A multi-layer guided reinforcement learning-based tasks offloading in edge computing
Alberto Robles-Enciso, Antonio Skármeta
Abstract
The breakthrough in Machine Learning (ML) techniques and the popularity of the Internet of Things (IoT) has increased interest in applying Artificial Intelligence (AI) techniques to the new paradigm of Edge Computing. One of the challenges in edge computing architectures is the optimal distribution of the generated tasks between the devices in each layer (i.e., cloud-fog-edge). In this paper, we propose to use Reinforcement Learning (RL) to solve the Task Assignment Problem (TAP) at the edge layer and then we propose a novel multi-layer extension of RL (ML-RL) techniques that allows edge agents to query an upper-level agent with more knowledge to improve the performance in complex and uncertain situations. We first formulate the task assignment process considering the trade-off between energy consumption and execution time. We then present a greedy solution as a baseline and implement our multi-layer RL proposal in the PureEdgeSim simulator. Finally several simulations of each algorithm are evaluated with different numbers of devices to verify scalability. The simulation results show that reinforcement learning solutions outperformed the heuristic-based solutions and our multi-layer approach can significantly improve performance in high device density scenarios.