after Fast Fourier Transform (FFT), the modulus value of each Frequency point is exactly the amplitude characteristic under the frequency values, wherein
The maximum point of modulus value is dominant frequency freq, then dominant frequency inverse is the period, then period m=1/freq.
In practical application scene, the period of operational indicator is unobvious or business changes and causes the period as time goes by
Change, by being manually specified, to will lead to forecasting inaccuracy true；Operational indicator reports daily to be counted and is saved in database, according to most
The case where not only having reduced manual intervention but also be suitable for mechanical periodicity, it is quasi- to increase prediction for the automatic detection cycle of new operational indicator
True property.
2, historical data M of the capacity management device to operational indicatortDo primary (m is odd number) or the twice m of (m is even number)
Phase rolling average obtains trend component Tt。
3, trend component data M has been rejected in acquisitiont-Tt, and rejecting trend component data Mt-TtOn the basis of, to complete
I-th of time point in portion's period takes the average estimation as i-th of time point, obtains seasonal component St。
4, to trend component TtIt is fitted, capacity management device provides a variety of model of fit；And based on input data
Curvilinear characteristic and error of fitting automatically select most matched trend prediction model from a variety of (here for 7 kinds) models.
Specific: capacity management device selects the trend prediction mould for most matching operational indicator from multiple model of fit
Type.
112 timing of cloud controller database sends each operational indicator data in capacity management database 114, capacity
Management database 114 saves each operational indicator data, that is to say, that the related data of operational indicator is generating always new
Data are needed according to a period of time newest data correction model of fit, and the specific time oneself can set, such as: one week, one
A month or a season etc..
Capacity management device 108 obtains the number of the newest operational indicator of a period of time from capacity management database 114
According to；Model judging characteristic is extracted, whether judging characteristic is constant, is considered as and corresponding Model Matching if it is constant；Different moulds
The judging characteristic of type is respectively:
Linear feature
Feature1 (i)=L (i+1)-L (i)
Feature1=(Feature1-min (Feature1))/mean (Feature1-min (Feature1))；
Index characteristic
Feature2 (i)=log (L (i+1))-log (L (i))；
Feature2=(Feature2-min (Feature2))/mean (Feature2-min (Feature2))；
Modified index feature:
Feature3 (i)=(L (i+2)-L (i+1))/(L (i+1)-L (i))；
Compertz curvilinear characteristic:
Feature4 (i)=(log (L (i+2))-log (L (i+1)))/(log (L (i+1))-log (L (i)))；
S curve feature:
Feature5 (i)=((1/L (i+2))-(1/L (i+1)))/((1/L (i+1))-(1/L (i)))；
Conic section feature:
Feature6 (i)=Feature1 (i+1)-Feature1 (i)；
Feature6=(Feature6-min (Feature6))/mean (Feature6-min (Feature6))；
Logarithmic curve feature:
Feature7 (i)=(L (i+1)-L (i))/(log (i+1)-log (i))
If all aspect of model do not comply with, carry out curve fitting respectively by different models, each model of one by one inspection
The side's r value, the side r illustrates that models fitting is better closer to 1, selects model of the side r closest to 1 as trend prediction model.
In this way, carry out curve fitting respectively using above-mentioned 7 kinds of models, select optimal model as becoming from 7 kinds of models
The accuracy of gesture prediction model, such prediction can be higher.
5, according to the trend prediction model of selection, it is fitted the trend component of service decomposition indexAnd by the business of prediction
The trend component of Breaking index downIt is recorded in database.
With the development of business, the trend prediction model of fitting may change, for example in the developing stage of business, presentation refers to
Number increases, fit indices prediction model, but more period of expansion enters the stage of stable development, linear prediction model may be fitted, so often
Secondary prediction requires to go amendment fitting trend prediction model according to business latest data.
