Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for predicting SFARIMA network traffic

A prediction method and network traffic technology, applied in data exchange network, digital transmission system, electrical components, etc., can solve the problems of large prediction error, inability to realize online prediction, and delay in prediction, etc.

Inactive Publication Date: 2009-12-09
JIANGSU XINWANG TEC TECH
View PDF0 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The main problems existing in the existing traffic forecasting methods are: large forecasting error, short-term forecasting only, online forecasting not possible, and forecasting time delay

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for predicting SFARIMA network traffic
  • Method for predicting SFARIMA network traffic
  • Method for predicting SFARIMA network traffic

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] First collect network traffic, and use collection tools such as WinPcap to extract traffic sequences. The experimental platform is Matlab7.0, and the captured bottom flow data is used as input, and this method is used to predict the flow, observe the output, and compare it with the actual flow. Through our actual operation and testing, the network traffic is well predicted, the prediction delay is eliminated, and the accuracy of this method is verified.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a method for predicting network traffic and a prediction algorithm. The method, which compensates for the time-delay effect by continuously carrying out the time sliding on prediction sequences, comprises the following steps: Step 1, extracting a sample array from real traffic sequences, designating the sample array as FArray and initiating the values of three variables, M, N and m; Step 2, calculating the self-similarity index H of the sample array FArray on the basis of methods, such as periodogram, R / S analysis, wavelet analysis and the like; Step 3, estimating the order of the sample array by the AIC (Akaike Information Criterion), wherein, AIC(n,m) = lnsa + 2(n+m+1) / N (1), and determining that the order of the model is (p,q), if AIC(p,q) = min AIC(n,m); Step 4, calculating the model parameter ARMA [phi, theta], wherein, ARMA [phi, theta] = ARMA (pbest, qbest), and the calculating method comprises the following steps: (1) estimating the parameter of the autoregression part, and (2) estimating the average sliding coefficient; Step 5, calculating the coefficient vector, pij = theta1pij-1+ theta2pij-2+lambada+thetaqpij-q+phij(j>0), wherein, pi0 is equal to negative 1, and when j is larger than the sum of p and d, phij is equal to 0; and Step 6, predicting the network traffic according to the following formula: X(h) = *pij[(h)]X[t+h-j].

Description

technical field [0001] The invention is a prediction method of network flow, which is mainly used for solving the delay problem of flow prediction, and belongs to the field of computer application technology. Background technique [0002] The problem of traffic forecasting can be briefly summarized as: a set of traffic data X before the current moment is known n-i , (i=0, 1, 2Λ), then the flow rate X at a certain moment in the future n+k Available by X n-i inferred. Here k is called the step size, when k=1, it is a single-step prediction, and when k>1, it is a multi-step prediction. Studies have shown that 80% of network traffic can be predicted, and the accuracy of prediction is related to the selected time scale, and different sequences correspond to different optimal scales. This illustrates the high predictability of network traffic. [0003] The principle of the network traffic forecasting problem is that the sequences have a correlation structure or a fixed rel...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): H04L12/26
Inventor 丁元彬张顺颐颜学智王攀
Owner JIANGSU XINWANG TEC TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products