CN108632082A - A kind of prediction technique and device of the load information of server - Google Patents
A kind of prediction technique and device of the load information of server Download PDFInfo
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- CN108632082A CN108632082A CN201810258539.5A CN201810258539A CN108632082A CN 108632082 A CN108632082 A CN 108632082A CN 201810258539 A CN201810258539 A CN 201810258539A CN 108632082 A CN108632082 A CN 108632082A
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- 238000010586 diagram Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 7
- 238000004590 computer program Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000012731 temporal analysis Methods 0.000 description 3
- 238000000700 time series analysis Methods 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 2
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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
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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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
- H04L41/14—Network analysis or design
- H04L41/147—Network analysis or design for predicting network behaviour
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Abstract
The embodiment of the present invention provides a kind of prediction technique and device of the load information of server, the method includes：Obtain the load information and the corresponding acquisition time of the load information of server；According to the corresponding acquisition time of the load information, generated time sequence；According to the time series and preset model, the load information to the server in predetermined time is predicted.Described device executes the above method.The prediction technique and device of the load information of server provided in an embodiment of the present invention can accurately predict the load information of server by time series and preset model.
Description
Technical field
The present embodiments relate to server technology fields, and in particular to a kind of prediction technique of the load information of server
And device.
Background technology
Available data center usually have hundreds of, thousands of or even tens thousand of server nodes, these server continuous services,
Energy consumption is huge.But the server load of data center is not to be always held at higher state, load can with time change,
For example, daytime, load was higher, load at night relatively low；The daily load that works is higher, and festivals or holidays load is relatively low.How accurately to predict
Server load, it appears particularly important.
Existing load estimation technology, including it is following several：
1) existing data center's infrastructure management (Data Center Infrastructure management, letter
Claim " DCIM ") software, the load of individual server node can not be usually perceived, be predicted, cabinet or some region can only be passed through
The factors such as temperature adjust the energy consumption situation of whole cabinet or whole region.2) load estimation based on temporal characteristics：By one
It is divided into some time in 24 hours, collects the load characteristic of each period in advance, these load characteristics is used in combination to go prediction not
Carry out the load of same time period.This method is static, and forecasting accuracy is low, does not account for the dynamic change of load, effect
Difference.
Therefore, drawbacks described above how is avoided, accurately the load information of server is predicted, becoming need solve
Problem.
Invention content
In view of the problems of the existing technology, the embodiment of the present invention provides a kind of prediction technique of the load information of server
And device.
In a first aspect, the embodiment of the present invention provides a kind of prediction technique of the load information of server, the method includes：
Obtain the load information and the corresponding acquisition time of the load information of server；
According to the corresponding acquisition time of the load information, generated time sequence；
According to the time series and preset model, the load information to the server in predetermined time is predicted.
Second aspect, the embodiment of the present invention provide a kind of prediction meanss of the load information of server, and described device includes：
Acquiring unit, the load information for obtaining server and the corresponding acquisition time of the load information；
Generation unit, for according to the corresponding acquisition time of the load information, generated time sequence；
Predicting unit, for according to the time series and preset model, to the server predetermined time load
Information is predicted.
The third aspect, the embodiment of the present invention provide a kind of electronic equipment, including：Processor, memory and bus, wherein
The processor and the memory complete mutual communication by the bus；
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to refer to
Order is able to carry out following method：
Obtain the load information and the corresponding acquisition time of the load information of server；
According to the corresponding acquisition time of the load information, generated time sequence；
According to the time series and preset model, the load information to the server in predetermined time is predicted.
Fourth aspect, the embodiment of the present invention provide a kind of non-transient computer readable storage medium, including：
The non-transient computer readable storage medium stores computer instruction, and the computer instruction makes the computer
Execute following method：
Obtain the load information and the corresponding acquisition time of the load information of server；
According to the corresponding acquisition time of the load information, generated time sequence；
According to the time series and preset model, the load information to the server in predetermined time is predicted.
The prediction technique and device of the load information of server provided in an embodiment of the present invention by time series and are preset
Model can accurately predict the load information of server.
Description of the drawings
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 technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Some bright embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the prediction technique flow diagram of the load information of server of the embodiment of the present invention；
Fig. 2 is the system architecture diagram that the load information of server of the embodiment of the present invention acquires；
Fig. 3 is the prediction meanss structural schematic diagram of the load information of server of the embodiment of the present invention；
Fig. 4 is electronic equipment entity structure schematic diagram provided in an embodiment of the present invention.
