Abstract
The fog paradigm extends the cloud capabilities at the edge of the network. Fog computing-based real-time applications (Online gaming, 5G, Healthcare 4.0, Industrial IoT, autonomous vehicles, virtual reality, augmented reality, and many more) are growing at a very fast pace. There are limited resources at the fog layer compared to the cloud, which leads to resource constraint problems. Edge resources need to be utilized efficiently to fulfill the growing demand for a large number of IoT devices. Lots of work has been done for the efficient utilization of edge resources. This paper provided a systematic review of fog resource management literature from the year 2016–2021. In this review paper, the fog resource management approaches are divided into 9 categories which include resource scheduling, application placement, load balancing, resource allocation, resource estimation, task offloading, resource provisioning, resource discovery, and resource orchestration. These resource management approaches are further subclassified based on the technology used, QoS factors, and data-driven strategies. Comparative analysis of existing articles is provided based on technology, tools, application area, and QoS factors. Further, future research prospects are discussed in the context of QoS factors, technique/algorithm, tools, applications, mobility support, heterogeneity, AI-based, distributed network, hierarchical network, and security. A systematic literature review of existing survey papers is also included. At the end of this work, key findings are highlighted in the conclusion section.
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Kansal, P., Kumar, M. & Verma, O.P. Classification of resource management approaches in fog/edge paradigm and future research prospects: a systematic review. J Supercomput 78, 13145–13204 (2022). https://doi.org/10.1007/s11227-022-04338-1
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DOI: https://doi.org/10.1007/s11227-022-04338-1