Energy Aware Dynamic Workflow Application Partitioning and Task Scheduling in Heterogeneous Mobile Cloud Network

Mobile Edge Cloud Computing Architecture (MEC-CA) presents key opportunities in performance improvement and energy saving for resource constrained mobile device. The mobile applications such as healthcare application, Augmented Reality can be modeled by task graphs. This work investigates the problem of dynamic application and scheduling tasks (which belong to the same or possibly different applications) in the MCCA environment. Nevertheless, the existing offloading system algorithms did not consider network failure and cloud resource in their MCCA paradigm. More specilically, we suggest the dynamic application partitioning and task scheduling (DAPTS) algorithm such that the application completion time constraint are satisfied while the total energy dissipation of the mobile device and cloud resources is minimized. Performance evaluations result demonstrates that proposed DAPTS outperform minimize the objective function in context of completion time and energy use as compared to the baseline approaches.