A comprehensive survey on scheduling algorithms using fuzzy systems in distributed environments This paper presents a review of high-quality articles on fuzzy-based scheduling algorithms in grid, cloud, and fog computing from 2005 to June 2023. It discusses and compares fuzzy-based scheduling schemes based on their merits, demerits, evaluation techniques, simulation environments, and important parameters. The paper introduces distributed environments, scheduling processes, and their surveys. It summarizes several domains where fuzzy logic is used in distributed systems, addresses the basic concepts of fuzzy inference systems and motivations of fuzzy theory in schedulers, and discusses open challenges and promising future directions in fuzzy-based scheduling. The paper analyzes fuzzy logic applications in distributed systems and the advantages and disadvantages of fuzzy logic. It presents a comprehensive analysis of fuzzy-based scheduling in grid, cloud, and fog computing. It develops a taxonomy to categorize grid, cloud, and fog scheduling algorithms. It compares existing fuzzy-based scheduling algorithms according to QoS criteria and their appropriate applications. It investigates the details of fuzzy systems used in schedulers. It analyzes the evaluation tools used to evaluate different approaches. It proposes possible future research challenges and open issues. The paper discusses the importance of task scheduling in distributed systems, the challenges of managing IT systems, and the need for effective scheduling algorithms. It reviews existing surveys on task scheduling in distributed systems, highlighting the lack of attention to QoS metrics and the absence of fuzzy-based scheduling algorithms in most studies. The paper identifies the challenges of fuzzy logic, the need for choosing the best simulator, and the importance of identifying open challenges and future research directions. The paper is structured as follows: Section 2 provides an introduction to distributed systems, grid, cloud, and fog computing, scheduling, and fuzzy inference systems. Section 3 describes the research methodology. Section 4 describes a taxonomy of fuzzy-based scheduling algorithms. Section 5 compares these algorithms. Section 6 discusses future research directions and open issues. Section 7 presents the conclusion. The paper discusses the evolution of distributed systems, the characteristics of grid, cloud, and fog computing, and the scheduling process. It explains the challenges of task scheduling in distributed systems, the importance of resource management, and the role of fuzzy logic in scheduling. It reviews various fuzzy-based scheduling algorithms for cloud, fog, and grid environments, highlighting their strengths, weaknesses, and applications. The paper identifies open challenges and future research directions in fuzzy-based scheduling.A comprehensive survey on scheduling algorithms using fuzzy systems in distributed environments This paper presents a review of high-quality articles on fuzzy-based scheduling algorithms in grid, cloud, and fog computing from 2005 to June 2023. It discusses and compares fuzzy-based scheduling schemes based on their merits, demerits, evaluation techniques, simulation environments, and important parameters. The paper introduces distributed environments, scheduling processes, and their surveys. It summarizes several domains where fuzzy logic is used in distributed systems, addresses the basic concepts of fuzzy inference systems and motivations of fuzzy theory in schedulers, and discusses open challenges and promising future directions in fuzzy-based scheduling. The paper analyzes fuzzy logic applications in distributed systems and the advantages and disadvantages of fuzzy logic. It presents a comprehensive analysis of fuzzy-based scheduling in grid, cloud, and fog computing. It develops a taxonomy to categorize grid, cloud, and fog scheduling algorithms. It compares existing fuzzy-based scheduling algorithms according to QoS criteria and their appropriate applications. It investigates the details of fuzzy systems used in schedulers. It analyzes the evaluation tools used to evaluate different approaches. It proposes possible future research challenges and open issues. The paper discusses the importance of task scheduling in distributed systems, the challenges of managing IT systems, and the need for effective scheduling algorithms. It reviews existing surveys on task scheduling in distributed systems, highlighting the lack of attention to QoS metrics and the absence of fuzzy-based scheduling algorithms in most studies. The paper identifies the challenges of fuzzy logic, the need for choosing the best simulator, and the importance of identifying open challenges and future research directions. The paper is structured as follows: Section 2 provides an introduction to distributed systems, grid, cloud, and fog computing, scheduling, and fuzzy inference systems. Section 3 describes the research methodology. Section 4 describes a taxonomy of fuzzy-based scheduling algorithms. Section 5 compares these algorithms. Section 6 discusses future research directions and open issues. Section 7 presents the conclusion. The paper discusses the evolution of distributed systems, the characteristics of grid, cloud, and fog computing, and the scheduling process. It explains the challenges of task scheduling in distributed systems, the importance of resource management, and the role of fuzzy logic in scheduling. It reviews various fuzzy-based scheduling algorithms for cloud, fog, and grid environments, highlighting their strengths, weaknesses, and applications. The paper identifies open challenges and future research directions in fuzzy-based scheduling.