Name
Abstract | ||
|---|---|---|
With the growing popularity of smart phones and the rapid development of Internet, the traditional network management system cannot adapt itself to the requirement intelligently. If we can use historic data to predict the trend of network traffic accurately, a better planning of network is more likely to be made and limited resource can be allocated and scheduled reasonably. However, massive amounts of data collected by network operators cannot be effectively processed. Therefore, in this paper, we design a network traffic prediction system based on Hadoop platform to process the real mobile network traffic data for a major network operator in China. With the Echo State Network (ESN), a new kind of Recurrent Neural Network (RNN) structure, the system can make accurate predictions of traffic variation trend for various network applications. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/WPMC.2014.7014785 | WPMC |
Keywords | Field | DocType |
mapreduce,parallel processing,network traffic prediction,computer network management,rnn structure,internet rapid development,echo state network,traffic prediction,mobile radio,data handling,recurrent neural network structure,telecommunication traffic,smart phones,recurrent neural nets,hadoop platform,hadoop,network management system,network traffic prediction system,esn,data models,predictive models,mobile computing,reservoirs,mobile communication | Traffic generation model,Computer science,Computer network,Network architecture,Network simulation,Real-time computing,Network monitoring,Network traffic simulation,Network traffic control,Intelligent computer network,Network management station | Conference |
ISSN | Citations | PageRank |
1347-6890 | 1 | 0.37 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyan Cui | 1 | 53 | 20.53 |
| Yuan Yao | 2 | 1 | 0.37 |
| Kuo Zhang | 3 | 311 | 20.43 |
| Fangfang Sun | 4 | 1 | 0.37 |
| yunjie | 5 | 220 | 29.40 |