  Network Function Virtualization (NFV) carries the potential for on-demand
deployment of network algorithms in virtual machines (VMs). In large clouds,
however, VM resource allocation incurs delays that hinder the dynamic scaling
of such NFV deployment. Parallel resource management is a promising direction
for boosting performance, but it may significantly increase the communication
overhead and the decline ratio of deployment attempts. Our work analyzes the
performance of various placement algorithms and provides empirical evidence
that state-of-the-art parallel resource management dramatically increases the
decline ratio of deterministic algorithms but hardly affects randomized
algorithms. We, therefore, introduce APSR -- an efficient parallel random
resource management algorithm that requires information only from a small
number of hosts and dynamically adjusts the degree of parallelism to provide
provable decline ratio guarantees. We formally analyze APSR, evaluate it on
real workloads, and integrate it into the popular OpenStack cloud management
platform. Our evaluation shows that APSR matches the throughput provided by
other parallel schedulers, while achieving up to 13x lower decline ratio and a
reduction of over 85% in communication overheads.
