CN105071963A - Multi-service traffic estimation method facing big data Internet - Google Patents
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Abstract
The invention provides a multi-service traffic estimation method facing a big data Internet, and belongs to the technical field of communication networks. The method comprises the following steps: step 1 of acquiring a backbone network link load by a network management station by adopting a simple network management protocol; step 2 of acquiring a route matrix by the network management station according to the routing table information in a network topology and a router; step 3 of generating into one random Bernoulli matrix by the network management station, and determining partial directly measured end-to-end network traffics according to the matrix; step 4 of approximately describing a traffic matrix by using a principal component analysis method; step 5 of constructing a network tomography model according to the steps 1, 2 and 4; step 6 of constructing a linear measurement model according to the steps 3 and 4; step 7 of estimating the traffic matrix by the network management station through the method of constructing an optimizing model according to the models in steps 5 and 6. According to the multi-service traffic estimation method facing the big data Internet provided by the invention, the linear measurement model is constructed through the Bernoulli matrix and a traffic estimation model is construction by using a network tomography technology.
Description
Technical Field
The invention belongs to the technical field of communication networks, and particularly relates to a multi-service traffic estimation method for a big data internet.
Background
With the continuous development of information and communication technologies, internet technologies have been deeply developed into the current social life and industrial production. Particularly, the proposal of the internet + further promotes the popularization of the internet technology in the fields of industry, agriculture and the like, and promotes the information process of the social industry. While the internet technology is gradually deepened into various social fields, the problems of mass data processing, information safety and the like are increasingly highlighted, and the problems bring higher requirements for network management.
In recent years, the internet has become a complex heterogeneous network in the background of providing diverse network services to different end users. Network management functions are introduced into the network to guarantee the quality of service for each user. For a network administrator to perform an effective network management function, it first needs to know the status information of the end-to-end network traffic. In practice, the end-to-end network traffic information may be described by a traffic matrix, which is an important input parameter in the network management process.
Although the traffic matrix has an extremely important role, it is not simple to acquire the traffic matrix for a large-scale backbone network. The reason for this is multiple, and first, for a large-scale backbone network, it is impractical to collect network traffic information directly. In this case, researchers are more inclined to estimate network traffic indirectly rather than collecting network traffic information directly. In many network traffic estimation methods, network traffic is usually inferred from other effective network information, such as network tomography, which estimates network traffic from link load and routing information. However, the network tomography model has a high pathological characteristic, so the research for estimating the network traffic has many challenges.
Disclosure of Invention
Aiming at the defects of the existing method, the invention provides a multi-service traffic estimation method facing to a big data internet, so as to obtain an accurate backbone network traffic estimation value and lay a solid foundation for realizing effective network management and network planning.
A big data internet-oriented multi-service traffic estimation method comprises the following steps:
step 1: the network management station adopts a simple network management protocol to obtain backbone network link load;
step 2: the network management station acquires a routing matrix according to the network topology structure and the routing table information in the router;
and step 3: the network management station generates a random Bernoulli matrix, and determines part of directly measured end-to-end network flow according to the matrix;
and 4, step 4: approximately describing the flow matrix by using a principal component analysis method;
and 5: constructing a network tomography model according to the steps 1, 2 and 4;
step 6: constructing a linear measurement model according to the steps 3 and 4;
and 7: according to the models in the steps 5 and 6, the network management station estimates the flow matrix by a method of constructing an optimized model.
The network management station in step 1 adopts a simple network management protocol to obtain the load of a backbone network link, and the method comprises the following steps: when using matrixLWhen indicating link load, for one withNA node andPbackbone network of bar link, interceptTLink load data for each time slot, thenLIs oneP×TOf the matrix of (a).
The network management station in step 2 obtains the routing matrix according to the network topology and the routing table information in the router, and the method is as follows: when in useRWhen representing a routing matrix, for one hasNA node andPbackbone network of bar links, routing matrixRIs oneP×N 2Of the matrix of (a).
The network management station in step 3 generates a random bernoulli matrix, and determines part of directly measured end-to-end network flow according to the matrix, and the specific steps are as follows:
step 3-1: by means of matricesMRepresents a traffic matrix, the traffic matrix is then oneN 2×TEach end-to-end network traffic gets an identity, 1 toN 2；
Step 3-2: the network management station generates oneQ×N 2Random Bernoulli matrix of symbolsBRepresents that the elements are 1 or 0, respectively. Matrix arrayBEach of which is independently and identically distributed, anb q,n ～Bern(z) Whereinq=1,2,…,Q，n=1,2,…,N 2，zIs as followsb q,n Probability when = 1;
step 3-3: P-Bernoulli matrixBElement of each column ofTaking and collecting the elements, i.e.
