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41st Annual Symposium on Foundations of Computer Science
Detecting a network failure
Redondo Beach, California
November 12November 14
ISBN: 0769508502
ASCII Text  x  
J. Kleinberg, "Detecting a network failure," 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 231, 41st Annual Symposium on Foundations of Computer Science, 2000.  
BibTex  x  
@article{ 10.1109/SFCS.2000.892110, author = {J. Kleinberg}, title = {Detecting a network failure}, journal ={2013 IEEE 54th Annual Symposium on Foundations of Computer Science}, volume = {0}, year = {2000}, issn = {02725428}, pages = {231}, doi = {http://doi.ieeecomputersociety.org/10.1109/SFCS.2000.892110}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  CONF JO  2013 IEEE 54th Annual Symposium on Foundations of Computer Science TI  Detecting a network failure SN  02725428 SP EP A1  J. Kleinberg, PY  2000 KW  software agents; Internet; computer network reliability; network failure detection; global measurements; Internet topology discovery tools; remote software agents; Web pages; random sampling; VCdimension; disjoint paths; matchings VL  0 JA  2013 IEEE 54th Annual Symposium on Foundations of Computer Science ER   
Measuring the properties of a large, unstructured network can be difficult: one may not have full knowledge of the network topology, and detailed global measurements may be infeasible. A valuable approach to such problems is to take measurements from selected locations within the network and then aggregate them to infer largescale properties. One sees this notion applied in settings that range from Internet topology discovery tools to remote software agents that estimate the download times of popular Web pages. Some of the most basic questions about this type of approach, however, are largely unresolved at an analytical level. How reliable are the results? How much does the choice of measurement locations affect the aggregate information one infers about the network? We describe algorithms that yield provable guarantees for a particular problem of this type: detecting a network failure. Suppose we want to detect events of the following form: an adversary destroys up to k nodes or edges, after which two subsets of the nodes, each at least an /spl epsi/ fraction of the network, are disconnected from one another. We call such an event an (/spl epsi/,k) partition. One method for detecting such events would be to place "agents" at a set D of nodes, and record a fault whenever two of them become separated from each other. To be a good detection set, D should become disconnected whenever there is an (/spl epsi/,k)partition; in this way, it "witnesses" all such events. We show that every graph has a detection set of size polynomial in k and /spl epsi//sup 1/, and independent of the size of the graph itself. Moreover, random sampling provides an effective way to construct such a set. Our analysis establishes a connection between graph separators and the notion of VCdimension, using techniques based on matchings and disjoint paths.
Index Terms:
software agents; Internet; computer network reliability; network failure detection; global measurements; Internet topology discovery tools; remote software agents; Web pages; random sampling; VCdimension; disjoint paths; matchings
Citation:
J. Kleinberg, "Detecting a network failure," focs, pp.231, 41st Annual Symposium on Foundations of Computer Science, 2000
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