R. Sebastian, M. Weise, et al.
ECPPM 2022
We present an adaptive load shedding approach for windowed stream joins. In contrast to the conventional approach of dropping tuples from the input streams, we explore the concept of selective processing for load shedding. We allow stream tuples to be stored in the windows and shed excessive CPU load by performing the join operations, not on the entire set of tuples within the windows, but on a dynamically changing subset of tuples that are learned to be highly beneficial. We support such dynamic selective processing through three forms of runtime adaptations: adaptation to input stream rates, adaptation to time correlation between the streams and adaptation to join directions. Our load shedding approach enables us to integrate utility-based load shedding with time correlation-based load shedding. Indexes are used to further speed up the execution of stream joins. Experiments are conducted to evaluate our adaptive load shedding in terms of output rate and utility. The results show that our selective processing approach to load shedding is very effective and significantly outperforms the approach that drops tuples from the input streams. © Springer-Verlag London Limited 2006.
R. Sebastian, M. Weise, et al.
ECPPM 2022
Rei Odaira, Jose G. Castanos, et al.
IISWC 2013
Joxan Jaffar
Journal of the ACM
Daniel Karl I. Weidele, Hendrik Strobelt, et al.
SysML 2019