Name
Abstract | ||
|---|---|---|
Many cloud applications in modern datacenters have very demanding latency requirements, making flow completion time (FCT) an important metric for evaluating the network performance. Existing network flow scheduling methods either base on pre-known information or have poor performance. Therefore, we present LAFS, an efficient learning-based flow scheduling approach which minimizes the FCT with esti... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IPCCC51483.2021.9679437 | 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC) |
Keywords | DocType | ISSN |
Learning systems,Measurement,Analytical models,Processor scheduling,Conferences,Computational modeling,Prototypes | Conference | 1097-2641 |
ISBN | Citations | PageRank |
978-1-6654-4331-9 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feixue Han | 1 | 0 | 0.68 |
| Qing Li | 2 | 3222 | 433.87 |
| Keke Zhu | 3 | 0 | 0.34 |
| Jianer Zhou | 4 | 0 | 1.69 |
| Yong Jiang | 5 | 19 | 15.92 |
| Zhuyun Qi | 6 | 0 | 0.34 |
| Fuliang Li | 7 | 18 | 7.12 |