CN108540331B - Network flow prediction method based on improved ESN - Google Patents
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Abstract
The invention provides a network flow prediction method based on an improved ESN, which comprises the steps of continuously collecting network flow data, and then carrying out noise reduction processing on the original network flow data on the basis of the collected network flow data to obtain noise-reduced network flow data; meanwhile, a network flow prediction model based on the improved ESN is constructed, the network flow data after noise reduction and the original network flow data are combined to be used as input, and a double-ring reserve pool structure with a fixed structure is constructed to replace a random reserve pool structure of the original ESN; finally, the ESN is improved through training, and a network traffic prediction model based on the improved ESN and used for network traffic prediction is obtained. By the method, the accuracy of the network flow prediction result can be improved, and a better prediction effect can be obtained in the nonlinear time series prediction.
Description
Technical Field
The invention relates to a network flow prediction method based on an improved ESN, belonging to the technical field of computer application.
Background
The network plays an important role in social life, enterprise production and management. With the development of internet technology, the network scale is continuously enlarged, the complexity of the network is higher and higher, and the requirements of people on network management are higher and higher. The network flow is an important parameter for evaluating the network load and the operation state, and is an important means for mastering the network operation state and realizing effective management and control by continuously monitoring the network flow and realizing accurate prediction. Therefore, it is of great significance to study the prediction of network traffic.
The network flow has the characteristics of self-similarity, long correlation, periodicity, chaos and the like. In recent years, with the continuous expansion of network scale, traditional linear prediction methods such as AR, ARMA, poisson model and the like cannot accurately describe the complex nonlinear relation of network traffic. With the intensive research on network traffic by researchers, most network traffic prediction models based on the traditional neural network have relatively good prediction performance, are simple and convenient, but have unstable performance. Therefore, compared with the traditional neural Network, the Echo State Network (ESN) has strong nonlinear processing capability and faster training speed, and can obtain better prediction effect in nonlinear time series prediction.
Disclosure of Invention
The invention provides a network flow prediction method based on an improved ESN, which improves the accuracy of a network flow prediction result and can obtain a better prediction effect in nonlinear time series prediction.
The network flow prediction method based on the improved ESN is characterized by comprising the following steps:
step 1: collecting network flow data;
step 2: carrying out noise reduction processing on the network flow data;
and step 3: constructing an improved ESN network flow prediction model with a double-ring reserve pool structure;
and 4, step 4: training the network flow prediction model based on the improved ESN constructed in the step 3 by using the collected network flow data and the noise-reduced network flow data;
and 5, predicting the network traffic at the future moment by using the network traffic prediction model trained in the step 4 and based on the improved ESN.
Further, in the step 1, in the step of collecting the network traffic data, the total number of data packets per minute is counted in the specified sampling time, and finally a certain number of network traffic data sets are obtained,WhereinIs the network traffic data at time t.
Further, in step 2, performing noise reduction processing on the acquired network traffic data requires performing phase space reconstruction on the network traffic data, then performing noise reduction processing on the known network traffic data by using a local projection method, and finally obtaining a certain number of noise-reduced network traffic data sets,WhereinAnd reducing noise data for the network flow at the moment t.
Further, in the step 2, performing noise reduction processing on the network traffic data includes the following steps:
step 2-1: for collected traffic data setsThe time delay required by phase space reconstruction is obtained by using an interactive information method;
Step 2-2: for collected traffic data setsDetermining the embedding dimension for phase space reconstruction by using improved false nearest neighbor method;
Step 2-3: according to time delayAnd embedding dimensionPerforming phase space reconstruction on the known flow data, and storing the phase space reconstruction;
step 2-4: for each phase point in the phase space, selecting a corresponding local neighborhood, and performing noise reduction processing on the known network traffic data by adopting a local projection method to obtain a set of noise-reduced network traffic data。
Further, in step 3, weight needs to be set for constructing the dual-ring reserve pool structureSize of reserve poolBicyclic neuronal septaAnd number of rings(ii) a Constructing a network traffic prediction model based on the improved ESN requires determining input and output vectors of the improved ESN; the input vector of the improved ESN is network flow historical data and the network flow historical data after noise reduction,WhereinBefore time tA collection of historical data of individual network traffic,is the embedding dimension of the original network traffic sequence,whereinBefore time tA set of denoised network traffic history data,for the embedding dimension of the noise-reduced sequence of network traffic,the total embedding dimension and the number of neurons in the input layer; output vector。
