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Adaptive Online First-Order Monitoring

  • Joshua SchneiderEmail author
  • David Basin
  • Frederik Brix
  • Srđan KrstićEmail author
  • Dmitriy TraytelEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11781)

Abstract

Online first-order monitoring is the task of detecting temporal patterns in streams of events carrying data. Considerable research has been devoted to scaling up monitoring using parallelization by partitioning events based on their data values and processing the partitions concurrently. To be effective, partitioning must account for the event stream’s statistics, e.g., the relative event frequencies, and these statistics may change rapidly. We develop the first parallel online first-order monitor capable of adapting to such changes. A central problem we solve is how to manage and exchange states between the parallel executing monitors. To this end, we develop state exchange operations and prove their correctness. Moreover, we extend the implementation of the MonPoly monitoring tool with these operations, thereby supporting parallel adaptive monitoring, and show empirically that adaptation can yield up to a tenfold improvement in run-time.

Notes

Acknowledgment

Christian Fania helped us implement and evaluate our adaptive monitoring framework. The anonymous reviewers gave numerous helpful comments on earlier drafts of this paper. Joshua Schneider is supported by the US Air Force grant “Monitoring at Any Cost” (FA9550-17-1-0306). Srđan Krstić is supported by the Swiss National Science Foundation grant “Big Data Monitoring” (167162).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Information Security, Department of Computer ScienceETH ZürichZurichSwitzerland

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