CN111327494A - Multi-domain SDN network traffic situation assessment method and system - Google Patents
Multi-domain SDN network traffic situation assessment method and system Download PDFInfo
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Abstract
The utility model discloses a multi-domain SDN network traffic situation assessment method and system, comprising: acquiring multi-domain SDN network traffic situation influence factors and constructing a multi-domain SDN network traffic situation index set; calculating an index initial weight; determining a weight change interval based on the index initial weight; optimizing the initial weight of the index based on the weight change interval to obtain the optimal weight of the index; carrying out weighted calculation on the optimal index weight and the index value to obtain a multi-domain SDN network flow situation value; and determining the current time period multi-domain SDN network flow state by combining the multi-domain SDN network flow state level table and the current time period multi-domain SDN network flow state value.
Description
Technical Field
The disclosure relates to the technical field of network traffic situation assessment, and in particular relates to a multi-domain SDN network traffic situation assessment method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
With the continuous increase of the scale of the internet and the continuous increase of the number of users, various problems are exposed to the traditional network system architecture, such as distributed deployment, poor controllability, poor mobility and the like. These disadvantages severely restrict the rapid development of network technology, and therefore, a network architecture capable of breaking this situation is urgently needed. Software-defined Networks (SDN) have also come to light. The essence of a software defined network is to implement a decoupling of the control plane and the data plane so that a network administrator can control the entire network through software programming. Compared with the traditional network, the advantages are mainly reflected in the following two aspects:
1) the SDN separates a control plane and a data plane of a current network, the data plane performs simple data forwarding by actual network devices (such as switches and routers), and the control plane implements regulation and control of the data plane by a controller (or called a network operating system) that is logically centrally managed, so as to simplify policy execution, optimization and network configuration.
2) The SDN improves the programmability of a network system by providing a powerful and extensible programming interface for developers. For the upper layer, developers do not need to pay attention to hardware details of the bottom layer, and only need to flexibly design own application on the basis of the northbound interface; for the controller, the logic of the upper layer application can be realized by the south interface to be compatible with different network devices. The SDN separates the forwarding function and the control function of the network, and has more obvious advantages in the aspects of data forwarding, network management and the like.
Network traffic management has been the focus of SDN research. With the continuous development of the SDN, frequent information interaction causes network traffic to increase, complexity of network operation conditions is increased, and researchers propose to apply traffic management technologies such as traffic engineering, anomaly detection, traffic statistics and the like to the SDN. However, the description of the flow rate has the disadvantages of limitation and incompleteness. And evaluating the SDN network traffic state by the network traffic situation to enhance the overall performance capability of network resources. The network flow situation assessment refers to the steps of refining, fusing and analyzing a large amount of acquired heterogeneous data on the premise that a certain space-time environment perceives relevant network flow elements, and quantifying the current network flow state according to a specific situation assessment algorithm. Through network flow situation evaluation, the problem of single data source can be avoided, the defects that the acquired information is mutually independent and effective correlation analysis cannot be carried out can be overcome, in addition, the flow state can be evaluated from the global perspective, and the current network flow situation value can be calculated by utilizing an advanced calculation technology. Network traffic situation assessment SDN network traffic state enables a network manager to know the current traffic situation in time by analyzing network global traffic information, so that the long-term running state of the network is represented, an important basis is provided for daily network maintenance and network resource allocation in the future, and the SDN network traffic situation assessment SDN traffic state plays a vital role in network management.
In the course of implementing the present disclosure, the inventors found that the following technical problems exist in the prior art: currently, the situation evaluation focusing on network traffic is in a primary stage; the flow situation of the multi-domain SDN network cannot be accurately evaluated.
Disclosure of Invention
The purpose of the present disclosure is to solve the above problems, and provide a method and a system for evaluating a multi-domain SDN network traffic situation, so as to accurately evaluate the multi-domain SDN network traffic situation.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
in a first aspect, the present disclosure provides a method for evaluating a multi-domain SDN network traffic situation;
a multi-domain SDN network traffic situation assessment method comprises the following steps:
acquiring multi-domain SDN network traffic situation influence factors and constructing a multi-domain SDN network traffic situation index set;
calculating an index initial weight; determining a weight change interval based on the index initial weight;
optimizing the initial weight of the index based on the weight change interval to obtain the optimal weight of the index;
carrying out weighted calculation on the optimal index weight and the index value to obtain a multi-domain SDN network flow situation value;
and determining the current time period multi-domain SDN network flow state by combining the multi-domain SDN network flow state level table and the current time period multi-domain SDN network flow state value.
