CN107171868A - The malfunctioning node detection method and its malfunctioning node detection system of SDN - Google Patents

The malfunctioning node detection method and its malfunctioning node detection system of SDN Download PDF

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CN107171868A
CN107171868A CN201710537009.XA CN201710537009A CN107171868A CN 107171868 A CN107171868 A CN 107171868A CN 201710537009 A CN201710537009 A CN 201710537009A CN 107171868 A CN107171868 A CN 107171868A
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node
mrow
probe
probability
influence
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殷波
王志军
冯伟斌
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China United Network Communications Group Co Ltd
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China United Network Communications Group Co Ltd
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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
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0677—Localisation of faults
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
    • H04L43/0817—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Environmental & Geological Engineering (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

The invention belongs to communication technical field, and in particular to the malfunctioning node detection method and its malfunctioning node detection system of a kind of SDN.The malfunctioning node detection method of the SDN, including：Step 1：Probe is set in node, and obtains the state of the node belonging to the probe；Step 2：Calculate the factor of influence of the node belonging to the probe；Step 3：The path of the node belonging to the result of detection and the probe of the state of the node according to belonging to every probe, calculates the probability of malfunction of the node；Step 4：The condition when probability of malfunction for finding out the node reaches maximum；Step 5：Export the node set that all probability reach maximum, that is, the malfunctioning node set obtained.Its using Bayesian network disposed in SDN probe realize to malfunctioning node detect, when can effectively solve failure node in current SDN, because network structure is huge, be difficult find its trouble point and by localization of fault out the problem of.

Description

The malfunctioning node detection method and its malfunctioning node detection system of SDN
Technical field
The invention belongs to communication technical field, and in particular to the malfunctioning node detection method and its failure of a kind of SDN Node detection system.
Background technology
Data forwarding and control are realized in SDN (Software Defined Network, i.e. software defined network) System separation, is a kind of data control separation, the network architecture of software programmable, it uses the control plane and distribution of centralization Forwarding plane, control plane and Forwarding plane be separated from each other, and control plane is using southbound interface agreement on Forwarding plane The network equipment carries out centerized fusion, and provides flexible programmability by open northbound interface for network.
SDN is inevitably present the problem of node failure fails, and current SDN Technical comparing is novel, on The fault diagnosis of SDN, the research of the SDN fault diagnosis especially under virtual private cloud environment is seldom.How to enter The nodal fault diagnostics of row SDN, carry out fault recovery in time, it is ensured that the continuity of SDN institute bearer service, is SDN Network fault diagnosis needs the subject matter solved.
The content of the invention
The technical problems to be solved by the invention are that there is provided a kind of SDN nets for above shortcomings in the prior art The malfunctioning node detection method and its malfunctioning node detection system of network, it can at least partly solve asking for SDN fault diagnosis Topic.
The technical scheme that solution present invention problem is used is the malfunctioning node detection method of the SDN, including Step：
Step 1：Probe is set in node, and obtains the state of the node belonging to the probe；
Step 2：Calculate the factor of influence of the node belonging to the probe；
Step 3：The affiliated institute of result of detection and the probe of the state of the node according to belonging to every probe The path of node is stated, the probability of malfunction of the node is calculated；
Step 4：The condition when probability of malfunction for finding out the node reaches maximum；
Step 5：Export the node set that all probability reach maximum, that is, the malfunctioning node set obtained.
Preferably, in step 1：The part node sets the probe in a network, and passes through the probe institute Belong to the node state set obtain the probe paths traversed the node state sparse matrix.
Preferably, in step 2, the calculation formula of the factor of influence of the node belonging to the probe is：
IF(v0)=α Fin (v0)+βFout(v0)
Wherein：V is some specific node in network, and Fin (v0) is the internal influence factor of the node, and α is inside Influence coefficient；Fout (v0) is the external action factor of the node, and β is external action coefficient, and Sn is what the node influenceed Business number, the numerical value that Li is quantified by the node to the influence degree of some business, K (v) is the interior subordinate of the node Property value.
