Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2927

Network topology inference based on end-to-end measurements

Authors Jin, Xing
Yiu, W. -P. Ken
Chan, S. -H. Gary
Wang, Yajun
Issue Date 2006
Source IEEE journal on selected areas in communications, v. 24, (12), 2006, DEC, p. 2182-2195
Summary We consider using traceroute-like end-to-end measurement to infer the underlay topology for a group of hosts. One major issue is the measurement cost. Given N hosts in an asymmetric network without anonymous routers, traditionally full N(N - 1) traceroutes are needed to determine the underlay topology. We investigate how to efficiently infer an underlay topology with low measurement cost, and propose a heuristic called Max-Delta. In the heuristic, a server selects appropriate host-pairs to measure in each iteration so as to reveal the most undiscovered information on the underlay. We further observe that the presence of anonymous routers significantly distorts and inflates the inferred topology. Previous research has shown that obtaining both exact and approximate topology in the presence of anonymous routers under certain consistency constraints is intractable. We hence propose fast algorithms on how to practically construct an approximate topology by relaxing some constraints. We investigate and compare two algorithms to merge anonymous routers. The first (me uses Isomap to map routers into a multidimensional space and merges anonymous routers according to their interdistances. The second algorithm is based on neighbor router information, which trades off some accuracy with speed. We evaluate our inference algorithms on Internet-like and real Internet topologies. Our results show that almost full measurement is needed to fully discover the underlay topology. However, substantial reduction in measurements can be achieved if a little accuracy, say 5\%, can be compromised. Moreover, our merging algorithms in the presence of anonymous routers can efficiently infer an underlay topology with good accuracy.
Subjects
ISSN 0733-8716
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Language English
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