Name
Abstract | ||
|---|---|---|
Reducing Coflow Completion Time (CCT) has a significant impact on application performance in data-parallel frameworks. Most existing works assume that the endpoints of constituent flows in each coflow are predetermined. We argue that CCT can be further optimized by treating flows' destinations as an additional optimization dimension via reducer placement. In this paper, we propose and implement RPC, a joint online Reducer Placement and Coflow bandwidth scheduling framework, to minimize the average CCT in cloud clusters. We first develop a 2-approximation algorithm to minimize the CCT of a single coflow, then schedule all the coflows following the Shortest Remaining Time First (SRTF) principle. We use a real testbed implementation and extensive large-scale simulations to demonstrate that RPC can reduce the average CCT by 64.98% compared with state-of-the-art technologies. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICNP.2018.00028 | 2018 IEEE 26th International Conference on Network Protocols (ICNP) |
Keywords | Field | DocType |
Cloud computing,Reducer placement,Flow scheduling | Cluster (physics),Bandwidth scheduling,Computer science,Testbed,Flow scheduling,Reducer,Shortest remaining time,Distributed computing,Cloud computing | Conference |
ISSN | ISBN | Citations |
1092-1648 | 978-1-5386-6044-7 | 0 |
PageRank | References | Authors |
0.34 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yangming Zhao | 1 | 124 | 12.45 |
| Chen Tian | 2 | 37 | 8.36 |
| Jingyuan Fan | 3 | 7 | 2.47 |
| Guan Tong | 4 | 17 | 4.09 |
| Chunming Qiao | 5 | 3971 | 400.49 |