Impact-minimizing Runtime Switching of Distributed Stream Processing Algorithms.
Cui Qin, Holger Eichelberger · 2016
Stream processing is a popular paradigm to process huge amounts of data. During processing, the actual characteris-tics of the analyzed data streams may vary, e.g., in terms of volume or velocity. To provide a steady quality of the anal-ysis results, runtime adaptation of the data processing is de-sirable. 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 pro-cessing, 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 ap-proach, 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. Keywords Data stream processing; runtime adaptation; impact-mini-mizing adaptation enactment; algorithm switching 1.