CN108038049A - Real-time logs control system and control method, cloud computing system and server - Google Patents

Real-time logs control system and control method, cloud computing system and server Download PDF

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CN108038049A
CN108038049A CN201711333074.7A CN201711333074A CN108038049A CN 108038049 A CN108038049 A CN 108038049A CN 201711333074 A CN201711333074 A CN 201711333074A CN 108038049 A CN108038049 A CN 108038049A
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sequence
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failure
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CN108038049B (en
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裴庆祺
赵伟伟
王磊
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Xidian University
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    • 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
    • G06F11/3452—Performance evaluation by statistical analysis
    • 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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Abstract

The invention belongs to field of cloud computer technology, disclose a kind of real-time logs control system and control method, cloud computing system and server, pass through the analysis for log events, error message is classified, is filtered, converging operation, extraction becomes sequence, simultaneously the sequence of calculation belongs to the probability of failure sequence and the probability of non-faulting sequence to training fault model, is obtained a result, made prediction using Bayes classification theory.The present invention passes through the analysis for log events, the operation such as all error messages are classified, are filtered, are polymerize, extraction becomes sequence, train fault model and calculate the sequence and belong to the probability of failure sequence and the probability of non-faulting sequence, obtained a result using Bayes classification theory, make prediction, compared with improving judgement speed for substantial amounts of rule match；Failure predication research loses caused by reducing network failure and is of great significance for the burden for mitigating network management and safeguarding.

Description

Real-time logs control system and control method, cloud computing system and server
Technical field
The invention belongs to field of cloud computer technology, more particularly to a kind of real-time logs control system and control method, cloud meter Calculation system and server.
Background technology
With the rapid development of computer technology, cloud computing becomes one of most important computer realm, cloud computing service It is deep among everyone live and work.Can be by the calculating to real time data, based on machine learning algorithm for cloud The failure that may occur in computing system carries out look-ahead, reserves failure response time, while also supports elastic Horizon to expand The disposal ability of cluster is opened up, to adapt to ever-increasing data volume and user demand.Massive logs data are calculated in real time Processing, mining analysis has good developing direction and application prospect in terms of going out the state of system, failure predication from data.
In conclusion problem existing in the prior art is：In original failure predication model, on the one hand, when state continues Between be distributed and be defaulted as exponential type distribution mostly, and the state probability of failure changes and is unsatisfactory for exponential type in practice；On the other hand, Detected value probability of nonserviceabling has done sliding-model control, this carries out experimental analysis to big data environment and has unexpected shadow Ring, therefore this content adoption status continuous time and its distribution and state observation value probability distribution an ancient unit of weight carry out serialization distribution and assumes prestige cloth You are distributed, and the probable value of diagnosis and prediction can be improved using improved prediction model.
The content of the invention
In view of the problems of the existing technology, the present invention provides a kind of real-time logs control system and control method, cloud Computing system and server.
The present invention is achieved in that a kind of real-time logs control method, the real-time logs control method by for The analysis of log events, error message is classified, is filtered, converging operation, and extraction becomes sequence, training fault model And the sequence of calculation belongs to the probability of failure sequence and the probability of non-faulting sequence, is obtained a result, done using Bayes classification theory Go out prediction.
