# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
###### Abstract  
The Internet of Things (IoT) uptake brought a paradigm shift in application deployment. Indeed, IoT applications are not centralized in cloud data centers, but the computation and storage are moved close to the consumers, creating a computing continuum between the edge of the network and the cloud. This paradigm shift is called fog computing, a concept introduced by Cisco in 2012. Scheduling applications in this decentralized, heterogeneous, and resource-constrained environment is challenging. The task scheduling problem in fog computing has been widely explored and addressed using many approaches, from traditional operational research to heuristics and machine learning. This paper aims to analyze the literature on task scheduling in fog computing published in the last five years to classify the criteria used for decision-making and the technique used to solve the task scheduling problem. We propose a taxonomy of task scheduling algorithms, and we identify the research gaps and challenges.  
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RQ2:What techniques are used to solve the task scheduling problem in fog computing? RQ3:What are the open challenges of task scheduling in fog computing?  
Unlike previous surveys, our review takes a contemporary, comprehensive approach, addressing the latest developments and research directions in fog task scheduling. We reviewed many recent publications and learned about emerging technologies and real-world uses from various sources. A key innovation introduced through our literature review is the development of a novel taxonomy and classification framework. This framework integrates state-of-the-art technologies and classifies the current task-scheduling techniques. Researchers can better grasp the changing fog computing landscape with its help, as it offers a comprehensive reference guide and acts as a dynamic roadmap.  
In this research paper, we conduct a comprehensive literature review of the existing research work done in the fog computing task scheduling field. Key contributions are:  
* The definition of a taxonomy for task scheduling algorithms in fog computing, which classifies them along two dimensions: the technique for solving the scheduling problem and the metric used to make scheduling decisions.
* A comprehensive literature review of existing scheduling algorithms, particularly focusing on intelligent dynamic scheduling techniques based on machine learning, fuzzy logic, reinforcement learning, and deep reinforcement learning, describing their strengths and weaknesses.
* Identification of the research gaps and challenges for task scheduling in fog computing for future research work in this field.  
The remainder of this paper is organized as follows. Section 2 explains the concept of fog computing, its architecture, and its characteristics. The task scheduling problem is fog computing is formulated in Section 3. Section 4 introduces the research methodology adopted. The categorization of the literature is proposed in Section 5. The analysis of the optimization criteria is presented in Section 6, and the analysis of problem solution techniques is discussed in Section 7. Research gaps and challenges are presented in Section 8. Finally, Section 9 concludes the paper.