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STAR: Self-Tuning Aggregation for Scalable Monitoring

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STAR: Self-Tuning Aggregation for Scalable Monitoring
We present STAR, a self-tuning algorithm that adaptively sets numeric precision constraints to accurately and efficiently answer continuous aggregate queries over distributed data streams. Adaptivity and approximation are essential for both robustness to varying workload characteristics and for scalability to large systems. In contrast to previous studies, we treat the problem as a workload-aware optimization problem whose goal is to minimize the total communication load for a multi-level aggregation tree under a fixed error budget. STAR's hierarchical algorithm takes into account the update rate and variance in the input data distribution in a principled manner to compute an optimal error distribution, and it performs cost-benefit throttling to direct error slack to where it yields the largest benefits. Our prototype implementation of STAR in a large-scale monitoring system provides (1) a new distribution mechanism that enables selftuning error distribution and (2) an optimizati...
Navendu Jain, Michael Dahlin, Yin Zhang, Dmitry Ki
Added 05 Dec 2009
Updated 05 Dec 2009
Type Conference
Year 2007
Where VLDB
Authors Navendu Jain, Michael Dahlin, Yin Zhang, Dmitry Kit, Prince Mahajan, Praveen Yalagandula
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