Efficient and dynamic routing topology inference from end-to-end measurements

Authors:
Jian Ni;Haiyong Xie;Sekhar Tatikonda;Yang Richard Yang
Affiliations:
Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL;Akamai Technologies, San Mateo, CA;Department of Electrical Engineering, Yale University, New Haven, CT;Department of Computer Science, Yale University, New Haven, CT
Venue:
IEEE/ACM Transactions on Networking (TON)
Year:
2010

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Abstract

Inferring the routing topology and link performance from a node to a set of other nodes is an important component in network monitoring and application design. In this paper, we propose a general framework for designing topology inference algorithms based on additive metrics. The framework can flexibly fuse information from multiple measurements to achieve better estimation accuracy. We develop computationally efficient (polynomial-time) topology inference algorithms based on the framework. We prove that the probability of correct topology inference of our algorithms converges to one exponentially fast in the number of probing packets. In particular, for applications where nodes may join or leave frequently such as overlay network construction, application-layer multicast, and peer-to-peer file sharing/streaming, we propose a novel sequential topology inference algorithm that significantly reduces the probing overhead and can efficiently handle node dynamics. We demonstrate the effectiveness of the proposed inference algorithms via Internet experiments.