  Task offloading is a promising technology to exploit the benefits of fog
computing. An effective task offloading strategy is needed to utilize the
computational resources efficiently. In this paper, we endeavor to seek an
online task offloading strategy to minimize the long-term latency. In
particular, we formulate a stochastic programming problem, where the
expectations of the system parameters change abruptly at unknown time instants.
Meanwhile, we consider the fact that the queried nodes can only feed back the
processing results after finishing the tasks. We then put forward an effective
algorithm to solve this challenging stochastic programming under the
non-stationary bandit model. We further prove that our proposed algorithm is
asymptotically optimal in a non-stationary fog-enabled network. Numerical
simulations are carried out to corroborate our designs.
