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https://doi.org/10.1109/infcom...
Article . 2007 . Peer-reviewed
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Detection and Localization of Network Black Holes

Authors: Albert Greenberg; Alex C. Snoeren; Jennifer Yates; Ramana Rao Kompella;

Detection and Localization of Network Black Holes

Abstract

Internet backbone networks are under constant flux, struggling to keep up with increasing demand. The pace of technology change often outstrips the deployment of associated fault monitoring capabilities that are built into today's IP protocols and routers. Moreover, some of these new technologies cross networking layers, raising the potential for unanticipated interactions and service disruptions that the built-in monitoring systems cannot detect. In such instances, failures may cause data packets to be silently dropped inside the network without triggering any alarms or responses (e.g., the failure is not routed around). So-called "silent failures" or "black holes" represent a critical threat to today's rapidly evolving networks. In this paper, we present a simple and effective method to detect and diagnose such silent failures. Our method uses active measurement between edge routers to raise alarms whenever end-to-end connectivity is disrupted, regardless of the cause. These alarms feed localization agents that employ spatial correlation techniques to isolate the root-cause of failure. Using data from two real systems deployed on sections of a tier-I ISP network, we successfully detect and localize three known black holes. Further, we present simulation results demonstrating that our system accurately and precisely (both greater than 80% according to our metrics) localizes a variety of failures classes.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
125
Top 10%
Top 1%
Top 10%