Further, in the step 3, constructing an improved ESN network traffic prediction model with a dual-ring reserve pool structure includes the following steps:
step 3-1: according to the size of the reserve tankAnd input layer neuron numberConstructing the absolute value of the non-zero element as the weightA size ofInput connection matrix of;
Step 3-2: according to bicyclic neuronal spacingAnd number of ringsConstructing the absolute value of the non-zero element as the weightA size ofReserve pool connection matrix;
Step 3-3: for input vectorUse ofAs a function of excitation inside the reserve tankThe excitation state of the internal neurons in the improved ESN reserve pool is obtained by the following formula,;
Step 3-4: connecting matrices according to outputsInput vectorAnd neuron excitation state inside the reserve poolUsing an identity function as the output excitation functionObtaining an output vector by the following formula,。
Further, in the step 4, the specific steps of training the network traffic prediction model based on the improved ESN are as follows:
Step 4-2: bringing the training sample pairs into the improved ESN, the collection timeArrival timeInput vector ofAnd an internal state vectorForm a state vectorObtained in a size ofState matrix of(ii) a Collecting timeArrival timeDesired output vector ofTo obtain a size ofDesired output matrix of; Set to 100; the method is realized by the following formula:
step 4-3: using pseudo-inverse metersComputing an output connection matrix(ii) a Computing network actual outputAnd desired outputMean square error ofSo that the mean square errorMinimum;is a state matrixThe pseudo-inverse of (1); the method is realized by the following formula:
further, in the step 5, the total number of the packets per minute is counted to obtain a new network traffic data setAnd carrying out noise reduction treatment on the network flow data by using a local noise reduction projection method so as to obtain a noise-reduced network flow data set. Improved ESN-based mesh using training completionThe network flow prediction model predicts the future network flow data and outputs the network flow data at the next moment。
Advantageous effects
The invention is characterized in that the network flow data is continuously collected, and then the original network flow data is subjected to noise reduction treatment on the basis of the continuous collection of the network flow data to obtain the noise-reduced network flow data; meanwhile, a network flow prediction model based on the improved ESN is constructed as shown in FIG. 1, the network flow data after noise reduction and the original network flow data are combined to be used as input, and a double-ring reserve pool structure with a fixed structure is constructed to replace a random reserve pool structure of the original ESN; finally, the ESN is improved through training, and a network traffic prediction model based on the improved ESN and used for network traffic prediction is obtained. By the method, the accuracy of the network flow prediction result can be improved.
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Fig. 1 is a schematic diagram of a network traffic prediction model based on improved ESN.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
The invention provides a Network flow prediction method based on an improved Echo State Network (ESN), which comprises the following specific steps:
step 1: counting the total number of data packets per minute within a specified sampling time, and finally obtaining a certain number of network flow data sets,WhereinIs the network traffic data at time t.
Step 2: and carrying out noise reduction processing on the acquired network flow data. Firstly, reconstructing the phase space of network flow data, then using local projection method to make noise reduction treatment on the known network flow data, finally obtaining a certain quantity of noise-reduced network flow data set,WhereinAnd reducing noise data for the network flow at the moment t. The network flow data denoising steps are as follows:
step 2-1: for collected traffic data setsThe time delay required by phase space reconstruction is obtained by using an interactive information method。
Step 2-2: for collected traffic data setsDetermining the embedding dimension for phase space reconstruction by using improved false nearest neighbor method。
Step 2-3: according to time delayAnd embedding dimensionAnd performing phase space reconstruction on the known flow data and storing the data.
Step 2-4:for each phase point in the phase space, selecting a corresponding local neighborhood, and performing noise reduction processing on the known network traffic data by adopting a local projection method to obtain a set of noise-reduced network traffic data。
And step 3: and constructing an improved ESN network flow prediction model with a double-ring reserve pool structure. Weight value required to be set for constructing double-ring storage pool structureSize of reserve poolBicyclic neuronal septaAnd number of rings. Constructing a network traffic prediction model based on the improved ESN requires determining the input and output vectors of the improved ESN. The input vector of the improved ESN is network flow historical data and the network flow historical data after noise reduction,WhereinBefore time tA collection of network traffic history data, whereinIs the embedding dimension of the original network traffic sequence,whereinBefore time tA set of denoised network traffic history data,for the embedding dimension of the noise-reduced sequence of network traffic,the total embedding dimension, and the number of input layer neurons. Output vector. The specific steps of constructing an improved ESN network flow prediction model with a double-ring reserve pool structure are as follows:
