Privacy-Aware Load Balancing in Fog Networks: A Reinforcement Learning Approach

TL;DR

This paper introduces a privacy-preserving reinforcement learning-based load balancing algorithm for fog networks, optimizing IoT workload distribution without exposing sensitive node information, and demonstrating superior performance in simulated environments.

Contribution

It presents a novel RL approach that maintains privacy of Fog nodes while effectively balancing loads, outperforming existing methods in dynamic IoT scenarios.

Findings

Outperforms baseline load balancing methods across various workloads

Ensures privacy of Fog service providers during load balancing

Environment representation improves RL performance and privacy

Abstract

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.