Network Economic Model for Resource Utilization in Fog-based RAN

The exponential growth of communication devices such as Internet of Things (IoT) devices, Augmented Reality/Virtual Reality (AR/VR) devices, sensors/actuators, mobile devices, and many others have led to a substantial increase in the quantity of traffic generated. This heavy traffic may cause significant delays for delay-sensitive applications and burden the fronthaul in Cloud-RAN architecture. Supporting all types of communications with the C-RAN architecture will be expensive and inefficient for telecom network operators (TNOs) and devices. Consequently, a fog computing-based RAN has evolved to reduce the heavy burden on fronthaul and ensure timely delivery of requested content for delay-sensitive applications. Fog computing resolves this problem by bringing computational resources and networking closer to users or devices, which is particularly useful for delay-sensitive applications. The cloud has extensive computing and resource capabilities, but its latency is more significant than fog computing-based RAN. In this paper, we examine a market in which TNOs lease third-party deployed fog access points (F-APs) in order to optimize the utilization of fog computing-based RAN architecture and F-APs resources. By employing a fog computing paradigm and efficiently using available fog node resources, the fronthaul infrastructure can relieve some of its burdens. Reducing service latency for end users is one of the most promising advantages of the fog paradigm. A user agrees to utilize fog resources in this endeavor and vice versa. Users and fog nodes do not provide usable utility and cost functions, so we implement a market controller to regulate the market. To maximize the social welfare of the network for participating users and fog nodes, we devise a two-sided auction method to assign computational resources and appropriate compensation.