Machine Learning-Based Runtime Scheduler for Mobile Offloading Framework

Remote offloading techniques have been proposed to overcome the limited resources of mobile platforms by leveraging external powerful resources such as personal work-stations or cloud servers. Prior studies have primarily focused on core mechanisms for offloading. Yet, adaptive scheduling in such systems is important because offloading effectiveness can be influenced by varying network conditions, workload requirements, and load at the target device. In this paper, we present a study on the feasibility of applying machine learning techniques to address the adaptive scheduling problem in mobile offloading framework. The study considers 19 different machine learning algorithms and four workloads, with a dataset obtained through the deployment of an Android-based remote offloading framework prototype on actual mobile and cloud resources. From this set, a subset of machine learning algorithms, which have relatively high scheduling accuracy, is selected to implement an offline offloading scheduler. Finally, by taking computational cost and the scheduling performance into account, we use Instance-Based Learning to evaluate an online adaptive scheduler for mobile offloading. In our evaluation, we observe that an Instance Learning-based online offloading scheduler selects the best scheduling decision in 87.5% instances, in an experiment setup in which an image processing workload is offloaded while subject to varying network bandwidth conditions and the amount of data transfer.