FoGMatch: An Intelligent Multi-Criteria IoT-Fog Scheduling Approach Using Game Theory

Abstract

Cloud computing has long been the main backbone that Internet of Things (IoT) devices rely on to accommodate their storage and analytical needs. However, the fact that cloud systems are often located quite far from the IoT devices and the emergence of delay-critical IoT applications urged the need for extending the cloud architecture to support delay-critical services. Given that fog nodes possess low resource capabilities compared to the cloud, matching the IoT services to appropriate fog nodes while guaranteeing minimal delay for IoT services and efficient resource utilization on fog nodes becomes quite challenging. In this context, the main limitation of existing approaches is addressing the scheduling problem from one side perspective, i.e., either fog nodes or IoT devices. To address this problem, we propose in this paper a multi-criteria intelligent IoT-Fog scheduling approach using game theory. Our solution consists of designing (1) preference functions for the IoT and fog layers to enable them to rank each other based on several criteria latency and resource utilization and (2) centralized and distributed intelligent scheduling algorithms that capitalize on matching theory and consider the preferences of both parties. Simulation results reveal that our solution outperforms the two common Min-Min and Max-Min scheduling approaches in terms of IoT services execution makespan and fog nodes resource consolidation efficiency.