Further, the real-time logs control method specifically includes：
Step 1, collects the log file data on each node in distributed system, will newly be produced by increment inspection Daily record data is sent to collecting terminal in real time；
Step 2, deletes the same type event of same position report in a certain period of time, deletes redundancy event, pass through Time threshold is setRepresent to be used for the time window for performing event filtering；By removing in certain time period by multiple The similar case of diverse location report, deletes the redundancy event in daily record, data flow is saved in time series database；Use phase Like property Sim (D1, D2) judge：
Wherein D1, D2Represent two sequences, W1K, W2KRepresent the vector entries of D1, D2 sequence, similarity i.e. two vector angle Cosine value represent, Sim (D1, D2) bigger, represent that both similarities are higher；
Step 3, when every data is stored to tables of data, using SQL statement according to timestamp, process number, record level Not, scheduler module, separator, record information cutting recording；
Processed standard format data are carried out persistent storage by step 4 using SQL statement；
Step 5, extracts daily record failure sequence；
Step 6, clusters likelihood value of the standard according to sequenceCalculate as metric, calculated using hierarchical clustering Method realizes that failure dependent event is grouped, wherein：
S=[si] represent an a length of L status switch,For in state si(k) initial state probability vector π= [πi] under observation probability matrix；
Step 7, is combined using improved HSMM and Bayesian network BayesNet, and real-time logs data are made with event Barrier prediction；
Standard HSMM can be by transition probability matrix G (t) between state=[gij(t)], state si(k) in initial state probabilities Vectorial π=[πi] under observation probability matrix B=bi(k), it is defined asBy state duration probability It is distributed serialization；Handled using the distribution of state duration as continuously distributed, and assume that it is obeyed Weibull distribution and comes State duration probability distribution, the state duration probability distribution f of state are describedi(l) it is：
fi(l)=α β (α l)β-1e-(αl)β；
In formula：α, β are respectively the scale parameter and form parameter of Weibull distribution；
By status monitoring value probability distribution serialization；Equally set it and obey Weibull distribution, state-detection value probability point Cloth function ξi(θ) is：
Wherein αi、βiFor the parameter of the Weibull distribution of each state phase；Improved HSMM models can be described as
Step 8, failure and non-faulting model are trained, parameterWithTarget is assessment, gives an observation sequence Arrange O=[o1, o2..., ol] whether it is failure correlated series；The sequence likelihood value of disaggregated model is calculated, is then classified as nothing Failure or failure Bayesian decision theory；
Step 9, fail result anticipation：
One sequence mark is become into failure dependent event sequence, system sends failure predication；WhereinRepresent mistake Failure correlated series is judged into the cost as failure unrelated sequences, P (F) represents the probability of failure,Expression pair Sequence likelihood value is taken the logarithm.
Further, the extraction daily record failure sequence specifically includes：
The first step, extracts error event sequence：Using SQL statement, the record of ERROR ranks is crossed according to logging level and is carried Take out, retention time stamp and text message information；
Second step, merges similar error event：Levenshtein editing distance algorithms are utilized to sequence of events, will be similar Larger error event is spent to merge；Smallest edit distance includes sub- smallest edit distance；
Wherein d[i-1, j]+ 1, which represents target journaling, is inserted into a letter, d[i, j-1]- 1, which represents matching daily record, deletes a letter； Then xi=yjWhen, it is not necessary to change, so with previous step d[i-1, j-1]+ 1 cost is identical, otherwise+1, d[i,j]Represent above three Middle minimum one；
3rd step, error event classification：After previous step merges error event, according to the text message of error event In keyword similar error event is sorted out, and assignment ID, preserves in the database；
4th step, abstraction sequence：Sequentially in time, failure is extracted in occur for the previous periodInterior event, setting For failure dependent event sequence,For the failure lead time, current failure event is dependent failure event；Non-faulting correlation thing Part sequence is then the sequence of events in the time interval that system does not break down.
Another object of the present invention is to provide a kind of real-time logs control system of the real-time logs control method, institute Stating real-time logs control system includes：Log information processing module, daily record failure analysis module.
Further, the daily record failure analysis module includes：
Collector journal information unit, for collecting the log file data in distributed system on each node, daily record is received Collection function should allow the self-defined journal file to be monitored, by the method for increment inspection, will newly produce daily record data reality When be sent to collecting terminal；
Log information filter element, for carrying out de-redundancy and the filtering of data；
Log information standard format unit, data standard formatting is carried out for processed log information；
Log storage unit, for processed standard format data to be carried out persistent storage.
Further, the daily record failure analysis module includes：
Extract log event sequence units；
Failure dependent event cluster cell, for training a small hidden semi-Markov model in advance using event, Seek sequence likelihood value；
Failure predication unit, it is theoretical using hidden semi-Markov model and Bayes's decibel, judge whether sequence is failure Correlated series；
Fail result judges output unit：When being determined as failure correlated series, system sends failure warning stream, exports shape State fault pre-alarming.
