  Distributed Nearest Neighbor Search (DNNS) locates service nodes that have
shortest interactive delay towards requesting hosts. DNNS provides an important
service for large-scale latency sensitive networked applications, such as VoIP,
online network games, or interactive network services on the cloud. Existing
work assumes the delay to be symmetric, which does not generalize to
applications that are sensitive to one-way delays, such as the multimedia video
delivery from the servers to the hosts. We propose a relaxed inframetric model
for the network delay space that does not assume the triangle inequality and
delay symmetry to hold. We prove that the DNNS requests can be completed
efficiently if the delay space exhibits modest inframetric dimensions, which we
can observe empirically. Finally, we propose a DNNS method named HybridNN
(\textit{Hybrid} \textit{N}earest \textit{N}eighbor search) based on the
inframetric model for fast and accurate DNNS. For DNNS requests, HybridNN
chooses closest neighbors accurately via the inframetric modelling, and
scalably by combining delay predictions with direct probes to a pruned set of
neighbors. Simulation results show that HybridNN locates nearly optimally the
nearest neighbor. Experiments on PlanetLab show that HybridNN can provide
accurate nearest neighbors that are close to optimal with modest query overhead
and maintenance traffic.
