Method for predicating long correlation sequences by utilizing short correlation model

A model prediction and long correlation technology, applied in the field of self-similar network traffic prediction, can solve the problems of complex algorithm and low prediction ability, and achieve the effect of high prediction accuracy, reduced complexity and low complexity

Inactive Publication Date: 2013-01-23
HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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Problems solved by technology

[0004] The technical problem solved by the present invention is to construct a method for predicting the long correlation sequence of network self-similar traffic by using the short correlation model, and overcome the technical problems of complex algorithms and low prediction ability in the prior art

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  • Method for predicating long correlation sequences by utilizing short correlation model
  • Method for predicating long correlation sequences by utilizing short correlation model
  • Method for predicating long correlation sequences by utilizing short correlation model

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Embodiment Construction

[0033] The technical solutions of the present invention will be further described below in conjunction with specific embodiments.

[0034] Such as figure 1 As shown, the specific embodiment of the present invention is: provide a kind of method utilizing short correlation model to predict long correlation sequence, comprise the steps:

[0035] Step 100: Decompose the self-similar network traffic, that is, take the maximum and minimum points of the signal x(t) to be analyzed and fit them with two cubic spline curves to obtain the upper and lower extremes of x(t) Value envelope, use m(t) to represent the average value of the two envelopes, let h(t)=x(t)-m(t), if the number of h(t) signal extreme points is the same as the zero-crossing point The numbers must be equal or differ by at most one, and at any point in time, the mean value of the envelope defined by the local maximum and local minimum of the h(t) signal is zero, then h(t) is the first IMF, otherwise Treat h(t) as x(t),...

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Abstract

The invention relates to a method for predicating long correlation sequences by utilizing a short correlation model. Aiming at self-similarity network flow, the invention provides an ARMA (autoregressive moving average model) self-similarity sequence predicating method based on EMD (empirical mode decomposition). The method comprises the following steps of: firstly decomposing the self-similarity network flow into a plurality of IMFs (Intrinsic Mode Functions) by adopting the EMD method, wherein due to the narrow-band characteristic of the IMF, the IMF is provided to be a short correlation sequence, so that the problem of modeling predication of the long correction sequences is converted into the modeling and predicating for the plurality of short correlation sequences, and the complexity of the model is effectively reduced; secondly predicating the decomposed IMF sequences by utilizing excellent short correlation modeling predication capacity of an ARMA model; and finally providing a method for improving the predication precision of the model, so as to effectively reducing the normalization error of mean square of the predication result. The method provided by the technical scheme of the invention has the advantages of high predication precision and low complexity, and the predication precision of self-similarity flow is higher than that of a neural network model.

Description

technical field [0001] The invention relates to a method for predicting self-similar network traffic, in particular to a method for predicting a long correlation sequence by using a short correlation model. Background technique [0002] The modeling and forecasting of network traffic is the basis for studying network performance, management, protocol and service quality, and is of great significance to network planning and design. Therefore, modeling and forecasting of network traffic has attracted much attention. Due to the self-similarity of network traffic, the occupation of network data packet cache is larger than the results of traditional queuing theory analysis, resulting in a larger packet loss rate and delay. Therefore, in order to ensure the quality of service of the network, the construction of network traffic In the process of modeling and forecasting, it is necessary to conduct in-depth research on the self-similar nature of network traffic to find out a model t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24H04L12/26
Inventor 张钦宇高波于佳
Owner HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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