Adaptive Online First-Order Monitoring

Schneider, Joshua; Basin, David; Brix, Frederik; Krstić, Srđan; Traytel, Dmitriy

doi:10.1007/978-3-030-31784-3_8

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…4.2 cannot be adaptive because they must behave uniformly on the event stream.) We have already made first steps towards computing stream statistics online [30] and performing adaptive slicing [52].…”

Section: Discussionmentioning

confidence: 99%

“…However, the automatic splitting strategy can be dynamically reconfigured by redistributing the submonitors' states coupled with the online collection of the statistics. We have already made some progress in implementing this extension and analyzing the tradeoff between the reconfiguration costs and the cost of using an imperfect splitting strategy [30,52]. We also plan to refine our automatic splitting strategy to account explicitly for communication costs and to evaluate our approach in a distributed cluster.…”

Section: Discussionmentioning

confidence: 99%

“…We therefore omit the index θ and write γ (r ), λ( f ), and so forth. We have started to address timevarying statistics in a separate work [52].…”

Section: Examplementioning

confidence: 99%

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Scalable Online First-Order Monitoring

Schneider

Basin

Brix

et al. 2018

Runtime Verification

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Online monitoring is the task of identifying complex temporal patterns while incrementally processing streams of data-carrying events. Existing state-of-the-art monitors for first-order patterns, which may refer to and quantify over data values, can process streams of modest velocity in real-time. We show how to scale up first-order monitoring to substantially higher velocities by slicing the stream, based on the events' data values, into substreams that can be monitored independently. Because monitoring is not embarrassingly parallel in general, slicing can lead to data duplication. To reduce this overhead, we adapt hash-based partitioning techniques from databases to the monitoring setting. We implement these techniques in an automatic data slicer based on Apache Flink and empirically evaluate its performance using two tools-MonPoly and DejaVu-to monitor the substreams. Our evaluation attests to substantial scalability improvements for both tools.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%