Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm

TL;DR

This paper introduces a reinforcement learning-based load balancing algorithm for Fog networks that minimizes latency and overload probability by optimizing task offloading decisions without relying on specific network assumptions.

Contribution

It proposes a novel RL-based load balancing method for Fog computing that guarantees convergence and outperforms traditional schemes in reducing overload and delay.

Findings

Achieves 1.17% lower overload probability than random offloading.

Achieves 3.21% lower overload probability than nearest offloading.

Significant performance improvements over traditional methods in average delay.

Abstract

The powerful paradigm of Fog computing is currently receiving major interest, as it provides the possibility to integrate virtualized servers into networks and brings cloud service closer to end devices. To support this distributed intelligent platform, Software-Defined Network (SDN) has emerged as a viable network technology in the Fog computing environment. However, uncertainties related to task demands and the different computing capacities of Fog nodes, inquire an effective load balancing algorithm. In this paper, the load balancing problem has been addressed under the constraint of achieving the minimum latency in Fog networks. To handle this problem, a reinforcement learning based decision-making process has been proposed to find the optimal offloading decision with unknown reward and transition functions. The proposed process allows Fog nodes to offload an optimal number of tasks among incoming tasks by selecting an available neighboring Fog node under their respective resource capabilities with the aim to minimize the processing time and the overall overloading probability. Compared with the traditional approaches, the proposed scheme not only simplifies the algorithmic framework without imposing any specific assumption on the network model but also guarantees convergence in polynomial time. The results show that, during average delays, the proposed reinforcement learning-based offloading method achieves significant performance improvements over the variation of service rate and traffic arrival rate. The proposed algorithm achieves 1.17%, 1.02%, and 3.21% lower overload probability relative to random, least-queue and nearest offloading selection schemes, respectively.