Name
Abstract | ||
|---|---|---|
In recent years, several frameworks have been introduced to facilitate massively-parallel data processing on shared-nothing architectures like compute clouds. While these frameworks generally offer good support in terms of task deployment and fault tolerance, they only provide poor assistance in finding reasonable degrees of parallelization for the tasks to be executed. However, as billing models of clouds enable the utilization of many resources for a short period of time for the same cost as utilizing few resources for a long time, proper levels of parallelization are crucial to achieve short processing times while maintaining good resource utilization and therefore good cost efficiency. In this paper, we present and evaluate a solution for detecting CPU and I/O bottlenecks in parallel DAG-based data flow programs assuming capacity constrained communication channels. The detection of bottlenecks represents an important foundation for manually or automatically scaling out and tuning parallel data flow programs in order to increase performance and cost efficiency. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/MTAGS.2010.5699429 | MTAGS@SC |
Keywords | Field | DocType |
cloud computing,data flow computing,data flow graphs,fault tolerance,multiprocessing systems,parallel architectures,resource allocation,task analysis,cpu bottleneck detection,directed acyclic graph,parallel dag-based data flow program,parallel data processing,resource utilization,shared-nothing architecture,task deployment,classification algorithms,data flow,data processing,communication channels,cost efficiency,instruction sets,fault tolerant,shared nothing architecture | Data processing,Computer science,Instruction set,Real-time computing,Shared nothing architecture,Fault tolerance,Resource allocation,Distributed computing,Data flow diagram,Cost efficiency,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-4244-9705-8 | 6 | 0.57 |
References | Authors | |
16 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominic Battré | 1 | 257 | 20.40 |
| Matthias Hovestadt | 2 | 270 | 22.24 |
| bjorn lohrmann | 3 | 6 | 0.57 |
| alexander stanik | 4 | 6 | 0.57 |
| Daniel Warneke | 5 | 601 | 27.20 |