step 3-1: according to the size of the reserve tankAnd input layer neuron numberConstructing the absolute value of the non-zero element as the weightA size ofInput connection matrix of。
Step 3-2: according to bicyclic neuronal spacingAnd number of ringsConstructing the absolute value of the non-zero element as the weightA size ofReserve pool connection matrix。
Step 3-3: for input vectorUse ofAs a function of excitation inside the reserve tankThe excitation state of the internal neurons in the improved ESN reserve pool is obtained by the following formula,。
Step 3-4: connecting matrices according to outputsInput vectorAnd neuron excitation state inside the reserve poolUsing an identity function as the output excitation functionObtaining an output vector by the following formula,。
And 4, step 4: and (3) training the network traffic prediction model based on the improved ESN, which is constructed in the step (3), by using the network traffic data collected in the step (1) and the noise-reduced network traffic data in the step (2). The specific steps of training the network traffic prediction model based on the improved ESN are as follows:
Step 4-2: bringing the training sample pairs into the improved ESN, the collection timeArrival timeInput vector ofAnd an internal state vectorForm a state vectorObtained in a size ofState matrix of. Collecting timeArrival timeDesired output vector ofTo obtain a size ofDesired output matrix of. In the inventionSet to 100. The method is realized by the following formula:
step 4-3: obtaining an output connection matrix by using a pseudo-inverse method:. Computing network actual outputAnd desired outputMean square error ofSo that the mean square errorAnd minimum.Is a state matrixThe pseudo-inverse of (1). The method is realized by the following formula:
and 5: and (4) predicting the network traffic at the future time by using the trained network traffic prediction model based on the improved ESN in the step 4. Counting the total number of data packets per minute to obtain a new network traffic data setAnd carrying out noise reduction treatment on the network flow data by using a local noise reduction projection method so as to obtain a noise-reduced network flow data set. Modification based on use of training completionThe network flow prediction model of the ESN predicts the future network flow data and outputs the network flow data at the next moment。
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.
Claims (7)
1. The network flow prediction method based on the improved ESN is characterized by comprising the following steps:
step 1: collecting network flow data;
step 2: carrying out noise reduction processing on the network flow data;
and step 3: constructing an improved Echo State Network (ESN) network flow prediction model with a double-ring reserve pool structure, wherein the ESN network flow prediction model is based on a double-ring neuron interval dmaxAnd a ring number c, constructing a reserve pool connection matrix W with the absolute value of non-zero elements as a weight r and the size of NxN;
in the step 3, the steps of constructing the improved ESN network flow prediction model with the double-ring reserve pool structure are as follows:
step 3-1: constructing an input connection matrix W with the absolute value of non-zero elements as a weight r and the size of KxN according to the scale N of the reserve pool and the number K of neurons in an input layerin;
Step 3-2: according to the spacing d of bicyclic neuronsmaxAnd a ring number c, constructing a reserve pool connection matrix W with the absolute value of non-zero elements as a weight r and the size of NxN;
step 3-3: for the input vector u (t), tanh (-) is used as the incentive function f inside the poolinThe excitation state x (t), x (t) ═ f of the internal neurons in the improved ESN reservoir is obtained by the following formulain(Winu(t)+Wx(t-1));
Step 3-4: according to the output connection matrix WoutInput ofVector u (t) and reservoir internal neuron excitation state x (t), using an identity function as output excitation function foutThe output vector y (t), y (t) f is obtained by the following formulaout(Wout(u(t),x(t)));
And 4, step 4: training the network flow prediction model based on the improved ESN constructed in the step 3 by using the collected network flow data and the noise-reduced network flow data;
and 5: and (4) predicting the network traffic at the future time by using the trained network traffic prediction model based on the improved ESN in the step 4.
2. The improved ESN-based network traffic prediction method of claim 1, wherein: in the step 1, in the network traffic data, the total number of packets per minute is counted within a specified sampling time, and finally a certain number of network traffic data sets tr (t) are obtained, where tr (t) is the network traffic data at time t, { tr (1), tr (2),.., tr (t) }.
3. The improved ESN-based network traffic prediction method of claim 1, wherein: in the step 2, performing noise reduction processing on the acquired network traffic data requires performing phase space reconstruction on the network traffic data, then performing noise reduction processing on the known network traffic data by using a local projection method, and finally obtaining a certain number of noise-reduced network traffic data sets Trden(t),Trden(t)={trden(1),trden(2),...,trden(t) }, in which trdenAnd (t) is network traffic noise reduction data at the time t.
