  In this paper, we propose a load balancing algorithm based on Reinforcement
Learning (RL) to optimize the performance of Fog Computing for real-time IoT
applications. The algorithm aims to minimize the waiting delay of IoT workloads
in dynamic environments with unpredictable traffic demands, using intelligent
workload distribution. Unlike previous studies, our solution does not require
load and resource information from Fog nodes to preserve the privacy of service
providers, who may wish to hide such information to prevent competitors from
calculating better pricing strategies. The proposed algorithm is evaluated on a
Discrete-event Simulator (DES) to mimic practical deployment in real
environments, and its generalization ability is tested on simulations longer
than what it was trained on. Our results show that our proposed approach
outperforms baseline load balancing methods under different workload generation
rates, while ensuring the privacy of Fog service providers. Furthermore, the
environment representation we proposed for the RL agent demonstrates better
performance compared to the commonly used representations for RL solutions in
the literature, which compromise privacy.