Preferably, in step 3, the calculation formula of the probability of malfunction of the node is：
P(V1,V2,…Vi... Vn,T1,T2,...Tj,...Tm)=
P(T1|Pa(T1))P(T2|Pa(T2))...P(Tm|Pa(Tm))P(V1)P(V2)...P(Vn)
Wherein：P (V1, V2 ... Vi ... Vn, T1, T2 ... Tj ... Tm) be all nodes conditional probability, Characterize the influence that the failure of the node is brought to whole network；
(Pa (Tj) represents all links that the probe Tj passes through, when having node failure in the probe Tj, p (Tj= 1 | Pa (Tj))=1, p (Tj=0 | Pa (Tj))=0, when the probe Tj fault-frees, and p (Tj=1 | Pa (Tj))=0, p (Tj =0 | Pa (Tj))=1；Tj=1 represents normal, and Tj=0 represents failure；
Vi is node described in i-th, and Tj is probe described in j-th strip, and n is the node number, and m is the probe bar number (m ＜ n).
Preferably, after the factor of influence of the node belonging to the probe is calculated, the event of the calculating node Also include step before hindering probability：According to the sparse matrix, being obtained using maximal margin matrix algorithm includes all nodes State perfect matrix.
A kind of malfunctioning node detection system of SDN, including acquiring unit, computing unit, positioning unit and output are single Member, wherein：
The acquiring unit, the state for obtaining nodes, and by the state transfer of the node to the meter Calculate unit；
The computing unit, the state for receiving the node, the factor of influence of node described in calculating network and The node faulty probability in the paths；
The positioning unit, for the faulty probability in the paths of the node according to the computing unit, finds out The probability of malfunction of the node reaches condition during maximum；
The output unit, the node set of maximum is reached for exporting all probability, that is, the failure section obtained Point set.
Preferably, in the acquiring unit：The part node sets the probe in a network, and by described The state set of the node belonging to probe obtains the sparse matrix of the state of the node of the probe paths traversed.
Preferably, the computing unit includes factor of influence computing module, in the factor of influence computing module, institute The calculation formula for stating the factor of influence of the node belonging to probe is：
IF(v0)=α Fin (v0)+βFout(v0)
Wherein：V is some specific node in network, and Fin (v0) is the internal influence factor of the node, and α is inside Influence coefficient；Fout (v0) is the external action factor of the node, and β is external action coefficient, and Sn is what the node influenceed Business number, the numerical value that Li is quantified by the node to the influence degree of some business, K (v) is the interior subordinate of the node Property value.
Preferably, the computing unit includes node probability evaluation entity, in the node probability evaluation entity, institute The calculation formula for stating the probability of malfunction of node is：
P(V1,V2,…Vi... Vn,T1,T2,...Tj,...Tm)=
P(T1|Pa(T1))P(T2|Pa(T2))...P(Tm|Pa(Tm))P(V1)P(V2)...P(Vn)
Wherein：P (V1, V2 ... Vi ... Vn, T1, T2 ... Tj ... Tm) be all nodes conditional probability, Characterize the influence that the failure of the node is brought to whole network；
(Pa (Tj) represents all links that the probe Tj passes through, when having node failure in the probe Tj, p (Tj= 1 | Pa (Tj))=1, p (Tj=0 | Pa (Tj))=0, when the probe Tj fault-frees, and p (Tj=1 | Pa (Tj))=0, p (Tj =0 | Pa (Tj))=1；Tj=1 represents normal, and Tj=0 represents failure；
Vi is node described in i-th, and Tj is probe described in j-th strip, and n is the node number, and m is the probe bar number (m ＜ n).
Preferably, the computing unit also includes completion module, and the completion module is used to calculate the probe After the factor of influence of the affiliated node, before the probability of malfunction of the calculating node：According to the sparse matrix, using most Big marginalisation matrix algorithm obtains the perfect matrix for the state for including all nodes.
The beneficial effects of the invention are as follows：The malfunctioning node detection method and its malfunctioning node detection system of the SDN, profit Probe is disposed in SDN to realize to malfunctioning node detection, can effectively solve to occur in current SDN with Bayesian network During malfunctioning node, due to the huge structure of network, it is difficult to find its trouble point, and the problem of localization of fault is come out.
Brief description of the drawings
Fig. 1 is the flow chart of the malfunctioning node detection method of SDN in the embodiment of the present invention；
Fig. 2 is the schematic diagram of network element connection figure in the embodiment of the present invention；
Fig. 3 is the schematic diagram of MMMF algorithm principles in the embodiment of the present invention；
Fig. 4 is the schematic diagram of the relation between interior joint of the embodiment of the present invention and probe；
Fig. 5 is the structural representation of the malfunctioning node detection system of SDN in the embodiment of the present invention；
1- acquiring units；2- computing units；3- positioning units；4- output units.