The extraction log event sequence units further comprise：
Error event recording unit is extracted, the record of ERROR ranks is crossed according to logging level and is extracted, retention time Stamp, scheduler module and text message information；
Merge similar error event elements, error event sequence is utilized into Levenshtein editing distance algorithms, will be similar Larger error event is spent to merge；
Error event taxon, uses Levenshtein editing distance algorithms, by similar wrong thing to sequence of events Part is sorted out, and assignment ID；
Failure correlated series unit is extracted, according to time order and function order, extraction failure interior event for the previous period, setting For the preposition event of failure.
Another object of the present invention is to provide a kind of cloud computing system using the real-time logs control method.
Failure predication research work now mainly has three classes method, including the Fault Model based on daily record frequency, base In message frequency Fault Model and based on state transfer Fault Model.
The real-time collecting log information of the invention in system operation time simultaneously carries out clustering processing, by analyzing event log Using the algorithm and model of machine learning, the prediction for the failure that system future may occur is realized, in system operation The system failure is investigated and positioned in advance, for improving system O＆M efficiency and prevention emergency event.The present invention is logical The analysis for log events is crossed, the operation such as all error messages are classified, are filtered, are polymerize, extraction becomes sequence Row, training fault model simultaneously calculate the sequence and belong to the probability of failure sequence and the probability of non-faulting sequence, use Bayes point Class theory is obtained a result, and is made prediction.
Effective criterion of this method mainly determines by three parameters, i.e. accuracy rate, recall rate and F-measure Parameter, accuracy rate reaction is correct ratio in all predictions, recall rate reaction be institute it is faulty in be predicted correctly out The ratio come, F.measure are a comprehensive metrics with reference to accuracy rate and recall rate；
Prediction case such as table 1 below：
Prediction result actual result The system failure System is normal
The system failure TruePositive(TP) FalsePositive(FP)
System is normal FalseNegative(FN) TrueNegative(TN)
1 prediction case of table
Predictive validity parameter such as table 2：
2 validity parameter expression formula of table
Following data conclusion is drawn by system experimentation, it can be seen that this subsystem is in accuracy rate better than before not improving
Brief description of the drawings
Fig. 1 is real-time logs control system architecture schematic diagram provided in an embodiment of the present invention；
In figure：1st, log information processing module；1-1, collector journal information unit；1-2, log information filter element；1- 3rd, log information standard format unit；1-4, log storage unit；2nd, daily record failure analysis module；2-1, extraction log event Sequence units；2-1-1, extraction error event recording unit；2-1-2, merge similar error event elements；2-1-3, error event Taxon；2-1-4, extraction failure correlated series unit；2-2, failure dependent event cluster cell；2-3, failure predication list Member；2-4, fail result judge output unit.
Fig. 2 is real-time logs control method flow chart provided in an embodiment of the present invention.
Fig. 3 is that real-time logs control method provided in an embodiment of the present invention realizes flow chart.
Fig. 4 is failure sequence extraction schematic diagram provided in an embodiment of the present invention.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to Limit the present invention.
The application principle of the present invention is explained in detail below in conjunction with the accompanying drawings.
As shown in Figure 1, real-time logs control system provided in an embodiment of the present invention includes：Log information processing module 1, day Will failure analysis module 2.
Daily record failure analysis module 1 includes：
Collector journal information unit 1-1：For collecting the log file data in distributed system on each node, daily record Collecting function should allow the self-defined journal file to be monitored, by the method for increment inspection, will newly produce daily record data Collecting terminal is sent in real time.
Log information filter element 1-2：For carrying out de-redundancy and the filtering of data.
Log information standard format unit 1-3：Data standard formatting is carried out for processed log information, such as According to：Timestamp, process number, record rank, scheduler module, separator, record information, wherein, record rank is divided into several major classes, Including：ERROR, WARING, TRACE, INFO, DUBUG, CRITICAL, AUDIT, rank is more forward, and higher grade, and higher grade The significance level for representing event is higher.
