Computation Offloading and Resource Allocation in D2D-Enabled Mobile Edge Computing

In this paper, we develop computation offloading scheme based on device-to-device (D2D) communications. The scheme is proposed for effective computation execution where some mobile devices (MDs) could offload their computation intensive tasks to appropriate nearby MDs where necessary, with the assistance of the base station. Accordingly, we formulate a stochastic optimization problem to minimize the average expenses (e.g., wireless communication expense, computation service expense) of MDs in task offloading, while considering the computation resource budget constraint to restraint the behavior of the overuse of computation resource and guarantee mobile users' motivation for collaboration not impaired. To solve this problem, we propose an algorithm that does not need any prior- knowledge of available resources of MDs, referred to as the SEEP. To address a couple and mixed combinational subproblem in the SEEP, we decouple optimization variables for suboptimal. By doing so, both task scheduling and subcarrier assignment are obtained in closed forms, while power allocation is solved by developing efficient iterative algorithm that exploits D.C. (difference of convex functions) structure. Simulation results show the convergence of the SEEP, and illustrate SEEP can flexibly coordinate the tradeoff between expenses and delay, and can substantially reduce expenses of MDs against other existing schemes.