On the Cost of Network Inference Mechanisms

A number of network path delay, loss, or bandwidth inference mechanisms have been proposed over the past decade. Concurrently, several network measurement services have been deployed over the Internet and intranets. We consider inference mechanisms that use O(n) end-to-end measurements to predict the O(n虏) end-to-end pairwise measurements among n nodes, and investigate when it is beneficial to use them in measurement services. In particular, we address the following questions : 1) For which measurement request patterns would using an inference mechanism be advantageous? 2) How does a measurement service determine the set of hosts that should utilize inference mechanisms, as opposed to those that are better served using direct end-to-end measurements? We explore three solutions that identify groups of hosts which are likely to benefit from inference. We compare these solutions in terms of effectiveness and algorithmic complexity. Results with synthetic data sets and data sets from a popular peer-to-peer system demonstrate that our techniques accurately identify host subsets that benefit from inference, in significantly less time than an algorithm that identifies optimal subsets. The measurement savings are large when measurement request patterns exhibit small-world characteristics, which is often the case. (Part of this work (focusing on one of three solutions presented in this paper) appeared in [1]).