Optimizing Fog Computing Efficiency: Exploring the Role of Heterogeneity in Resource Allocation and Task Scheduling

Abstract

Fog computing is a promising solution for latency-sensitive applications in the Internet of Things (IoT) era. This research thoroughly investigates the performance characteristics of fog computing systems, specifically focusing on the impact of architectural heterogeneity. The study explores how architectural diversity, deadline constraints, and the count of Mobile Data Centers (MDCs) influence key performance metrics. Through experimental simulations, the research assesses success ratio, rejection ratio, and resource utilization across various architectural models. The findings emphasize the significance of adopting heterogeneous architectures and wider deadline ranges, leading to improved success ratio and reduced job rejection. Moreover, increasing the number of MDCs positively affects resource utilization and overall system performance. This research offers valuable insights for optimizing fog computing systems, enabling the development of efficient solutions for real-world applications.