MPLS: technology and applications
MPLS: technology and applications
Fault Tolerance and Load Balancing in QoS Provisioning with Multiple MPLS Paths
IWQoS '01 Proceedings of the 9th International Workshop on Quality of Service
Touring the internet in a TCP sidecar
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
All of Nonparametric Statistics (Springer Texts in Statistics)
All of Nonparametric Statistics (Springer Texts in Statistics)
Discarte: a disjunctive internet cartographer
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Traceroute probe method and forward IP path inference
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Scamper: a scalable and extensible packet prober for active measurement of the internet
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
A learning-based approach for IP geolocation
PAM'10 Proceedings of the 11th international conference on Passive and active measurement
Revealing MPLS tunnels obscured from traceroute
ACM SIGCOMM Computer Communication Review
Quantifying violations of destination-based forwarding on the internet
Proceedings of the 2012 ACM conference on Internet measurement conference
Network fingerprinting: TTL-based router signatures
Proceedings of the 2013 conference on Internet measurement conference
DataTraffic Monitoring and Analysis
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Multi-Protocol Label Switching (MPLS) is a mechanism that enables service providers to specify virtual paths through IP networks. The use of MPLS in the open Internet (i.e., public end-to-end paths) has important implications for users and network neutrality since MPLS is frequently used in traffic engineering applications today. In this paper we present a longitudinal study of the prevalence and characteristics of MPLS deployments in the open Internet. We use path measurement data collected over the past 3.5 years by the CAIDA Archipelago project (Ark), which consist of over 10 billion individual traceroutes between hosts throughout the Internet. We use two different techniques for identifying MPLS paths in Ark data: direct observation via ICMP extensions that include MPLS label information, and inference using a Bayesian data fusion methodology. Our direct observation method can only identify uniform-mode tunnels, which very likely underestimates MPLS deployments. Nonetheless, our results show that the total number of tunnels observed in a given measurement period has varied widely over time with the largest deployments in tier-1 providers. About 7% of all autonomous systems deploy MPLS and this level of deployment has been consistent over the past three years. The average length of an MPLS tunnel has decreased from 4 hops in 2008 to 3 hops in 2011, and the path length distribution is heavily skewed. About 25% of all paths in 2011 cross at least one MPLS tunnel, while 4% cross more than one. Finally, data observed in MPLS headers suggest that many ASes employ some types of traffic classification and engineering in their tunnels.