Energy-Efficient Computation Offloading for Multicore-Based Mobile Devices

Modern mobile devices are equipped with multicore-based processors, which introduce new challenges on computation offloading. With the big.LITTLE architecture, instead of only deciding locally or remotely running a task in the traditional architecture, we have to consider how to exploit the new architecture to minimize energy while satisfying application completion time constraints. In this paper, we address the problem of energy-efficient computation offloading on multicore-based mobile devices running multiple applications. We first formalize the problem as a mixed-integer nonlinear programming problem that is NP-hard, and then propose a novel heuristic algorithm to jointly solve the offloading decision and task scheduling problems. The basic idea is to prioritize tasks from different applications to make sure that both application time constraints and task dependency requirements are satisfied. To find a better schedule while reducing the schedule searching overhead, we propose a critical path based solution which recursively checks the tasks and moves tasks to the right CPU cores to save energy. Simulation and experimental results show that our offloading algorithm can significantly reduce the energy consumption of mobile devices while satisfying the application completion time constraints.