CN107273273A - A kind of distributed type assemblies hardware fault method for early warning and system - Google Patents
A kind of distributed type assemblies hardware fault method for early warning and system Download PDFInfo
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- CN107273273A CN107273273A CN201710501250.7A CN201710501250A CN107273273A CN 107273273 A CN107273273 A CN 107273273A CN 201710501250 A CN201710501250 A CN 201710501250A CN 107273273 A CN107273273 A CN 107273273A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3466—Performance evaluation by tracing or monitoring
- G06F11/3476—Data logging
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Abstract
This application discloses a kind of distributed type assemblies hardware fault method for early warning and system, including：Each index item data of the real-time acquisition hardware of performance monitoring module is simultaneously transmitted to data analysis module；Each index item data of the whole hardware collected is carried out neural network learning analysis by data analysis module, and whether prediction hardware will break down；Data analysis module is built based on neural network model；If predicting hardware will break down, hardware forewarning management module controls alarm module to start alarm by the webserver.The application improves the availability for solving failure efficiency and group system due to enabling hardware fault to be detected before failure generation by analysis of neural network using data analysis module, and improves the accuracy of fault detect.
Description
Technical field
The present invention relates to technical field of memory, more particularly to a kind of distributed type assemblies hardware fault method for early warning and it is
System.
Background technology
With the development and the quickening of enterprises IT application process of internet, enterprise is in order to meet ever-increasing industry
Business demand, gradually adopts server cluster to carry out the processing and storage of big data quantity.But increasing server component increase
The difficulty of cluster unified management, while improve the probability that cluster breaks down to a certain extent, therefore one effective
Group system hardware fault early warning scheme is significant to solving cluster integrity problem.
Distributed cluster system typically has multiple servers composition, and these server groups are externally provided into a cluster is unified
Service.Distributed type assemblies hardware fault early warning mechanism traditional at present can only typically check system current state, it is impossible to before
Cluster monitoring data are analyzed, so that rational System Performance Analysis, optimization and system cluster hardware detection can not be completed, it is existing
There is working method more passive so that failure can be just resolved after occurring, and not only extend fault time, and drop significantly
The low availability of group system.This method can only carry out inspection processing to the present situation of database running status, not have
The analysis ability that gives warning in advance of group system.
The content of the invention
In view of this, it is an object of the invention to provide a kind of distributed type assemblies hardware fault method for early warning and system, make
Obtaining hardware fault can detect before failure generation, and then raising solves the availability of failure efficiency and group system,
And improve the accuracy of fault detect.Its concrete scheme is as follows：
A kind of distributed type assemblies hardware fault method for early warning, including：
Each index item data of the real-time acquisition hardware of performance monitoring module is simultaneously transmitted to data analysis module；
Each index item data of the whole collected the hardware is carried out neural network learning point by the data analysis module
Analysis, predicts whether the hardware will break down；The data analysis module is built based on neural network model；
If predicting the hardware will break down, hardware forewarning management module passes through webserver control alarm mould
Block starts alarm.
Preferably, in above-mentioned distributed type assemblies hardware fault method for early warning provided in an embodiment of the present invention, the data
Each index item data of the whole collected the hardware is carried out neural network learning analysis by analysis module, predicts that the hardware is
It is no to break down, specifically include：
The data analysis module by each index of the hardware currently collected as data, and the institute collected in the past
State each index item data of hardware to be trained collectively as training data, obtain parameters revision amount；
With reference to the parameters revision amount, the parameter to the neural network model is updated；
According to the parameter of the neural network model after renewal, the hardware state variation tendency is judged, prediction is described
Whether hardware will break down.
Preferably, in above-mentioned distributed type assemblies hardware fault method for early warning provided in an embodiment of the present invention, the data
Analysis module by each index of the hardware currently collected as data, and each index item number of the hardware collected in the past
It is trained according to collectively as training data, obtains corresponding parameters revision amount, the neural network model parameter is carried out more
Newly, specifically include：
The data analysis module is by each index item data of the hardware currently collected, and the institute collected in the past
Each index item data of hardware is stated as training data, the positive processing of progress on multiple layers of the neural network model, and
Control information is obtained at the end of the positive processing；
The control information is carried out reverse process by the data analysis module by Back Propagation Algorithm, in the god
Reverse process through network model produces parameters revision amount.
Preferably, in above-mentioned distributed type assemblies hardware fault method for early warning provided in an embodiment of the present invention, with reference to described
Parameters revision amount, the parameter to the neural network model is updated, and is specifically included：
With reference to the parameters revision amount, the threshold value of weights and output result to the input parameter of the neural network model
Constantly adjusted, so that error sum of squares is minimum.
Preferably, in above-mentioned distributed type assemblies hardware fault method for early warning provided in an embodiment of the present invention, collection in the past
To each index item data of the hardware include each index item data of the hardware collected when hardware failure.
