  In this paper, we consider how to provide fast estimates of flow-level tail
latency performance for very large scale data center networks. Network tail
latency is often a crucial metric for cloud application performance that can be
affected by a wide variety of factors, including network load, inter-rack
traffic skew, traffic burstiness, flow size distributions, oversubscription,
and topology asymmetry. Network simulators such as ns-3 and OMNeT++ can provide
accurate answers, but are very hard to parallelize, taking hours or days to
answer what if questions for a single configuration at even moderate scale.
Recent work with MimicNet has shown how to use machine learning to improve
simulation performance, but at a cost of including a long training step per
configuration, and with assumptions about workload and topology uniformity that
typically do not hold in practice.
  We address this gap by developing a set of techniques to provide fast
performance estimates for large scale networks with general traffic matrices
and topologies. A key step is to decompose the problem into a large number of
parallel independent single-link simulations; we carefully combine these
link-level simulations to produce accurate estimates of end-to-end flow level
performance distributions for the entire network. Like MimicNet, we exploit
symmetry where possible to gain additional speedups, but without relying on
machine learning, so there is no training delay. On large-scale networks where
ns-3 takes 11 to 27 hours to simulate five seconds of network behavior, our
techniques run in one to two minutes with 99th percentile accuracy within 9%
for flow completion times.
