A D2D offloading approach to efficient mobile edge resource pooling

Abstract

The explosion of resource-hungry mobile applications has posed great challenges on the underlying mobile devices which typically have limited computation resource. In response, device-to-device (D2D) computation offloading is envisioned as a promising approach to the problem by gearing resource-rich devices and resource-poor devices. Towards real-time and efficient computation offloading, in this paper, we proposed a novel edge resource pooling framework called ERP, in which a massive crowd of devices at the network edge exploit D2D collaboration for pooling and sharing computation resource with each other. Specifically, we first formulate the utility maximization problem under both computation and communication constraints as a mixed-integer linear programming (MILP), which is further proven to be NP-hard. To address this challenge, we propose a centralized greedy heuristic based on the classical maximum network flow problem, which schedules the task offloading in a cost-efficient manner. Rigorous theoretical analysis and extensive evaluations demonstrate the effectiveness of the heuristic to some extent.