Nonlinear Pricing Based Distributed Offloading in Multi-User Mobile Edge Computing

Mobile edge computing (MEC) has emerged as a promising solution to alleviate mobile devices' (MUs') computational burden by offloading part or all of their computational tasks to a nearby edge server. To promote the wide deployment of MEC, charging the MUs and rewarding the edge server is a good mechanism to motivate the edge server to offer computing service. Current related literature usually assume a linear pricing strategy, in which the unit price in time is a linear function with the served computing capability. In this work, however, we explore the nonlinear pricing strategy for the first time, as the cost of a CPU presents super-linear feature with the computing capability. A MEC system with multiple MUs and a single edge server is considered and a two-level Stackelberg game with the leader being the edge server and the followers being MUs is formulated, whereby edge server's revenue is maximized in the upper level via optimizing the nonlinear pricing function while the defined cost function of individual MUs is minimized by deciding the amount of data for offloading and computing capability to purchase from the edge server in the lower level. Through analysis, steps of transformations, and relaxation, closed-form optimal solution for lower-level problem is derived, and the solution for upper-level problem is presented although it is non-convex. Numerical results verify the superiority of our proposed pricing strategy over traditional linear pricing strategy.