Embodiment
To make those skilled in the art more fully understand technical scheme, below in conjunction with the accompanying drawings and specific embodiment party Formula is described in further detail to the malfunctioning node detection method and its malfunctioning node detection system of SDN of the present invention.
The present invention proposes a kind of detection method of the malfunctioning node in SDN, and it is by the probe skill in traditional IP network Art is incorporated into SDN, and disposing probe as few as possible realizes the state acquisition to SDN, then using Bayes Network is analyzed network state, realizes the fault detect of SDN.
Below in conjunction with the flow chart of the malfunctioning node location algorithm based on probe shown in Fig. 1, illustrate allowed for influencing factors SDN malfunctioning node detection method：
Step 1：Probe is set in node, and obtains the state detection result of the affiliated node of probe.
In this step, in order to reduce the load of network, a small amount of probe is sent into SDN first, actually should In, probe can be one section of program being deployed on the node of each in SDN, can obtain the status information of node；So Go out the result of remaining probe using probe prediction technological prediction end to end afterwards, the device node of linking probe is selected at random. Here, without sending probe to all nodes in SDN, first to random placement in SDN during probe deployment A part of probe, then detects the deployed position of remaining probe using Predicting Technique end to end.Random selection device section Point deployment probe, other nodes are attached with the probe being deployed to.
The step sends multiple probes by the Predicting Technique of probe, but not fully sends to all nodes and be Wherein a little node is sent, as long as the probe of deployment, which can be connected, covers all network equipments, from And can ensure to complete the detection to SDN with a small amount of probe.
Probe deployment can be obtained in a sparse matrix, sparse matrix on node by the probe set A sent Each numerical value represent whether probe paths traversed normal.The mode of acquisition is：In this sparse matrix where element Row correspond to the start node in the path that the probe that has sent passes through, row correspond to the end in the path that the probe sent passes through Node, so that the access path between forming probe, returning result represents whether the link connected between two nodes connects.If The result that this probe is returned is normal (being connected between two nodes where representing probe), then corresponds to element in sparse matrix It is worth for -1；If the result that this probe is returned is failure, the value for corresponding to element in sparse matrix is 1；The probe not sent, The value for then corresponding to element in sparse matrix is 0, that is to say, that the element in sparse matrix is constituted by 1,0, -1.
Step 2：Calculate the factor of influence of the affiliated node of probe.
There is a factor of influence to the whole network in whole SDN in the node belonging to probe.In this step, utilize Formula (1)-formula (3) calculates the factor of influence of each node.
For SDN, its business trend is more fixed, and N-1 principles are followed simultaneously for important business, i.e., every Individual important service can all have a more than one path, and how much the significance level of business can be characterized with optional number of paths, such as Network element connection figure shown in Fig. 2 is to be used as network model using the sub-fraction node connection in network.
First, network model in fig. 2 does service definition：Because set scale of model is smaller, so limiting industry The quantity of business, provides to have 5 business, i.e. S1-S5 in a model now.SDN business have itself the characteristics of and type, in order to The bearing mode and content of these business are distinguished, and the emphasis of concern is embodied, is convenient for based on bearer service resource The analysis of side, here to set probe to carry out example eight nodes, the form of common factor is taken with resource come table by business S1-S5 Show, user's mathematical formulae is described as follows：
S1：X1∩X2∩X5 S2：X3∩X2∩X6 S3：X6∩X7∩X8
S4：X4∩X6∩X7 S5：X3∩X6∩X8
Next, the impacted degree of analysis business：Because business is moved towards to fix, various failures are to the influence degree of business Different, for quantitative mark business degree of susceptibility, the standard that influence degree is provided is reported for service impact, with reference to actual motion In possibility situation, according to assume the problem of model, business degree of susceptibility can be drawn as shown in Table 1：
The impacted degree of the business of table one
Influence degree Description
Unaffected (L1) Service operation is normal
Potential impact (L2) Business can normally be run, but there are potential faults
Performance is impacted (L3) Business can be run, but service quality is poor
Have a strong impact on (L4) Business can not be run
Node is represented with X, and L is identified as influence degree, using v as corresponding variable in calculating process, according to Each node failure of Business Process Analysis for business influence degree, as a result as shown in Table 2：
The node disturbance degree of table two
V1 V2 V3 V4 V5 V6 V7 V8
S1 L4 L3 L1 L2 L4 L2 L1 L1
S2 L2 L3 L4 L2 L2 L4 L1 L1
S3 L1 L1 L1 L1 L1 L4 L3 L4
S4 L1 L2 L1 L4 L2 L3 L4 L1
S5 L2 L2 L4 L2 L2 L3 L3 L4
Then, the factor of influence of calculate node：In the case where network element device breaks down, can with objective method come Measure influence of the SDN equipment to business.Node failure can not only directly affect business, can also indirectly influence associated section Point, utilizes Complex Networks Theory, it is considered to the built-in attribute and external attribute of node, the comprehensive factor of influence to seek node.