The embodiment of the present invention additionally provides a kind of distributed type assemblies hardware fault early warning system, including：Performance monitoring module,
Data analysis module, hardware forewarning management module and alarm module；
The performance monitoring module, for each index item data of real-time acquisition hardware and is transmitted to the data analysis mould
Block；
The data analysis module, for each index item data of the whole collected the hardware to be carried out into Neural Network Science
Analysis is practised, predicts whether the hardware will break down；The data analysis module is built based on neural network model；
The hardware forewarning management module, if will be broken down for predicting the hardware, passes through the webserver
The alarm module is controlled to start alarm.
Preferably, in above-mentioned distributed type assemblies hardware fault early warning system provided in an embodiment of the present invention, the data
Analysis module, specifically for each index of the hardware that will currently collect as data, and the hardware collected in the past
Each index item data is trained collectively as training data, obtains parameters revision amount；With reference to the parameters revision amount, to described
The parameter of neural network model is updated；According to the parameter of the neural network model after renewal, the hardware shape is judged
State variation tendency, predicts whether the hardware will break down.
Preferably, in above-mentioned distributed type assemblies hardware fault early warning system provided in an embodiment of the present invention, collection in the past
To each index item data of the hardware include each index item data of the hardware collected when hardware failure.
A kind of distributed type assemblies hardware fault method for early warning provided by the present invention and system, including：Performance monitoring module
Real-time each index item data of acquisition hardware is simultaneously transmitted to data analysis module；Data analysis module is each by the whole hardware collected
Index item data carries out neural network learning analysis, and whether prediction hardware will break down；Data analysis module is based on nerve
Network model is built；If predicting hardware will break down, hardware forewarning management module controls to alert by the webserver
Module starts alarm.The present invention by analysis of neural network using data analysis module due to enabling hardware fault in failure
Detected before generation, and then improve the availability for solving failure efficiency and group system, and improve the standard of fault detect
True property.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
The embodiment of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis
The accompanying drawing of offer obtains other accompanying drawings.
Fig. 1 is the flow chart of distributed type assemblies hardware fault method for early warning provided in an embodiment of the present invention；
Fig. 2 is the structural representation of distributed type assemblies hardware fault early warning system provided in an embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
The present invention provides a kind of distributed type assemblies hardware fault method for early warning, as shown in figure 1, comprising the following steps：
Each index item data of the real-time acquisition hardware of S101, performance monitoring module is simultaneously transmitted to data analysis module；
Each index item data of the whole hardware collected is carried out neural network learning analysis by S102, data analysis module,
Whether prediction hardware will break down；Data analysis module is built based on neural network model；
If S103, predicting hardware and will break down, hardware forewarning management module passes through webserver control alarm
Module starts alarm.
In above-mentioned distributed type assemblies hardware fault method for early warning provided in an embodiment of the present invention, first using performance monitoring
Each index item data of the real-time acquisition hardware of module is simultaneously transmitted to data analysis module；Then it will be collected using data analysis module
Each index item data of whole hardware carry out neural network learning analysis, whether prediction hardware will break down；Data analysis
Module is built based on neural network model；It is logical using hardware forewarning management module if finally predicting hardware will break down
Cross webserver control alarm module and start alarm.The application using data analysis module by analysis of neural network due to being made
Obtaining hardware fault can detect before failure generation, and then raising solves the availability of failure efficiency and group system,
And improve the accuracy of fault detect and the competitiveness of distributed type assemblies.
It should be noted that considering the particularity in cluster device running, hardware failure detection scheme is changed into one kind certainly
Adapt to hardware failure detection pattern.The method is not to single Indexs measure, by the intelligent hardware of cluster management system
Comprehensive information is analyzed, and can more accurately position fault rootstock, is improved and is solved failure efficiency.And the letter of the method way to manage
Single, obtained warning information is without secondary analysis, and the technical merit to system manager requires relatively low, and its applicability is stronger.
Further, in the specific implementation, in above-mentioned distributed type assemblies hardware fault early warning provided in an embodiment of the present invention
In method, each index item data of the whole hardware collected is carried out neural network learning point by step S102 data analysis modules
Whether analysis, prediction hardware will break down, and specifically may comprise steps of：
Step 1: data analysis module by each index of the hardware currently collected as data, and collect in the past it is hard
Each index item data of part is trained collectively as training data, obtains parameters revision amount；
Step 2: incorporating parametric correction, the parameter to neural network model is updated；
Step 3: according to the parameter of the neural network model after renewal, judging hardware state variation tendency, prediction hardware is
It is no to break down.
Above-mentioned steps are by certain study and analysis ability (to substantial amounts of input-output mode map relation
Practise and analyze), can whether normal with the current running status of Accurate Prediction system equipment, realize the look-ahead and hair of failure
It is existing, improve the operational efficiency of system.
