.png){kind=icon}
Return
Elastic Scaling for Data Stream Processing
.png){kind=icon}
PRE
AI
DOI：10.1109/TPDS.2013.295.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article addresses the profitability problem associated with auto-parallelization of general-purpose distributed data stream processing applications. Auto-parallelization involves locating regions in the application's data flow graph that can be replicated at run-time to apply data partitioning, in order to achieve scale. In order to make auto-parallelization effective in practice, the profitability question needs to be answered: How many parallel channels provide the best throughput? The answer to this question changes depending on the workload dynamics and resource availability at run-time. In this article, we propose an elastic auto-parallelization solution that can dynamically adjust the number of channels used to achieve high throughput without unnecessarily wasting resources. Most importantly, our solution can handle partitioned stateful operators via run-time state migration, which is fully transparent to the application developers. We provide an implementation and evaluation of the system on an industrial-strength data stream processing platform to validate our solution.
Keywords:
Data stream processing
parallelization
elasticity
Journal
IF:
6
Papers:
5.2K
Citations:
1.1W
Organization
Cited Papers
Metal carbonyls—II Carbon monoxide and chloride exchange with some cyclopentadienyl metal carbonyls
PREAI
ShareSaveShareSaveMultiple-mouse magnetic resonance imaging with cryogenic radiofrequency probes for evaluation of brain development.png)
NeuroImage
OAAI
NeuroImageIF0
OAAIShareSaveShareSaveShareSave
View more