Comparing static and dynamic measurements and models of the internet's AS topology

Seung Taek Park, David M. Pennock, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Citations (Scopus)

Abstract

Capturing a precise snapshot of the Internet's topology is nearly impossible. Recent efforts have produced autonomous-system (AS) level topologies with noticeably divergent characteristics even calling into question the widespread belief that the Internet's degree distribution follows a power law. In turn, this casts doubt on Internet modeling efforts, since validating a model on one data set does little to ensure validity on another data set, or on the (unknown) actual Internet topology. We examine six metrics - three existing metrics and three of our own - applied to two large publicly-available topology data sets. Certain metrics highlight differences between the two topologies, while one of our static metrics and several dynamic metrics display an invariance between the data sets. Invariant metrics may capture properties inherent to the Internet and independent of measurement methodology, and so may serve as better gauges for validating models. We continue by testing nine models - seven existing models and two of our own - according to these metrics applied to the two data sets. We distinguish between growth models that explicitly add nodes and links over time in a dynamic process, and static models that add all nodes and links in a batch process. All existing growth models show poor performance according to at least one metric, and only one existing static model, called Inet, matches all metrics well. Our two new models - growth models that are modest extensions of one of the simplest existing growth models - perform better than any other growth model across all metrics. Compared with Inet, our models are very simple. As growth models, they provide a possible explanation for the processes underlying the Internet's growth, explaining, for example, why the Internet's degree distribution is more skewed than baseline models would predict.

Original languageEnglish (US)
Title of host publicationIEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies
Pages1616-1627
Number of pages12
DOIs
StatePublished - Nov 22 2004
EventIEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies - Hongkong, China
Duration: Mar 7 2004Mar 11 2004

Publication series

NameProceedings - IEEE INFOCOM
Volume3
ISSN (Print)0743-166X

Other

OtherIEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies
CountryChina
CityHongkong
Period3/7/043/11/04

Fingerprint

Topology
Internet
Invariance
Gages

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

Park, S. T., Pennock, D. M., & Giles, C. L. (2004). Comparing static and dynamic measurements and models of the internet's AS topology. In IEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies (pp. 1616-1627). (Proceedings - IEEE INFOCOM; Vol. 3). https://doi.org/10.1109/INFCOM.2004.1354574
Park, Seung Taek ; Pennock, David M. ; Giles, C. Lee. / Comparing static and dynamic measurements and models of the internet's AS topology. IEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies. 2004. pp. 1616-1627 (Proceedings - IEEE INFOCOM).
@inproceedings{348d4133c890427e94ef229861b9aff4,
title = "Comparing static and dynamic measurements and models of the internet's AS topology",
abstract = "Capturing a precise snapshot of the Internet's topology is nearly impossible. Recent efforts have produced autonomous-system (AS) level topologies with noticeably divergent characteristics even calling into question the widespread belief that the Internet's degree distribution follows a power law. In turn, this casts doubt on Internet modeling efforts, since validating a model on one data set does little to ensure validity on another data set, or on the (unknown) actual Internet topology. We examine six metrics - three existing metrics and three of our own - applied to two large publicly-available topology data sets. Certain metrics highlight differences between the two topologies, while one of our static metrics and several dynamic metrics display an invariance between the data sets. Invariant metrics may capture properties inherent to the Internet and independent of measurement methodology, and so may serve as better gauges for validating models. We continue by testing nine models - seven existing models and two of our own - according to these metrics applied to the two data sets. We distinguish between growth models that explicitly add nodes and links over time in a dynamic process, and static models that add all nodes and links in a batch process. All existing growth models show poor performance according to at least one metric, and only one existing static model, called Inet, matches all metrics well. Our two new models - growth models that are modest extensions of one of the simplest existing growth models - perform better than any other growth model across all metrics. Compared with Inet, our models are very simple. As growth models, they provide a possible explanation for the processes underlying the Internet's growth, explaining, for example, why the Internet's degree distribution is more skewed than baseline models would predict.",
author = "Park, {Seung Taek} and Pennock, {David M.} and Giles, {C. Lee}",
year = "2004",
month = "11",
day = "22",
doi = "10.1109/INFCOM.2004.1354574",
language = "English (US)",
isbn = "0780383559",
series = "Proceedings - IEEE INFOCOM",
pages = "1616--1627",
booktitle = "IEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies",

}

Park, ST, Pennock, DM & Giles, CL 2004, Comparing static and dynamic measurements and models of the internet's AS topology. in IEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings - IEEE INFOCOM, vol. 3, pp. 1616-1627, IEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies, Hongkong, China, 3/7/04. https://doi.org/10.1109/INFCOM.2004.1354574

Comparing static and dynamic measurements and models of the internet's AS topology. / Park, Seung Taek; Pennock, David M.; Giles, C. Lee.

IEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies. 2004. p. 1616-1627 (Proceedings - IEEE INFOCOM; Vol. 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Comparing static and dynamic measurements and models of the internet's AS topology

AU - Park, Seung Taek

AU - Pennock, David M.

AU - Giles, C. Lee

PY - 2004/11/22

Y1 - 2004/11/22

N2 - Capturing a precise snapshot of the Internet's topology is nearly impossible. Recent efforts have produced autonomous-system (AS) level topologies with noticeably divergent characteristics even calling into question the widespread belief that the Internet's degree distribution follows a power law. In turn, this casts doubt on Internet modeling efforts, since validating a model on one data set does little to ensure validity on another data set, or on the (unknown) actual Internet topology. We examine six metrics - three existing metrics and three of our own - applied to two large publicly-available topology data sets. Certain metrics highlight differences between the two topologies, while one of our static metrics and several dynamic metrics display an invariance between the data sets. Invariant metrics may capture properties inherent to the Internet and independent of measurement methodology, and so may serve as better gauges for validating models. We continue by testing nine models - seven existing models and two of our own - according to these metrics applied to the two data sets. We distinguish between growth models that explicitly add nodes and links over time in a dynamic process, and static models that add all nodes and links in a batch process. All existing growth models show poor performance according to at least one metric, and only one existing static model, called Inet, matches all metrics well. Our two new models - growth models that are modest extensions of one of the simplest existing growth models - perform better than any other growth model across all metrics. Compared with Inet, our models are very simple. As growth models, they provide a possible explanation for the processes underlying the Internet's growth, explaining, for example, why the Internet's degree distribution is more skewed than baseline models would predict.

AB - Capturing a precise snapshot of the Internet's topology is nearly impossible. Recent efforts have produced autonomous-system (AS) level topologies with noticeably divergent characteristics even calling into question the widespread belief that the Internet's degree distribution follows a power law. In turn, this casts doubt on Internet modeling efforts, since validating a model on one data set does little to ensure validity on another data set, or on the (unknown) actual Internet topology. We examine six metrics - three existing metrics and three of our own - applied to two large publicly-available topology data sets. Certain metrics highlight differences between the two topologies, while one of our static metrics and several dynamic metrics display an invariance between the data sets. Invariant metrics may capture properties inherent to the Internet and independent of measurement methodology, and so may serve as better gauges for validating models. We continue by testing nine models - seven existing models and two of our own - according to these metrics applied to the two data sets. We distinguish between growth models that explicitly add nodes and links over time in a dynamic process, and static models that add all nodes and links in a batch process. All existing growth models show poor performance according to at least one metric, and only one existing static model, called Inet, matches all metrics well. Our two new models - growth models that are modest extensions of one of the simplest existing growth models - perform better than any other growth model across all metrics. Compared with Inet, our models are very simple. As growth models, they provide a possible explanation for the processes underlying the Internet's growth, explaining, for example, why the Internet's degree distribution is more skewed than baseline models would predict.

UR - http://www.scopus.com/inward/record.url?scp=8344223693&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=8344223693&partnerID=8YFLogxK

U2 - 10.1109/INFCOM.2004.1354574

DO - 10.1109/INFCOM.2004.1354574

M3 - Conference contribution

AN - SCOPUS:8344223693

SN - 0780383559

T3 - Proceedings - IEEE INFOCOM

SP - 1616

EP - 1627

BT - IEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies

ER -

Park ST, Pennock DM, Giles CL. Comparing static and dynamic measurements and models of the internet's AS topology. In IEEE INFOCOM 2004 - Conference on Computer Communications - Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies. 2004. p. 1616-1627. (Proceedings - IEEE INFOCOM). https://doi.org/10.1109/INFCOM.2004.1354574