Vantage Point Selection Algorithms for Bottleneck Capacity Estimation

TL;DR

This paper studies algorithms for selecting vantage points in a network to efficiently estimate bottleneck capacities, proposing approximation algorithms for both non-adaptive and adaptive scenarios with theoretical performance guarantees.

Contribution

It introduces novel algorithms and bounds for vantage point selection to maximize bottleneck capacity discovery in networks, addressing both non-adaptive and adaptive settings.

Findings

Achieves a 1-1/e approximation for the non-adaptive model.

Provides bounds on optimal solutions for trees and planar graphs.

Analyzes the complexity of vantage point selection in capacity estimation.

Abstract

Motivated by the problem of estimating bottleneck capacities on the Internet, we formulate and study the problem of vantage point selection. We are given a graph $G=(V, E)$ whose edges $E$ have unknown capacity values that are to be discovered. Probes from a vantage point, i.e, a vertex $v \in V$, along shortest paths from $v$ to all other vertices, reveal bottleneck edge capacities along each path. Our goal is to select $k$ vantage points from $V$ that reveal the maximum number of bottleneck edge capacities. We consider both a non-adaptive setting where all $k$ vantage points are selected before any bottleneck capacity is revealed, and an adaptive setting where each vantage point selection instantly reveals bottleneck capacities along all shortest paths starting from that point. In the non-adaptive setting, by considering a relaxed model where edge capacities are drawn from a random permutation (which still leaves the problem of maximizing the expected number of revealed edges NP-hard), we are able to give a $1-1/e$ approximate algorithm. In the adaptive setting we work with the least permissive model where edge capacities are arbitrarily fixed but unknown. We compare with the best solution for the particular input instance (i.e. by enumerating all choices of $k$ tuples), and provide both lower bounds on instance optimal approximation algorithms and upper bounds for trees and planar graphs.