Towards Computational Offloading in Mobile Device Clouds

Abstract

Many mobile applications overcome their device limitations in computational, energy, or data resources by offloading computations to the cloud. In this paper, we consider environments in which computational offloading occurs amongst a set of mobile devices. We call such an environment a mobile device cloud (MDC). In this work, we first highlight the gain in computation time and energy consumption that can be achieved by offloading tasks to nearby devices within an MDC compared to a cloud. We then propose and implement an MDC platform that enables the creation and assessment of various offloading algorithms in MDCs. This platform consists of an Android application deployable across MDC devices, and a test bed to measure power being consumed by a mobile device. We utilize this platform to carry out various offloading experiments on an MDC test bed from which we gain interesting insights into the potential for MDC offloading. Results from these experiments show up to 50% gain in time and 26% gain in energy. Finally, we address the off loadee selection problem in MDCs by proposing several social-based algorithms. The potential promise of this approach is shown by evaluating these algorithms using real data sets that include contact traces and social information of mobile devices in a conference setting.