Abstract
Mobile devices can now support a wide range of applications, many of which demand high computational power. Backed by the virtually unbounded resources of cloud computing, today's mobile-cloud (MC) computing can meet the demands of even the most computationally and resource intensive applications. However, many existing MC hybrid applications are inefficient in terms of achieving objectives like minimizing battery power consumption and network bandwidth usage, which form a tradeoff. To counter this problem we propose a technique that: 1) measures, at run time, how well the MC application meets these two objectives; and 2) allows arbitrary configurations to be applied to the MC application in order to optimize the efficiency tradeoff. Our experimental evaluation considers two MC hybrid applications. We modularized them first, based on computationally-intensive tasks, and then executed them using a simple MC framework while measuring the power and bandwidth consumption at run-time. Analysis of results shows that efficient configurations of the apps can be obtained in terms of minimizing the two objectives. However, there remain challenges such as scalability and automation of the process, which we discuss.
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