A Novel Framework for Mobile-Edge Computing by Optimizing Task Offloading

Abstract

With the emergence of mobile computing offloading paradigms, such as mobile-edge computing (MEC), many Internet of Things applications can take advantage of the computing powers of end devices to perform local tasks without the need to rely on a centralized server. Computation offloading is becoming a promising technique that helps to prolong the device's battery life and reduces the computing tasks' execution time. Many previous works have discussed task offloading to the cloud. However, these schemes do not differentiate between types of application tasks. It is not reasonable to offload all application tasks into the cloud. Some application tasks with low computing and high communication cost are more suitable to be executed on the end devices. On the other hand, most resources on the end devices are idle and can be used to process tasks with low computing and high communication cost. In this article, a three-layer task offloading framework named DCC is proposed, which consists of the device layer, cloudlet layer and cloud layer. In DCC, the tasks with high computing requirement are offloaded to the cloudlet layer and cloud layer. Whereas tasks with low computing and high communication cost are executed on the device layer, hence DCC avoids transmitting large amount of data to the cloud, and can effectively reduce the processing delay. We have introduced a greedy task graph partition offloading algorithm, where the tasks scheduling process is assisted according to the device computing capabilities following a greedy optimization approach to minimize the tasks communication cost. To show the effectiveness of the proposed framework, We have implemented a facial recognition system as usecase scenario. Furthermore, experiment and simulation results show that DCC can achieve high performance when compared to state-of-the-art computational offloading techniques.