Log storage unit 1-4：For processed standard format data to be carried out persistent storage, easy to rear issue According to extraction and analysis.
Daily record failure analysis module 2 includes：
Extract log event sequence units 2-1：
Failure dependent event cluster cell 2-2, for training a small hidden semi-Markov in advance using event (HSMM) model, the observation sequence for asking sequence likelihood value i.e. given sequence to utilize training pattern to produce；
Failure predication unit 2-3：It is theoretical using hidden semi-Markov model and Bayes's decibel, judge whether sequence is event Hinder correlated series；
Fail result judges output unit 2-4：When being determined as failure correlated series, system sends failure warning stream, defeated Do well fault pre-alarming.
Extraction log event sequence units 2-1 further comprises：
Extract error event recording unit 2-1-1：The record of ERROR ranks is crossed according to logging level and is extracted, is protected Stay the information such as timestamp, scheduler module and text message；
Merge similar error event elements 2-1-2：Error event sequence is utilized into Levenshtein editing distance algorithms, The larger error event of similarity is merged；
Error event taxon 2-1-3：Levenshtein editing distance algorithms are used to sequence of events, will be similar Error event is sorted out, and assignment ID；
Extract failure correlated series unit 2-1-4：According to time order and function order, extraction failure interior thing for the previous period Part, is set as the preposition event of failure.
As shown in Fig. 2, real-time logs control method provided in an embodiment of the present invention comprises the following steps：
S201：By the analysis for log events, all error messages are classified, filtered, are polymerize Operation, extraction become sequence；
S202：Train fault model and calculate the sequence and belong to the probability of failure sequence and the probability of non-faulting sequence, make Obtained a result, made prediction with Bayes classification theory.
The application principle of the present invention is further described below in conjunction with the accompanying drawings.
Carried out compared with using failure keyword for substantial amounts of rule match, it is in the present invention, (hidden using improved HSMM Markov model) and Bayesdecisiontheory (Bayes classification theory), directly calculate a faulty sequence and belong to event Hinder the probability of sequence, raising judges speed.
As shown in figure 3, real-time logs control method provided in an embodiment of the present invention comprises the following steps that：
1st, log information processing procedure
Step 1, log information is collected
System should be able to collect the log file data on each node in distributed system, and log collection function should Allow the self-defined journal file to be monitored, by the method for increment inspection, will newly produce daily record data and send in real time To collecting terminal.
Step 2, log information filters
There are two methods：One is temporal filtering, the other is spatial filtering.When system detectio is to exception, in system Before breaking down, system can continue output warning message stream.Similarly, once system jam, is solving failure problems May repeatedly break down information repeatedly in daily record before.
Temporal filtering method is by deleting the same type event that same position is reported in a certain period of time, so as to delete Redundancy event, by setting time thresholdRepresent to be used for the time window for performing event filtering.Spatial filtering method is led to The similar case for removing and being reported in certain time period by multiple and different positions is crossed, the redundancy event in daily record is deleted, by data flow It is saved in time series database, saves space and improve efficiency.Usually using similitude Sim (D1, D2) judge：
Wherein D1, D2Represent two sequences, W1K, W2KRepresent the vector entries of D1, D2 sequence, similarity i.e. two vector angle Cosine value represent, Sim (D1, D2) bigger, represent that both similarities are higher.
Step 3, journal format standardizes.
When that will be stored per data to tables of data, using SQL statement according to timestamp, process number, record rank, process The cutting recordings such as module, separator, record information.
Step 4, daily record stores.
Processed standard format data are subjected to persistent storage using SQL statement, easy to the extraction of later data Analysis.
2. daily record accident analysis：
Failure performance system mode between establish be based on probability causal relation, by failure appearance prior probability come Hidden Semi-Markov Process and Bayesian network are trained, bug list now each germline is solved according to prior probability during diagnosis The posterior probability of system state, it is directly perceived to express the joint probability distribution of variable, while calculate the probability that each feature causes failure.
