Maximizing MapReduce job speed and reliability in the mobile cloud by optimizing task allocation

Abstract

Mobile cloud computing has become a widespread phenomenon owing to the rapid development and proliferation of mobile devices all over the globe. Furthermore, revolutionary mobile hardware technologies, such as 5G and IoT, have led to increased competition for mobile intelligence among tech-giants, like Google, Apple, and Facebook, further leading to developments in the field of mobile cloud computing. However, several challenges still remain; above all, resolving the task allocation problem that determines the nodes on which tasks will be executed is of paramount importance, and therefore, is the focus of many previous studies on mobile cloud. To this end, we propose a novel Mobile MapReduce Task Allocation(MTA) strategy that simultaneously maximizes both job speed and reliability by modeling communication delay and task reliability. Based on extensive evaluations using various task allocation strategies, representative workloads, and real mobility traces on a mobile MapReduce simulator validated against a platform running on an actual smartphone cluster, we show that MTA significantly outperformed the state-of-the-art task allocation algorithms by up to 3.7 times and 41%, respectively, in terms of job speed and reliability, confirming its resource efficiency and scalability as well.