A Reinforcement Learning Method for Joint Minimization of Energy Consumption and Delay in Fog Computing

nowadays, there is a growing demand for the use of fog computing in applications such as e-health, agriculture, industry, and intelligent transportation management. In fog computing, optimal offloading is of crucial importance due to the limited energy of mobile devices. In this regard, using machine learning methods has recently attracted much attention. This paper presents a reinforcement learning-based approach to motivate users to offload their tasks. We propose a self-organizing algorithm for offloading based on Q-learning theory. Performance evaluation of the proposed method against traditional and state-of-the-art methods shows that it consumes less energy. It also reduces the execution time of tasks and results in less consumption of network resources.

A Reinforcement Learning Method for Joint Minimization of Energy Consumption and Delay in Fog Computing Keywords:

Fog computing , Computation offloading , Optimization , reinforcement learning , Q-Learning