Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

TL;DR

This paper provides a comprehensive taxonomy of scheduling strategies for IoT applications in Fog computing, analyzing current research, identifying gaps, and suggesting future directions for scalable and adaptive scheduling mechanisms.

Contribution

It introduces a new classification scheme for IoT scheduling in Fog computing and analyzes existing literature to identify research gaps and future opportunities.

Findings

Current scheduling approaches lack scalability and adaptability.

Research gaps identified in classification schemes.

Future directions include developing more dynamic scheduling algorithms.

Abstract

Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This paper presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described.