In a second aspect, the present disclosure provides a multi-domain SDN network traffic situation assessment server;
a multi-domain SDN network traffic situation assessment server, comprising:
an index set building module configured to: acquiring multi-domain SDN network traffic situation influence factors and constructing a multi-domain SDN network traffic situation index set;
an index initial weight calculation module configured to: calculating an index initial weight; determining a weight change interval based on the index initial weight;
an index optimal weight calculation module configured to: optimizing the initial weight of the index based on the weight change interval to obtain the optimal weight of the index;
a weight calculation module configured to: carrying out weighted calculation on the optimal index weight and the index value to obtain a multi-domain SDN network flow situation value;
a multi-domain SDN network traffic state output module configured to: and determining the current time period multi-domain SDN network flow state by combining the multi-domain SDN network flow state level table and the current time period multi-domain SDN network flow state value.
In a third aspect, the present disclosure also provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present disclosure also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
In a fifth aspect, the present disclosure provides a multi-domain SDN network traffic situation assessment system;
a multi-domain SDN network traffic situation assessment system comprises: a multi-domain SDN network traffic situation assessment server of the third aspect; the system further comprises:
a multi-domain SDN network comprising a number of SDN networks;
each SDN network is communicated with a corresponding intra-domain controller, and the intra-domain controller is responsible for collecting network flow situation influence factors in the corresponding SDN network; uploading the acquired network traffic situation influence factors to a database by the intra-domain controller;
the method comprises the following steps that network traffic situation influence factors between the SDN network and other SDN networks are collected by an inter-domain controller, and the inter-domain controller uploads the collected network traffic situation influence factors to a database;
the multi-domain SDN network traffic situation evaluation server extracts network traffic situation influence factors from a database, processes the extracted network traffic situation influence factors and determines the multi-domain SDN network traffic state in the current time period.
The beneficial effect of this disclosure:
(1) a multi-domain SDN network traffic situation assessment method is provided. Calculating initial index weight, determining a weight change interval on the basis of obtaining an index initial weight result, optimizing the index weight, calculating the optimal weight of each index, obtaining a corresponding flow situation value of the SDN by adopting a weighted comprehensive evaluation method, and determining the network flow state in the current period by combining a multi-domain SDN flow situation level table.
(2) And designing a network flow situation evaluation model by combining the characteristics of the multi-domain SDN network. The multi-domain SDN network adopts a vertical architecture, a database is introduced as a main information storage mode in order to store network situation index information and historical processing results with large data volume, and meanwhile, a network situation evaluation server is introduced to evaluate the network flow situation in the current time period in order to reduce communication cost and calculation cost.
(3) Analyzing the multi-domain SDN network flow situation influence factors and constructing a flow situation evaluation index system. In order to comprehensively reflect the SDN network traffic situation, situation influence factors are analyzed from four aspects of an SDN application plane, a control plane, a data plane and a transmission process, and an SDN network traffic situation index set is constructed.
Drawings
Fig. 1 is a model diagram for evaluating a multi-domain SDN network traffic situation according to a first embodiment of the present disclosure;
fig. 2 is a multi-domain SDN network traffic situation index system according to a first embodiment of the present disclosure;
fig. 3 is a flowchart of initial weight calculation based on PCA traffic situation indicators in a first embodiment of the present disclosure;
FIG. 4 is a Gaussian distribution plot according to a first embodiment of the present disclosure;
fig. 5 is a flowchart of weight optimization based on the grayish wolf algorithm according to a first embodiment of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
In a first embodiment, the present embodiment provides a method for evaluating a multi-domain SDN network traffic situation;
a multi-domain SDN network traffic situation assessment method comprises the following steps:
s1: acquiring multi-domain SDN network traffic situation influence factors and constructing a multi-domain SDN network traffic situation index set;
s2: calculating an index initial weight; determining a weight change interval based on the index initial weight;
s3: optimizing the initial weight of the index based on the weight change interval to obtain the optimal weight of the index;
s4: carrying out weighted calculation on the optimal index weight and the index value to obtain a multi-domain SDN network flow situation value;
s5: and determining the current time period multi-domain SDN network flow state by combining the multi-domain SDN network flow state level table and the current time period multi-domain SDN network flow state value.
Further, the acquiring of the multi-domain SDN network traffic situation influencing factors is acquired from four aspects of an application plane, a control plane, a data plane and a transmission process of the multi-domain SDN network.
Further, the calculating of the initial weight of the index is performed by using a principal component analysis algorithm.