In SDN, built-in attribute is influence of the node failure for other nodes in network topology, including node Degree, betweenness, tight ness rating；External attribute is combined influence degree of the node failure to each business.By quantifying these indexs, obtain To the numerical value of each node factor of influence.
In order to corresponding with built-in attribute, quantify external attribute first, for one numerical value of each grade regulation, such as table three It is shown：
Combined influence degree of the node failure of table three for business
L1 L2 L3 L4
0 0.3 0.7 1
Influence degree level value in table three is example, in fact should can be arbitrarily defined as times between 0-1 What numerical value.
Node failure influence value：
Wherein：V is some specific node in SDN, and Sn is the business number that node influences, and Li is node to some The numerical value (numerical value i.e. in table three) that the influence degree of business is quantified, according to the node of SDN and the relationship characteristic of business, Node failure combined influence value is the average value for each service impact, is also the outside shadow of a node in SDN Ring power.
SDN is designated as G, wherein there is the link E between node V and node, then has in G=(V, E), node v0 shadow The factor is rung to be defined as follows：
IF(v0)=α Fin (v0)+βFout(v0) (2)
Wherein, Fin (v0) is the internal influence factor of node, and α is internal influence coefficient；Fout (v0) is the outside of node Factor of influence, β is external action coefficient, and meets alpha+beta=1, wherein, two values of α, β can be according to actual conditions and SDN Structure is manually set, and the two, which is added, is equal to 1, and the larger then internal influence coefficient value of internal influence is a little high；External action compared with Greatly, then external action coefficient value is a little high, can set at random without limiting.For example in the present embodiment more consideration is section The influence that point is brought for external business, therefore the proportion of the external action factor is larger, and β is taken to relatively large value, preferably passes through Value is tested for α=0.2, β=0.8.
Fout (v0) is the external action factor, and as formula (1) calculates the fault impact value Cd (V0) of obtained node, Fin (v0) is the internal influence factor, is calculated and obtained by following formula (3), it is possible thereby to calculate egress according to formula (2) Factor of influence IF numerical value.
Here,
Wherein：K (v0) is the built-in attribute value of node, can use the node self attributes such as the number of degrees, betweenness, tight ness rating, due to α value is smaller in the regulation of formula (2), so specifically choosing influence of which built-in attribute value to result and little；max (K (v)) is normalization factor, in order to eliminate the influence of network size logarithm value so that it is interval interior that index is unified in [0,1].
Step 3：The road passed by according to the result of detection of the state of the affiliated node of every probe and the affiliated node of probe Footpath, the probability of malfunction of calculate node.
Probe deployment can interconnect to after node, be related to multilink.In this step, every probe is inputted The result of detection of the state of affiliated node, on the basis of the factor of influence of above-mentioned node, using Bayesian network model, The factor of influence of node is applied in new probability formula to position the node broken down in network.I.e. according to the spy of every probe Survey result, the probability of malfunction of the path that probe is passed by and node, calculate respectively formula P (T1 | Pa (T1)) P (T2 | Pa (T2)) ... P (Tm | Pa (Tm)) P (V1) P (V2) ... P (Vn) numerical value.
Step 4：The condition when probability of malfunction for finding out step 3 interior joint reaches maximum.
