Name
Abstract | ||
|---|---|---|
Many systems for big data processing have been developed to analyze and process huge amount of data. One of them is Storm, whose target is stream data processing. The default scheduler in Storm uses the round robin method to assign tasks, which is not optimal for heterogeneous computing environments. In this paper, we proposed and implemented a new Storm scheduling algorithm, named G-Storm, which takes the GPU capacity into consideration and can make better use of GPU to speed up the overall performance. The experimental results show that G-Storm can achieve 1.65x to 2.04x performance improvement on lightly weight and heavily loading of topology, comparing to the results of the original Storm scheduler. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.130 | 2016 IEEE 14TH INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, 14TH INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, 2ND INTL CONF ON BIG DATA INTELLIGENCE AND COMPUTING AND CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/DATACOM/CYBERSC |
Keywords | Field | DocType |
Apache Storm, scheduling, GPU, stream processing, heterogeneous clusters | Big data processing,Computer science,Scheduling (computing),Parallel computing,Stream data,Symmetric multiprocessor system,Storm,Real-time computing,Speedup,Performance improvement | Conference |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi-Ren Chen | 1 | 8 | 3.98 |
| Che-Rung Lee | 2 | 9 | 6.64 |