First Hop Mobile Offloading of DAG Computations

In recent years, Mobile Cloud Computing (MCC) has been proposed to increase battery lifetime of mobile devices. However, offloading on Cloud infrastructures may be infeasible for latency critical applications, due to the geographical distribution of Cloud data centers that increases offloading time. In this paper, we investigate the use of Mobile Edge Cloud Offloading (MECO), namely offloading to a heterogeneous computing infrastructure featuring both Cloud and Edge nodes, where Edge nodes are geographically closer to the mobile device. We evaluate improvements of MECO in comparison with MCC for objectives such as applications' runtime, mobile device battery lifetime and cost for the user. Afterwards, we propose the Edge Cloud Heuristic Offloading (ECHO) approach to find a trade-off solution between the aforementioned objectives, according to user's preferences. We evaluate our approach by simulating offloading of Directed Acyclic Graphs (DAGs) representing mobile applications through the use of Monte-Carlo simulations. The results show that (1) MECO can reduce application runtime by up to 70.7% and cost by up to 70.6% in comparison to MCC and (2) ECHO allows user to select a trade-off solution with at most 18% MAPE for runtime, 16% for cost and 0.5% for battery lifetime, according to user's preferences.