Step 1, daily record failure sequence is extracted.
The first step, extracts error event sequence：Using SQL statement, the record of ERROR ranks is crossed according to logging level and is carried Take out, the information such as retention time stamp and text message；
Second step, merges similar error event：The sequence of events of previous step is calculated using Levenshtein editing distances Method, the larger error event of similarity is merged；
The algorithm has used the algorithm policy of Dynamic Programming, which possesses optimal minor structure, and smallest edit distance includes Sub- smallest edit distance；
Wherein d[i-1, j]+ 1, which represents target journaling, is inserted into a letter, d[i, j-1]+ 1, which represents matching daily record, deletes a letter； Then xi=yjWhen, it is not necessary to change, so with previous step d[i-1, j-1]+ 1 cost is identical, otherwise+1, d[i, j]Represent above three Middle minimum one；
3rd step, error event classification：After previous step merges error event, according to the text message of error event In keyword similar error event is sorted out, and assignment ID, preserves in the database；
4th step, abstraction sequence：Sequentially in time, failure is extracted in occur for the previous periodFor event, setting For failure dependent event sequence,For the failure lead time, current failure event is dependent failure event；Non-faulting correlation thing Part sequence is then the sequence of events in the time interval that system does not break down, as shown in Figure 4：
Step 2, failure dependent event clusters.
In practice, the same system failure may be caused by having a variety of failure dependent event sequences, and this is a variety of former Barrier dependent event sequence be characterized in it is different, therefore need clustered.
Cluster standard can be according to the likelihood value of sequenceCalculate as metric, finally calculated using hierarchical clustering Method realizes that failure dependent event is grouped, wherein：
S=[si] represent an a length of L status switch, bsi(oi) it is in state si(k) initial state probability vector π= [πi] under observation probability matrix.
Step 3, prediction model is established in training.
Prediction model is the key of network failure prediction, and the feature constructed directly affects the performance of prediction model.This It is combined using hidden semi-Markov model (HSMM) and Bayesian network (Bayes Net), is made for real-time logs data Failure predication.
Standard HSMM can be by transition probability matrix G (t) between state=[gij(t)], state si(k) in initial state probabilities Vectorial π=[πi] under observation probability matrix B=bi(k), it is defined as
Have in terms of this improvement to HSMM：By state duration probability distribution serialization.By state duration Distribution is handled as continuously distributed, and assumes to describe state duration probability distribution, i.e., it obeys Weibull distribution The state duration probability distribution f of statei(l) it is：
fi(l)=α β (α l)β-1e-(αl)β；
In formula：D, β is respectively the scale parameter and form parameter of Weibull distribution；
By status monitoring value probability distribution serialization.Equally set it and obey Weibull distribution, state-detection value probability point Cloth function ξi(θ) is：
Wherein αi、βiFor the parameter of the Weibull distribution of each state phase；Therefore improved HSMM models can be described as
Step 4, failure predication.
The failure and non-faulting model of hypothesis are trained, i.e. parameterWithTarget is assessment, gives an observation sequence Arrange (faulty sequence) O=[o1, o2..., ol] whether it is failure correlated series.The sequence likelihood value of disaggregated model is calculated first, Then it is classified as fault-free or failure Bayesian decision theory.
Step 5, fail result prejudges：
When formula is set up above, a sequence mark is become into failure dependent event sequence, system sends failure predication.Its InRepresent cost failure correlated series judged as failure unrelated sequences of mistake, P (F) represents the probability of failure,Sequence likelihood value is taken the logarithm in expression, can so prevent that sequence likelihood value is too small and overflow problem occurs.Pass through Such method, can judge each sequence, make failure predication.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention All any modification, equivalent and improvement made within refreshing and principle etc., should all be included in the protection scope of the present invention.