6, trend component and seasonal component are rejected from initial data, are obtained random component, are estimated the mean μ of random component
And standard deviation sigma, specifically:
7, by the trend component of fitting, the seasonal component and random component of estimation are overlapped, and obtain prediction model:
8, then according to the operational indicator data of prediction model and history, obtain the prediction result of operational indicator
Step 203: obtaining the association factor of the initial service index from database, and obtain the association factor
Historical data.
Under complicated application scenarios, such as: under telecommunication service scene, has between many operational indicators and there is association pass
System, an operational indicator depend on another operational indicator or multiple operational indicators, that is, depend on multiple dependent variables, therefore industry
The period for index of being engaged in and trend are that the superposition of multiple dependent variables directly predicts this operational indicator without obvious characteristic,
It is true to will lead to forecasting inaccuracy.In order to solve such case, needs first to isolate dependent variable, dependent variable is predicted, then will be each
The predicted value of dependent variable is overlapped, and is calculated the predicted value of prediction index, can be increased the accuracy of prediction in this way.
Incidence relation between operational indicator can be embodied by association factor, such as: operational indicator 2 is business
The association factor of index 1, therefore, if the second operational indicator influences the first operational indicator, the second operational indicator is the first industry
The association factor for index of being engaged in.In embodiments of the present invention, operational indicator can be specially service involved in some business, than
Such as, in following example, service A is the first operational indicator, and service B is the association factor of the first operational indicator, services C, clothes
Business D is the association factor for servicing B.
Under distributed, large-scale cluster system scene, new service is often disposed, the incidence relation between service needs
It often updates, is difficult to go to safeguard by hand.And under distributed, large-scale cluster system scene, before primary request can trigger hundreds of times
The calling of rear end, multi-level service call, be difficult with manpower go for out servicing between incidence relation.
Wherein key point is the acquisition of the association factor of operational indicator, that is, obtains the incidence relation between service, the present invention
Embodiment proposes two kinds of solutions, can automatically analyze and record the incidence relation between service, one is pass through cloud controller
It is obtained when deployment services, one is by calling chain apparatus to obtain.Both acquisition modes are introduced respectively below:
First way: by being obtained when cloud controller deployment services.Because often disposing new clothes under cloud scene
Business, user specifies the incidence relation of the service and other services when deployment of new services, and cloud controller is by the service and other services
Between incidence relation store into cloud controller database, cloud controller database can deposit the incidence relation between service
It stores up in capacity management database.Specific process is as follows:
Administrator is by cloud controller deployment services A, the pass of configuration service A and other services (such as: service B services C)
Connection relationship；Cloud controller can search available resources in Service Source pond, and service A is deployed in the above-mentioned available resources found
It goes.After cloud controller has disposed new demand servicing, which service node service A is deployed on, the association of this service A and other services
The information preservations such as relationship are into cloud controller database.Cloud controller database by these service between incidence relation be synchronized to
In capacity management database.Then capacity management device can get service A and other clothes from capacity management database
Incidence relation between business.
The second way: by calling chain apparatus to obtain.
Under distributed, large-scale cluster system scene, primary request can trigger the tune between the service of hundreds of secondary front and back ends
With these some problems called will affect current request, and certain steps can drag slow entire process flow, in large-scale festivals or holidays
The peak traffic phase needs that application cluster distribution how many machine given, these are all that O&M is in need of consideration, but transfer environment is answered
Miscellaneous degree has been difficult to be done with manpower and has accurately analyzed and have evaluated；In face of the log of magnanimity, it would be desirable to be able to automatically primary request
In " isolated " the log string of different components that is related to together, restore more valuable information, auxiliary problem (AP) is delimited
Positioning, capacity planning and stability analysis.That is, obtaining the relationship between service by call chain.Detailed process is such as
Under:
Assuming that there are three kinds of services, front end services, the first back-end services, the second back-end services, these three types service be all
The service issued in Service Source pond.At this point, when some external request accesses front end services, after front end services also need calling
End service 1 and back-end services 2 could complete external request requirement.With the calling between each service, each service is tune
It is recorded in log with process, then by log collection, and the log of collection is aggregated into and is called in catenary system.
