research-article

Traceroute probe method and forward IP path inference

Authors:

Matthew Luckie,

Bradley HuffakerAuthors Info & Claims

IMC '08: Proceedings of the 8th ACM SIGCOMM conference on Internet measurement

Pages 311 - 324

https://doi.org/10.1145/1452520.1452557

Published: 20 October 2008 Publication History

Abstract

Several traceroute probe methods exist, each designed to perform better in a scenario where another fails. This paper examines the effects that the choice of probe method has on the inferred forward IP path by comparing the paths inferred with UDP, ICMP, and TCP-based traceroute methods to (1) a list of routable IP addresses, (2) a list of known routers, and (3) a list of well-known websites. We further compare methods by examining seven months of macroscopic Internet topology data collected by CAIDA's Archipelago infrastructure.

We found significant differences in the topology observed using different probe methods. In particular, we found that ICMP-based traceroute methods tend to successfully reach more destinations, as well as collect evidence of a greater number of AS links. UDP-based methods infer the greatest number of IP links, despite reaching the fewest destinations. We hypothesise that some per-flow load balancers implement different forwarding policies for TCP and UDP, and run a specific experiment to confirm this hypothesis.

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Index Terms

Traceroute probe method and forward IP path inference
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
    2. Metrics

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Published In

cover image ACM Conferences

IMC '08: Proceedings of the 8th ACM SIGCOMM conference on Internet measurement

October 2008

352 pages

ISBN:9781605583341

DOI:10.1145/1452520

Program Chairs:
Konstantina Papagiannaki
Intel Research Pittsburgh, USA
,
Zhi-Li Zhang
University of Minnesota, USA

Copyright © 2008 ACM.

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Published: 20 October 2008

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IMC08

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IMC08: Internet Measurement Conference

October 20 - 22, 2008

Vouliagmeni, Greece

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Li NLi TMa ZHu XZhang SLiu FQuan XLuo XRen GFeng HZhang S(2024)HpGraphNEI: A network entity identification model based on heterophilous graph learningInformation Processing & Management10.1016/j.ipm.2024.10381061:5(103810)Online publication date: Sep-2024
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Sommers JRudolph NDurairajan R(2023)Schooling NOOBs with eBPFProceedings of the 1st Workshop on eBPF and Kernel Extensions10.1145/3609021.3609302(21-27)Online publication date: 10-Sep-2023
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