Abstract : In the IoT-based systems, the fog computing allowsthe fog nodes to offload and process tasks requested from IoTenableddevices in a distributed manner instead of the centralizedcloud servers to reduce the response delay. However, achievingsuch a benefit is still challenging in the systems with high rate ofrequests, which imply long queues of tasks in the fog nodes, thusexposing probably an inefficiency in terms of latency to offloadthe tasks. In addition, a complicated heterogeneous degree inthe fog environment introduces an additional issue that many ofsingle fogs can not process heavy tasks due to lack of availableresources or limited computing capabilities. Reinforcement learningis a rising component of machine learning, which providesintelligent decision making for agents to response effectively tothe dynamics of environment. This vision implies a great potentialof application of RL in the concept of fog computing regardingresource allocation for task offloading and execution to achievethe improved performance. This work presents an overview of RLapplications to solve the resource allocation related problems inthe fog computing environment. The open issues and challengesare explored and discussed for further study.
Index terms : Fog computing, machine learning, performance improvement, reinforcement learning, resource allocation, task offloading, task scheduling.