Then call chain apparatus log is analyzed, analysis call path processing, serviced between calling source,
Incidence relation between each service, and be recorded in call chain database.
It will record calling log, including track identification (TraceID), sequence number in the calling process of each service
(SeqNo), the information such as service and called service are called.Wherein, it is generated when TraceID is external request access front end services
One unique mark ID, TraceID is constant in entire call chain.A sequencing is buried in SeqNo mark call chain,
SeqNo is the unfixed character string of length, format are as follows: X.X....X.X, in which: each X corresponds to level-one calling, and the number of X is
To call depth；X is incremented by since 1 by calling sequencing in same level calling；Call chain root node (generates TraceID's
Node) SeqNo be 1.It is identical with the SeqNo of corresponding with service call request that service call responds SeqNo.Each service log calls
The format of log are as follows: TraceID | SeqNo | operation flow | called side | called side.
For example, as shown in figure 3, the schematic diagram of the call relation between service, service A are front end services, service B, clothes
Be engaged in C, and service D and service E are back-end services.In an operation flow, service A has received a request of applications, raw
At TraceID be 100001, SeqNo 1, the log of record are as follows: 100001 | 1 | operation flow A | applications | service A；It should
Service A to need to call service B, then SeqNo be revised as 1.1, the log of record are as follows: 100001 | 1.1 | operation flow A | service A
| service B, further, service B also need to call service E, then SeqNo are revised as 1.1.2, the log of record: 100001 |
1.1.2 | operation flow A | service B | service E.In above-mentioned each record log storage call chain database.
When call relation between each service for needing to analyze some business, call chain apparatus from call chain database
In get record log between each service of the specific business of said one, and analyze above-mentioned day, wherein TraceID phase
Same is a same calling service, is then known that successive calling sequence according to SeqNo, such as: by analyzing log, can obtain
Knowing that TraceID is 10001 is the same calling service, then can know the precedence relationship of SeqNo: 1,1.1,1.1.2, this
Sample has been known that service A calls service B, service B to call service E.Thus between available each service to the business
Call relation.
After calling chain apparatus to get the call relation between service, the incidence relation between above-mentioned service is synchronized to appearance
Buret is managed in database, and such capacity management device can get the calling between service from capacity management database and close
System.
In this way, being obtained from capacity management database after capacity management device gets the association factor of operational indicator
To the historical data of association factor.
Step 204: according to the historical data of association factor and traffic forecast algorithm, association factor being predicted, is obtained
The prediction result of association factor, the prediction result for the initial service index predicted according to step 202 and the association factor of prediction
Prediction result calculates the predicted value for obtaining operational indicator, and the predicted value of the operational indicator of prediction is stored in capacity management number
According in library.
In this way according to the process of step 202-204, it is predicted that the prediction result of the operational indicator of each business
Step 205: from capacity management database, obtaining the prediction of the operational indicator of each business run on server
As a result, the resource consumption of predictive server.
For server resource often by multiple business joint effect, each service period, trend, busy period are different, each industry
It is engaged in after the influence superposition to server resource, the period of server resource and trend are unobvious, directly carry out to system resource pre-
It surveys, it is true to will lead to forecasting inaccuracy.
Fish stock assessment models are pre-established according to telecommunication service feature: such as St=a × M1t+b×M2t+c×M3t+ d, S
For system resource index, M1, M2, M3 indicate each operational indicator, and a, b, c, d indicate the resource consumption value of constituent parts business, every time
It needs to calculate and correct each service unit resource consumption value before stock assessment；That is, according to service decomposition index and multinomial
The model evaluator factor, such as St=a × M1t+b×M2t+c×M3t+ d, wherein factor of a polynomial has 4, therefore only needs to obtain
4 groups of newest service decomposition indexs, can calculate the resource consumption value of constituent parts business.
