  Edge computing is a promising approach for localized data processing for many
edge applications and systems including Internet of Things (IoT), where
computationally intensive tasks in IoT devices could be divided into sub-tasks
and offloaded to other IoT devices, mobile devices, and / or servers at the
edge. However, existing solutions on edge computing do not address the full
range of challenges, specifically heterogeneity; edge devices are highly
heterogeneous and dynamic in nature. In this paper, we develop a predictive
edge computing framework with hard deadlines. Our algorithm; PrComp (i)
predicts the uncertain dynamics of resources of devices at the edge including
energy, computing power, and mobility, and (ii) makes sub-task offloading
decisions by taking into account the predicted available resources, as well as
the hard deadline constraints of tasks. We evaluate PrComp on a testbed
consisting of real Android-based smartphones, and show that it significantly
improves energy consumption of edge devices and task completion delay as
compared to baselines.
