  We consider a general online resource allocation model with bandit feedback
and time-varying demands. While online resource allocation has been well
studied in the literature, most existing works make the strong assumption that
the demand arrival process is stationary. In practical applications, such as
online advertisement and revenue management, however, this process may be
exogenous and non-stationary, like the constantly changing internet traffic.
Motivated by the recent Online Algorithms with Advice framework [Mitazenmacher
and Vassilvitskii, \emph{Commun. ACM} 2022], we explore how online advice can
inform policy design. We establish an impossibility result that any algorithm
perform poorly in terms of regret without any advice in our setting. In
contrast, we design an robust online algorithm that leverages the online
predictions on the total demand volumes. Empowered with online advice, our
proposed algorithm is shown to have both theoretical performance and promising
numerical results compared with other algorithms in literature. We also provide
two explicit examples for the time-varying demand scenarios and derive
corresponding theoretical performance guarantees. Finally, we adapt our model
to a network revenue management problem, and numerically demonstrate that our
algorithm can still performs competitively compared to existing baselines.