The embodiment of the present invention not only only accounts for initial service index in the resources for carrying out server, it is also necessary to examine
Consider the association factor for influencing initial service index, is in this way that resources will be more accurate to server.Further, originally
Existing scheme is optimized in the prediction algorithm of invention, increase the period detect automatically, trend fitting model selection function, saving
Human cost reduces interference from human factor and increases the accuracy of prediction in the case where mechanical periodicity, Long-term change trend.In reality
In the case of prediction index by Multiple factors collective effect, period and trend are unobvious, directly prediction error it is larger, in order to solve this
Kind situation designs business model according to telecommunication service feature, increases coupling index prediction and calculation of natural resources function, increases prediction
Accuracy.
As shown in figure 4, a kind of capacity management device provided by the invention, comprising:
First prediction module 41, for the historical data according to traffic forecast algorithm and initial service index, to described first
Beginning operational indicator is predicted, the prediction result of the initial service index is obtained；
Module 42 is obtained, for obtaining the association factor of the initial service index, and obtains going through for the association factor
History data；
Second prediction module 43, for the historical data and traffic forecast algorithm according to the association factor, to the pass
The connection factor is predicted, and obtains the prediction result of the association factor；
Third prediction module 44, for according to the prediction result of the initial service index and the prediction of the association factor
As a result, the prediction result of final service index is obtained, and according to the prediction result of the final service index, predictive server
Resource consumption.
Optionally, module 42 is obtained, specifically for obtaining the association factor of the initial service index, institute from database
State what association factor was determined by cloud controller in deployment services.
Optionally, module 42 is obtained, specifically for obtaining the association factor of the initial service index from database,
In, the association factor by calling chain apparatus by calling path to obtain between Analysis Service, remembered by the calling path
Record is in record log.
Optionally, third prediction module 44, specifically for when running multiple business on server, according to the just beginning of the school year
The prediction result for index of being engaged in and the prediction result of the association factor, obtain the prediction of the final service index of each business respectively
As a result；According to the prediction result of the final service index of each business and the corresponding unit resource consumption value of each business,
The resource consumption of predictive server.
The embodiment of the present invention not only only accounts for initial service index in the resources for carrying out server, it is also necessary to examine
Consider the association factor for influencing initial service index, is in this way that resources will be more accurate to server.
It should be noted that being schematical, only a kind of logic to the division of modules in the embodiment of the present invention
The division of function, there may be another division manner in actual implementation, in addition, each function mould in each embodiment of the application
Block can integrate in a processing module, is also possible to modules and physically exists alone, can also be with two or more
Module is integrated in a module.Above-mentioned integrated module both can take the form of hardware realization, can also use software function
The form of energy module is realized.
If the integrated module is realized in the form of software function module and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
It is each that equipment (can be personal computer, server or the network equipment etc.) or processor (processor) execute the application
The all or part of the steps of embodiment the method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory
(ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic or disk
Etc. the various media that can store program code.
The embodiment of the invention also provides a kind of capacity management devices, as shown in figure 5, Fig. 5 is to hold in the embodiment of the present invention
The structural schematic diagram of managing device is measured, which includes processor 51 and memory 52.Wherein, it processor 51 and deposits
Reservoir 52 is connected.The specific connection medium between above-mentioned component is not limited in the embodiment of the present invention.Selection of the embodiment of the present invention
Connected between processor 51 and memory 52 by bus 53 in Fig. 5, bus is indicated in Fig. 5 with thick line, other components it
Between connection type, be only to be schematically illustrated, do not regard it as and be limited.It is total that the bus can be divided into address bus, data
Line, control bus etc..Only to be indicated with a thick line in Fig. 5, it is not intended that an only bus or a type convenient for indicating
The bus of type.
