We investigate the problem of stochastic network optimization in the presence
of imperfect state prediction and non-stationarity. Based on a novel
distribution-accuracy curve prediction model, we develop the predictive
learning-aided control (PLC) algorithm, which jointly utilizes historic and
predicted network state information for decision making. PLC is an online
algorithm that requires zero a-prior system statistical information, and
consists of three key components, namely sequential distribution estimation and
change detection, dual learning, and online queue-based control.
Specifically, we show that PLC simultaneously achieves good long-term
performance, short-term queue size reduction, accurate change detection, and
fast algorithm convergence. In particular, for stationary networks, PLC
achieves a near-optimal [O(ϵ), O(log(1/ϵ)2)] utility-delay
tradeoff. For non-stationary networks, \plc{} obtains an [O(ϵ),O(log2(1/ϵ)+min(ϵc/2−1,ew/ϵ))]
utility-backlog tradeoff for distributions that last
Θ(ϵ1+amax(ϵ−c,ew−2)) time, where
ew is the prediction accuracy and a=Θ(1)>0 is a constant (the
Backpressue algorithm \cite{neelynowbook} requires an O(ϵ−2) length
for the same utility performance with a larger backlog). Moreover, PLC detects
distribution change O(w) slots faster with high probability (w is the
prediction size) and achieves an O(min(ϵ−1+c/2,ew/ϵ)+log2(1/ϵ)) convergence time. Our results demonstrate
that state prediction (even imperfect) can help (i) achieve faster detection
and convergence, and (ii) obtain better utility-delay tradeoffs