Harnessing Nature's Wisdom for Resource Optimization and Load Balancing in Edge and Fog Computing

With the new advancements and rapid evolution in the loT (Internet of Things) environment and the rise in demand for low-latency applications, fog and edge computing epitome have emerged as promising solutions to meet the stringent requirements of real-time data processing and analysis. However, efficient resource utilization and load balancing are critical challenges in fog and edge computing environments due to the heterogeneous nature of devices and dynamic workloads. So, here we propose an approach for resource management and task distribution in fog and edge computing environments leveraging nature-inspired algorithm The Clonal Selection Algorithm (CLONALG). The application of this algorithm provides an ideal or near-perfect solution. In this paper, we have presented a supported and detailed comparison of this nature- inspired meta-heuristic algorithm based on numerous factors that influence how well load balancing and resource optimization work together to produce better optimal solutions.