Memory 52 in the embodiment of the present invention, for the program code that storage processor 51 executes, memory 52 be can be
Volatile memory (English: volatile memory), such as random access memory (English: random-access
Memory, abbreviation: RAM)；Memory 52 is also possible to nonvolatile memory (English: non-volatile memory), example
Such as read-only memory (English: read-only memory, abbreviation: ROM), flash memory (English: flash memory), firmly
Disk (English: hard disk drive, abbreviation: HDD) or solid state hard disk (English: solid-state drive, abbreviation: SSD),
Or memory 52 can be used for carrying or store the expectation program code with instruction or data structure form and can be by
Any other memory of computer access, but not limited to this.In addition, memory 52 can also be the group of above-mentioned any memory
It closes.
In the embodiment of the present invention, processor 51 for calling the program code stored in memory 52 by bus, and leads to
The program code for executing and calling is crossed to execute:
According to the historical data of traffic forecast algorithm and initial service index, the initial service index is predicted,
Obtain the prediction result of the initial service index；The association factor of the initial service index is obtained, and obtains the association
The historical data of the factor；According to the historical data of the association factor and traffic forecast algorithm, the association factor is carried out pre-
It surveys, obtains the prediction result of the association factor；According to the prediction result of the initial service index and the association factor
Prediction result obtains the prediction result of final service index, and according to the prediction result of the final service index, prediction service
The resource consumption of device.
Processor 51 in the embodiment of the present invention can be a central processing unit (English: central
Processing unit, abbreviation CPU).
The embodiment of the invention also provides a kind of resources systems, as shown in fig. 6, including 61 sum number of capacity management device
According to library 62, wherein
The capacity management device 61, for the historical data according to traffic forecast algorithm and initial service index, to institute
It states initial service index to be predicted, obtains the prediction result of the initial service index；Obtain the initial service index
Association factor, and obtain the historical data of the association factor；It is calculated according to the historical data of the association factor and traffic forecast
Method predicts the association factor, obtains the prediction result of the association factor；According to the pre- of the initial service index
The prediction result of result and the association factor is surveyed, obtains the prediction result of final service index, and according to the final service
The prediction result of index, the resource consumption of predictive server；
The database 62, historical data, the initial service index institute for storing the initial service index are right
The historical data of the association factor and the association factor answered.
Optionally, the system, further includes: chain apparatus 63 is called, for getting record log from the database, point
The calling path in the record log is analysed, the association factor of initial service index is obtained.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the present invention, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, those skilled in the art can carry out various modification and variations without departing from this hair to the embodiment of the present invention
The spirit and scope of bright embodiment.In this way, if these modifications and variations of the embodiment of the present invention belong to the claims in the present invention
And its within the scope of equivalent technologies, then the present invention is also intended to include these modifications and variations.
Claims (10)
1. a kind of method of resources characterized by comprising
According to the historical data of traffic forecast algorithm and initial service index, the initial service index is predicted, is obtained
The prediction result of the initial service index；
The association factor of the initial service index is obtained, and obtains the historical data of the association factor；
According to the historical data of the association factor and traffic forecast algorithm, the association factor is predicted, is obtained described
The prediction result of association factor；
According to the prediction result of the prediction result of the initial service index and the association factor, final service index is obtained
Prediction result, and according to the prediction result of the final service index, the resource consumption of predictive server；
Wherein, the association factor for obtaining the initial service index, specifically includes:
The association factor of the initial service index is obtained from database, the association factor is by cloud controller in deployment services
When determine.
2. a kind of method of resources characterized by comprising
According to the historical data of traffic forecast algorithm and initial service index, the initial service index is predicted, is obtained
The prediction result of the initial service index；
The association factor of the initial service index is obtained, and obtains the historical data of the association factor；
According to the historical data of the association factor and traffic forecast algorithm, the association factor is predicted, is obtained described
The prediction result of association factor；
According to the prediction result of the prediction result of the initial service index and the association factor, final service index is obtained
Prediction result, and according to the prediction result of the final service index, the resource consumption of predictive server；
Wherein, the association factor for obtaining the initial service index, specifically includes:
The association factor of the initial service index is obtained from database, wherein the association factor is led to by calling chain apparatus
Crossing between Analysis Service calls path to obtain, and the calling path is recorded in record log.
