A Distributed Approach to End-to-End Network Topology Inference

To construct an efficient overlay network, the information of underlay is important. However, the inference of an underlay topology is not easy. We consider using end-to-end measurement tools such as traceroute to infer the underlay topology among a group of hosts. Since pair-wise traceroutes among hosts take a long time and generate much network traffic, Max-Delta has been proposed to infer a highly accurate topology with a low number of traceroutes. However, Max-Delta relies on a central server to collect traceroute results and to select paths for hosts to measure. It is hence not scalable to large groups. In this paper, we investigate a distributed version of Max-Delta scheme in order to support scalable inference. In our scheme, each host joins an overlay tree before conducting traceroutes. A host then independently selects paths to traceroute and exchanges traceroute results with others through the overlay tree. As a result, each host can maintain a partially discovered topology. We have studied two key issues in the scheme, i.e., how to construct a low-diameter overlay tree and how to reduce bandwidth consumption in measurements. As compared to Max-Delta, our scheme is fully distributed and scalable. In the scheme, each host computes its own traceroute targets, and the computational loads are distributed to all the hosts instead of a single server. Furthermore, each host only exchanges data with a few other hosts and does not need to set up connections with all the other hosts. Simulation results show that the constructed tree has a low diameter and can support quick data exchange among hosts, and that the use of a lookup table for routers can significantly reduce bandwidth consumption in data exchange.