  Latency is one of the most critical performance metrics for a wide range of
applications. Therefore, it is important to understand the underlying
mechanisms that give rise to the observed latency values and diagnose the ones
that are unexpectedly high. In this paper, we study the Internet delay space
via robust principal component analysis (RPCA). Using RPCA, we show that the
delay space, i.e. the matrix of measured round trip times between end hosts,
can be decomposed into two components - the expected latency between end hosts
with respect to the current state of the Internet and the inflation on the
paths between the end hosts. Using this decomposition, first we study the
well-known low-dimensionality phenomena of the delay space and ask what
properties of the end hosts define the dimensions. Second, using the
decomposition, we develop a filtering method to detect the paths which
experience unexpected latencies and identify routing anomalies. We show that
our filter successfully identifies an anomalous route even when its observed
latency is not obviously high in magnitude.
