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.
@article{arxiv.2208.11385,
title = {Efficient Data-Driven Network Functions},
author = {Zhiyuan Yao and Yoann Desmouceaux and Juan-Antonio Cordero-Fuertes and Mark Townsley and Thomas Heide Clausen},
journal= {arXiv preprint arXiv:2208.11385},
year = {2022}
}