In this step, find out P (V1, V2 ... Vi ... Vn, T1, T2 ... Tj ... Tm) bar of the value in maximum The specific node V of some in part, i.e. network value (being 0 or 1), wherein：0 represents node failure, and 1 represents that node is normal.
MMMF algorithms are maximal margin matrix algorithm (Maximum-Margin Ma-trix Factorization), such as Fig. 3 show MMMF algorithm principle schematic diagrames.It by original matrix by being decomposed into factor matrix U and V, while to object function Optimize；Then normal form constraint is carried out to factor matrix U and V, factor matrix U is multiplied with V, so as to obtain predicting square Battle array Y.
When MMMF algorithms are applied in the malfunctioning node detection method of the SDN of the present embodiment, according to the detection of probe As a result the sparse matrix (matrix is an ill-conditioned matrix) obtained obtains a perfect matrix, and the calculating process namely will All elements are predicted for 0 probe in above-mentioned sparse matrix, i.e., the state of the node of probe is not provided with predicted path. MMMF is a synchronous study characteristic vector v and coefficient vector u process, and original matrix Y is decomposed into the matrix of low norm.It is low Order constraint is substantially exactly the dimension in binding characteristic space, and it is exactly one in fact in lower dimensional space that Y each row, which are predicted, On prediction task.A characteristic vector v and a coefficient vector u are calculated using the nonindependence between element in matrix, Then a perfect matrix is obtained using the two vectorial products, and this perfect matrix is approximately estimating for ill-conditioned matrix Meter, the detection event of all probes is represented with this.It should be understood that the state of the probe of deployment is also without all detecting simultaneously Return, as long as the state for the probe that return path is related to.
The positioning of malfunctioning node is carried out using Bayesian network model after matrix is predicted.What probe was detected Information, actually failure occur after by various entities interact and the external manifestation of generation, so now network therefore Barrier positioning just embodies its uncertain feature.And Bayesian network has under condition of uncertainty environment, to causality The advantage of diagnosis.Bayesian network goes to express the joint probability distribution and its conditional independence of variable, energy in the form of figure simultaneously It is enough to connect Bayesian network model with the node in SDN and detection, form one two layers direct digraph.
The relation between node and probe is illustrated in figure 4, the wherein device node in network is considered as father node, and probe is considered as Child node, the result of probe depends on the result of its father node, the shape of probe when and if only if its all father node is correct State is just correct, by calculating the node that the probability of malfunction of each node can be most possibly to break down in location path.
Calculate all according to parameter (prior probability and the result of all probes that include all-network node) to be positioned The conditional probability of network node, that is, calculate P (V1, V2 ... Vi ... Vn, T1, T2 ... Tj ... Tm) probability, wherein Vi For i-th of network node, Tj is j-th strip probe, and n is network node number, and m is probe bar number (m ＜ n).P (V1, V2, ... Vi ... Vn, T1, T2 ... Tj ... Tm) calculation formula be：
P(V1,V2,…Vi... Vn,T1,T2,...Tj,...Tm)=
P(T1|Pa(T1))P(T2|Pa(T2))...P(Tm|Pa(Tm))P(V1)P(V2)...P(Vn)
Wherein：
(1) P (V1, V2 ... Vi ... Vn, T1, T2 ... Tj ... Tm) be all nodes conditional probability, characterize section The influence that point failure is brought to whole network；
(2) Pa (Tj) represents all links that probe Tj passes through, when having node failure in probe Tj, and p (Tj=1 | Pa (Tj))=1, p (Tj=0 | Pa (Tj))=0, p when trouble-free (Tj=1 | Pa (Tj))=0, p (Tj=0 | Pa (Tj))= 1；Tj=1 represents normal, and Tj=0 represents failure.
(3) prior probability is the out of order probability of network node obtained according to previous experiences and analysis, is an estimation Value in value, different SDN systems is different.In the model of the present embodiment, the Prior Probability of node failures is 0.1, the Prior Probability p (Xi)=0.9 during node fault-free, therefore, when faulty the probability of malfunction of node be p (Xi)= 0.1*IF (Xi), that is, consider the failure probability of node and the product of factor of influence, because the characteristic of Bayesian network, using The method of prior probability * factors of influence, obtained instant posterior probability, can more be accurately positioned malfunctioning node.Here, failure probability As prior probability, probability of malfunction is posterior probability=prior probability * factors of influence.
