  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 $[O(\epsilon)$, $O(\log(1/\epsilon)^2)]$ utility-delay
tradeoff. For non-stationary networks, \plc{} obtains an $[O(\epsilon),
O(\log^2(1/\epsilon)$ $+ \min(\epsilon^{c/2-1}, e_w/\epsilon))]$
utility-backlog tradeoff for distributions that last
$\Theta(\frac{\max(\epsilon^{-c}, e_w^{-2})}{\epsilon^{1+a}})$ time, where
$e_w$ is the prediction accuracy and $a=\Theta(1)>0$ is a constant (the
Backpressue algorithm \cite{neelynowbook} requires an $O(\epsilon^{-2})$ 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 $O(\min(\epsilon^{-1+c/2},
e_w/\epsilon)+\log^2(1/\epsilon))$ 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.
