Streaming analytics deploy Kleene pattern queries to detect and aggregate event trends against high-rate data streams. Despite increasing workloads, most state-of-the-art systems process each query independently, thus missing cost-saving sharing opportunities. Sharing complex event trend aggregation poses several technical challenges. First, the execution of nested and diverse Kleene patterns is difficult to share. Second, we must share aggregate computation without the exponential costs of constructing the event trends. Third, not all sharing opportunities are beneficial because sharing aggregation introduces overhead. We propose a novel framework, Muse (Multi-query Snapshot Execution), that shares aggregation queries with Kleene patterns while avoiding expensive trend construction. It adopts an online sharing strategy that eliminates re-computations for shared sub-patterns. To determine the beneficial sharing plan, we introduce a cost model to estimate the sharing benefit and design the Muse refinement algorithm to efficiently select robust sharing candidates from the search space. Finally, we explore optimization decisions to further improve performance. Our experiments over a wide range of scenarios demonstrate that Muse increases throughput by 4 orders of magnitude compared to state-of-the-art approaches with negligible memory requirements.
| Identifer | oai:union.ndltd.org:wpi.edu/oai:digitalcommons.wpi.edu:etd-theses-2400 |
| Date | 07 May 2020 |
| Creators | Rozet, Allison M. |
| Contributors | Elke A. Rundensteiner, Advisor |
| Publisher | Digital WPI |
| Source Sets | Worcester Polytechnic Institute |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses (All Theses, All Years) |
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