Joint Task Offloading, D2D Pairing, and Resource Allocation in Device-Enhanced MEC: A Potential Game Approach
Tao Fang, Yuan Feng, Liang Ao, Jiaxin Chen
Abstract
The emergence of intelligent applications produces the demand for computing. How to reduce the computation pressure in mobile-edge computing (MEC) under massive computation demand is an urgent problem to solve. Specifically, the allocation of heterogeneous resources, including communication resources and computing resources, needs to be optimized simultaneously. From the perspective of joint optimization of channel allocation, device-to-device (D2D) pairing, and offloading mode, this article studies the multiuser computing task offloading problem in device-enhanced MEC. The objective is maximizing the aggregate offloading benefits, i.e., the tradeoff between delay and energy consumption, of all compute-intensive users in the network. By introducing game theory, the problem is modeled as a multiuser computation task offloading game, which is proved to be an exact potential game (EPG) with at least one pure-strategy Nash equilibrium (NE) solution. In order to find a desirable solution, this article proposes a better reply-based distributed multiuser computation task offloading algorithm (BR-DMCTO). Simulation results show that the proposed offloading mechanism can improve the benefit of users, and verify the effectiveness and convergence of the proposed algorithm.