Abstract
This letter investigates energy-efficient computation offloading designs for device-to-device (D2D) cooperative computing between the two users, in which each user has time-varying computation task arrivals. In this setup, the two users can dynamically exchange the computation loads via D2D offloading for reducing the overall energy consumption. In particular, we minimize the weighted sum-energy consumption of both users over a finite time horizon, by jointly optimizing their local computing and task exchange (offloading) decisions over time, subject to the newly introduced task causality and completion constraints. By applying the convex optimization technique, we obtain the well-structured optimal solution to this problem. Numerical results show that by enabling bidirectional computation sharing between the users, the proposed D2D cooperative computing design significantly reduces the system energy consumption, as compared with other benchmark schemes.
