Enhancing IoT Workloads: A Comparative Study of Fog and Edge Computing Scheduling Algorithms

This work presents a comparative analysis of fog and edge computing scheduling algorithms, focusing on their impact on IoT workload optimization. The analysis considers task allocation, load balancing, energy efficiency, latency, and scalability. An advanced experimental setup with diverse workload scenarios simulates real-world IoT environments. Existing algorithms, alongside modifications and novel introductions, are evaluated. Real-world applications showcase practical considerations and the adaptability of these algorithms. Refined metrics capture both immediate and long-term effects on scalability. The results inform future fog and edge computing research, considering emerging technologies. The study identifies key factors for optimizing IoT workloads and highlights the importance of scheduling algorithms for maximizing performance.