It should be understood that the principal component analysis algorithm is used for calculating the initial weight of the index, because when the SDN network traffic situation is evaluated, the contribution degree of each index to the traffic situation is different, and different weight levels need to be given to the evaluation index according to the importance degree of each index factor.
Further, determining a weight change interval based on the index initial weight; a gaussian distribution is used to determine the weight change interval.
Further, optimizing the initial weight of the index based on the weight change interval to obtain the optimal weight of the index; the initial weight of the index is optimized by adopting a wolf optimization algorithm to obtain the optimal weight of the index.
It should be understood that the set of multi-domain SDN network traffic situation indicators is constructed as follows:
Xt＝{x1(t),x2(t),...,xm(t)}，
wherein ,XtSet of situational indicators, x, representing time ti(t) represents each individual situational index value at time t, m is the number of situational indexes in the set, i is 1, 2.
It should be understood that for the research of the SDN traffic situation index set, the selected index should have strong integrity, reflect the overall operation state of the network as much as possible, and consider the influence of data from multiple sources on the network traffic situation evaluation result. According to the multi-domain SDN network flow situation index set, situation indexes are extracted from an application plane, a control plane, a data plane and a transmission process respectively, multi-source data consideration is carried out specifically from flow conditions and equipment states of an application program, a switch, a controller and a link, and the multi-domain SDN network flow situation index set is shown in figure 2.
It should be understood that as the network scale increases, due to the limited performance of the controllers, it is difficult for a single domain SDN with a single controller to meet the network requirements, and a cooperative multi-controller needs to be deployed to form a multi-domain SDN network. Monitoring and analysis of network traffic are always the key points of SDN research, and in order to accurately and comprehensively evaluate the multi-domain SDN network traffic condition, the multi-domain SDN-oriented network traffic situation awareness method is provided in the disclosure.
The multi-domain SDN network traffic situation influence factors obtained from an application plane of the multi-domain SDN network comprise: application presence rate, controller presence rate, switch presence rate, link presence rate, or REST request rate.
The multi-domain SDN network traffic situation influence factors obtained from a control plane of the multi-domain SDN network comprise: a cross-domain request rate, a Packet in rate, or a controller online rate.
The multi-domain SDN network traffic situation influence factors obtained from a data plane of the multi-domain SDN network comprise: switch packet reception rate, switch packet transmission rate, or switch on-line rate.
The multi-domain SDN network traffic situation influence factors obtained from the transmission process of the multi-domain SDN network comprise: bit error rate, bandwidth utilization, maximum transmission time delay, packet loss rate, throughput, or link on-line rate.
The indexes related to the multi-domain SDN network traffic situation index set comprise:
(1) application/controller/switch/link online rate: the ratio of the number of applications/controllers/switches/links currently active in the network to the total number of applications/controllers/switches/links.
(2) REST request rate: the number of REST packets per unit time that all applications interact with the controller. The controller provides REST API to the application plane, and the SDN application communicates with the controller by sending REST request to monitor and optimize the whole network.
(3) Cross-domain request rate: and the number of cross-domain request packets interacted between the inter-domain controller and the intra-domain controller in unit time. The inter-domain controller is responsible for communication and management of the inter-domain network managed by the inter-domain controller, when a cross-domain request comes, the inter-domain controller sends the cross-domain request to the inter-domain controller, and the inter-domain controller calculates a cross-domain path based on the global topology information.
(4) Packet _ in rate: and (3) the number of Packet _ in packets interacted between all the switches and the controller in unit time is shown as a formula (1). After a new flow arrives at the switch, if no flow table entry matched with the new flow exists on the switch, the switch forwards the data Packet to the controller for processing through a Packet _ in message.
Wherein T is a sampling period; packet _ in-num is the number of Packet _ in packets in the period.
(5) Switch packet transmission/reception rate: the number of data packets sent/received by the switch in unit time of all the current ports.
(6) Throughput: the number of successfully transmitted packets per unit time. The greater the throughput, the better the quality of service of the network.
(7) Packet loss amount: the number of packets lost per unit time. The packet loss can characterize the problems in the network to a certain extent, and the smaller the packet loss rate is, the better the service quality of the network is.
(8) Maximum transmission delay: the maximum amount of time a packet will experience from the source to the sink. The transmission quality of the network can be well represented by measuring the maximum time delay, when the time delay is too large, the network is congested on some links or nodes, and the loss of data packets is easily caused by the large time delay.
