Energy-aware task scheduling in mobile cloud computing

The limited energy supply, computing, storage and transmission capabilities of mobile devices pose a number of challenges for improving the quality of service (QoS) of various mobile applications, which has stimulated the emergence of many enhanced mobile computing paradigms, such as mobile cloud computing (MCC), fog computing, mobile edge computing (MEC), etc. The mobile devices need to partition mobile applications into related tasks and decide which tasks should be offloaded to remote computing facilities provided by cloud computing, fog nodes etc. It is very important yet tough to decide which tasks to be uploaded and where they are scheduled since this could greatly impact the applications' timeliness and mobile devices' lifetime. In this paper, we model the task scheduling problem at the end-user mobile device as an energy consumption optimization problem, while taking into account task dependency, data transmission and other constraint conditions such as task deadline and cost. We further present several heuristic algorithms to solve it. A series of simulation experiments are conducted to evaluate the performance of the algorithms and the results show that our proposed algorithms outperform the state-of-the-art algorithms in energy efficiency as well as response time.