Name
Abstract | ||
|---|---|---|
We describe an approach to elastically scale the performance of a data analytics operator that is part of a streaming application. Our techniques focus on dynamically adjusting the amount of computation an operator can carry out in response to changes in incoming workload and the availability of processing cycles. We show that our elastic approach is beneficial in light of the dynamic aspects of streaming workloads and stream processing environments. Addressing another recent trend, we show the importance of our approach as a means to providing computational elasticity in multicore processor-based environments such that operators can automatically find their best operating point. Finally, we present experiments driven by synthetic workloads, showing the space where the optimizing efforts are most beneficial and a radioastronomy imaging application, where we observe substantial improvements in its performance-critical section. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IPDPS.2009.5161036 | IPDPS |
Keywords | Field | DocType |
incoming workload,dynamic aspect,computational elasticity,data parallel operator,stream processing environment,multicore processor-based environment,data analytics operator,elastic approach,best operating point,synthetic workloads,radioastronomy imaging application,elastic scaling,parallel processing,availability,intelligent sensors,stream processing,programming,multicore processor,application software,data analysis,multicore processing,critical section,multicore processors,data mining,computer science,probability density function,elasticity | Yarn,Data analysis,Computer science,Operating point,Parallel computing,Real-time computing,Operator (computer programming),Stream processing,Scaling,Multi-core processor,Distributed computing,Computation | Conference |
ISSN | Citations | PageRank |
1530-2075 | 72 | 2.59 |
References | Authors | |
14 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Scott Schneider | 1 | 90 | 3.90 |
| Henrique Andrade | 2 | 431 | 23.85 |
| Bugra Gedik | 3 | 2397 | 108.79 |
| Alain Biem | 4 | 288 | 18.64 |
| Kun-Lung Wu | 5 | 2849 | 389.90 |