，
Wherein,w n is composed ofBThe calculation result of the union of the elements of each column in the tree;
step 3-4: when in usew n When the value of (1) is equal to 1, the network management station measures the serial number as the number by using the NetFlow function of the router through a method for controlling the port of the routernEnd-to-end network traffic;
step 3-5: putting directly measured end-to-end network traffic into a traffic matrixMMedium then traffic matrixMEnd-to-end network traffic known through direct measurement and unknown traffic not measured are included;
step 3-6: a linear system is obtained according to steps 3-5, i.e.
，
Wherein, the matrixYReferred to as measured values, from the above linear relationship,Ytraffic-only matrixMIs related to known end-to-end network traffic and is not related to unknown traffic, so the measurementsYAs is known to us, end-to-end network traffic and Bernoulli matrix, which can be measured directlyBAnd multiplying and calculating.
The method for approximately describing the flow matrix by utilizing the principal component analysis method in the step 4 comprises the following specific steps:
step 4-1: the network management station gathers a historical traffic matrix and represents it asM *；
Step 4-2: method for decomposing historical flow matrix by using singular value decompositionM *By means of, i.e.
，
Wherein, sigma*Is a diagonal matrix whose values on diagonal elements are matricesM *TThe singular value of (a).V *Is an orthogonal matrix, a matrixU *Describing historical traffic matrix dynamic change information;
step 4-3: approximately describing matrix M by principal components T*I.e. by
，
Wherein the diagonal matrix Σ* KFor extraction ofKA matrix of singular values after the principal component, which step is equivalent to preservingKThe largest singular value and setting the other small singular values as 0;
step 4-4: using historical traffic matricesM *Approximately describes the flow matrixMI.e. by
，
Wherein, the matrixU TA traffic matrix is describedMThe dynamic variation characteristic of (2).
Step 5, constructing a network tomography model according to the steps 1, 2 and 4, wherein the method comprises the following steps: the network tomography model is expressed as
。
Step 6, constructing a linear measurement model according to steps 3 and 4, the method comprising: the constructed model is represented as:
。
step 7, according to the models in steps 5 and 6, the network management station estimates the traffic matrix by a method for constructing an optimized model, and the specific steps are as follows:
step 7-1: the models in steps 5 and 6 construct an optimization model as follows
；
Step 7-2: obtaining a matrix by solving the optimized model in step 7-1U T Expressed as EsU T Great, then flow matrix estimated value EsM}=V *Σ* K Es{U T }。
The invention has the advantages that: the invention relates to a multi-service traffic estimation method facing a big data internet, which adopts a compressed sensing technology and a network tomography technology, on one hand, overcomes the underdetermined characteristic of the network tomography technology; on the other hand, the huge overhead problem of directly measuring all end-to-end network traffic is avoided. The method of the invention obtains better flow matrix estimation value, has obvious improvement on the aspect of estimation error, and reduces the estimation error by 16 percent.
Drawings
FIG. 1 is a network topology structure of a multi-service traffic estimation method for big data Internet according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a big data Internet-oriented multi-service traffic estimation method of the present invention;
FIG. 3 is a flow chart of a big data Internet oriented multi-service traffic estimation method of the present invention;
FIG. 4 is a schematic diagram showing comparison between a true value and an estimated value of end-to-end network traffic, which is labeled 99, of a big data internet-oriented multi-service traffic estimation method of the present invention;
FIG. 5 is a schematic diagram showing comparison between a true value and an estimated value of end-to-end network traffic, which is labeled 105, of a big data internet-oriented multi-service traffic estimation method according to the present invention;
FIG. 6 is a schematic diagram of relative root mean square error of a big data internet oriented multi-service traffic estimation method of the present invention;
fig. 7 is a schematic diagram of a cumulative distribution function of relative root mean square errors of a big data internet-oriented multi-service traffic estimation method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The present embodiment estimates data of an Abilene backbone network, and a topology of the Abilene backbone network is shown in fig. 1, which includes 12 nodes and 54 unidirectional links (including 24 external links and 30 internal links). Thus traffic matrixMThe number of intermediate end-to-end network traffic isN 2=122=144, number of link loadsP= 54. For Bernoulli matrixBNumber of rows thereofQ=144, andzand = 0.01. In the process of principal component analysis, the number of principal components is takenK=6。
The embodiment of the method for estimating the multi-service traffic for the big data internet is shown in fig. 2.
