Dependent task offloading with energy-latency tradeoff in mobile edge computing

With the rapid development of Internet-of-Things (IoT) and mobile devices, the IoT applications become more computation-intensive and latency-sensitive, which bring severe challenges to the resource-limited devices. Mobile Edge Computing has served as a key promising method to enhance the network's computing capability by enabling resource-constrained devices to offload tasks to the edge servers. A major challenge, which has been overlooked by most existing works on task offloading, is the dependencies among tasks and subtasks. In this paper, the subtask offloading with logical dependency for IoT applications is focused on. Specifically, subtask dependent graphs are employed to explore the dependency of subtasks and consider the priority of task scheduling. Further, an offloading scheme is put forward for minimizing both task latency and energy consumption of the device with dependency guarantees for all IoT tasks in multi-server edge networks. Finaly, the simulation results demonstrate that the overall reduction rate is around 14% and relatively stable can effectively reduce task latency in multi-server edge networks.