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


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: © 2018 The Authors. Transactions on Emerging Telecommunications Technologies published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: Electrical and Electronic Engineering
Last Modified: 20 May 2024 07:25
Date Deposited: 28 Sep 2018 12:52
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Related URLs: https://onlinel ... 0.1002/ett.3526 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2019-08-01
Published Online Date: 2018-10-22
Accepted Date: 2018-09-24
Authors: Akbar, Aamir
Lewis, Peter R. (ORCID Profile 0000-0003-4271-8611)



Version: Published Version

License: Creative Commons Attribution

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