Abstract
Estimating network distance between arbitrary Internet endpoints is an essential primitive in applications ranging from performance optimization to network debugging and auditing. Enabling such a primitive without deploying new infrastructure was demonstrated via DNS. However, the proliferation of DNS hosting has made DNS-based measurement techniques far less dependable. In this paper, we show that the heterogeneous infrastructure of different CDNs, combined with the proliferation of the EDNS0 client-subnet extension (ECS), enables novel infrastructureless measurement. We design Fury Route, a system that estimates network distance by utilizing ECS to construct a virtual path between endpoints via intermediate CDN replicas. Fury Route requires no additional infrastructure to be deployed. The measured endpoints do not need to participate by sending or responding to probes. Fury Route further generates no load on endpoints. It only queries DNS, whose infrastructure is designed for large loads. We extensively evaluate Fury Route and demonstrate that (i) the key to Fury Route’s ability to construct virtual paths lies in the heterogeneity of the underlying CDNs, (ii) Fury Route is effective in revealing relative network distance, needed in many real-world scenarios, (iii) caching can dramatically reduce Fury Route’s DNS overhead, making it a useful system in practice.
| Original language | English (US) |
|---|---|
| Title of host publication | Passive and Active Measurement - 19th International Conference, PAM 2018, Proceedings |
| Editors | Anja Feldmann, Georgios Smaragdakis, Robert Beverly |
| Publisher | Springer Verlag |
| Pages | 87-99 |
| Number of pages | 13 |
| ISBN (Print) | 9783319764801 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Event | 19th International Conference on Passive and Active Measurement, PAM 2018 - Berlin, Germany Duration: Mar 26 2018 → Mar 27 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10771 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 19th International Conference on Passive and Active Measurement, PAM 2018 |
|---|---|
| Country | Germany |
| City | Berlin |
| Period | 3/26/18 → 3/27/18 |
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ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
Fury Route : Leveraging CDNs to Remotely Measure Network Distance. / Flores, Marcel; Wenzel, Alexander; Chen, Kevin; Kuzmanovic, Aleksandar.
Passive and Active Measurement - 19th International Conference, PAM 2018, Proceedings. ed. / Anja Feldmann; Georgios Smaragdakis; Robert Beverly. Springer Verlag, 2018. p. 87-99 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10771 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Fury Route
T2 - Leveraging CDNs to Remotely Measure Network Distance
AU - Flores, Marcel
AU - Wenzel, Alexander
AU - Chen, Kevin
AU - Kuzmanovic, Aleksandar
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Estimating network distance between arbitrary Internet endpoints is an essential primitive in applications ranging from performance optimization to network debugging and auditing. Enabling such a primitive without deploying new infrastructure was demonstrated via DNS. However, the proliferation of DNS hosting has made DNS-based measurement techniques far less dependable. In this paper, we show that the heterogeneous infrastructure of different CDNs, combined with the proliferation of the EDNS0 client-subnet extension (ECS), enables novel infrastructureless measurement. We design Fury Route, a system that estimates network distance by utilizing ECS to construct a virtual path between endpoints via intermediate CDN replicas. Fury Route requires no additional infrastructure to be deployed. The measured endpoints do not need to participate by sending or responding to probes. Fury Route further generates no load on endpoints. It only queries DNS, whose infrastructure is designed for large loads. We extensively evaluate Fury Route and demonstrate that (i) the key to Fury Route’s ability to construct virtual paths lies in the heterogeneity of the underlying CDNs, (ii) Fury Route is effective in revealing relative network distance, needed in many real-world scenarios, (iii) caching can dramatically reduce Fury Route’s DNS overhead, making it a useful system in practice.
AB - Estimating network distance between arbitrary Internet endpoints is an essential primitive in applications ranging from performance optimization to network debugging and auditing. Enabling such a primitive without deploying new infrastructure was demonstrated via DNS. However, the proliferation of DNS hosting has made DNS-based measurement techniques far less dependable. In this paper, we show that the heterogeneous infrastructure of different CDNs, combined with the proliferation of the EDNS0 client-subnet extension (ECS), enables novel infrastructureless measurement. We design Fury Route, a system that estimates network distance by utilizing ECS to construct a virtual path between endpoints via intermediate CDN replicas. Fury Route requires no additional infrastructure to be deployed. The measured endpoints do not need to participate by sending or responding to probes. Fury Route further generates no load on endpoints. It only queries DNS, whose infrastructure is designed for large loads. We extensively evaluate Fury Route and demonstrate that (i) the key to Fury Route’s ability to construct virtual paths lies in the heterogeneity of the underlying CDNs, (ii) Fury Route is effective in revealing relative network distance, needed in many real-world scenarios, (iii) caching can dramatically reduce Fury Route’s DNS overhead, making it a useful system in practice.
UR - http://www.scopus.com/inward/record.url?scp=85043588353&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043588353&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-76481-8_7
DO - 10.1007/978-3-319-76481-8_7
M3 - Conference contribution
SN - 9783319764801
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 87
EP - 99
BT - Passive and Active Measurement - 19th International Conference, PAM 2018, Proceedings
A2 - Feldmann, Anja
A2 - Smaragdakis, Georgios
A2 - Beverly, Robert
PB - Springer Verlag
ER -