Specific implementation mode
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
The every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Fig. 1 is the prediction technique flow diagram of the load information of server of the embodiment of the present invention, as shown in Figure 1, this hair
A kind of prediction technique of the load information for server that bright embodiment provides, includes the following steps：
S1：Obtain the load information and the corresponding acquisition time of the load information of server.
Specifically, device obtains the load information and the corresponding acquisition time of the load information of server.Device can
To be interpreted as the management equipment of the upper level of server, such as central server etc..Load information may include cpu busy percentage,
Memory usage and network bandwidth utilization factor etc., but be not especially limited./ proc file system acquisition load letter can be passed through
Breath, can be primary at interval of acquisition in ten minutes, and the time of origin of acquisition action is acquisition time.
Fig. 2 is the system architecture diagram that the load information of server of the embodiment of the present invention acquires, as shown in Fig. 2, system can be with
Including data collector (configuring on each server in advance), (reception configures data collector on each server in advance
Data collector send the load information).The load information that data collector receives has null value, error value sometimes
Etc. abnormal conditions therefore data cleansing can be carried out to the load information that receives, and the load after data cleansing is believed
Breath, is stored in database (database can be disposed in the device).The step of specific data cleansing may include removal null value,
Excessive or too small value etc. is rejected according to standard deviation.
In order to further discriminate between the load estimation of each server as a result, clothes can be preset on each server
Business device title ID and/or server ip.
In order to further ensure efficient, the stable acquisition of the load information to server, TCP/IP networks can be passed through
Agreement receives the load information that the data collector configured on each server in advance is sent.
S2：According to the corresponding acquisition time of the load information, generated time sequence.
Specifically, device is according to the corresponding acquisition time of the load information, generated time sequence.Time series refer to by
The numerical value of same statistical indicator (can be any one of cpu busy percentage, memory usage and network bandwidth utilization factor) is by it
Ordered series of numbers made of the chronological order arrangement of acquisition.The main purpose of time series analysis is according to existing historical data pair
Future is predicted.Time series analysis, the continuous regularity exactly developed according to objective things, with past history number
According to further to speculate following development trend by statistical analysis.Things can be extended to this following supposed premise packet in the past
Containing two layers of meaning:First, unexpected jump variation will not occur, advanced with relatively small paces；Second is that showing in the past with current
As that can be shown that the movable development and change of the present and the future tend to.This just determines under normal circumstances, time series analysis method
For short, last-period forecast than more significant, but farther future is such as extended to, just will appear significant limitation, lead to predicted value
Deviate practical larger and makes incorrect decision.
S3：According to the time series and preset model, the load information to the server in predetermined time carries out in advance
It surveys.
Specifically, device, according to the time series and preset model, the load to the server in predetermined time is believed
Breath is predicted.Load information that can be according to following formula to the server in predetermined time is predicted：
Wherein, XtIt is that the prediction result predicted load information in predetermined time t to the server, c indicate pre-
Survey constant, the ε of stabilitytIndicate default error, p and the q of predetermined time t all indicate according to the time series determine
The quantity for the acquisition time that participation continuous in time calculates,Indicate time series relevant parameter, the θ of i-th of acquisition timej
Indicate default error relevant parameter, the X of j-th of acquisition timet-iIt is in predetermined time t-i to the server to load information
The prediction result predicted, εt-jIndicate the default error in predetermined time t-j.Above-mentioned XtBe each index it is corresponding
The prediction result that predetermined time t predicts load information, such as：Cpu busy percentage is predicted in predetermined time t pre-
Survey result is Xt, for memory usage and network bandwidth utilization factor, repeat no more.C can be independently arranged according to actual conditions,
It can be selected as 5, εtIt can be independently arranged according to actual conditions, 0.1 can be selected as；If necessary to 5 before predetermined time t
Hour load information predicted that with reference to above description, acquisition load information is primary within every ten minutes, then when acquisition in total
Between have 30 (5 hours=300 minutes), i.e. p and q are equal to 30.And θjConcrete numerical value can be carried out according to artificial experience
Rationally setting.Xt-iThe prediction result at certain moment before being predetermined time t；εt-jBefore being above-mentioned predetermined time t
The prediction result at certain moment and the practical error measured between acknowledged accurate result.
