Aggregate Continuous Queries (ACQs) are among the most common Continuous Queries across all classes of monitoring applications and typically have a high execution cost. As such, optimizing the processing of ACQs is imperative for Data Stream Management Systems to reach their full potential. Existing multiple ACQs optimization schemes focus on ACQs with varying window specifications and pre-aggregation filters and assume a processing model where each ACQ is computed as a final-aggregation of a sub-aggregation. In this paper, we propose a novel processing model for ACQs, called TriOps, that minimizes the repetition of operations at the sub-aggregation level, and a new multiple ACQs optimizer, called TriWeave, that is TriOps-aware. We analytically and experimentally demonstrate the performance gains of our proposed schemes, showing their superiority over alternative schemes. Finally, we generalize TriWeave to incorporate the classical subsumption-based multiquery optimization techniques for handling overlapping group-by attributes.
Data Streams Management Systems are designed to support monitoring applications which require the processing of hundreds of Aggregate Continuous Queries (ACQs). These ACQs typically have different time granularities, with possibly different selection predicates and group-by attributes. In order to achieve scalability in the presence of heavy workloads, in this paper, we introduce the concept of 'Weaveability' as an indicator of the potential gains of sharing the processing of ACQs. We then propose Weave Share, a cost-based optimizer that exploits weaveability to optimize the shared processing of ACQs. Our experimental analysis shows that Weave Share outperforms the alternative sharing schemes generating up to four orders of magnitude better quality plans. Finally, we describe a practical implementation of the Weave Share optimizer.
Amazon, Google, and IBM now sell cloud computing services. We consider the setting of a for-profit business selling data stream monitoring/management services and we investigate auction-based mechanisms for admission control of continuous queries. When submitting a query, each user also submits a bid of how much she will commit to paying for that query to run. The admission control auction mechanism then determines which queries to admit, and how much to charge each user in a way that maximizes system revenue while incentivizing users to use the system honestly. Specifically, we require that each user maximizes her payoff by bidding her true value of having her query run. We further consider the requirement that the mechanism be sybil-immune, that is, that no user can increase her payoff by submitting queries that she does not value. The main combinatorial challenges come from the difficulty of effectively taking advantage of the shared processing between queries. We design several payment mechanisms and experimentally evaluate them. We describe the provable game theoretic characteristics of each mechanism alongside its performance with respect to maximizing profit, total user payoff, and rate of admission, showing what tradeoffs may be in store for implementers.
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