CN109784504A - Data center's long-distance intelligent operation management method and system - Google Patents
Data center's long-distance intelligent operation management method and system Download PDFInfo
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- 238000007726 management method Methods 0.000 title claims abstract description 39
- 230000007613 environmental effect Effects 0.000 claims abstract description 33
- 230000005012 migration Effects 0.000 claims description 13
- 238000013508 migration Methods 0.000 claims description 13
- 238000003066 decision tree Methods 0.000 claims description 4
- 238000003062 neural network model Methods 0.000 claims description 2
- 238000012706 support-vector machine Methods 0.000 claims description 2
- 108010001267 Protein Subunits Proteins 0.000 claims 1
- 230000006641 stabilisation Effects 0.000 abstract description 3
- 238000011105 stabilization Methods 0.000 abstract description 3
- 238000012544 monitoring process Methods 0.000 description 9
- 238000000034 method Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000004378 air conditioning Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000005057 refrigeration Methods 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000032258 transport Effects 0.000 description 1
- 239000011800 void material Substances 0.000 description 1
Abstract
The present invention relates to data center's O&M fields, disclose data center's long-distance intelligent operation management method and system, by obtaining data center's operation data and environmental data；According to decision rule and prediction model, the operation data and the environmental data are predicted；Management and fault pre-alarming are scheduled to data center apparatus according to prediction result.The present invention can effectively reduce the energy of data center, provide fault pre-alarming function, reduce failure rate, improve the utilization rate of resource and maintain the stabilization of system.
Description
Technical field
The present invention relates to data center's O&M field more particularly to data center's long-distance intelligent operation management method and it is
System.
Background technique
In recent years, with the fast development of information technology and network technology, especially in cloud computing, big data, Internet of Things
Development and promotion under, data center also tends to high-density development, and data center's structure is increasingly complicated, and enterprise is in data
The demand of the heart, server, link, service response etc. and requirement to operation system operational reliability, good experience
It is higher and higher, meanwhile, to data center monitoring, higher requirements are also raised, and professional is in short supply, the O&M of data center
Management work is also faced with huge challenge.
One typical data center contains various elements, and in software and service layer, it includes network, application, void
Quasi-ization, server, storage etc.；In facilities such as infrastructure levels, including power, environment, HVAC, security protection；In O&M level, packet
Include daily maintenance, inspection and anti-natural calamity etc..It can be said that data center is a complicated combined system.
Due to data center apparatus huge number, it is tired that the horizontal irregular one side of equipment manufacturer results in monitoring of tools
Difficulty, fault location is difficult, on the other hand not in time due to equipment manufacturer's after-sale service, causes service response slow, can not quickly solve
Failure problems.In the prior art, most data center all builds the infrastructure monitoring system (DCIM system) for having oneself and solves
The technical problem, and DCIM system in the prior art remains in simple monitoring and data statistics is shown, it is not right
Monitoring data carries out profound analysis, it is difficult to find the inducement and general character of failure.
Summary of the invention
The present invention provides data center's long-distance intelligent operation management method and system, solves data center in the prior art and transports
Dimension monitoring does not carry out profound analysis to monitoring data, it is difficult to the technical issues of finding the inducement and general character of failure.
The purpose of the present invention is what is be achieved through the following technical solutions:
Data center's long-distance intelligent operation management method, comprising:
Obtain data center's operation data and environmental data, wherein the operation data includes that operation of air conditioner data, IT are set
Standby operation data, network management data and UPS data, the environmental data include data center computer room environmental data and climatic environment number
According to；
According to decision rule and prediction model, the operation data and the environmental data are predicted；
Management and fault pre-alarming are scheduled to data center apparatus according to prediction result.
Data center's long-distance intelligent operation management system, comprising:
Module is obtained, for obtaining data center's operation data and environmental data, wherein the operation data includes air-conditioning
Operation data, information technoloy equipment operation data, network management data and UPS data, the environmental data include data center computer room environment number
According to weather environmental data；
Prediction module, for being carried out to the operation data and the environmental data according to decision rule and prediction model
Prediction；
Operation module, for being scheduled management and fault pre-alarming to data center apparatus according to prediction result.
