  As wireless services and applications become more sophisticated and require
faster and higher-capacity networks, there is a need for an efficient
management of the execution of increasingly complex tasks based on the
requirements of each application. In this regard, fog computing enables the
integration of virtualized servers into networks and brings cloud services
closer to end devices. In contrast to the cloud server, the computing capacity
of fog nodes is limited and thus a single fog node might not be capable of
computing-intensive tasks. In this context, task offloading can be particularly
useful at the fog nodes by selecting the suitable nodes and proper resource
management while guaranteeing the Quality-of-Service (QoS) requirements of the
users. This paper studies the design of a joint task offloading and resource
allocation control for heterogeneous service tasks in multi-fog nodes systems.
This problem is formulated as a partially observable stochastic game, in which
each fog node cooperates to maximize the aggregated local rewards while the
nodes only have access to local observations. To deal with partial
observability, we apply a deep recurrent Q-network (DRQN) approach to
approximate the optimal value functions. The solution is then compared to a
deep Q-network (DQN) and deep convolutional Q-network (DCQN) approach to
evaluate the performance of different neural networks. Moreover, to guarantee
the convergence and accuracy of the neural network, an adjusted
exploration-exploitation method is adopted. Provided numerical results show
that the proposed algorithm can achieve a higher average success rate and lower
average overflow than baseline methods.
