Name
Abstract | ||
|---|---|---|
Efficient execution of distributed database operators such as joining and aggregating is critical for the performance of big data analytics. With the increase of the compute speedup of modern CPUs, reducing the network communication time of these operators in large systems is becoming increasingly important, and also challenging current techniques. Significant performance improvements have been achieved by using state-of-the-art methods, such as reducing network traffic designed in the data management domain, and data flow scheduling in the data communications domain. However, the proposed techniques in both fields just view each other as a black box, and performance gains from a co-optimization perspective have not yet been explored. In this paper, based on current research in coflow scheduling, we propose a novel Coflow-based Co-optimization Framework (CCF), which can co-optimize application-level data movement and network-level data communications for distributed operators, and consequently contribute to their performance in large distributed environments. We present the detailed design and implementation of CCF, and conduct an experimental evaluation of CCF using large-scale simulations on large data joins. Our results demonstrate that CCF can always perform faster than current approaches on network communications in large-scale distributed scenarios. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICPP.2017.48 | 2017 46th International Conference on Parallel Processing (ICPP) |
Keywords | Field | DocType |
big data,coflow scheduling,distributed joins,network communications,data-intensive applications | Data modeling,Data analysis,Computer science,Scheduling (computing),Parallel computing,Distributed database,Big data,Data management,Distributed computing,Data flow diagram,Speedup | Conference |
ISSN | ISBN | Citations |
0190-3918 | 978-1-5386-1043-5 | 2 |
PageRank | References | Authors |
0.42 | 21 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Long Cheng | 1 | 91 | 16.99 |
| Ying Wang | 2 | 276 | 55.61 |
| Yulong Pei | 3 | 47 | 13.84 |
| Dick H. J. Epema | 4 | 3134 | 180.80 |