Claims (9)

1. a kind of real-time logs control method, it is characterised in that the real-time logs control method passes through for log recording thing The analysis of part, error message is classified, is filtered, converging operation, and extraction becomes sequence, training fault model and the sequence of calculation Belong to the probability of failure sequence and the probability of non-faulting sequence, obtained a result, made prediction using Bayes classification theory.
2. real-time logs control method as claimed in claim 1, it is characterised in that the real-time logs control method is specifically wrapped Include：
Step 1, collects the log file data on each node in distributed system, daily record will be newly produced by increment inspection Data are sent to collecting terminal in real time；
Step 2, deletes the same type event of same position report in a certain period of time, deletes redundancy event, pass through setting Time thresholdRepresent to be used for the time window for performing event filtering；By removing in certain time period by multiple and different The similar case of position report, deletes the redundancy event in daily record, data flow is saved in time series database；Use similitude Sim(D1, D2) judge：
<mrow> <mi>S</mi> <mi>i</mi> <mi>m</mi> <mrow> <mo>(</mo> <msub> <mi>D</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>D</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mi>&amp;theta;</mi> <mo>=</mo> <mfrac> <mrow> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> <mi>n</mi> </msubsup> <msub> <mi>W</mi> <mrow> <mn>1</mn> <mi>K</mi> </mrow> </msub> <mo>&amp;times;</mo> <msub> <mi>W</mi> <mrow> <mn>2</mn> <mi>K</mi> </mrow> </msub> </mrow> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> <mi>n</mi> </msubsup> <msub> <mi>W</mi> <mrow> <mn>1</mn> <mi>K</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>&amp;times;</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> <mi>n</mi> </msubsup> <msub> <mi>W</mi> <mrow> <mn>2</mn> <mi>K</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mfrac> <mo>;</mo> </mrow>
Wherein D1, D2Represent two sequences, W1K, W2KRepresent D1, D2 sequence vector entries, similarity i.e. two vector angle it is remaining String value represents, Sim (D1, D2) bigger, represent that both similarities are higher；
Step 3, every data store to tables of data when, using SQL statement according to timestamp, process number, record rank, into Journey module, separator, record information cutting recording；
Processed standard format data are carried out persistent storage by step 4 using SQL statement；
Step 5, extracts daily record failure sequence；
Step 6, clusters likelihood value of the standard according to sequenceCalculated as metric, it is real using hierarchical clustering algorithm Existing failure dependent event packet, wherein：
S=[si] represent an a length of L status switch,For in state si(k) in initial state probability vector π=[πi] under Observation probability matrix；
Step 7, is combined using hidden semi-Markov model HSMM and Bayesian network Bayes Net, to real-time logs data Make failure predication；
Standard HSMM can be by transition probability matrix G (t) between state=[gij(t)], state si(k) in initial state probability vector π=[πi] under observation probability matrix B=bi(k), it is defined as λ=(π, G (t), B)；By state duration probability distribution Serialization；Handled using the distribution of state duration as continuously distributed, and assume that it obeys Weibull distribution to describe State duration probability distribution, the state duration probability distribution f of statei(l) it is：
fi(l)=α β (α l)β-1e-(αl)β；
In formula:α, β are respectively the scale parameter and form parameter of Weibull distribution；
By status monitoring value probability distribution serialization；Equally set it and obey Weibull distribution, state-detection value probability distribution letter Number ξi(θ) is：
<mrow> <msub> <mi>&amp;xi;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>&amp;theta;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>P</mi> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>t</mi> </msub> <mo>=</mo> <mi>&amp;theta;</mi> <mo>|</mo> <msub> <mi>q</mi> <mi>t</mi> </msub> <mo>=</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <msub> <mi>&amp;beta;</mi> <mi>i</mi> </msub> <msup> <mrow> <mo>(</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mi>&amp;theta;</mi> <mo>)</mo> </mrow> <mrow> <msub> <mi>&amp;beta;</mi> <mi>i</mi> </msub> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msup> <mi>e</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mi>&amp;theta;</mi> <mo>)</mo> <msub> <mi>&amp;beta;</mi> <mi>i</mi> </msub> </mrow> </msup> <mo>;</mo> </mrow>
Wherein αi、βiFor the parameter of the Weibull distribution of each state phase；Improved HSMM models can be described as
Step 8, failure and non-faulting model are trained, parameterWithTarget is assessment, gives an observation sequence O= [o1, o2..., ol] whether it is failure correlated series；Calculate disaggregated model sequence likelihood value, be then classified as fault-free or Failure Bayesian decision theory；
Step 9, fail result anticipation：
One sequence mark is become into failure dependent event sequence, system sends failure predication；WhereinRepresent mistake will therefore Hindering correlated series to judge to become the cost of failure unrelated sequences, P (F) represents the probability of failure,Represent to sequence Likelihood value is taken the logarithm.
