Decentralized Computation of Threshold Crossing Alerts

  • Fetahi Wuhib
  • Mads Dam
  • Rolf Stadler
  • Alexander Clemm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3775)


Threshold crossing alerts (TCAs) indicate to a management system that a management variable, associated with the state, performance or health of the network, has crossed a certain threshold. The timely detection of TCAs is essential to proactive management. This paper focuses on detecting TCAs for network-level variables, which are computed from device-level variables using aggregation functions, such as SUM, MAX, or AVERAGE. It introduces TCA-GAP, a novel protocol for producing network-wide TCAs in a scalable and robust manner. The protocol maintains a spanning tree and uses local thresholds, which adapt to changes in network state and topology, by allowing nodes to trade unused “threshold space”. Scalability is achieved through computing the thresholds locally and through distributing the aggregation process across all nodes. Fault-tolerance is achieved by a mechanism that reconstructs the spanning tree after node addition, removal or failure. Simulation results on an ISP topology show that the protocol successfully concentrates traffic overhead to periods where the aggregate is close to the given threshold.


Span Tree Aggregation Function Random Walk Model Local Weight Global Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© IFIP International Federation for Information Processing 2005

Authors and Affiliations

  • Fetahi Wuhib
    • 1
  • Mads Dam
    • 1
  • Rolf Stadler
    • 1
  • Alexander Clemm
    • 2
  1. 1.KTH Royal Institute of TechnologyStockholmSweden
  2. 2.Cisco SystemsSan JoseUSA

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