Name
Abstract | ||
|---|---|---|
In the data flow models of today's data center applications such as MapReduce, Spark and Dryad, multiple flows can comprise a coflow group semantically. Only completing all flows in a coflow is meaningful to an application. To optimize application performance, routing and scheduling must be jointly considered at the level of a coflow rather than individual flows. However, prior solutions have significant limitation: they only consider scheduling, which is insufficient. To this end, we present Rapier, a coflow-aware network optimization framework that seamlessly integrates routing and scheduling for better application performance. Using a small-scale testbed implementation and large-scale simulations, we demonstrate that Rapier significantly reduces the average coflow completion time (CCT) by up to 79.30% compared to the state-of-the-art scheduling-only solution, and it is readily implementable with existing commodity switches. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/INFOCOM.2015.7218408 | 2015 IEEE Conference on Computer Communications (INFOCOM) |
Keywords | Field | DocType |
coflow-aware network optimization framework,RAPIER,coflow-aware data center networks | Spark (mathematics),Fair-share scheduling,Computer science,Scheduling (computing),Computer network,Testbed,Two-level scheduling,Dynamic priority scheduling,Data center,Data flow diagram,Distributed computing | Conference |
ISSN | Citations | PageRank |
0743-166X | 32 | 1.16 |
References | Authors | |
24 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yangming Zhao | 1 | 124 | 12.45 |
| Kai Chen | 2 | 744 | 59.02 |
| Wei Bai 0001 | 3 | 190 | 13.46 |
| Minlan Yu | 4 | 1855 | 107.25 |
| Chen Tian | 5 | 1119 | 84.93 |
| Yanhui Geng | 6 | 88 | 8.65 |
| zhang | 7 | 67 | 4.46 |
| Dan Li | 8 | 1441 | 88.77 |
| Sheng Wang | 9 | 240 | 33.31 |