Abstract
In the computational offloading problem of edge cloud computing (ECC), almost all researches develop the offloading strategy by optimizing the user cost, but most of them only consider the delay and energy consumption, and seldom consider the task waiting delay. This is very unfavorable for tasks with high sensitive latency requirements in the current era of intelligence. In this paper, by using D2D (Device-to-Device) technology, we propose a D2D-assisted collaboration computational offloading strategy (D-CCO) based on user cost optimization to obtain the offloading decision and the number of tasks that can be offloaded. Specifically, we first build a task queue system with multiple local devices, peer devices and edge processors, and compare the execution performance of computing tasks on different devices, taking into account user costs such as task delay, power consumption, and wait delay. Then, the stochastic optimization algorithm and the back-pressure algorithm are used to develop the offloading strategy, which ensures the stability of the system and reduces the computing cost to the greatest extent, so as to obtain the optimal offloading decision. In addition, the stability of the proposed algorithm is analyzed theoretically, that is, the upper bounds of all queues in the system are derived. The simulation results show the stability of the proposed algorithm, and demonstrate that the D-CCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the user cost.