Adaptive load shedding for windowed stream joins

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, focusing on costly stream joins such as those over set-valued or weighted set-valued attributes. The main idea of our adaptive load shedding approach is two-fold. First, we allow stream tuples to be stored in the windows and shed excessive CPU load by performing the stream join operations, not on the entire set of tuples within the windows, but on a dynamically changing subset of tuples that are highly beneficial. Second, we support such dynamic selective processing through three forms of runtime adaptations: By adaptation to input stream rates, we perform partial processing based load shedding and dynamically determine the fraction of the windows to be processed by comparing the tuple consumption rate of join operation to the incoming stream rates. By adaptation t...

• Type: Conference
• Year: 2005
• Where: CIKM
• Authors: Bugra Gedik, Kun-Lung Wu, Philip S. Yu, Ling Liu
• Keywords: CIKM 2005, Load Shedding, Load Shedding Approach, Stream Joins