Estimating network layer subnet characteristics via statistical sampling

Authors:
M. Engin Tozal;Kamil Sarac
Affiliations:
Department of Computer Science, The University of Texas at Dallas, Richardson, TX;Department of Computer Science, The University of Texas at Dallas, Richardson, TX
Venue:
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part I
Year:
2012

Citing 7
Cited 1

On power-law relationships of the Internet topology

Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Measuring ISP topologies with rocketfuel

IEEE/ACM Transactions on Networking (TON)
On the bias of traceroute sampling: or, power-law degree distributions in regular graphs

Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Discarte: a disjunctive internet cartographer

Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Census and survey of the visible internet

Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
TraceNET: an internet topology data collector

IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Palmtree: An IP alias resolution algorithm with linear probing complexity

Computer Communications

Internet topology discovery

DataTraffic Monitoring and Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

Network layer Internet topology consists of a set of routers connected to each other through subnets. Recently, there has been a significant interest in studying topological characteristics of subnets in addition to routers in the Internet. However, given the size of the Internet, constructing complete subnet level topology maps is neither practical nor economical. A viable solution, then, is to sample subnets in the target domain and estimate their global characteristics. In this study, we propose a sampling framework for subnets; derive proper estimators for various subnet characteristics including total number of subnets, subnet prefix length distribution, mean subnet degree, and IP address utilization; and analyze the theoretical and empirical aspects of these estimators.