Adaptive Load Shedding for Windowed Stream Joins

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Adaptive Load Shedding for Windowed Stream Joins (2005)

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by Bugra Gedik , Kun-Lung Wu , Philip Yu , Ling Liu

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18 - 5 self

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@MISC{Gedik05adaptiveload,
    author = {Bugra Gedik and Kun-Lung Wu and Philip Yu and Ling Liu},
    title = {Adaptive Load Shedding for Windowed Stream Joins},
    year = {2005}
}

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Abstract

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:Byadaptation 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 to time correlation between the streams, we dynamically determine the number of basic windows to be used and prioritize the tuples for selective processing, encouraging CPU-limited execution of stream joins in high priority basic windows. By adaptation to join directions, we dynamically determine the most beneficial direction to perform stream joins in order to process more useful tuples under heavy load conditions and boost the utility or number of output tuples produced. Our load shedding framework not only enables us to integrate utility-based load shedding with time correlation-based load shedding, but more importantly, it also allows load shedding to be adaptive to various dynamic stream properties. Inverted indexes are used to further speed up the execution of stream joins based on set-va...

Keyphrases

windowed stream join stream join adaptive load shedding selective processing adaptive load input stream load shedding conventional approach cpu-limited execution stream tuples basic window runtime adaptation partial processing useful tuples stream join operation dynamic selective processing join operation high priority basic window stream rate utility-based load beneficial direction costly stream join incoming stream rate time correlation-based load shedding main idea time correlation heavy load condition output tuples tuple consumption rate excessive cpu load entire set weighted set-valued attribute various dynamic stream property

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