(9) Bandwidth utilization: the average of the ratio of the bandwidth used by the link to the bandwidth in the sampling period. For characterizing the ability of the link to transmit data.
(10) Amount of error code: the number of erroneously transmitted data packets per unit time. The smaller the bit error amount, the better the service quality of the network.
It should be understood that, in the dynamic operation process of the multi-domain SDN network, the global state of the network cannot be interpreted through a single index, such as throughput, time delay, packet loss rate, and the like. In order to evaluate the multi-domain SDN network traffic situation, after a network traffic situation index set is established, further data mining is required, and a current traffic state of the network is quantitatively described.
Further, performing weighted calculation on the optimal index weight and the index value to obtain a multi-domain SDN network traffic situation value; specifically, a multi-domain SDN network flow situation value is obtained by adopting a weighted comprehensive evaluation method.
It should be understood that in S5, a multi-domain SDN network traffic state in the current period is determined by combining the multi-domain SDN network traffic state level table and the multi-domain SDN network traffic state value in the current period; the method specifically comprises the following steps:
under different network operation states, the network traffic situation corresponds to different division results. For SDN network traffic situation, it can be classified into 4-level network morphology, as shown in table 1. And determining the flow state of the multi-domain SDN network by combining the flow situation value of the multi-domain SDN network at the current time.
TABLE 1 Multi-Domain SDN network traffic situation level Table
It should be understood that the calculating of the initial index weight by using the principal component analysis algorithm specifically includes:
principal component analysis is a method of mathematically reducing the dimensions of data. The basic idea is to try to combine a plurality of original indexes X with certain correlation1，X2，…，XP(e.g., P indicators) are recombined into a set of a smaller number of uncorrelated composite indicators FmTo replace the original index. How the comprehensive index should be extracted can make it reflect the original variable X to the maximum extentPThe represented information can ensure that the new indexes are kept independent of each other (the information is not overlapped).
Let F1Principal component index formed by the first linear combination of the original variables:
F1＝a11X1+a21X2+...+ap1Xp,
as can be seen from the mathematical knowledge, the amount of information extracted from each principal component can be measured by its variance, Var (F)1) Larger, denotes F1The more information that is contained. It is often desirable to have the first principal component F1The maximum amount of information contained, and therefore F, selected among all linear combinations1Should be X1，X2，…，XPIs the largest among all linear combinations of (1), so called F1Is the first main component.
If the first principal component is not enough to represent the original P indexes, then consider selecting the second principal component index F2To effectively reflect the original information, F1The existing information does not need to be presented in F1In (i) F2And F1To remain independent and uncorrelated, the covariance Cov (F) is expressed in mathematical language1,F1) Not equal to 0, so F2Is a reaction of with F1Uncorrelated X1，X2，…，XPIs the largest among all linear combinations of (1), so called F2F constructed as the second principal component, and so on1、F2、……、FmIs an index X of a primary variable1，X2，…，XPThe corresponding relation among the first, second, … … and mth principal components is shown in formula (2).
Further, the calculating of the index initial weight by using the principal component analysis algorithm specifically includes:
s201: constructing a sample set; the sample set comprises a plurality of indexes of the initial weight of the index to be calculated;
s202: carrying out standardization processing on the sample set;
s203: calculating the correlation coefficient between each index after the normalization processing to obtain a correlation coefficient matrix:
s204: calculating the characteristic variance contribution rate of each component according to the correlation coefficient matrix;
s205: taking k components of the characteristic variance contribution rate and a set threshold as principal components;
s206: calculating a component load matrix of each main component and each index according to the obtained plurality of main components;
s207: calculating a comprehensive score coefficient of each index on the principal component according to the component load matrix of each principal component and each index;
s208: and calculating the initial weight of each index according to the comprehensive score coefficient of each index on the principal component.
Further, the specific step S201 includes: the sample data set X contains n sample variable indexes, each index collects m pieces of sample data, a positive index in the sample data is an original value, a negative index in the sample data is an opposite number of the index value, and the sample set is represented by an equation (3) matrix:
further, the step S202: carrying out standardization treatment on the sample set, and specifically comprising the following steps:
the Z-score is adopted to carry out standardization processing on the data, and the method has the advantages that the information of sample data is fully utilized, the data after the standardization processing can be objectively obtained, and the standardized formula is shown as a formula (4).