The prediction technique of the load information of server provided in an embodiment of the present invention, by time series and preset model,
Accurately the load information of server can be predicted.
On the basis of the above embodiments, described according to the time series and preset model, to the server pre-
If the load information at moment is predicted, including：
Load information according to following formula to the server in predetermined time is predicted：
Wherein, XtIt is that the prediction result predicted load information in predetermined time t to the server, c indicate pre-
Survey constant, the ε of stabilitytIndicate default error, p and the q of predetermined time t all indicate according to the time series determine
The quantity for the acquisition time that participation continuous in time calculates,Indicate time series relevant parameter, the θ of i-th of acquisition timej
Indicate default error relevant parameter, the X of j-th of acquisition timet-iIt is in predetermined time t-i to the server to load information
The prediction result predicted, εt-jIndicate the default error in predetermined time t-j.
Specifically, load information of the device according to following formula to the server in predetermined time is predicted：
Wherein, XtIt is that the prediction result predicted load information in predetermined time t to the server, c indicate pre-
Survey constant, the ε of stabilitytIndicate default error, p and the q of predetermined time t all indicate according to the time series determine
The quantity for the acquisition time that participation continuous in time calculates,Indicate time series relevant parameter, the θ of i-th of acquisition timej
Indicate default error relevant parameter, the X of j-th of acquisition timet-iIt is in predetermined time t-i to the server to load information
The prediction result predicted, εt-jIndicate the default error in predetermined time t-j.Above-described embodiment is can refer to, is repeated no more.
The prediction technique of the load information of server provided in an embodiment of the present invention passes through time series and above-mentioned formula institute
The preset model of expression is further able to the load information accurately to server and predicts.
On the basis of the above embodiments, the load information includes cpu busy percentage, memory usage and network bandwidth profit
With rate.
Specifically, the load information in device includes cpu busy percentage, memory usage and network bandwidth utilization factor.
Above-described embodiment is can refer to, is repeated no more.
The prediction technique of the load information of server provided in an embodiment of the present invention, may further be accurately to CPU profits
It is predicted with rate, memory usage and network bandwidth utilization factor.
On the basis of the above embodiments, the load information for obtaining server, including：
Receive the load information that the data collector configured on each server in advance is sent.
Specifically, device receives the load information that the data collector configured on each server in advance is sent.
Above-described embodiment is can refer to, is repeated no more.
The prediction technique of the load information of server provided in an embodiment of the present invention, by receiving in advance in each server
The load information that the data collector of upper configuration is sent, can efficiently obtain the load information of each server.
On the basis of the above embodiments, what the data collector that the reception configures on each server in advance was sent
After the step of load information, the method further includes：
Data cleansing is carried out to the load information received.
Specifically, device carries out data cleansing to the load information received.Above-described embodiment is can refer to, it is no longer superfluous
It states.
And it by the load information after data cleansing, preserves in the database.
Specifically, device and by the load information after data cleansing, preserves in the database.Above-described embodiment is can refer to,
It repeats no more.
The prediction technique of the load information of server provided in an embodiment of the present invention, by data cleansing, and data are clear
Load information after washing preserves in the database, can effectively reject invalid data, saves the storage money of load information occupancy
Source.
On the basis of the above embodiments, the method further includes：
Each server is preset with server name ID and/or server ip, to distinguish each server.
Specifically, each server is preset with server name ID and/or server ip, so that device differentiation is described every
A server.Above-described embodiment is can refer to, is repeated no more.
The prediction technique of the load information of server provided in an embodiment of the present invention, by being preset with for each server
Server name ID and/or server ip facilitate and distinguish each server, and then facilitate and distinguish the corresponding load of each server
The prediction result of information.
On the basis of the above embodiments, the method further includes：
The described negative of the data collector configured on each server in advance transmission is received by TCP/IP procotols
Information carrying ceases.
It is sent out specifically, device receives the data collector configured on each server in advance by TCP/IP procotols
The load information sent.Above-described embodiment is can refer to, is repeated no more.
The prediction technique of the load information of server provided in an embodiment of the present invention is received pre- by TCP/IP procotols
The load information that the data collector first configured on each server is sent, is further able to efficiently obtain each clothes
The load information of business device.