The present invention provides data center's long-distance intelligent operation management method and system, by obtaining data center's operation data
And environmental data；According to decision rule and prediction model, the operation data and the environmental data are predicted；According to pre-
It surveys result and management and fault pre-alarming is scheduled to data center apparatus.The present invention can effectively reduce the energy of data center, mention
For fault pre-alarming function, failure rate is reduced, the utilization rate of resource is improved and maintains the stabilization of system.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also obtain according to these attached drawings
Obtain other attached drawings.
Fig. 1 is data center's long-distance intelligent operation management method flow diagram of the embodiment of the present invention；
Fig. 2 is data center's long-distance intelligent operation management system structural schematic diagram of the embodiment of the present invention.
Specific embodiment
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
Present invention implementation provides data center's long-distance intelligent operation management method, as shown in Figure 1, comprising:
Step 101 obtains data center's operation data and environmental data；
Wherein, the operation data includes operation of air conditioner data, information technoloy equipment operation data, network management data and UPS number
According to the environmental data includes data center computer room environmental data and weather environmental data；
Step 102, according to decision rule and prediction model, the operation data and the environmental data are predicted；
Step 103 is scheduled management and fault pre-alarming to data center apparatus according to prediction result.
Wherein, step 102 can specifically include:
Step 102-1, virtual machine (vm) migration operation is carried out to data center apparatus according to prediction result or server suspend mode is grasped
Work or server power-off operation；Alternatively,
Step 102-2, early warning instruction is carried out according to failure of the prediction result to data center apparatus.
Step 102-1 can specifically include:
A, judge whether the cpu busy percentage of server is greater than cpu busy percentage upper limit threshold；
B, when cpu busy percentage is not more than cpu busy percentage upper limit threshold, judge whether cpu busy percentage is less than cpu busy percentage
Lower threshold；When cpu busy percentage is less than cpu busy percentage lower threshold, time series autoregression AR model prediction future m is utilized
The cpu busy percentage at a moment, when whether the cpu busy percentage for judging m moment is respectively less than cpu busy percentage lower threshold, into
The operation of row virtual machine (vm) migration or server sleep operation or server power-off operation；Wherein, the time series autoregression AR mould
Type can be substituted by support vector machines or neural network model.
Wherein, consuming energy for server of leaving unused is about the 60% of full-load operation energy consumption, and method through this embodiment will be not busy
It sets the virtual machine run on server to be migrated, and suspend mode or power-off operation is carried out to server, can effectively reduce server
Energy consumption.
C, when cpu busy percentage is greater than cpu busy percentage upper limit threshold, time series autoregression AR model prediction future is utilized
The cpu busy percentage at m moment, when whether the cpu busy percentage for judging m moment is all larger than cpu busy percentage upper limit threshold, into
The operation of row virtual machine (vm) migration or server sleep operation or server power-off operation.
Wherein, in order to ensure the happy operation of service, the virtual machine loaded in high service is migrated, realizes load
The function of sharing.
In addition, weather monitoring can also be carried out by the external environment to data center in the present embodiment, by outside
Environmental data inside environmental monitoring data and computer room carries out confluence analysis, the trip information of bonding apparatus, temperature information,
Fault message etc. constructs simulation model, establishes the incidence relation between external environment condition parameter and computer room items operating parameter, thus
Realize that the automated intelligent of air-conditioning setting cryogenic temperature and humidity is adjusted, in the case where ensuring that various equipment are normally and efficiently run,
Extend the natural cooling time, the shortening electric refrigerating operaton time reaches reduction so as to save the energy consumption of HVAC refrigeration system
PUE, the purpose for realizing energy-saving run.
Step 102-2 can specifically include:
Operation data is analyzed according to decision-tree model or Apriori model, is determined in data by decision rule
The failure risk grade of heart equipment sends early warning instruction to operator according to risk class.Such as: by obtaining key equipment
The historical operating parameter and its essential attribute information of such as battery, historical failure information, building environment parameter, to these data
Mining analysis is carried out, a prediction model is constructed, then by the prediction model in originally implementing, in conjunction with corresponding Risk-warning
Rule, so that it may look-ahead and identification a part there are the battery packs of high risk likelihood of failure, and by warning information with
The operational system on foreground is integrated, regular real-time update risk label, so that reminding operation maintenance personnel to safeguard and replace in advance should
Group battery reduces the possibility of delay machine to avoid the generation of failure.