wherein ,
the mean value of each index in the sample set is shown as the formula (5)
Further, the step S203: calculating a correlation coefficient between each index to obtain a correlation coefficient matrix; the method comprises the following specific steps:
the correlation coefficient matrix of the sample can express the correlation among the indexes, and the correlation coefficient matrix is shown as the formula (6):
wherein ,rijThe correlation coefficient of the ith and jth index variables is defined as formula (7), and rij＝rji，rii＝1，
For the mean value of each sample data, the mean value of the ith sample data is shown in equation (8):
further, the step S204: calculating the characteristic variance contribution rate of each component according to the correlation coefficient matrix; the method comprises the following specific steps:
finding out the characteristic root of the correlation coefficient matrix R, and sorting the characteristic root according to a descending rule, namely lambda1≥λ2≥…≥λnNot less than 0; and (3) solving the single characteristic variance contribution rate of the components according to the characteristic root, wherein the formula is shown as (9):
further, the specific step of S205 includes: selecting main components:
the single contribution rates are added from large to small in sequence to obtain the cumulative variance contribution rate, and k components with the contribution rate larger than 85% are used as main components, as shown in formula (10):
further, the specific step of S206 includes: and calculating a component load matrix of the main component and each index according to the obtained main component:
the magnitude of the influence coefficient of each variable index on the principal component can be represented by the load of the variable index on the principal component. And calculating a corresponding unit feature vector according to the size of k:
the unit feature matrix formed by the method is a component load matrix, and is shown as a formula (11).
wherein ,SijThe load of the ith index on the jth principal component is shown.
Further, the step S207 specifically includes: calculating the comprehensive score coefficient of each index on the principal component:
score contribution rate H of each index on principal componentijAs shown in the formula (12), the overall score coefficient U of each indexiAs shown in equation (13).
Further, the specific step of S208 includes: calculating the weight value of each index:
normalizing the comprehensive score coefficient of each index to obtain the weight value P of each indexiAs shown in formula (14):
the flow of initial weight calculation based on PCA traffic situation indicator is shown in fig. 3.
Further, the specific step of determining the weight change interval by using gaussian distribution includes:
and adopting [ w-3 sigma, w +3 sigma ] as a change interval of the weight, wherein w represents the weight, and sigma represents the variance.
It should be understood that the specific step of determining the weight change interval by using the gaussian distribution includes:
after the initial value of the weight is determined, in order to achieve the optimal weight, the weight needs to be continuously searched within a certain weight interval, and the reasonable determination of the range of the change of the index weight is very important for the weight optimization result.
The interval of the cumulative variance contribution rate is obtained from the formula [0.85,1 ]]Namely, it is
When the cumulative variance contribution rate is higher, the number of the selected principal components is larger, and when the cumulative variance contribution rate reaches 1, all the components are selected. The interval for making the cumulative variance contribution rate be [0.85,1 ] is extracted]The index weight under each component combination is calculated, k index weight distribution schemes are provided, and each index has k weights which are in Gaussian distribution. Assuming that consumer satisfaction investigation is performed on item indexes (brick and mortar, reputation, business image, service) reflecting certain store performance, the characteristic values of the first component, the second component, the third component and the fourth component are 2.721, 1.111, 0.94 and 0.227 respectively, and the single characteristic variance contribution rates are 54.426%, 22.22%, 18.802% and 4.552% respectively. Selecting the interval of the cumulative variance contribution rate as 0.85,1]A combination of ingredients of [ first ingredient, second ingredient, third ingredient ] respectively]And [ first component, second component, third component, fourth component ]]And two combinations, namely calculating the index weights under the two combinations respectively.
In order to reasonably give the change interval of each index weight, a 3 sigma characterization method is adopted to judge the weight distribution result. As described above, under different weight distribution schemes, the weight of each index follows gaussian distribution, the gaussian distribution diagram is shown in fig. 4, and the calculation process is as follows:
wherein ,
is a Gaussian distribution of the i-th index，ci and σiUnder k weight distribution schemes, the mean and variance of the ith index weight, wijThe weight of the ith index under the jth distribution scheme is taken as k is the number of the weight distribution schemes, and [ w-3 sigma, w +3 sigma ] is adopted]As the interval of change of the weight.
It should be understood that the grey wolf optimization algorithm is adopted to optimize the initial weight of the index to obtain the optimal weight of the index, the grey wolf algorithm is used as a novel swarm intelligence optimization algorithm, the rank system and the hunting of the grey wolf society are simulated, the behavior is optimized, the grey wolf colony has a strict pyramid social rank system in the hunting process, the wolf with the highest rank in the whole grey wolf colony is α and is responsible for decision making and management of the whole wolf colony in the hunting process, β wolfs and delta wolfs are groups with good fitness and assist α wolfs to manage the whole wolf colony and have decision making rights in the hunting process, and the rest of the grey wolf is defined as omega to assist α and delta to attack hunting objects.