Fig. 3 is the prediction meanss structural schematic diagram of the load information of server of the embodiment of the present invention, as shown in figure 3, this hair
Bright embodiment provides a kind of prediction meanss of the load information of server, including acquiring unit 1, generation unit 2 and prediction list
Member 3, wherein：
Acquiring unit 1 is used to obtain the load information and the corresponding acquisition time of the load information of server；It generates
Unit 2 is used for according to the corresponding acquisition time of the load information, generated time sequence；Predicting unit 3 be used for according to it is described when
Between sequence and preset model, the load information to the server in predetermined time predicts.
Specifically, when acquiring unit 1 is used to obtain load information and the load information corresponding acquisition of server
Between；Generation unit 2 is used for according to the corresponding acquisition time of the load information, generated time sequence；Predicting unit 3 is used for basis
The time series and preset model, the load information to the server in predetermined time are predicted.
The prediction meanss of the load information of server provided in an embodiment of the present invention, by time series and preset model,
Accurately the load information of server can be predicted.
The prediction meanss of the load information of server provided in an embodiment of the present invention specifically can be used for executing above-mentioned each side
The process flow of method embodiment, details are not described herein for function, is referred to the detailed description of above method embodiment.
Fig. 4 is electronic equipment entity structure schematic diagram provided in an embodiment of the present invention, as shown in figure 4, the electronic equipment
Including：Processor (processor) 401, memory (memory) 402 and bus 403；
Wherein, the processor 401, memory 402 complete mutual communication by bus 403；
The processor 401 is used to call the program instruction in the memory 402, to execute above-mentioned each method embodiment
The method provided, such as including：Obtain the load information and the corresponding acquisition time of the load information of server；Root
According to the corresponding acquisition time of the load information, generated time sequence；According to the time series and preset model, to the clothes
Business device is predicted in the load information of predetermined time.
The present embodiment discloses a kind of computer program product, and the computer program product includes being stored in non-transient calculating
Computer program on machine readable storage medium storing program for executing, the computer program include program instruction, when described program instruction is calculated
When machine executes, computer is able to carry out the method that above-mentioned each method embodiment is provided, such as including：Obtain the load of server
Information and the corresponding acquisition time of the load information；According to the corresponding acquisition time of the load information, generated time sequence
Row；According to the time series and preset model, the load information to the server in predetermined time is predicted.
The present embodiment provides a kind of non-transient computer readable storage medium, the non-transient computer readable storage medium
Computer instruction is stored, the computer instruction makes the computer execute the method that above-mentioned each method embodiment is provided, example
Such as include：Obtain the load information and the corresponding acquisition time of the load information of server；According to the load information pair
The acquisition time answered, generated time sequence；According to the time series and preset model, to the server in predetermined time
Load information is predicted.
One of ordinary skill in the art will appreciate that：Realize that all or part of step of above method embodiment can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can be stored in a computer read/write memory medium, the program
When being executed, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes：ROM, RAM, magnetic disc or light
The various media that can store program code such as disk.
The embodiments such as electronic equipment described above are only schematical, illustrate as separating component wherein described
Unit may or may not be physically separated, and the component shown as unit may or may not be object
Manage unit, you can be located at a place, or may be distributed over multiple network units.It can select according to the actual needs
Some or all of module therein is selected to achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying wound
In the case of the labour for the property made, you can to understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It is realized by the mode of software plus required general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be expressed in the form of software products in other words, should
Computer software product can store in a computer-readable storage medium, such as ROM/RAM, magnetic disc, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
Finally it should be noted that：The above various embodiments is only to illustrate the technical solution of the embodiment of the present invention rather than right
It is limited；Although the embodiment of the present invention is described in detail with reference to foregoing embodiments, the ordinary skill of this field
Personnel should understand that：It still can be with technical scheme described in the above embodiments is modified, or to which part
Or all technical features carries out equivalent replacement；And these modifications or replacements, it does not separate the essence of the corresponding technical solution
The range of each embodiment technical solution of the embodiment of the present invention.
Claims (10)
1. a kind of prediction technique of the load information of server, which is characterized in that including：
Obtain the load information and the corresponding acquisition time of the load information of server；
According to the corresponding acquisition time of the load information, generated time sequence；
According to the time series and preset model, the load information to the server in predetermined time is predicted.
