  The powerful paradigm of Fog computing is currently receiving major interest,
as it provides the possibility to integrate virtualized servers into networks
and brings cloud service closer to end devices. To support this distributed
intelligent platform, Software-Defined Network (SDN) has emerged as a viable
network technology in the Fog computing environment. However, uncertainties
related to task demands and the different computing capacities of Fog nodes,
inquire an effective load balancing algorithm. In this paper, the load
balancing problem has been addressed under the constraint of achieving the
minimum latency in Fog networks. To handle this problem, a reinforcement
learning based decision-making process has been proposed to find the optimal
offloading decision with unknown reward and transition functions. The proposed
process allows Fog nodes to offload an optimal number of tasks among incoming
tasks by selecting an available neighboring Fog node under their respective
resource capabilities with the aim to minimize the processing time and the
overall overloading probability. Compared with the traditional approaches, the
proposed scheme not only simplifies the algorithmic framework without imposing
any specific assumption on the network model but also guarantees convergence in
polynomial time. The results show that, during average delays, the proposed
reinforcement learning-based offloading method achieves significant performance
improvements over the variation of service rate and traffic arrival rate. The
proposed algorithm achieves 1.17%, 1.02%, and 3.21% lower overload probability
relative to random, least-queue and nearest offloading selection schemes,
respectively.