The algorithm is described in detail as follows:
on the search space in D dimension, the position of each wolf is a vector:
Xi(t)＝(Xi,1(t),Xi,2(t),…,Xi,D(t))，
wherein, i is 1,2,3, …, N represents the wolf group and N is composed of wolfs. Grey wolf XiWhen updating the position, firstly, three wolf Xs with the best position are calculatedbest(t)＝{Xα(t),Xβ(t),Xδ(t) }, the calculation formula is shown in equation (18):
wherein ,Xi(t) represents the location of the ith wolf at time t. Dα、Dβ、DδRespectively represent the current time gray wolf XiDistances from α, β, delta, Ck＝2·r1，r1Is a random number between 0 and 1, and k takes the values of 1,2 and 3.
Then, XiThrough a maleFormula (II)
And updating the position of the user, wherein the user represents approaching to the optimal three wolfs in the wolf group, namely the optimal solution direction.
Wherein X (t +1) represents the final movement result of the gray wolf, X'1(t)、X′2(t)、X′3(t) are respectively Grey wolf XiFor the vectors to be moved towards α, β, δ, the calculation formula is shown in equation (19):
wherein ,Ak＝2a·r2-a, k ═ 1,2,3, where r2Is a random number between 0 and 1. a is a convergence factor, which is introduced for better constraint parameter optimization, and the calculation formula is that a is 2-2T/TmaxT is the current iteration number, TmaxFor the maximum number of iterations, it can be seen that as the number of iterations increases, the value of the convergence factor decreases linearly from 2 to 0, resulting in | AkThe value of | is reduced from greater than 1 to a state less than 1, thereby controlling the direction of optimization. The convergence factor can be automatically adjusted to achieve a faster convergence effect, and meanwhile, when the convergence factor is large, the local optimal solution can be skipped, and when the convergence factor is small, the oscillation in the vicinity of the global optimal solution can be avoided.
And (3) optimizing the weight: the gray wolf algorithm is applied to the solving process of the multi-domain SDN flow situation index system weight, 14 indexes are provided in total, and the variation range of each index is determined. Defining the final index weight as
The weight change section corresponding to the jth index is
Different evaluation results can be obtained corresponding to different weight combinations, the weights are substituted into a formula to calculate evaluation results of different sample data under the weights and calculate variance of the evaluation results, and the variance maximization is taken as adaptationSolving the following function most value problem by the function of the degree of response through the Grey wolf algorithm:
max f＝D(Z) (20)
wherein, d (z) represents the variance of the traffic situation assessment results of the m sample data; z represents a flow situation evaluation result matrix of m sample data, ZiRepresenting the final evaluation result of the ith sample; w is ajRepresenting the weight of the jth index in the index system;
and
is the weight wjThe upper and lower bounds of the fluctuation, j ═ 1,2, …, n.
Further, as shown in fig. 5, the initial weight of the index is optimized by using the grayish wolf optimization algorithm to obtain the optimal weight of the index; the method comprises the following specific steps:
s501: initializing the parameters of the gray wolf algorithm;
s502: calculating the fitness value of each gray wolf in the gray wolf group;
s503: updating the individual positions of the grey wolves according to the fitness value of each grey wolve in the grey wolve group;
s504: and repeating S502-S503 to carry out parameter iterative updating until convergence or until the iteration times are met, and recording the optimal individual with the maximum final fitness function as a weight distribution result.
Further, the step S501: initializing the parameters of the gray wolf algorithm; the method comprises the following specific steps:
determining the number N of wolf individuals, the maximum iteration times L and an initial population position matrix D, D ═ pos (i) }of the wolf optimization algorithmi∈N. The individuals in the population are numerical values of a group of weights, an initial population is generated by adopting a random uniform distribution function, the variation range of the weight interval is limited when the initial population is generated, the total sum of the weights is adjusted to be 1, the individuals which do not meet the weight variation interval after the weights are adjusted are removed, and the process is circulated until the generated population size is N.
Further, in S502, calculating a fitness value of each gray wolf in the gray wolf population; the method comprises the following specific steps:
and calculating the fitness value of each gray wolf in the gray wolf group. For gray wolf i, the gray wolf position pos (i) is decomposed into the corresponding index weight w1，w2，…，w14And substituting the weight population into a gray wolf algorithm fitness calculation formula to calculate the fitness value of each individual.
