Name
Abstract | ||
|---|---|---|
Network traffic identification has become more and more important in recent years. However, as the Internet backbone bandwidth continuously grows, traditional flow-based traffic identification methods gradually become impractical. In order to improve the performance of traffic identification, this paper proposes an ingenious and practical flow dispatching mechanism named Emilie, which intelligently predicts the elephant flows using only the first three packets of each flow. By discriminating mouse flows against elephant flows, methods with various complexity are utilized to identify the application-level protocol type of elephant and mouse flows separately. Emilie utilizes Machine Learning techniques to achieve high accuracy as well as keep fast speed in predicting elephant flows. Experimental results on real network traffic traces illustrate that around 88% precision, 85% recall and over 85% accuracy are gained on average, which is much better than existing solutions. To the best of our knowledge, this is the first practical and efficient work that supports inline elephant flow prediction. Flow dispatching based on Emilie empowers traffic identification systems to achieve both high accuracy and fast speed. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICCNC.2014.6785300 | ICNC |
Keywords | Field | DocType |
protocols,mouse flow,application-level protocol,emilie,flow dispatching mechanism,flow dispatch,elephant flows prediction,learning (artificial intelligence),flow-based traffic identification methods,telecommunication power management,internet back- bone bandwidth,machine learning techniques,internet,inline elephant flow prediction,telecommunication traffic,power enhancement,traffic identification,network traffic identification,learning artificial intelligence | Traffic identification,Simulation,Network packet,Flow (psychology),Real-time computing,Bandwidth (signal processing),Engineering,Elephant flow,The Internet | Conference |
ISSN | Citations | PageRank |
2325-2626 | 2 | 0.42 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiyang Shao | 1 | 2 | 1.09 |
| Baohua Yang | 2 | 97 | 8.21 |
| Jingjie Jiang | 3 | 2 | 0.42 |
| Yibo Xue | 4 | 230 | 33.06 |
| Jun Li | 5 | 338 | 38.15 |