The present invention provides data center's long-distance intelligent operation management method, by obtaining data center's operation data and environment
Data；According to decision rule and prediction model, the operation data and the environmental data are predicted；According to prediction result
Management and fault pre-alarming are scheduled to data center apparatus.The present invention can effectively reduce the energy of data center, provide failure
Warning function reduces failure rate, improves the utilization rate of resource and maintain the stabilization of system.
The embodiment of the invention also provides data center's long-distance intelligent operation management systems, as shown in Figure 2, comprising:
Module 210 is obtained, for obtaining data center's operation data and environmental data, wherein the operation data includes
Operation of air conditioner data, information technoloy equipment operation data, network management data and UPS data, the environmental data include data center computer room ring
Border data and weather environmental data；
Prediction module 220, for according to decision rule and prediction model, to the operation data and the environmental data into
Row prediction；
Operation module 230, for being scheduled management and fault pre-alarming to data center apparatus according to prediction result.
Wherein, the operation module 230, comprising:
Scheduling unit 231, for carrying out virtual machine (vm) migration operation or server to data center apparatus according to prediction result
Sleep operation or server power-off operation；
Prewarning unit 232, for carrying out early warning instruction according to failure of the prediction result to data center apparatus.
The scheduling unit 231, comprising:
Judgment sub-unit 2311, for judging whether the cpu busy percentage of server is greater than cpu busy percentage upper limit threshold；
First executes subelement 2312, for judging CPU benefit when cpu busy percentage is not more than cpu busy percentage upper limit threshold
Whether it is less than cpu busy percentage lower threshold with rate；When cpu busy percentage is less than cpu busy percentage lower threshold, time series is utilized
The cpu busy percentage at autoregression AR m moment of model prediction future, when whether the cpu busy percentage for judging m moment is respectively less than
When cpu busy percentage lower threshold, virtual machine (vm) migration operation or server sleep operation or server power-off operation are carried out；
Second executes subelement 2313, for utilizing time sequence when cpu busy percentage is greater than cpu busy percentage upper limit threshold
The cpu busy percentage at column autoregression AR m moment of model prediction future, when whether the cpu busy percentage for judging m moment is all larger than
When cpu busy percentage upper limit threshold, virtual machine (vm) migration operation or server sleep operation or server power-off operation are carried out.
The prewarning unit 232 is specifically used for analyzing operation data according to decision-tree model or Apriori model,
The failure risk grade that data center apparatus is determined by decision rule sends early warning to operator according to risk class and refers to
Show.
Through the above description of the embodiments, those skilled in the art can be understood that the present invention can be by
Software adds the mode of required hardware platform to realize, naturally it is also possible to all implemented by hardware, but in many cases before
Person is more preferably embodiment.Based on this understanding, technical solution of the present invention contributes to background technique whole or
Person part can be embodied in the form of software products, which can store in storage medium, such as
ROM/RAM, magnetic disk, CD etc., including some instructions are used so that a computer equipment (can be personal computer, service
Device or the network equipment etc.) execute method described in certain parts of each embodiment of the present invention or embodiment.
The present invention is described in detail above, specific case used herein is to the principle of the present invention and embodiment party
Formula is expounded, and the above description of the embodiment is only used to help understand the method for the present invention and its core ideas；Meanwhile it is right
In those of ordinary skill in the art, according to the thought of the present invention, change is had in specific embodiments and applications
Place, in conclusion the contents of this specification are not to be construed as limiting the invention.
Claims (9)
1. data center's long-distance intelligent operation management method characterized by comprising
Obtain data center's operation data and environmental data, wherein the operation data includes operation of air conditioner data, information technoloy equipment fortune
Row data, network management data and UPS data, the environmental data include data center computer room environmental data and weather environmental data；
According to decision rule and prediction model, the operation data and the environmental data are predicted；
Management and fault pre-alarming are scheduled to data center apparatus according to prediction result.