3. a kind of method of resources characterized by comprising
According to the historical data of traffic forecast algorithm and initial service index, the initial service index is predicted, is obtained
The prediction result of the initial service index；
The association factor of the initial service index is obtained, and obtains the historical data of the association factor；
According to the historical data of the association factor and traffic forecast algorithm, the association factor is predicted, is obtained described
The prediction result of association factor；
According to the prediction result of the prediction result of the initial service index and the association factor, final service index is obtained
Prediction result, and according to the prediction result of the final service index, the resource consumption of predictive server；
Wherein, when running multiple business on server, the prediction result according to the initial service index and the pass
Join the prediction result of the factor, obtain the prediction result of final service index, and according to the prediction result of the final service index,
The resource consumption of predictive server, specifically includes:
According to the prediction result of the prediction result of the initial service index and the association factor, each business is obtained respectively
The prediction result of final service index；
According to the prediction result of the final service index of each business and the corresponding unit resource consumption value of each business, in advance
Survey the resource consumption of server.
4. a kind of capacity management device characterized by comprising
First prediction module, for the historical data according to traffic forecast algorithm and initial service index, to the initial service
Index is predicted, the prediction result of the initial service index is obtained；
Module is obtained, for obtaining the association factor of the initial service index, and obtains the historical data of the association factor；
Second prediction module, for the historical data and traffic forecast algorithm according to the association factor, to the association factor
It is predicted, and obtains the prediction result of the association factor；
Third prediction module, for according to the prediction result of the initial service index and the prediction result of the association factor,
The prediction result of final service index is obtained, and according to the prediction result of the final service index, the resource of predictive server
Consumption；
Wherein, the acquisition module, specifically for obtaining the association factor of the initial service index, the pass from database
The connection factor is determined by cloud controller in deployment services.
5. a kind of capacity management device characterized by comprising
First prediction module, for the historical data according to traffic forecast algorithm and initial service index, to the initial service
Index is predicted, the prediction result of the initial service index is obtained；
Module is obtained, for obtaining the association factor of the initial service index, and obtains the historical data of the association factor；
Second prediction module, for the historical data and traffic forecast algorithm according to the association factor, to the association factor
It is predicted, and obtains the prediction result of the association factor；
Third prediction module, for according to the prediction result of the initial service index and the prediction result of the association factor,
The prediction result of final service index is obtained, and according to the prediction result of the final service index, the resource of predictive server
Consumption；
Wherein, the acquisition module, specifically for obtaining the association factor of the initial service index from database, wherein
By calling chain apparatus by calling path to obtain between Analysis Service, the calling path is recorded in the association factor
In record log.
6. a kind of capacity management device characterized by comprising
First prediction module, for the historical data according to traffic forecast algorithm and initial service index, to the initial service
Index is predicted, the prediction result of the initial service index is obtained；
Module is obtained, for obtaining the association factor of the initial service index, and obtains the historical data of the association factor；
Second prediction module, for the historical data and traffic forecast algorithm according to the association factor, to the association factor
It is predicted, and obtains the prediction result of the association factor；
Third prediction module, for according to the prediction result of the initial service index and the prediction result of the association factor,
The prediction result of final service index is obtained, and according to the prediction result of the final service index, the resource of predictive server
Consumption；
Wherein, the third prediction module, specifically for being referred to according to the initial service when running multiple business on server
The prediction result of target prediction result and the association factor obtains the prediction knot of the final service index of each business respectively
Fruit；According to the prediction result of the final service index of each business and the corresponding unit resource consumption value of each business, in advance
Survey the resource consumption of server.
