  Open Radio Access Network systems, with their virtualized base stations
(vBSs), offer operators the benefits of increased flexibility, reduced costs,
vendor diversity, and interoperability. Optimizing the allocation of resources
in a vBS is challenging since it requires knowledge of the environment, (i.e.,
"external'' information), such as traffic demands and channel quality, which is
difficult to acquire precisely over short intervals of a few seconds. To tackle
this problem, we propose an online learning algorithm that balances the
effective throughput and vBS energy consumption, even under unforeseeable and
"challenging'' environments; for instance, non-stationary or adversarial
traffic demands. We also develop a meta-learning scheme, which leverages the
power of other algorithmic approaches, tailored for more "easy'' environments,
and dynamically chooses the best performing one, thus enhancing the overall
system's versatility and effectiveness. We prove the proposed solutions achieve
sub-linear regret, providing zero average optimality gap even in challenging
environments. The performance of the algorithms is evaluated with real-world
data and various trace-driven evaluations, indicating savings of up to 64.5% in
the power consumption of a vBS compared with state-of-the-art benchmarks.
