Learning-Based Pricing for Privacy-Preserving Job Offloading in Mobile Edge Computing

Abstract

This paper considers a scenario in which an access point (AP) is equipped with a mobile edge server (MEC) of finite computing power, and serves multiple resource-hungry mobile users by charging users a price. This price helps to regulate users' behavior in offloading computation jobs to the AP. To that end, first we introduce an economics model for MEC bearing physical layer offloading intuition. We then propose a learning based pricing mechanism, in which with no direct control and no knowledge of users' private information, the AP learns the optimal price. Under our mechanism, the AP induces self-interested users to make socially optimal offloading decisions, thus maximizing the system-wide welfare.