2. according to the method described in claim 1, it is characterized in that, described according to the time series and preset model, to institute
The load information that server is stated in predetermined time predicted, including：
Load information according to following formula to the server in predetermined time is predicted：
Wherein, XtIt is prediction result, the c expression prediction stabilizations predicted load information in predetermined time t the server
Constant, the ε of propertytIt indicates all to indicate to be determined in time according to the time series in default error, p and the q of predetermined time t
The quantity of the continuous acquisition time for participating in calculating,Indicate time series relevant parameter, the θ of i-th of acquisition timejIndicate jth
Default error relevant parameter, the X of a acquisition timet-iIt is to be predicted load information in predetermined time t-i the server
Prediction result, εt-jIndicate the default error in predetermined time t-j.
3. according to the method described in claim 1, it is characterized in that, the load information includes cpu busy percentage, memory usage
And network bandwidth utilization factor.
4. method according to any one of claims 1 to 3, which is characterized in that the load information for obtaining server, packet
It includes：
Receive the load information that the data collector configured on each server in advance is sent.
5. according to the method described in claim 4, it is characterized in that, the data that the reception configures on each server in advance
After the step of load information that collector is sent, the method further includes：
Data cleansing is carried out to the load information received；
And it by the load information after data cleansing, preserves in the database.
6. according to the method described in claim 4, it is characterized in that, the method further includes：
Each server is preset with server name ID and/or server ip, to distinguish each server.
7. according to the method described in claim 4, it is characterized in that, the method further includes：
The load letter that the data collector configured on each server in advance is sent is received by TCP/IP procotols
Breath.
8. a kind of prediction meanss of the load information of server, which is characterized in that including：
Acquiring unit, the load information for obtaining server and the corresponding acquisition time of the load information；
Generation unit, for according to the corresponding acquisition time of the load information, generated time sequence；
Predicting unit, for according to the time series and preset model, to the server predetermined time load information
It is predicted.
9. a kind of electronic equipment, which is characterized in that including：Processor, memory and bus, wherein
The processor and the memory complete mutual communication by the bus；
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to instruct energy
Enough methods executed as described in claim 1 to 7 is any.
10. a kind of non-transient computer readable storage medium, which is characterized in that the non-transient computer readable storage medium is deposited
Computer instruction is stored up, the computer instruction makes the computer execute the method as described in claim 1 to 7 is any.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111491027A (en) * | 2020-04-16 | 2020-08-04 | 北京雷石天地电子技术有限公司 | Load balancing method, load balancing device and readable storage medium |
| CN111598296A (en) * | 2019-10-16 | 2020-08-28 | 中国南方电网有限责任公司 | Power load prediction method, power load prediction device, computer equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050089063A1 (en) * | 2003-10-24 | 2005-04-28 | Takaaki Haruna | Computer system and control method thereof |
| CN103748993B (en) * | 2009-05-31 | 2011-02-16 | 北京理工大学 | A kind of host load prediction method based on multisequencing combination |
| CN105550323A (en) * | 2015-12-15 | 2016-05-04 | 北京国电通网络技术有限公司 | Load balancing prediction method of distributed database, and predictive analyzer |
-
2018
- 2018-03-27 CN CN201810258539.5A patent/CN108632082A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050089063A1 (en) * | 2003-10-24 | 2005-04-28 | Takaaki Haruna | Computer system and control method thereof |
| CN103748993B (en) * | 2009-05-31 | 2011-02-16 | 北京理工大学 | A kind of host load prediction method based on multisequencing combination |
| CN105550323A (en) * | 2015-12-15 | 2016-05-04 | 北京国电通网络技术有限公司 | Load balancing prediction method of distributed database, and predictive analyzer |
Non-Patent Citations (1)
| Title |
|---|
| 王浩等: "基于负载预测的虚拟机动态调度算法研究与实现", 《计算机工程与科学》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111598296A (en) * | 2019-10-16 | 2020-08-28 | 中国南方电网有限责任公司 | Power load prediction method, power load prediction device, computer equipment and storage medium |
| CN111491027A (en) * | 2020-04-16 | 2020-08-04 | 北京雷石天地电子技术有限公司 | Load balancing method, load balancing device and readable storage medium |
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Application publication date: 20181009 |