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Deployment strategies for distributed complex event processing

Published: 01 February 2013 Publication History

Abstract

Several complex event processing (CEP) middleware solutions have been proposed in the past. They act by processing primitive events generated by sources, extracting new knowledge in the form of composite events, and delivering them to interested sinks. Event-based applications often involve a large number of sources and sinks, possibly dispersed over a wide geographical area. To better support these scenarios, the CEP middleware can be internally built around several, distributed processors, which cooperate to provide the processing and routing service. This paper introduces and compares different deployment strategies for a CEP middleware, which define (i) how the processing load is distributed over different processors and (ii) how these processors interact to produce the required results and to deliver them to sinks. Our evaluation compares the presented solutions and shows their benefits with respect to a centralized deployment, both in terms of network traffic and in terms of forwarding delay.

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  • (2023)What's the Difference? Incremental Processing with Change Queries in SnowflakeProceedings of the ACM on Management of Data10.1145/35897761:2(1-27)Online publication date: 20-Jun-2023
  • (2023)INEv: In-Network Evaluation for Event Stream ProcessingProceedings of the ACM on Management of Data10.1145/35889551:1(1-26)Online publication date: 30-May-2023
  • (2022)Runtime Adaptation of Data Stream Processing Systems: The State of the ArtACM Computing Surveys10.1145/351449654:11s(1-36)Online publication date: 9-Sep-2022
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Information & Contributors

Information

Published In

cover image Computing
Computing  Volume 95, Issue 2
February 2013
75 pages

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2013

Author Tags

  1. 68U35
  2. Complex event processing
  3. Distributed processing
  4. Event based systems
  5. Routing

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Cited By

View all
  • (2023)What's the Difference? Incremental Processing with Change Queries in SnowflakeProceedings of the ACM on Management of Data10.1145/35897761:2(1-27)Online publication date: 20-Jun-2023
  • (2023)INEv: In-Network Evaluation for Event Stream ProcessingProceedings of the ACM on Management of Data10.1145/35889551:1(1-26)Online publication date: 30-May-2023
  • (2022)Runtime Adaptation of Data Stream Processing Systems: The State of the ArtACM Computing Surveys10.1145/351449654:11s(1-36)Online publication date: 9-Sep-2022
  • (2021)MuSE Graphs for Flexible Distribution of Event Stream Processing in NetworksProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457318(10-22)Online publication date: 9-Jun-2021
  • (2020)A Survey of Multitier ProgrammingACM Computing Surveys10.1145/339749553:4(1-35)Online publication date: 26-Sep-2020
  • (2020)Towards Multiple Pattern Type Privacy Protection in Complex Event Processing Through Event Obfuscation StrategiesData Privacy Management, Cryptocurrencies and Blockchain Technology10.1007/978-3-030-66172-4_11(178-194)Online publication date: 17-Sep-2020
  • (2019)Language-integrated privacy-aware distributed queriesProceedings of the ACM on Programming Languages10.1145/33605933:OOPSLA(1-30)Online publication date: 10-Oct-2019
  • (2019)Complex event recognition in the Big Data era: a surveyThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00557-w29:1(313-352)Online publication date: 25-Jul-2019
  • (2018)Preserving Privacy and Quality of Service in Complex Event Processing through Event ReorderingProceedings of the 12th ACM International Conference on Distributed and Event-based Systems10.1145/3210284.3210296(40-51)Online publication date: 25-Jun-2018
  • (2018)TCEPProceedings of the 12th ACM International Conference on Distributed and Event-based Systems10.1145/3210284.3210292(136-147)Online publication date: 25-Jun-2018
  • Show More Cited By

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