Distributed Game-Theoretical D2D-Enabled Task Offloading in Mobile Edge Computing

Mobile Edge Computing (MEC) has been envisioned as a promising distributed computing paradigm where mobile users offload their tasks to edge nodes to decrease the cost of energy and computation. However, most of the existing studies only consider the congestion of wireless channels as a crucial factor affecting the strategy-making process, while ignoring the impact of offloading among edge nodes. In addition, centralized task offloading strategies result in enormous computation complexity in center nodes. Along this line, we take both the congestion of wireless channels and the offloading among multiple edge nodes into consideration to enrich users' offloading strategies and propose the Parallel User Selection Algorithm (PUS) and Single User Selection Algorithm (SUS) to substantially accelerate the convergence. More practically, we extend the users' offloading strategies to take into account idle devices and cloud services, which considers the potential computing resources at the edge. Furthermore, we construct a potential game in which each user selfishly seeks an optimal strategy to minimize its cost of latency and energy based on acceptable latency, and find the potential function to prove the existence of Nash equilibrium (NE). Additionally, we update PUS to accelerate its convergence and illustrate its performance through the experimental results of three real datasets, and the updated PUS effectively decreases the total cost and reaches Nash equilibrium.