Stackelberg Game for Service Deployment of IoT-Enabled Applications in 6G-Aware Fog Networks

Fog computing has emerged as a promising paradigm that borrows the user-oriented cloud services to the proximity of the Internet-of-Things (IoT) users in sixth-generation (6G) networks. Currently, service providers establish a proprietary fog architecture to prolong a specific group of IoT users by offering resources and services to the edge level. However, this sort of activity creates a service barrier and limits the development of fog services to the IoT-users. Keeping this in mind, we develop a 6G-aware fog federation model for utilizing maximum fog resources and providing demand specific services across the network while maximizing the revenue of fog service providers and guaranteeing the minimum service delay and price for IoT-users. To achieve this goal, we formulate our objective function into a mixed-integer nonlinear problem. By jointly optimizing the dynamic services cost and user demands, a noncooperative Stackelberg game interaction algorithm is formulated to schedule the fog and cloud resources distributively. Further maximizing the profit for the service providers and the seamless resource provisioning, a resource controller is initiated to manage the available fog resources. Extensive simulation analysis over 6G-aware Quality-of-Service parameters demonstrates the superiority of the proposed fog federation model and it reduces up to 15%-20% service delay and 20%-25% of service cost over the standalone fog and cloud frameworks.

Automated summary

Fog computing is a promising paradigm that brings cloud services to IoT users in 6G networks, and the 6G-aware fog federation model optimizes fog resources, provides demand-specific services, and ensures minimal service delay and price for IoT users. It utilizes a mixed-integer nonlinear problem formulation, a noncooperative Stackelberg game interaction algorithm, and a resource controller to maximize profit for service providers and streamline resource provisioning, reducing service delay by 15%-20% and service cost by 20%-25% compared to standalone fog and cloud frameworks through extensive simulation analysis.