Step 1: the network management station adopts a simple network management protocol to obtain backbone network link load;
when using matrixLWhen representing link load, for a backbone network with 12 nodes and 54 links, intercepting 1516 time slot link load data, thenLIs a 54 x 1516 matrix.
Step 2: the network management station acquires a routing matrix according to the network topology structure and the routing table information in the router;
when in useRRepresenting the routing matrix, the routing matrix is for a backbone network with 12 nodes and 54 linksRIs a 54 x 144 matrix.
And step 3: the network management station generates a random Bernoulli matrix, and determines part of directly measured end-to-end network flow according to the matrix, and the specific steps are as follows:
step 3-1: by means of matricesMRepresenting a flow matrix, wherein the flow matrix is a 144 x 1516 matrix, and each end-to-end network flow obtains an identification code from 1 to 144;
step 3-2: the network management station generates a 144 x 144 random Bernoulli matrix, symbolizedBRepresents that the elements are 1 or 0, respectively. Matrix arrayBEach of which is independently and identically distributed, anb q,n Bern (0.01), whereinq=1,2,…,144，n=1,2,…,144，zIs as followsb q,n Probability when = 1;
step 3-3: P-Bernoulli matrixBIs the union of the elements on each column of (i.e. is a
，
Wherein,w n is composed ofBThe calculation result of the union of the elements of each column in the tree;
step 3-4: when in usew n When the value of (a) is equal to 1,the network management station measures the serial number as the number by using the NetFlow function of the router through a method for controlling the port of the routernEnd-to-end network traffic;
step 3-5: putting directly measured end-to-end network traffic into a traffic matrixMMedium then traffic matrixMEnd-to-end network traffic known through direct measurement and unknown traffic not measured are included;
step 3-6: a linear system is obtained according to steps 3-5, i.e.
，
Wherein, the matrixYReferred to as measured values, from the above linear relationship,Ytraffic-only matrixMIs related to known end-to-end network traffic and is not related to unknown traffic, so the measurementsYAs is known to us, end-to-end network traffic and Bernoulli matrix, which can be measured directlyBAnd multiplying and calculating.
And 4, step 4: approximately describing the flow matrix by using a principal component analysis method, which comprises the following specific steps:
step 4-1: the network management station gathers a historical traffic matrix and represents it asM *；
Step 4-2: method for decomposing historical flow matrix by using singular value decompositionM *By means of, i.e.
，
Wherein, sigma*Is a diagonal matrix whose values on diagonal elements are matricesM T*The singular value of (a).V *Is an orthogonal matrix, a matrixU *Describing historical traffic matrix dynamic change information;
step 4-3: approximately describing matrices using principal componentsM T*I.e. by
，
Wherein the diagonal matrix Σ* K For the singular value matrix after 10 principal components are extracted, the step is equivalent to reserving 10 maximum singular values and setting the rest small singular values as 0;
step 4-4: using historical traffic matricesM *Approximately describes the flow matrixMI.e. by
，
Wherein, the matrixU T A traffic matrix is describedMThe dynamic variation characteristic of (2).
And 5: constructing a network tomography model according to the steps 1, 2 and 4; the network tomography model is expressed as
。
Step 6: constructing a linear measurement model according to the steps 3 and 4; the constructed model is expressed as
。
And 7: according to the models in the steps 5 and 6, the network management station estimates the flow matrix by a method for constructing an optimized model, and the specific steps are as follows:
step 7-1: the models in steps 5 and 6 construct an optimization model as follows
；
Step 7-2: obtaining a matrix by solving the optimized model in step 7-1U T Expressed as EsU T Great, then flow matrix estimated value EsM}=V *Σ* K Es{U T }。
In order to verify the effectiveness of the invention, the capability of tracking the end-to-end network traffic change trend of the big data internet-oriented multi-service traffic estimation method is firstly analyzed. Meanwhile, in the specific embodiment, the multi-service traffic estimation method for the big data internet is compared with three classical traffic matrix estimation methods, namely a Sparse Regularized Singular Value Decomposition (SRSVD) method, a Principal Component Analysis (PCA) method and a gravity model method (mobility) method.
In this embodiment two end-to-end network flows are selected and their true and estimated values are compared. Fig. 4 shows the true and estimated values of end-to-end network traffic, referenced 99. As can be seen from simulation results, all methods can track the variation trend of the number 99 end-to-end network traffic. However, the SRSVD has a large estimation error at 1200 slots. The mobility method is used for under-estimating in all estimation periods when estimating the end-to-end network flow. The PCA method has the greatest error among all methods. Next, the end-to-end network traffic labeled 105 is analyzed, and as shown in fig. 5, the four methods all obtain ideal estimation results. It is often very difficult to track jitter in the traffic. Nevertheless, four methods are able to track the detail change of the end-to-end network traffic, such as jitter between time slots 770-800.
