TY - GEN
T1 - Network topology inference with partial path information
AU - Holbert, B.
AU - Tati, S.
AU - Silvestri, S.
AU - Porta, T. La
AU - Swami, A.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/3/26
Y1 - 2015/3/26
N2 - Full knowledge of the routing topology of the Internet is useful for a multitude of network management tasks. However, the full topology is often not known and is instead estimated using topology inference algorithms. Many of these algorithms use Traceroute to probe paths in the network and then use the collected information to infer the topology. In practice routers may severely disrupt the operation of Traceroute and cause it to only provide partial information. We propose iTop, an algorithm for inferring the network topology when only partial information is available. iTop constructs a virtual topology, which overestimates the number of network components, and then repeatedly merges links in this topology to resolve it towards the structure of the true network. We perform extensive simulations to compare iTop to state of the art inference algorithms. Results show that iTop significantly outperforms previous approaches and its inferred topologies are within 5% of the original networks for all the considered metrics.
AB - Full knowledge of the routing topology of the Internet is useful for a multitude of network management tasks. However, the full topology is often not known and is instead estimated using topology inference algorithms. Many of these algorithms use Traceroute to probe paths in the network and then use the collected information to infer the topology. In practice routers may severely disrupt the operation of Traceroute and cause it to only provide partial information. We propose iTop, an algorithm for inferring the network topology when only partial information is available. iTop constructs a virtual topology, which overestimates the number of network components, and then repeatedly merges links in this topology to resolve it towards the structure of the true network. We perform extensive simulations to compare iTop to state of the art inference algorithms. Results show that iTop significantly outperforms previous approaches and its inferred topologies are within 5% of the original networks for all the considered metrics.
UR - http://www.scopus.com/inward/record.url?scp=84928018960&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84928018960&partnerID=8YFLogxK
U2 - 10.1109/ICCNC.2015.7069448
DO - 10.1109/ICCNC.2015.7069448
M3 - Conference contribution
AN - SCOPUS:84928018960
T3 - 2015 International Conference on Computing, Networking and Communications, ICNC 2015
SP - 796
EP - 802
BT - 2015 International Conference on Computing, Networking and Communications, ICNC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 International Conference on Computing, Networking and Communications, ICNC 2015
Y2 - 16 February 2015 through 19 February 2015
ER -