Impact-minimizing Runtime Switching of Distributed Stream Processing Algorithms
C. Qin, and H. Eichelberger. Proceedings of the Workshops of the EDBT/ICDT 2016 Joint Conference, EDBT/ICDT Workshops 2016, Bordeaux, France, March 15, 2016., CEUR-WS.org, (March 2016)
Stream processing is a popular paradigm to process huge
amounts of data. During processing, the actual characteristics
of the analyzed data streams may vary, e.g., in terms of
volume or velocity. To provide a steady quality of the analysis
results, runtime adaptation of the data processing is desirable.
While several techniques for changing data stream
processing at runtime do exist, one specific challenge is to
minimize the impact of runtime adaptation on the data processing,
in particular for real-time data analytics.
In this paper, we focus on the runtime switching among
alternative distributed algorithms as a means for adapting
complex data stream processing tasks. We present an approach,
which combines stream re-routing with buffering
and stream synchronization to reduce the impact on the
data streams. Finally, we analyze and discuss our approach
in terms of a quantitative evaluation.