Robust Task Offloading for IoT Fog Computing under Information Asymmetry and Information Uncertainty

With the wide development of smart devices, fog computing has emerged as a promising solution to accommodate the ever-increasing computational demands in Internet of things (IoT). However, there are two major obstacles hindering the wide deployment of IoT fog computing, i.e., how to realize server recruitment under information asymmetry and reliable task assignment under information uncertainty. In this article, we develop a robust two-stage task offloading algorithm by integrating contract theory with computational intelligence. In the first stage, we propose a contract based server recruitment scheme to motivate servers to share residual computational resources. In the second stage, by leveraging multi-armed bandit (MAB), we develop a reliable volatile upper confidence bound (RV-UCB) algorithm to minimize the long-term delay of task assignment, which lakes into account task awareness. occurrence awareness and location awareness. Finally, a series of stimulation results are carried out to validate the performance of the proposed algorithm.