In this paper, we proposed a D2D assisted hybrid framework, where the computation-intensive task can be offloaded to the cloud or neighboring users. Aiming at minimizing the total energy consumption under delay constraints, the joint optimization of the task offloading, task scheduling and computing resource allocation problem is formulated. Additionally, we model the duration of users’ intermittent connections to study the effect of user mobility on the task success rate. Then, the original problem is divided into two sub-problems to solved separately considering the coupling multiplicative variables. Further, a flexible proximal alternating direction method of multipliers (ADMM) based algorithm is proposed to solve the nonconvex sub-problem in a distributed way. Numerical results reveal the effectiveness of the algorithm on convergence and complexity reduction, and the proposed scheme achieves excellent performance when compared with other conventional schemes.