The importance of granularity in multiobjective optimization of mobile cloud hybrid applications

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 trade-off. To counter this problem, we propose a data-driven technique that (1) does instrumentation by allowing class-, method-, and hybrid-level configurations to be applied to the MC hybrid application and (2) measures, at runtime, how well the MC hybrid application meets these two objectives by generating data that are used to optimize the efficiency trade-off. Our experimental evaluation considers two MC hybrid Android-based applications. We modularized them first based on the granularity and the computationally intensive modules of the apps. They are then executed using a simple mobile cloud application framework while measuring the power and bandwidth consumption at runtime. Finally, the outcome is a set of configurations that consists of (1) statistically significant and nondominated configurations in collapsible sets and (2) noncollapsible configurations.The analysis of our results shows that from the measured data, Pareto-efficient configurations, in terms of minimizing the two objectives, of different levels of granularity of the apps can be obtained. Furthermore, the reduction of battery power consumption with the cost of network bandwidth usage, by using this technique, in the two MC hybrid applications was (1) 63.71% less power consumption in joules with the cost of using 1.07 MB of network bandwidth and (2) 34.98% less power consumption in joules with the cost of using 3.73 kB of network bandwidth.

A Self-Aware and Scalable Solution for Efficient Mobile-Cloud Hybrid Robotics

“…Previously, we have shown (Akbar and Lewis, 2018a ) the partitioning of applications by modularizing the code into different levels of granularity using annotations during development. We highlighted the importance of granularity for efficient partitioning of the applications.…”

“…To measure the power consumption of an application (as in Equation 3), there are platforms specific ways to use. In our previous work (Akbar and Lewis, 2017 , 2018a ), we have shown how to measure the power consumption for Android-based applications running on smartphones. In this work, we have targeted Python-based applications for battery-powered Raspberry Pi controlled robots.…”

“…To measure the total network usage (as in Equation 4), there are platforms specific ways to use. In our previous work (Akbar and Lewis, 2017 , 2018a ), we used a built-in Android Library, android.net.TrafficStats , for recording data-sent ( B Tx ) and data-received ( B Rx ), for Android-based MCH applications. In this work, since we are targeting Python-based MCH applications for Raspberry Pi, we are using T-shark (a command-line network packet analyser tool) for measuring network usage.…”

“…In MCC, an efficiency trade-off exists between the battery power consumption and network usage (Akbar and Lewis, 2017 , 2018a ). Due to which, the performance of Mobile-cloud hybrid (MCH) applications can be inefficient in terms of battery power consumption.…”

“…To optimize the two objectives, we proposed a technique (Akbar and Lewis, 2017 , 2018a ), for Android-based smartphones, to find and apply the optimal configurations of an MCH application. We define a configuration as, a valid mapping of all distinct and offloadable modules of an MCH application to a binary string .…”