Load balancing mechanisms in fog computing: A systematic review

TL;DR

This paper systematically reviews load balancing mechanisms in fog computing, analyzing classifications, metrics, evaluation techniques, challenges, and future trends to enhance service quality and resource management.

Contribution

It provides a comprehensive classification and analysis of load balancing techniques in fog computing, including metrics, evaluation methods, and future research directions.

Findings

Classification of load balancing methods into approximate, exact, fundamental, and hybrid.

Analysis of load balancing metrics, advantages, and disadvantages.

Identification of open challenges and future trends in fog load balancing.

Abstract

Recently, fog computing has been introduced as a modern distributed paradigm and complement to cloud computing to provide services. Fog system extends storing and computing to the edge of the network, which can solve the problem about service computing of the delay-sensitive applications remarkably besides enabling the location awareness and mobility support. Load balancing is an important aspect of fog networks that avoids a situation with some under-loaded or overloaded fog nodes. Quality of Service (QoS) parameters such as resource utilization, throughput, cost, response time, performance, and energy consumption can be improved with load balancing. In recent years, some researches in load balancing techniques in fog networks have been carried out, but there is no systematic review to consolidate these studies. This article reviews the load-balancing mechanisms systematically in fog computing in four classifications, including approximate, exact, fundamental, and hybrid methods (published between 2013 and August 2020). Also, this article investigates load balancing metrics with all advantages and disadvantages related to chosen load balancing mechanisms in fog networks. The evaluation techniques and tools applied for each reviewed study are explored as well. Additionally, the essential open challenges and future trends of these mechanisms are discussed.