(4) formula P (V are utilized1,V2..., Vn,T1,T2,...,Tm)=P (Vx)...P(Vy)*0.9n-k, wherein k is network The number of faults of the node of middle appearance, Vx...Vy is malfunctioning node, the probability of malfunction of P (Vx) ... P (Vy) correspondence respective nodes, pin To above-mentioned node, k is the number from 0-8, takes different values of the k from 0-8, can calculate different P (V1, V2 ... Vi, ... Vn, T1, T2 ... Tj ... Tm), the value under all situations is calculated, condition when taking its maximum judges V value.
Similarly, the node that there may be failure for being not provided with probe is also predicted, i.e. of V=0 malfunctioning node Number, then calculates the probability in the case of each node V=0 and V=1.Calculate under all situations P (V1, V2, ... Vi ... Vn, T1, T2 ... Tj ... Tm) after value, obtain P (V1, V2 ... Vi ... Vn, T1, T2 ... Tj ... Tm) It is worth the condition in maximum, i.e. V value (being 0 or 1).
Here it will be understood that to ensure computational efficiency, the present embodiment is only to calculate the shadow for the node that probe is related to Ring exemplified by the factor, now efficiency should be highest in theory.But, it is not excluded that the factor of influence of all nodes is all calculated Mode out, is not limited here.
Step 5：All V=0 node set is exported, obtained malfunctioning node set is as calculated.
Because nodes failure can cause service path to interrupt, using probe collector node information, shellfish is then utilized This network calculations of leaf are out of order probability.In this step, all V=0 node set is malfunctioning node set.
Accordingly, the present embodiment also provides a kind of malfunctioning node detection system of SDN, as shown in figure 5, it includes obtaining Unit 1, computing unit 2, positioning unit 3 and output unit 4 are taken, wherein：
Acquiring unit 1, the state for obtaining nodes, and by the state transfer of node to computing unit 2；
Computing unit 2, for the state of receiving node, the factor of influence and node of calculating network interior joint are in the paths Faulty probability；
Positioning unit 3, for according to the interior joint of computing unit 2 faulty probability in the paths, finding out the failure of node Probability reaches condition during maximum；
Output unit 4, the node set of maximum is reached for exporting all probability, that is, the malfunctioning node set obtained.
In the malfunctioning node detection system of the SDN, acquiring unit 1, computing unit 2, positioning unit 3 are corresponded to respectively The calculating of the information that the acquisition of detecting probe information in the malfunctioning node detection method of SDN, probe are obtained, malfunctioning node are determined Three steps in position.Acquiring unit 1 occurs in the form of multiple probes, by multiple node deployment probes in SDN, uses To obtain the state of nodes, while by the information transmission got to computing unit 2.
In acquiring unit 1：Part of nodes sets probe in a network, and is obtained by the state set of the affiliated node of probe To the sparse matrix of the state of the node of probe paths traversed.
Computing unit 2 is calculated the information that probe is got, and calculates the node factor of influence in network, Yi Jijie The probability that point breaks down in the paths.Computing unit 2 includes factor of influence computing module, in factor of influence computing module, The calculation formula of the factor of influence of the affiliated node of probe is：
IF(v0)=α Fin (v0)+βFout(v0)
Wherein：V is some specific node in network, and Fin (v0) is the internal influence factor of node, and α is internal influence Coefficient；Fout (v0) is the external action factor of node, and β is external action coefficient, and Sn is the business number that node influences, and Li is section The numerical value that point is quantified to the influence degree of some business, K (v) is the built-in attribute value of node.
Computing unit 2 includes node probability evaluation entity, in node probability evaluation entity, the meter of the probability of malfunction of node Calculating formula is：
P(V1,V2,…Vi... Vn,T1,T2,...Tj,...Tm)=
P(T1|Pa(T1))P(T2|Pa(T2))...P(Tm|Pa(Tm))P(V1)P(V2)...P(Vn)
Wherein：P (V1, V2 ... Vi ... Vn, T1, T2 ... Tj ... Tm) be all nodes conditional probability, characterize The influence that the failure of node is brought to whole network；
(Pa (Tj) represents all links that probe Tj passes through, when having node failure in probe Tj, and p (Tj=1 | Pa (Tj))=1, p (Tj=0 | Pa (Tj))=0, when probe Tj fault-frees, and p (Tj=1 | Pa (Tj))=0, p (Tj=0 | Pa (Tj))=1；Tj=1 represents normal, and Tj=0 represents failure；
Vi is i-th of node, and Tj is j-th strip probe, and n is node number, and m is probe bar number (m ＜ n).