Further, in the specific implementation, it is pre- in above-mentioned distributed type assemblies hardware fault provided in an embodiment of the present invention
In alarm method, data analysis module gathers each index of the hardware currently collected as data, and in the past in above-mentioned steps one
To each index item data of hardware be trained collectively as training data, corresponding parameters revision amount is obtained, to neutral net
Model parameter is updated, and specifically may comprise steps of：
First, data analysis module is by each index item data of the hardware currently collected, and the hardware collected in the past
Each index item data carries out positive processing as training data on multiple layers of neural network model, and in forward direction processing knot
Control information is obtained during beam；
Then, control information is carried out reverse process by data analysis module by Back Propagation Algorithm, in neutral net
The reverse process of model produces parameters revision amount.
The method detection hardware fault has subjective initiative, may be sent out with regard to that can predict failure before the failure occurs
It is existing, fault time is not only shortened, and substantially increase the availability of group system.
In addition, in the specific implementation, in above-mentioned distributed type assemblies hardware fault method for early warning provided in an embodiment of the present invention
In, incorporating parametric correction in above-mentioned steps two, the parameter to neural network model is updated, and can specifically be included：
Incorporating parametric correction, the threshold value of weights and output result to the input parameter of neural network model is carried out constantly
Ground is adjusted, so that error sum of squares is minimum.
Above-mentioned steps are incoming by constantly clustering performance data, and the study number of times of data analysis module is improved constantly, its
Precision of prediction is also constantly risen, and the hardware fault predictive ability of whole system is also improved constantly.
In the specific implementation, in above-mentioned distributed type assemblies hardware fault method for early warning provided in an embodiment of the present invention, it is
Further raising forecasting accuracy, each index item data of hardware for collecting in the past include collecting when hardware failure
Each index item data of hardware.
Based on same inventive concept, the embodiment of the present invention additionally provides a kind of distributed type assemblies hardware fault early warning system,
Because the principle that the system solves problem is similar to a kind of foregoing distributed type assemblies hardware fault method for early warning, therefore this method
Implementation may refer to the implementation of distributed type assemblies hardware fault method for early warning, repeats part and repeats no more.
In the specific implementation, distributed type assemblies hardware fault early warning system provided in an embodiment of the present invention, as shown in Fig. 2
Specifically include：Performance monitoring module 1, data analysis module 2, hardware forewarning management module 3 and alarm module 4；
Performance monitoring module 1, for each index item data of real-time acquisition hardware and is transmitted to data analysis module；
Data analysis module 2, for each index item data of the whole hardware collected to be carried out into neural network learning analysis,
Whether prediction hardware will break down；Data analysis module is built based on neural network model；
Hardware forewarning management module 3, if will break down for predicting hardware, controls to alert by the webserver
Module 4 starts alarm.
It should be noted that as shown in figure 1, performance monitoring module 1, data analysis module 2, hardware forewarning management module 3
It is to be electrically connected with each other, hardware forewarning management module 3 is electrically connected with by the webserver (Web server) and alarm module 4.
Distributed type assemblies are made up of Web server, the first child node and the second child node in Fig. 1, the first child node and the second son section
Point can be respectively by each index item data of the acquisition hardware of performance monitoring module 1, and each index item data of hardware collected passes through
Data analysis module 2 carries out neural network learning analysis, and whether prediction hardware will break down, and be hardware meeting when predicting the outcome
When breaking down and (such as predicting the outcome as just), hardware forewarning management module 3 feeds back to Web server warning information, and by accusing
Alert module 4 notifies keeper, and advance notice keeper's hardware state reaches the early warning effect for being preventive from possible trouble.
Further, in the specific implementation, in above-mentioned distributed type assemblies hardware fault early warning provided in an embodiment of the present invention
In system, data analysis module, specifically for by each index of the hardware currently collected as data, and collect in the past it is hard
Each index item data of part is trained collectively as training data, obtains parameters revision amount；Incorporating parametric correction, to nerve net
The parameter of network model is updated；According to the parameter of the neural network model after renewal, hardware state variation tendency is judged, predict
Whether hardware will break down.
In the specific implementation, in above-mentioned distributed type assemblies hardware fault early warning system provided in an embodiment of the present invention, with
Each index item data of hardware for including collecting when hardware failure toward each index item data of hardware collected.
A kind of distributed type assemblies hardware fault method for early warning provided in an embodiment of the present invention and system, including：Performance monitoring
Each index item data of the real-time acquisition hardware of module is simultaneously transmitted to data analysis module；Data analysis module is all hard by what is collected
Each index item data of part carries out neural network learning analysis, and whether prediction hardware will break down；Data analysis module is based on
Neural network model is built；If predicting hardware will break down, hardware forewarning management module passes through webserver control
Alarm module starts alarm.The application by analysis of neural network using data analysis module due to enabling hardware fault to exist
Failure is detected before occurring, and then improves the availability for solving failure efficiency and group system, and improves fault detect
Accuracy.
