  Future 5G wireless networks will rely on agile and automated network
management, where the usage of diverse resources must be jointly optimized with
surgical accuracy. A number of key wireless network functionalities (e.g.,
traffic steering, power control) give rise to hard optimization problems. What
is more, high spatio-temporal traffic variability coupled with the need to
satisfy strict per slice/service SLAs in modern networks, suggest that these
problems must be constantly (re-)solved, to maintain close-to-optimal
performance. To this end, we propose the framework of Online Network
Optimization (ONO), which seeks to maintain both agile and efficient control
over time, using an arsenal of data-driven, online learning, and AI-based
techniques. Since the mathematical tools and the studied regimes vary widely
among these methodologies, a theoretical comparison is often out of reach.
Therefore, the important question `what is the right ONO technique?' remains
open to date. In this paper, we discuss the pros and cons of each technique and
present a direct quantitative comparison for a specific use case, using real
data. Our results suggest that carefully combining the insights of problem
modeling with state-of-the-art AI techniques provides significant advantages at
reasonable complexity.