7. a kind of resources system characterized by comprising capacity management device and database, wherein
The capacity management device, for the historical data according to traffic forecast algorithm and initial service index, to described initial
Operational indicator is predicted, the prediction result of the initial service index is obtained；Obtain the association of the initial service index because
Son, and obtain the historical data of the association factor；According to the historical data of the association factor and traffic forecast algorithm, to institute
It states association factor to be predicted, obtains the prediction result of the association factor；According to the prediction result of the initial service index
With the prediction result of the association factor, the prediction result of final service index is obtained, and according to the final service index
Prediction result, the resource consumption of predictive server；
The database, for storing pass corresponding to the historical data of the initial service index, the initial service index
Join the historical data of the factor and the association factor；
Wherein, further includes: chain apparatus is called to analyze the record log for getting record log from the database
In calling path, obtain initial service index association factor.
8. a kind of resources system characterized by comprising
Capacity management device and database
Wherein, the capacity management device is the capacity management device described in claim 4 to 6 any one；
The database, for storing pass corresponding to the historical data of the initial service index, the initial service index
Join the historical data of the factor and the association factor.
9. a kind of capacity management device characterized by comprising
Processor and memory；
Wherein, processor is for calling the computer program stored in memory, to complete claims 1 to 3 any one institute
The method stated.
10. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has computer journey
Sequence, the computer program can be realized method described in claims 1 to 3 any one when being executed by processor.
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| CN201510590666.1A CN106549772B (en) | 2015-09-16 | 2015-09-16 | Resource prediction method, system and capacity management device |
| PCT/CN2016/089927 WO2017045472A1 (en) | 2015-09-16 | 2016-07-13 | Resource prediction method and system, and capacity management apparatus |
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Families Citing this family (29)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107346472A (en) * | 2017-06-29 | 2017-11-14 | 人民法院信息技术服务中心 | A kind of method and device that operation/maintenance data is handled by line modeling |
| CN110019110B (en) * | 2017-07-28 | 2022-11-18 | 腾讯科技（深圳）有限公司 | Capacity management method, device and equipment of service system and service system |
| CN109510715B (en) * | 2017-09-14 | 2022-02-08 | 中国电信股份有限公司 | Bandwidth allocation method and device, data center and storage medium |
| CN107943579B (en) * | 2017-11-08 | 2022-01-11 | 深圳前海微众银行股份有限公司 | Resource bottleneck prediction method, device, system and readable storage medium |
| CN108984304A (en) * | 2018-07-11 | 2018-12-11 | 广东亿迅科技有限公司 | Server expansion calculation method and device based on regression equation |
| CN109446041B (en) * | 2018-09-25 | 2022-10-28 | 平安普惠企业管理有限公司 | Server pressure early warning method and system and terminal equipment |
| CN113849372A (en) * | 2018-10-22 | 2021-12-28 | 创新先进技术有限公司 | Method and device for determining database capacity |
| CN109543891B (en) * | 2018-11-09 | 2022-02-01 | 深圳前海微众银行股份有限公司 | Method and apparatus for establishing capacity prediction model, and computer-readable storage medium |
| CN111181875B (en) * | 2018-11-12 | 2022-12-27 | 中移（杭州）信息技术有限公司 | Bandwidth adjusting method and device |
| CN110058939A (en) * | 2018-12-27 | 2019-07-26 | 阿里巴巴集团控股有限公司 | System Expansion method, device and equipment |
| CN111400147B (en) * | 2019-01-02 | 2023-05-05 | 中国移动通信有限公司研究院 | Service quality testing method, device and system |