Preferably, computing unit 2 also includes completion module, and completion module is used to calculate the shadow of the affiliated node of probe After the sound factor, before the probability of malfunction of calculate node：According to sparse matrix, included using maximal margin matrix algorithm The perfect matrix of the state of all nodes.
The invention provides a kind of fault detection method of SDN and its malfunction detection system of corresponding SDN, It uses Bayesian network to carry out the fault-finding of SDN：Node state is realized by disposing probe in a network Collection, while status data input Bayesian network to be calculated to the probability of node failure under traffic failure state, utilizes priori Probability * factors of influence obtain instant posterior probability, realize the prediction to malfunctioning node in SDN and positioning.
It is understood that the principle that embodiment of above is intended to be merely illustrative of the present and the exemplary implementation that uses Mode, but the invention is not limited in this.For those skilled in the art, the essence of the present invention is not being departed from In the case of refreshing and essence, various changes and modifications can be made therein, and these variations and modifications are also considered as protection scope of the present invention.

Claims (10)

1. the malfunctioning node detection method of a kind of SDN, it is characterised in that including step：
Step 1：Probe is set in node, and obtains the state of the node belonging to the probe；
Step 2：Calculate the factor of influence of the node belonging to the probe；
Step 3：The section belonging to the result of detection and the probe of the state of the node according to belonging to every probe The path of point, calculates the probability of malfunction of the node；
Step 4：The condition when probability of malfunction for finding out the node reaches maximum；
Step 5：Export the node set that all probability reach maximum, that is, the malfunctioning node set obtained.
2. the malfunctioning node detection method of SDN according to claim 1, it is characterised in that in step 1：In net The part node sets the probe in network, and obtains the probe by the state set of the node belonging to the probe The sparse matrix of the state of the node of paths traversed.
3. the malfunctioning node detection method of SDN according to claim 1, it is characterised in that in step 2, described The calculation formula of the factor of influence of the node is belonging to probe：
IF(v0)=α Fin (v0)+βFout(v0)
<mrow> <msub> <mi>C</mi> <mrow> <mi>d</mi> <mrow> <mo>(</mo> <mi>V</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mi>L</mi> <mi>i</mi> </mrow> <mrow> <mi>S</mi> <mi>n</mi> </mrow> </mfrac> <mo>&amp;times;</mo> <mn>100</mn> <mi>%</mi> </mrow>
<mrow> <mi>F</mi> <mi>i</mi> <mi>n</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>K</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mi>max</mi> <mrow> <mo>(</mo> <mi>K</mi> <mo>(</mo> <mi>v</mi> <mo>)</mo> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>,</mo> <mi>v</mi> <mo>&amp;Element;</mo> <mi>V</mi> </mrow>
Wherein：V is some specific node in network, and Fin (v0) is the internal influence factor of the node, and α is internal influence Coefficient；Fout (v0) is the external action factor of the node, and β is external action coefficient, and Sn is the business that the node influences Number, the numerical value that Li is quantified by the node to the influence degree of some business, K (v) is the built-in attribute value of the node.
4. the malfunctioning node detection method of SDN according to claim 1, it is characterised in that in step 3, the section The calculation formula of probability of malfunction of point is：
P(V1,V2,...Vi... Vn,T1,T2,...Tj,...Tm)=
P(T1|Pa(T1))P(T2|Pa(T2))...P(Tm|Pa(Tm))P(V1)P(V2)...P(Vn)
Wherein：P (V1, V2 ... Vi ... Vn, T1, T2 ... Tj ... Tm) be all nodes conditional probability, characterize The influence that the failure of the node is brought to whole network；
(Pa (Tj) represents all links that the probe Tj passes through, when having node failure in the probe Tj, and p (Tj=1 | Pa (Tj))=1, p (Tj=0 | Pa (Tj))=0, when the probe Tj fault-frees, p (Tj=1 | Pa (Tj))=0, p (Tj=0 | Pa (Tj))=1；Tj=1 represents normal, and Tj=0 represents failure；
Vi is node described in i-th, and Tj is probe described in j-th strip, and n is the node number, and m is probe bar number (the m ＜ n)。
5. the malfunctioning node detection method of SDN according to claim 2, it is characterised in that calculating the spy Also include step after the factor of influence of the node belonging to pin, before the probability of malfunction of the calculating node：According to described dilute Matrix is dredged, the perfect matrix for the state for including all nodes is obtained using maximal margin matrix algorithm.
