Name
Abstract | ||
|---|---|---|
ABSTRACT Traffic behavior in a ,large-scale network ,can be viewed ,as a complicated non-linear system, so it is very difficult to describe the long-term network traffic behavior in a large-scale network. In this paper, according to the non-linear character of network traffic, the time series of network ,traffic is decomposed ,into trend component, period component, mutation component and random component,by different ,mathematical ,tools. So the complicated traffic can be modeled with these four simpler sub-series tools. In order to check the decomposed model, the long-term traffic behavior of the CERNET backbone,network is analyzed by means of the decomposed,network traffic. The results are compared,with the ones of ARIMA model. According to the ,autocorrelation function value and predicting error function, the compounded model,can get higher error precision to describe ,the long-term traffic behavior. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11539117_50 | International Conference on Natural Computation |
Keywords | DocType | Volume |
large-scale network,long-term network traffic behavior,mutation component,time-series decomposed model,cernet backbone network,network traffic,long-term traffic behavior,decomposed network traffic,complicated traffic,period component,traffic behavior,arima model,autocorrelation function,prediction error,time series | Conference | 3611 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-28325-0 | 3 |
PageRank | References | Authors |
0.49 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guang Cheng | 1 | 61 | 26.17 |
| Gong Jian | 2 | 41 | 12.69 |
| Ding Wei | 3 | 3 | 1.84 |