| CN110109750B (en) * | 2019-04-03 | 2023-08-18 | 平安科技（深圳）有限公司 | Virtual resource acquisition method, device, computer equipment and storage medium |
| CN110109800A (en) * | 2019-04-10 | 2019-08-09 | 网宿科技股份有限公司 | A kind of management method and device of server cluster system |
| CN110096423A (en) * | 2019-05-14 | 2019-08-06 | 深圳供电局有限公司 | A kind of server memory capacity analyzing and predicting method based on big data analysis |
| CN110348684B (en) * | 2019-06-06 | 2023-07-18 | 创新先进技术有限公司 | Service call risk model generation method, prediction method and respective devices |
| CN110321240B (en) * | 2019-06-28 | 2023-06-09 | 创新先进技术有限公司 | Business impact assessment method and device based on time sequence prediction |
| CN111352733A (en) * | 2020-02-26 | 2020-06-30 | 北京奇艺世纪科技有限公司 | Capacity expansion and reduction state prediction method and device |
| CN111611517B (en) * | 2020-05-13 | 2023-07-21 | 咪咕文化科技有限公司 | Index monitoring method and device, electronic equipment and storage medium |
| CN111985713B (en) * | 2020-08-19 | 2023-08-18 | 中国银行股份有限公司 | Data index waveform prediction method and device |
| CN112685173A (en) * | 2020-12-22 | 2021-04-20 | 中通天鸿（北京）通信科技股份有限公司 | Intelligent route distribution system based on rich media |
| CN112559191B (en) * | 2020-12-23 | 2023-04-25 | 平安银行股份有限公司 | Method and device for dynamically deploying GPU resources and computer equipment |
| CN112702345A (en) * | 2020-12-24 | 2021-04-23 | 福建技术师范学院 | Information vulnerability risk assessment method and device based on data element characteristics |
| CN112734195B (en) * | 2020-12-31 | 2023-07-07 | 平安科技（深圳）有限公司 | Data processing method, device, electronic equipment and storage medium |
| CN113762688A (en) * | 2021-01-06 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Business analysis system, method and storage medium |
| CN112766698B (en) * | 2021-01-13 | 2024-02-09 | 中国工商银行股份有限公司 | Application service pressure determining method and device |
| CN112965956A (en) * | 2021-03-18 | 2021-06-15 | 上海东普信息科技有限公司 | Database horizontal capacity expansion method, device, equipment and storage medium |
| CN113220299A (en) * | 2021-05-28 | 2021-08-06 | 北京达佳互联信息技术有限公司 | Graphical display method and device |
| CN113923099B (en) * | 2021-09-03 | 2022-12-27 | 华为技术有限公司 | Root cause positioning method for communication network fault and related equipment |
| CN115080363B (en) * | 2022-08-23 | 2022-11-15 | 中国中金财富证券有限公司 | System capacity evaluation method and device based on service log |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN102103714A (en) * | 2009-12-22 | 2011-06-22 | 阿里巴巴集团控股有限公司 | Real-time processing platform for predicting service data and predicting method |
| CN103490956A (en) * | 2013-09-22 | 2014-01-01 | 杭州华为数字技术有限公司 | Self-adaptive energy-saving control method, device and system based on traffic predication |
| CN104125584A (en) * | 2013-04-27 | 2014-10-29 | 中国移动通信集团福建有限公司 | Service index realization prediction method aiming at network service and apparatus thereof |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103685347B (en) * | 2012-09-03 | 2017-01-18 | 阿里巴巴集团控股有限公司 | Method and device for allocating network resources |
| US9563663B2 (en) * | 2012-09-28 | 2017-02-07 | Oracle International Corporation | Fast path evaluation of Boolean predicates |
| CN103903069A (en) * | 2014-04-15 | 2014-07-02 | 广东电网公司信息中心 | Storage capacity predication method and storage capacity predication system |
-
2015
- 2015-09-16 CN CN201510590666.1A patent/CN106549772B/en active Active
-
2016
- 2016-07-13 WO PCT/CN2016/089927 patent/WO2017045472A1/en active Application Filing
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN102103714A (en) * | 2009-12-22 | 2011-06-22 | 阿里巴巴集团控股有限公司 | Real-time processing platform for predicting service data and predicting method |
| CN104125584A (en) * | 2013-04-27 | 2014-10-29 | 中国移动通信集团福建有限公司 | Service index realization prediction method aiming at network service and apparatus thereof |
| CN103490956A (en) * | 2013-09-22 | 2014-01-01 | 杭州华为数字技术有限公司 | Self-adaptive energy-saving control method, device and system based on traffic predication |
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