Name
Abstract | ||
|---|---|---|
With the explosion of multimedia applications, the network QoS is facing a set of challenges especially in congestion control. Active queue management (AQM), which plays an important role in network congestion control, has been proved necessary for decades. Recently, as the widespread bufferbloat being exposed, AQM has been paid more and more attention. However, traditional manually designed AQMs still exist some problems especially in parameter-tuning and scenarios adaption. Instead of designing a perfect AQM for all scenarios, which is nearly impossible, we try to make the computer generate an AQM for the scenario specified by users. We've developed a program called Kemy based on off-line machine learning technologies. The Kemy-generated AQM is evaluated in various scenarios and achieves the goals of solving bufferbloat problem. Compared to some representative human-designed AQMs, Kemy-generated AQM performs even better in some cases. |
Year | Venue | Keywords |
|---|---|---|
2015 | PROCEEDINGS OF THE 2015 10TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND NETWORKING IN CHINA CHINACOM 2015 | Active Queue Management, Congestion Control, Bufferbloat, Machine Learning |
Field | DocType | Citations |
Bufferbloat,Network congestion control,Computer science,Active queue management,Computer network,Quality of service,Real-time computing,Network congestion,Artificial intelligence,Machine learning,Distributed computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| XinAn Lin | 1 | 0 | 0.34 |
| Dong Zhang | 2 | 3 | 2.07 |