Finally, in addition it is also necessary to explanation, herein, such as first and second or the like relational terms be used merely to by
One entity or operation make a distinction with another entity or operation, and not necessarily require or imply these entities or operation
Between there is any this actual relation or order.Moreover, term " comprising ", "comprising" or its any other variant meaning
Covering including for nonexcludability, so that process, method, article or equipment including a series of key elements not only include that
A little key elements, but also other key elements including being not expressly set out, or also include be this process, method, article or
The intrinsic key element of equipment.In the absence of more restrictions, the key element limited by sentence "including a ...", is not arranged
Except also there is other identical element in the process including the key element, method, article or equipment.
Distributed type assemblies hardware fault method for early warning provided by the present invention and system are described in detail above, this
Apply specific case in text to be set forth the principle and embodiment of the present invention, the explanation of above example is only intended to
Help to understand method and its core concept of the invention；Simultaneously for those of ordinary skill in the art, the think of according to the present invention
Think, will change in specific embodiments and applications, in summary, this specification content should not be construed as pair
The limitation of the present invention.
Claims (8)
1. a kind of distributed type assemblies hardware fault method for early warning, it is characterised in that including：
Each index item data of the real-time acquisition hardware of performance monitoring module is simultaneously transmitted to data analysis module；
Each index item data of the whole collected the hardware is carried out neural network learning analysis by the data analysis module, in advance
Survey whether the hardware will break down；The data analysis module is built based on neural network model；
If predicting the hardware will break down, hardware forewarning management module controls alarm module to open by the webserver
Dynamic alarm.
2. distributed type assemblies hardware fault method for early warning according to claim 1, it is characterised in that the data analysis mould
Each index item data of the whole collected the hardware is carried out neural network learning analysis by block, predicts that the hardware whether will
Break down, specifically include：
The data analysis module by each index of the hardware currently collected as data, and collect in the past it is described hard
Each index item data of part is trained collectively as training data, obtains parameters revision amount；
With reference to the parameters revision amount, the parameter to the neural network model is updated；
According to the parameter of the neural network model after renewal, the hardware state variation tendency is judged, predict the hardware
Whether will break down.
3. distributed type assemblies hardware fault method for early warning according to claim 2, it is characterised in that the data analysis mould
Block is by each index of the hardware currently collected as data, and each index item data of the hardware collected in the past is common
It is trained as training data, obtains corresponding parameters revision amount, the neural network model parameter is updated, specifically
Including：
The data analysis module by each index item data of the hardware currently collected, and collect in the past it is described hard
Each index item data of part carries out positive processing as training data on multiple layers of the neural network model, and described
Control information is obtained at the end of forward direction processing；
The control information is carried out reverse process by the data analysis module by Back Propagation Algorithm, in the nerve net
The reverse process of network model produces parameters revision amount.
4. distributed type assemblies hardware fault method for early warning according to claim 3, it is characterised in that repaiied with reference to the parameter
Positive quantity, the parameter to the neural network model is updated, and is specifically included：
With reference to the parameters revision amount, the threshold value of weights and output result to the input parameter of the neural network model is carried out
Constantly adjust, so that error sum of squares is minimum.
5. the distributed type assemblies hardware fault method for early warning according to claim any one of 1-4, it is characterised in that adopt in the past
Each index item data of the hardware collected include each index item data of the hardware collected when hardware failure.
6. a kind of distributed type assemblies hardware fault early warning system, it is characterised in that including：Performance monitoring module, data analysis mould
Block, hardware forewarning management module and alarm module；
The performance monitoring module, for each index item data of real-time acquisition hardware and is transmitted to the data analysis module；
The data analysis module, for each index item data of the whole collected the hardware to be carried out into neural network learning point
Analysis, predicts whether the hardware will break down；The data analysis module is built based on neural network model；
The hardware forewarning management module, if will be broken down for predicting the hardware, passes through webserver control
The alarm module starts alarm.
7. distributed type assemblies hardware fault early warning system according to claim 6, it is characterised in that the data analysis mould
Block, specifically for each index of the hardware that will currently collect as data, and each index of the hardware collected in the past
Item data is trained collectively as training data, obtains parameters revision amount；With reference to the parameters revision amount, to the nerve net
The parameter of network model is updated；According to the parameter of the neural network model after renewal, the hardware state change is judged
Trend, predicts whether the hardware will break down.
8. the distributed type assemblies hardware fault early warning system according to claim any one of 6-7, it is characterised in that adopt in the past
Each index item data of the hardware collected include each index item data of the hardware collected when hardware failure.
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