Challenges and Opportunities in Fog Computing Scheduling: A Literature Review

The integration of the Internet of Things (IoT) has significantly transformed the way physical objects communicate, facilitating a wide range of applications. Nonetheless, both IoT and Artificial Intelligence (AI)-based applications face obstacles concerning the availability of computing resources, security, and constraints on energy consumption. While cloud computing provides certain resolutions, it also poses additional challenges, including heightened communication expenses and security vulnerabilities due to its physical disconnection from endpoint devices. As a countermeasure, fog computing emerges as a viable alternative by relocating computational resources nearer to the data origin at the edge of the network, yet it is not without its own set of issues such as the diversity of nodes and resource scarcity. This survey conducts an exhaustive analysis of the literature on scheduling within fog computing. It meticulously compares and contrasts various scheduling methodologies, taking into account critical aspects like the characteristics of fog computing, case studies, performance metrics, and simulation tools for evaluation, in addition to outlining their advantages and limitations. Moreover, it elaborates on a detailed taxonomy, assesses performance metrics, and examines performance parameters such as resource utilization, execution time, energy consumption, etc. Additionally, it identifies numerous prevailing challenges and issues. This survey paper aims to support the research community by highlighting potential avenues for future studies and key considerations in the development of scheduling strategies that leverage diverse contextual data.