In this embodiment, in order to quantitatively describe the estimation error of the large data internet-oriented multi-service traffic estimation method of the present invention, a Relative Root Mean Square Error (RRMSE) is introduced as a metric to compare the four methods. RRMSE is defined as
，
Wherein,m n (t) Are given the reference numeralsnThe end-to-end network traffic is in the time slottThe flow value of time. Esm n (t) Is asm n (t) An estimate of (d). Fig. 6 shows the relative root mean square error for the four methods. It can be seen that the RRMSE of the multi-service traffic estimation method for the big data internet is obviously lower than that of the SRSVD method and the Tomogravity method. The RRMSE of the PCA method fluctuates widely. The average values of the four methods RRMSE are 0.23, 0.29, 0.26 and 0.27 respectively. The cumulative distribution function of the four methods RRMSE is shown in fig. 7. Fig. 7 illustrates that the RRMSE of a multi-service traffic estimation method for large data internet of the present invention is the lowest in most cases. In other words, the RRMSE of the algorithm presented herein does not fluctuate significantly.
Claims (4)
1. A big data internet-oriented multi-service traffic estimation method is characterized by comprising the following steps: the method comprises the following steps:
step 1: the network management station adopts a simple network management protocol to obtain backbone network link load;
step 2: the network management station acquires a routing matrix according to the network topology structure and the routing table information in the router;
and step 3: the network management station generates a random Bernoulli matrix, and determines part of directly measured end-to-end network flow according to the matrix;
and 4, step 4: approximately describing the flow matrix by using a principal component analysis method;
and 5: constructing a network tomography model according to the steps 1, 2 and 4;
step 6: constructing a linear measurement model according to the steps 3 and 4;
and 7: according to the models in the steps 5 and 6, the network management station estimates the flow matrix by a method of constructing an optimized model.
2. The big data internet oriented multi-service traffic estimation method according to claim 1, wherein: the network management station in step 3 generates a random bernoulli matrix, and determines part of directly measured end-to-end network flow according to the matrix, which specifically comprises the following steps:
step 3-1: by means of matricesMRepresents a traffic matrix, the traffic matrix is then oneN 2×TEach end-to-end network traffic gets an identity, 1 toN 2；
Step 3-2: the network management station generates oneQ×N 2Random Bernoulli matrix of symbolsBRepresents the elements of 1 or 0, respectively, in a matrixBEach of which is independently and identically distributed, anb q,n ～Bern(z) Whereinq=1,2,…,Q，n=1,2,…,N 2，zIs as followsb q,n Probability when = 1;
step 3-3: P-Bernoulli matrixBIs the union of the elements on each column of (i.e. is a
，
Wherein,w n is composed ofBThe calculation result of the union of the elements of each column in the tree;
step 3-4: when in usew n When the value of (1) is equal to 1, the network management station utilizes the router NetFlo by a method for controlling the router portw function measurement number ofnEnd-to-end network traffic;
step 3-5: putting directly measured end-to-end network traffic into a traffic matrixMMedium then traffic matrixMEnd-to-end network traffic known through direct measurement and unknown traffic not measured are included;
step 3-6: a linear system is obtained according to steps 3-5, i.e.
，
Wherein, the matrixYReferred to as measured values, from the above linear relationship,Ytraffic-only matrixMIs related to known end-to-end network traffic and is not related to unknown traffic, so the measurementsYAs is known to us, end-to-end network traffic and Bernoulli matrix, which can be measured directlyBAnd multiplying and calculating.
3. The big data internet oriented multi-service traffic estimation method according to claim 1, wherein: step 6, constructing a linear measurement model according to the steps 3 and 4, and specifically comprises the following steps:
the constructed model is expressed as
。
4. The method for estimating the multi-service traffic oriented to the big data internet as claimed in claim 1, wherein the network management station estimates the traffic matrix by constructing an optimized model according to the models in steps 5 and 6 in step 7, and the specific steps are as follows:
step 7-1: the models in steps 5 and 6 construct an optimization model as follows
；
Step 7-2: obtaining a matrix by solving the optimized model in step 7-1U T Expressed as EsU T Great, then flow matrix estimated value EsM}=V *Σ* K Es{U T }。
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| CN113379092A (en) * | 2020-03-09 | 2021-09-10 | 西北工业大学青岛研究院 | Backbone network multi-service traffic estimation method and system facing big data |
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Application publication date: 20151118 |