Collaborative computation offloading and resource allocation based on dynamic pricing in mobile edge computing

The development of collaborative computing architecture in mobile edge computing provides an effective solution for the task processing of mobile devices with low computing capability, which can make up for computation resource and physical size restrictions of mobile devices and improve the performance of mobile communication networks in the future. In this paper, we focus on network congestion caused by increased load in hotspots. In particular, we consider the integrated network architecture of mobile edge computing (MEC) and device-to-device (D2D) communication to reduce the sum overhead of all task request users (TRUs). Accordingly, the computing offloading decision, wireless resource allocation strategy, computing resource allocation strategy, and assistant selection decision are optimized to minimize the sum overhead of the energy consumption and the cost of purchasing computing resource of all TRUs. The formulated overhead minimization problem is a mixed integer nonlinear programming problem. In order to tackle this problem, an algorithm based on the distance and the transaction price is first proposed to determine the target assistant user (TAU) of each TRU. Second, for a given TAU selection strategy, the original problem is further transformed into a convex problem and decomposed to make it more tractable. Finally, a distributed algorithm based on the alternating direction method of multipliers (ADMM) is adopted to solve the optimization problem. The simulation results prove the effectiveness of the proposed scheme in reducing user overhead.