An Empirical Approach to Modeling Inter-AS Traffic Matrices
Recently developed techniques have been very successful
in accurately estimating intra-Autonomous System (AS)
These techniques rely on link measurements,
flow measurements, or routing-related data to infer
traffic demand between every pair of ingress-egress
points of an AS. They also illustrate an inherent
mismatch between data needed (e.g., ingress-egress demand)
and data most readily available (e.g., link measurements).
This mismatch is exacerbated when we try to estimate
inter-AS traffic matrices, i.e., snapshots of
Internet-wide traffic behavior over coarse time scale
(a week or longer) between ASs. We present a method
for modeling inter-AS traffic demand that
relies exclusively on publicly available/obtainable
measurements. We first perform extensive Internet-wide
measurement experiments to infer the ``business rationale''
of individual ASs. We then use these business profiles to
characterize individual ASs, classifying them by
their "utility" into ASs providing Web hosting, residential
access, and business access.
We rank ASs by their utilities which drive our gravity-model based
approach for generating inter-AS traffic demand. In a first attempt to
validate our methodology, we test our inter-AS traffic
generation method on an inferred Internet AS graph
and present some preliminary findings about the resulting
inter-AS traffic matrices.
- An Empirical Approach to Modeling Inter-AS Traffic Matrices [pdf]
H. Chang, S. Jamin, Z. Mao, and W. Willinger.
in Proceedings of ACM Internet Measurement Conference 2005, New Orleans, LA, October, 2005.
If you have any question about the data sets, please contact hschang[at]eecs.umich.edu.
Web Service Utility
Our approach to quantifying web service utility
is based on locating popular content on the Internet, as revealed
by usage patterns of a popular search engine.
More specifically, we obtained a list of the top 10,000 search keywords
most popularly submitted to search engines in the years 2003-2004. For
each keyword, we queried the Google search
engine, using the Google Web API to retrieve a set of most
closely matched URLs.
By using these popular URLs, we infer ASs' web service utility
and rank them by the inferred utility.
Residential Access Utility
To infer an AS's utility in providing residential Internet access,
we estimate it by the number of P2P file sharing users of the AS.
Utilizing three major P2P file sharing applications, BitTorrent,
eDonkey and Gnutella, we crawled approximately 2.2 million distinct
P2P IP addresses. By mapping individual IP addresses to their corresponding
ASs, we obtain ASs' residential access utility and rank them by
the obtained utility.
Business Access Utility
Our approach to infer an AS's utility in providing business access
relies on publicly available BGP routing tables to estimate the AS's
bandwidth distribution. By using inferred provider-customer relationships
among ASs, we approximate an AS's bandwidth distribution as the number
of its downstream AS customers. We rank ASs' by the inferred
bandwidth distribution volumes.