  Comparing to cloud computing, fog computing performs computation and services
at the edge of networks, thus relieving the computation burden of the data
center and reducing the task latency of end devices. Computation latency is a
crucial performance metric in fog computing, especially for real-time
applications. In this paper, we study a peer computation offloading problem for
a fog network with unknown dynamics. In this scenario, each fog node (FN) can
offload their computation tasks to neighboring FNs in a time slot manner. The
offloading latency, however, could not be fed back to the task dispatcher
instantaneously due to the uncertainty of the processing time in peer FNs.
Besides, peer competition occurs when different FNs offload tasks to one FN at
the same time. To tackle the above difficulties, we model the computation
offloading problem as a sequential FN selection problem with delayed
information feedback. Using adversarial multi-arm bandit framework, we
construct an online learning policy to deal with delayed information feedback.
Different contention resolution approaches are considered to resolve peer
competition. Performance analysis shows that the regret of the proposed
algorithm, or the performance loss with suboptimal FN selections, achieves a
sub-linear order, suggesting an optimal FN selection policy. In addition, we
prove that the proposed strategy can result in a Nash equilibrium (NE) with all
FNs playing the same policy. Simulation results validate the effectiveness of
the proposed policy.
