Energy and Performance-Aware Task Scheduling in a Mobile Cloud Computing Environment

Abstract

Mobile cloud computing (MCC) offers significant opportunities in performance enhancement and energy saving in mobile, battery-powered devices. An application running on a mobile device can be represented by a task graph. This work investigates the problem of scheduling tasks (which belong to the same or possibly different applications) in an MCC environment. More precisely, the scheduling problem involves the following steps: (i) determining the tasks to be offloaded on to the cloud, (ii) mapping the remaining tasks onto (potentially heterogeneous) cores in the mobile device, and (iii) scheduling all tasks on the cores (for in-house tasks) or the wireless communication channels (for offloaded tasks) such that the task-precedence requirements and the application completion time constraint are satisfied while the total energy dissipation in the mobile device is minimized. A novel algorithm is presented, which starts from a minimal-delay scheduling solution and subsequently performs energy reduction by migrating tasks among the local cores or between the local cores and the cloud. A linear-time rescheduling algorithm is proposed for the task migration. Simulation results show that the proposed algorithm can achieve a maximum energy reduction by a factor of 3.1 compared with the baseline algorithm.