An Efficient Fog Computing Platform Through Genetic Algorithm-Based Scheduling

The Internet of Things (IoT) has led to the adoption of fog computing for data-intensive applications that require low-latency processing. Fog computing uses distributed nodes near the network edge to reduce delays and save bandwidth. To optimize resource usage and achieve goals in fog computing, task scheduling plays a crucial role. To tackle this challenge, we propose an approach based on genetic algorithms, inspired by genetics and natural selection. We evaluate GA algorithm based on their ability to complete tasks and prioritize them effectively. The results show that the genetic algorithm performs exceptionally well, completing more tasks and handling priorities efficiently. Its adaptability to changing situations enables optimal resource usage and task completion. Our findings underscore the significance of genetic algorithms in fog computing systems. The insights gained from this study contribute to the advancement of fog computing systems, benefiting IoT applications and services.