2. data center's long-distance intelligent operation management method according to claim 1, which is characterized in that described according to prediction
As a result the step of management and fault pre-alarming being scheduled to data center apparatus, comprising:
Virtual machine (vm) migration operation or server sleep operation are carried out to data center apparatus according to prediction result or server shuts down
Operation；Alternatively,
Early warning instruction is carried out according to failure of the prediction result to data center apparatus.
3. data center's long-distance intelligent operation management method according to claim 2, which is characterized in that described according to prediction
As a result the step of virtual machine (vm) migration operation or server sleep operation or server power-off operation being carried out to data center apparatus, packet
It includes:
Judge whether the cpu busy percentage of server is greater than cpu busy percentage upper limit threshold；
When cpu busy percentage is not more than cpu busy percentage upper limit threshold, judge whether cpu busy percentage is less than cpu busy percentage lower limit threshold
Value；When cpu busy percentage is less than cpu busy percentage lower threshold, the time series autoregression AR m moment of model prediction future is utilized
Cpu busy percentage carried out virtual when whether the cpu busy percentage for judging m moment is respectively less than cpu busy percentage lower threshold
Machine migration operation or server sleep operation or server power-off operation；
When cpu busy percentage is greater than cpu busy percentage upper limit threshold, when using time series autoregression AR model prediction future m
The cpu busy percentage at quarter carries out empty when whether the cpu busy percentage for judging m moment is all larger than cpu busy percentage upper limit threshold
Quasi- machine migration operation or server sleep operation or server power-off operation.
4. data center's long-distance intelligent operation management method according to claim 3, which is characterized in that described according to prediction
As a result the step of early warning instruction being carried out to the failure of data center apparatus, comprising:
Operation data is analyzed according to decision-tree model or Apriori model, determines that data center sets by decision rule
Standby failure risk grade sends early warning instruction to operator according to risk class.
5. data center's long-distance intelligent operation management method according to claim 3, which is characterized in that the time series
Autoregression AR model can be substituted by support vector machines or neural network model.
6. data center's long-distance intelligent operation management system characterized by comprising
Module is obtained, for obtaining data center's operation data and environmental data, wherein the operation data includes operation of air conditioner
Data, information technoloy equipment operation data, network management data and UPS data, the environmental data include data center computer room environmental data and
Climatic environment data；
Prediction module, for predicting the operation data and the environmental data according to decision rule and prediction model；
Operation module, for being scheduled management and fault pre-alarming to data center apparatus according to prediction result.
7. data center's long-distance intelligent operation management system according to claim 6, which is characterized in that the operation mould
Block, comprising:
Scheduling unit, for carrying out virtual machine (vm) migration operation or server sleep operation to data center apparatus according to prediction result
Or server power-off operation；
Prewarning unit, for carrying out early warning instruction according to failure of the prediction result to data center apparatus.
8. data center's long-distance intelligent operation management system according to claim 7, which is characterized in that the scheduling is single
Member, comprising:
Judgment sub-unit, for judging whether the cpu busy percentage of server is greater than cpu busy percentage upper limit threshold；
First executes subelement, for whether judging cpu busy percentage when cpu busy percentage is not more than cpu busy percentage upper limit threshold
Less than cpu busy percentage lower threshold；When cpu busy percentage is less than cpu busy percentage lower threshold, time series autoregression AR is utilized
The cpu busy percentage at m moment of model prediction future, when whether the cpu busy percentage for judging m moment is respectively less than cpu busy percentage
When lower threshold, virtual machine (vm) migration operation or server sleep operation or server power-off operation are carried out；
Second executes subelement, for utilizing time series autoregression when cpu busy percentage is greater than cpu busy percentage upper limit threshold
The cpu busy percentage at AR m moment of model prediction future is utilized when whether the cpu busy percentage for judging m moment is all larger than CPU
When rate upper limit threshold, virtual machine (vm) migration operation or server sleep operation or server power-off operation are carried out.
9. data center's long-distance intelligent operation management system according to claim 8, which is characterized in that the prewarning unit
Specifically for being analyzed according to decision-tree model or Apriori model operation data, determined in data by decision rule
The failure risk grade of heart equipment sends early warning instruction to operator according to risk class.
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Application publication date: 20190521 |