3. real-time logs control method as claimed in claim 2, it is characterised in that the extraction daily record failure sequence specifically wraps Include：
The first step, extracts error event sequence：Using SQL statement, the record of ERROR ranks is crossed according to logging level and is extracted Come, retention time stamp and text message information；
Second step, merges similar error event：Levenshtein editing distance algorithms are utilized to sequence of events, by similarity compared with Big error event merges；Smallest edit distance includes sub- smallest edit distance；
Wherein d[i-1, j]+ 1, which represents target journaling, is inserted into a letter, d[i, j-1]+ 1, which represents matching daily record, deletes a letter；Then xi=yjWhen, it is not necessary to change, so with previous step d[i-1, j-1]+ 1 cost is identical, otherwise+1, d[i, j]Represent in above three most Small one；
3rd step, error event classification：After previous step merges error event, according in the text message of error event Keyword is sorted out similar error event, and assignment ID, is preserved in the database；
4th step, abstraction sequence：Sequentially in time, failure is extracted in occur for the previous periodInterior event, is set as event Hinder dependent event sequence,For the failure lead time, current failure event is dependent failure event；Non-faulting dependent event sequence Row are then the sequences of events in the time interval that system does not break down.
A kind of 4. real-time logs control system of real-time logs control method as claimed in claim 1, it is characterised in that the reality Shi Zhi control systems include：Log information processing module, daily record failure analysis module.
5. real-time logs control system as claimed in claim 4, it is characterised in that the daily record failure analysis module includes：
Collector journal information unit, for collecting the log file data in distributed system on each node, log collection work( It can should allow the self-defined journal file to be monitored, by the method for increment inspection, will newly produce daily record data in real time It is sent to collecting terminal；
Log information filter element, for carrying out de-redundancy and the filtering of data；
Log information standard format unit, data standard formatting is carried out for processed log information；
Log storage unit, for processed standard format data to be carried out persistent storage.
6. real-time logs control system as claimed in claim 4, it is characterised in that the daily record failure analysis module includes：
Extract log event sequence units；
Failure dependent event cluster cell, for training a small hidden semi-Markov model in advance using event, seeks sequence Row likelihood value；
Failure predication unit, it is theoretical using hidden semi-Markov model and Bayes's decibel, judge whether sequence is failure correlation Sequence；
Fail result judges output unit：When being determined as failure correlated series, system sends failure warning stream, output state event Hinder early warning.
7. real-time logs control system as claimed in claim 6, it is characterised in that it is described extraction log event sequence units into One step includes：
Error event recording unit is extracted, the record of ERROR ranks is crossed according to logging level and is extracted, retention time stamp, Scheduler module and text message information；
Merge similar error event elements, error event sequence is utilized into Levenshtein editing distance algorithms, by similarity compared with Big error event merges；
Error event taxon, Levenshtein editing distance algorithms are used to sequence of events, by similar error event into Row is sorted out, and assignment ID；
Extract failure correlated series unit, according to time order and function order, extraction failure interior event for the previous period, be set as therefore Hinder preposition event.
A kind of 8. cloud computing system using real-time logs control method described in 3 any one of claims 1 to 3.
A kind of 9. cloud computing server using real-time logs control method described in 3 any one of claims 1 to 3.
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