Efficient and Dynamic Routing Topology Inference From End-to-End Measurements

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.