4. The improved ESN-based network traffic prediction method of claim 1, wherein: in the step 2, the denoising processing of the network traffic data is divided into the following steps:
step 2-1: for the collected flow data set Tr (t), calculating the time delay tau required by phase space reconstruction by using an interactive information method;
step 2-2: for the collected flow data set Tr (t), solving an embedding dimension m required by phase space reconstruction by using an improved false nearest point method;
step 2-3: performing phase space reconstruction on the known flow data according to the time delay tau and the embedding dimension m, and storing the phase space reconstruction;
step 2-4: for each phase point in the phase space, selecting a corresponding local neighborhood, and performing noise reduction processing on the known network traffic data by adopting a local projection method to obtain a set Tr of the noise-reduced network traffic dataden(t)。
5. The improved ESN-based network traffic prediction method of claim 1, wherein: in step 3, a weight r needs to be set to 0.5, a pool scale N needs to be set to 80, and a bicyclic neuron interval d needs to be set for constructing a bicyclic pool structuremax8 and the number of rings c 2; constructing a network traffic prediction model based on the improved ESN requires determining input and output vectors of the improved ESN; the input vector of the improved ESN is network flow historical data and the network flow historical data after noise reduction WhereinK before time t1Set of individual network traffic history data, k1Is the embedding dimension of the original network traffic sequence, whereinK before time t2A set of noise-reduced network traffic history data, k2For embedding dimension of noise-reduced network traffic sequence, K ═ K1+k2The total embedding dimension and the number of neurons in the input layer; the output vector y (t) { tr (t) }.
6. The improved ESN-based network traffic prediction method of claim 1, wherein: in the step 4, the specific steps of training the network traffic prediction model based on the improved ESN are as follows:
step 4-1: training sample pair for constructing input and output of improved ESN (Enterprise service network) { U }train,Ytrain}t={{utrain(1),ytrain(1)},{utrain(2),ytrain(2)},...,{utrain(t),ytrain(T) }, where T ═ 1, 2.., T }, T ═ 3000;
step 4-2: bringing the training sample pair into the improved ESN, collecting time t0Input vector u to time Ttrain(T) and an internal state vector x (T) constituting a state vector s (T) having a size of (K + N) x (T-T)0+1) state matrix S; collecting time t0Desired output vector y to time Ttrain(T) obtaining a size of Lx (T-T)0+1) desired output matrix Q; t is t0Set to 100; the method is realized by the following formula:
s(t)={utrain(t);x(t)}
S={s(t0),s(t0+1),...,s(T)}
Q={ytrain(t0),ytrain(t0+1),...,ytrain(T)}
step 4-3: obtaining an output connection matrix W using a pseudo-inverse computationout(ii) a Computing network actual outputAnd the desired output ytrainMean square error of (t)So that the mean square errorMinimum; s+Is the pseudo-inverse of the state matrix S; the method is realized by the following formula:
Wout=Q·S+。
7. the improved ESN-based network traffic prediction method of claim 1, wherein: in the step 5, the total number of the data packets per minute is counted to obtain a new network traffic data set Tr '(t), and the noise reduction processing is performed on the network traffic data by using a local noise reduction projection method to obtain a noise-reduced network traffic data set Tr' (t)den′(t); predicting future network traffic data by using trained network traffic prediction model based on improved ESN, and outputting network traffic data tr at next momentpred(t+1)。
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104463323A (en) * | 2013-09-17 | 2015-03-25 | 北京邮电大学 | Data prediction method and apparatus |
CN104915714A (en) * | 2014-03-13 | 2015-09-16 | 杨凤琴 | Predication method and device based on echo state network (ESN) |
CN105515857A (en) * | 2015-12-08 | 2016-04-20 | 江苏省邮电规划设计院有限责任公司 | Metropolitan area data network traffic calculation method based on RBF |
CN106789297A (en) * | 2016-12-29 | 2017-05-31 | 淮海工学院 | Predicting network flow system and its method for predicting based on neutral net |
CN107872457A (en) * | 2017-11-09 | 2018-04-03 | 北京明朝万达科技股份有限公司 | A kind of method and system that network operation is carried out based on predicting network flow |
-
2018
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Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104463323A (en) * | 2013-09-17 | 2015-03-25 | 北京邮电大学 | Data prediction method and apparatus |
CN104915714A (en) * | 2014-03-13 | 2015-09-16 | 杨凤琴 | Predication method and device based on echo state network (ESN) |
CN105515857A (en) * | 2015-12-08 | 2016-04-20 | 江苏省邮电规划设计院有限责任公司 | Metropolitan area data network traffic calculation method based on RBF |
CN106789297A (en) * | 2016-12-29 | 2017-05-31 | 淮海工学院 | Predicting network flow system and its method for predicting based on neutral net |
CN107872457A (en) * | 2017-11-09 | 2018-04-03 | 北京明朝万达科技股份有限公司 | A kind of method and system that network operation is carried out based on predicting network flow |
Non-Patent Citations (2)
Title |
---|
2D Orthogonal Locality Preserving Projection for Image Denoising;Gitam Shikkenawis 等;《IEEE TRANSACTIONS ON IMAGE PROCESSING》;20160131;第262-273页 * |
基于改进黑洞算法优化ESN 的网络流量短期预测;韩莹 等;《东北大学学报( 自然科学版)》;20180315;第39卷(第3期);第311-315页 * |
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