• DocumentCode
    3657129
  • Title

    Distributed Real-Time Event Analysis

  • Author

    Julian James Stephen;Daniel Gmach;Rob Block;Adit Madan;Alvin AuYoung

  • Author_Institution
    Purdue Univ., West Lafayette, IN, USA
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    11
  • Lastpage
    20
  • Abstract
    Security Information and Event Management (SIEM) systems perform complex event processing over a large number of event streams at high rate. As event streams increase in volume and event processing becomes more complex, traditional approaches such as scaling up to more powerful systems quickly become ineffective. This paper describes the design and implementation of DRES, a distributed, rule-based event evaluation system that can easily scale to process a large volume of non-trivial events. DRES intelligently forwards events across a cluster of nodes to evaluate complex correlation and aggregation rules. This approach enables DRES to work with any rules engine implementation. Our evaluation shows DRES scales linearly to more than 16 nodes. At this size it successfully processed more than half a million events per second.
  • Keywords
    "Engines","Correlation","Throughput","Real-time systems","Data structures","Servers","Connectors"
  • Publisher
    ieee
  • Conference_Titel
    Autonomic Computing (ICAC), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/ICAC.2015.12
  • Filename
    7266930