An incentive-aware job offloading control framework for multi-access edge computing

This paper considers a scenario in which an access point (AP) is equipped with a server of finite computing power, and serves multiple resource-hungry users by charging users a price. This price helps to regulate users' behavior in offloading jobs to the AP. However, existing works on pricing are based on abstract concave utility functions, giving no dependence on physical layer parameters. To that end, we first introduce a novel utility function, which measures the cost reduction by offloading as compared with executing jobs locally. Based on this utility function we then formulate two offloading games, with one maximizing individuals interest and the other maximizing the overall systems interest. We analyze the structural property of the games and admit in closed-form the Nash Equilibrium and the Social Equilibrium for the homogeneous user case, respectively. The proposed expressions are functions of user parameters such as the weights of time and energy, the distance from the AP, thus constituting an advancement over prior economic works that have considered only abstract functions. Finally, we propose an optimal price-based scheme, with which we prove that the interactive decision-making process with self-interested users converges to a Nash Equilibrium point equal to the Social Equilibrium point.