6. the malfunctioning node detection system of a kind of SDN, it is characterised in that including acquiring unit, computing unit, positioning unit And output unit, wherein：
The acquiring unit, the state for obtaining nodes, and the state transfer of the node is calculated single to described Member；
The computing unit, the state for receiving the node, the factor of influence of node described in calculating network and described Node faulty probability in the paths；
The positioning unit, for the faulty probability in the paths of the node according to the computing unit, finds out described The probability of malfunction of node reaches condition during maximum；
The output unit, the node set of maximum is reached for exporting all probability, that is, the malfunctioning node collection obtained Close.
7. the malfunctioning node detection system of SDN according to claim 6, it is characterised in that in the acquiring unit In：The part node sets the probe in a network, and is obtained by the state set of the node belonging to the probe The sparse matrix of the state of the node of the probe paths traversed.
8. the malfunctioning node detection system of SDN according to claim 6, it is characterised in that the computing unit bag Factor of influence computing module is included, in the factor of influence computing module, the factor of influence of the node belonging to the probe Calculation formula is：
IF(v0)=α Fin (v0)+βFout(v0)
<mrow> <msub> <mi>C</mi> <mrow> <mi>d</mi> <mrow> <mo>(</mo> <mi>V</mi> <mo>)</mo> </mrow> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mi>L</mi> <mi>i</mi> </mrow> <mrow> <mi>S</mi> <mi>n</mi> </mrow> </mfrac> <mo>&amp;times;</mo> <mn>100</mn> <mi>%</mi> </mrow>
<mrow> <mi>F</mi> <mi>i</mi> <mi>n</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>K</mi> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mi>max</mi> <mrow> <mo>(</mo> <mi>K</mi> <mo>(</mo> <mi>v</mi> <mo>)</mo> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>,</mo> <mi>v</mi> <mo>&amp;Element;</mo> <mi>V</mi> </mrow>
Wherein：V is some specific node in network, and Fin (v0) is the internal influence factor of the node, and α is internal influence Coefficient；Fout (v0) is the external action factor of the node, and β is external action coefficient, and Sn is the business that the node influences Number, the numerical value that Li is quantified by the node to the influence degree of some business, K (v) is the built-in attribute value of the node.
9. the malfunctioning node detection system of SDN according to claim 6, it is characterised in that the computing unit bag Node probability evaluation entity is included, in the node probability evaluation entity, the calculation formula of the probability of malfunction of the node is：
P(V1,V2,...Vi... Vn,T1,T2,...Tj,...Tm)=
P(T1|Pa(T1))P(T2|Pa(T2))...P(Tm|Pa(Tm))P(V1)P(V2)...P(Vn)
Wherein：P (V1, V2 ... Vi ... Vn, T1, T2 ... Tj ... Tm) be all nodes conditional probability, characterize The influence that the failure of the node is brought to whole network；
(Pa (Tj) represents all links that the probe Tj passes through, when having node failure in the probe Tj, and p (Tj=1 | Pa (Tj))=1, p (Tj=0 | Pa (Tj))=0, when the probe Tj fault-frees, p (Tj=1 | Pa (Tj))=0, p (Tj=0 | Pa (Tj))=1；Tj=1 represents normal, and Tj=0 represents failure；
Vi is node described in i-th, and Tj is probe described in j-th strip, and n is the node number, and m is probe bar number (the m ＜ n)。
10. the malfunctioning node detection system of SDN according to claim 7, it is characterised in that the computing unit is also Including completion module, the completion module is used for after the factor of influence of the node belonging to the probe is calculated, calculated Before the probability of malfunction of the node：According to the sparse matrix, being obtained using maximal margin matrix algorithm includes all sections The perfect matrix of the state of point.
CN201710537009.XA 2017-07-04 2017-07-04 The malfunctioning node detection method and its malfunctioning node detection system of SDN Pending CN107171868A (en)

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