  Network load balancers are important components in data centers to provide
scalable services. Workload distribution algorithms are based on heuristics,
e.g., Equal-Cost Multi-Path (ECMP), Weighted-Cost Multi-Path (WCMP) or naive
machine learning (ML) algorithms, e.g., ridge regression. Advanced ML-based
approaches help achieve performance gain in different networking and system
problems. However, it is challenging to apply ML algorithms on networking
problems in real-life systems. It requires domain knowledge to collect features
from low-latency, high-throughput, and scalable networking systems, which are
dynamic and heterogenous. This paper proposes Aquarius to bridge the gap
between ML and networking systems and demonstrates its usage in the context of
network load balancers. This paper demonstrates its ability of conducting both
offline data analysis and online model deployment in realistic systems. The
results show that the ML model trained and deployed using Aquarius improves
load balancing performance yet they also reveals more challenges to be resolved
to apply ML for networking systems.
