Name
Abstract | ||
|---|---|---|
Online flow distribution monitoring is critical in instruction detection. However, high-speed traffic monitoring is significantly challenging for a monitoring system with limited resources (e.g., memory and processing cycles). Flow and packet sampling techniques are commonly adopted to tackle this problem. Flow sampling are reduce the variance of the estimators in short flows; However, it increases the estimated error for the heavy-tailed flow. On the other hand, passive sampling presents an opposite results. In this paper, we propose a novel flow sampling approach by taking advantage of both packet and flow sampling techniques. An effective flow estimator is also introduced to estimate flow distributions. Extensive simulations are conducted with real traffic data from CERNET backbone network traffic traces to evaluate the system performance and compare it with other traffic sampling approaches. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/E2EMON.2007.375315 | E2EMON'07: FIFTH IEEE/IFIP WORKSHOP ON END-TO-END MONITORING TECHNIQUES AND SERVICES |
Keywords | Field | DocType |
packet sampling,flow sampling,hybrid sampling,flow distributions | Flow network,Computer science,Simulation,Network packet,Flow (psychology),Real-time computing,Sampling (statistics),Packet sampling,Intrusion detection system,Backbone network,Estimator | Conference |
Volume | Issue | ISSN |
null | null | null |
Citations | PageRank | References |
3 | 0.43 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guang Cheng | 1 | 61 | 26.17 |
| Jian Gong | 2 | 36 | 12.67 |
| Yongning Tang | 3 | 133 | 13.42 |