  Cloud environments require dynamic and adaptive networking policies. It is
preferred to use heuristics over advanced learning algorithms in Virtual
Network Functions (VNFs) in production becuase of high-performance constraints.
This paper proposes Aquarius to passively yet efficiently gather observations
and enable the use of machine learning to collect, infer, and supply accurate
networking state information-without incurring additional signalling and
management overhead. This paper illustrates the use of Aquarius with a traffic
classifier, an autoscaling system, and a load balancer-and demonstrates the use
of three different machine learning paradigms-unsupervised, supervised, and
reinforcement learning, within Aquarius, for inferring network state. Testbed
evaluations show that Aquarius increases network state visibility and brings
notable performance gains with low overhead.