Further, the step S503: updating the individual positions of the grey wolves according to the fitness value of each grey wolve in the grey wolve group; the method comprises the following specific steps:
comparing the individual fitness values of the wolfs, and sequentially storing the maximum 3 fitness values as fα、fβ and fδAnd updating the position pos (i) of the wolf individual according to a position updating formula, and in order to meet the condition that the sum of the updated weights of the positions is still 1, redistributing the weights by adopting a normalization weight method, as shown in a formula (24).
wherein ,wi(i is 1,2, …,14) is the gray wolf individual position after the position formula is updated, wi' (i ═ 1,2, …,14) is the normalized individual position of the gray wolf.
In a second embodiment, the present embodiment provides a multi-domain SDN network traffic situation assessment server;
a multi-domain SDN network traffic situation assessment server, comprising:
an index set building module configured to: acquiring multi-domain SDN network traffic situation influence factors and constructing a multi-domain SDN network traffic situation index set;
an index initial weight calculation module configured to: calculating an index initial weight; determining a weight change interval based on the index initial weight;
an index optimal weight calculation module configured to: optimizing the initial weight of the index based on the weight change interval to obtain the optimal weight of the index;
a weight calculation module configured to: carrying out weighted calculation on the optimal index weight and the index value to obtain a multi-domain SDN network flow situation value;
a multi-domain SDN network traffic state output module configured to: and determining the current time period multi-domain SDN network flow state by combining the multi-domain SDN network flow state level table and the current time period multi-domain SDN network flow state value.
In a third embodiment, the present embodiment further provides an electronic device, which includes a memory, a processor, and computer instructions stored in the memory and executed on the processor, where the computer instructions, when executed by the processor, implement the method in the first embodiment.
In a fourth embodiment, the present embodiment further provides a computer-readable storage medium for storing computer instructions, and the computer instructions, when executed by a processor, implement the method of the first embodiment.
Fifth, the present embodiment provides a multi-domain SDN network traffic situation assessment system;
a multi-domain SDN network traffic situation assessment system comprises: the multi-domain SDN network traffic situation assessment server in the third embodiment; the system further comprises:
a multi-domain SDN network comprising a number of SDN networks;
each SDN network is communicated with a corresponding intra-domain controller, and the intra-domain controller is responsible for collecting network flow situation influence factors in the corresponding SDN network; uploading the acquired network traffic situation influence factors to a database by the intra-domain controller;
the method comprises the following steps that network traffic situation influence factors between the SDN network and other SDN networks are collected by an inter-domain controller, and the inter-domain controller uploads the collected network traffic situation influence factors to a database;
the multi-domain SDN network traffic situation evaluation server extracts network traffic situation influence factors from a database, processes the extracted network traffic situation influence factors and determines the multi-domain SDN network traffic state in the current time period.
In order to timely and effectively master the situation of the traffic situation of the whole network, the multi-domain SDN network traffic situation assessment model is designed, and the assessment model is shown in figure 1 and comprises a plurality of SDN domains, a database and a situation assessment server.
(1) The multi-domain SDN network employs a vertical architecture. The system comprises an inter-domain controller and an intra-domain controller, wherein the intra-domain controller only manages and controls internal communication, the inter-domain communication is managed and controlled by the inter-domain controller, and the inter-domain controller masters a global network view.
(2) In order to store the network situation index information with large data volume and the historical processing result, a database is introduced as a main information storage mode, so that the system has the bearing capacity for a large amount of information. The method comprises the steps that a controller respectively collects information such as equipment states and flow in a multi-domain SDN, wherein intra-domain information is collected by an intra-domain controller, and inter-domain information is collected by an inter-domain controller; and writing the information into a database in a sub-table manner, and simultaneously obtaining flow situation index data by the database in a uniform resource arrangement manner and writing the flow situation index data into the database.
(3) In order to reduce communication overhead and calculation overhead, a network situation assessment server is introduced. The method is responsible for evaluating the network traffic situation at the current time period by using the current time period traffic situation index data in the database, returning the final evaluation result to the inter-domain controller and writing the final evaluation result into the database.
