Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing

Wearable devices have become essential in our daily activities. Due to battery constrains
the use of computing, communication, and storage resources is limited. Mobile Cloud Computing (MCC)
and the recently emerged Fog Computing (FC) paradigms unleash unprecedented opportunities to augment
capabilities of wearables devices. Partitioning mobile applications and offloading computationally heavy
tasks for execution to the cloud or edge of the network is the key. Offloading prolongs lifetime of
the batteries and allows wearable devices to gain access to the rich and powerful set of computing
and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss rationale
of application partitioning for MCC and FC. To experiment, we develop an Android-based application
and benchmark energy and execution time performance of multiple partitioning scenarios. The results
unveil architectural trade-offs that exist between the paradigms and devise guidelines for proper power management of service-centric Internet of Things (IoT) applications.