The SDN has the characteristic of separation of a control layer and a forwarding layer, can provide a clearer global view for a network technically, and enables acquisition of a large amount of real-time and historical data to be possible. As network size increases, a single common SDN controller has difficulty meeting network requirements due to limited performance of the controller, and deployment of multiple controllers working in coordination is required. The SDN multi-domain controller cooperative work mode includes networking divided into a horizontal architecture and a vertical architecture. The network domains in the horizontal architecture have equal relationships, are controlled by respective control planes, and exchange information with each other to coordinate inter-domain communication, such as distributed clustering. The vertical architecture comprises inter-domain controllers and intra-domain controllers, wherein the intra-domain controllers only control internal communication, the inter-domain communication is controlled by the inter-domain controllers, and the inter-domain controllers master the global network view.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. A multi-domain SDN network traffic situation assessment method is characterized by comprising the following steps:
acquiring multi-domain SDN network traffic situation influence factors and constructing a multi-domain SDN network traffic situation index set;
calculating an index initial weight; determining a weight change interval based on the index initial weight;
optimizing the initial weight of the index based on the weight change interval to obtain the optimal weight of the index;
carrying out weighted calculation on the optimal index weight and the index value to obtain a multi-domain SDN network flow situation value;
and determining the current time period multi-domain SDN network flow state by combining the multi-domain SDN network flow state level table and the current time period multi-domain SDN network flow state value.
2. The method of claim 1, wherein the calculating the initial weight of the indicator is performed by using a principal component analysis algorithm.
3. The method of claim 1, wherein the weight change interval is determined based on an index initial weight; a gaussian distribution is used to determine the weight change interval.
4. The method as claimed in claim 1, wherein the index initial weight is optimized based on the weight change interval to obtain an index optimal weight; the initial weight of the index is optimized by adopting a wolf optimization algorithm to obtain the optimal weight of the index.
5. The method as claimed in claim 2, wherein the calculating the initial weight of the index using the principal component analysis algorithm comprises:
s201: constructing a sample set; the sample set comprises a plurality of indexes of the initial weight of the index to be calculated;
s202: carrying out standardization processing on the sample set;
s203: calculating the correlation coefficient between each index after the normalization processing to obtain a correlation coefficient matrix:
s204: calculating the characteristic variance contribution rate of each component according to the correlation coefficient matrix;
s205: taking k components of the characteristic variance contribution rate and a set threshold as principal components;
s206: calculating a component load matrix of each main component and each index according to the obtained plurality of main components;
s207: calculating a comprehensive score coefficient of each index on the principal component according to the component load matrix of each principal component and each index;
s208: and calculating the initial weight of each index according to the comprehensive score coefficient of each index on the principal component.
6. The method as claimed in claim 4, wherein the initial weight of the index is optimized by using a grayish wolf optimization algorithm to obtain an optimal weight of the index; the method comprises the following specific steps:
s501: initializing the parameters of the gray wolf algorithm;
s502: calculating the fitness value of each gray wolf in the gray wolf group;
s503: updating the individual positions of the grey wolves according to the fitness value of each grey wolve in the grey wolve group;
s504: and repeating S502-S503 to carry out parameter iterative updating until convergence or until the iteration times are met, and recording the optimal individual with the maximum final fitness function as a weight distribution result.
7. A multi-domain SDN network traffic situation assessment server is characterized by comprising:
an index set building module configured to: acquiring multi-domain SDN network traffic situation influence factors and constructing a multi-domain SDN network traffic situation index set;
an index initial weight calculation module configured to: calculating an index initial weight; determining a weight change interval based on the index initial weight;
an index optimal weight calculation module configured to: optimizing the initial weight of the index based on the weight change interval to obtain the optimal weight of the index;
a weight calculation module configured to: carrying out weighted calculation on the optimal index weight and the index value to obtain a multi-domain SDN network flow situation value;
a multi-domain SDN network traffic state output module configured to: and determining the current time period multi-domain SDN network flow state by combining the multi-domain SDN network flow state level table and the current time period multi-domain SDN network flow state value.
8. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executable on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-6.
9. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 6.
10. A multi-domain SDN network traffic situation assessment system is characterized by comprising: the multi-domain SDN network traffic situation assessment server of claim 7; the system further comprises:
a multi-domain SDN network comprising a number of SDN networks;
each SDN network is communicated with a corresponding intra-domain controller, and the intra-domain controller is responsible for collecting network flow situation influence factors in the corresponding SDN network; uploading the acquired network traffic situation influence factors to a database by the intra-domain controller;
the method comprises the following steps that network traffic situation influence factors between the SDN network and other SDN networks are collected by an inter-domain controller, and the inter-domain controller uploads the collected network traffic situation influence factors to a database;
the multi-domain SDN network traffic situation evaluation server extracts network traffic situation influence factors from a database, processes the extracted network traffic situation influence factors and determines the multi-domain SDN network traffic state in the current time period.
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